diff --git a/.claude/agents/censorship-expert.md b/.claude/agents/censorship-expert.md deleted file mode 100644 index 2d222fd..0000000 --- a/.claude/agents/censorship-expert.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -name: censorship-expert -description: Use this agent for questions about privacy across technology domains — network privacy, device privacy, censorship circumvention, and privacy best practices. Invoke it when researching, writing, or reviewing knowledge under networking/censorship, or when the user needs privacy-focused analysis or recommendations. -tools: Read, Edit, Write, Bash, Grep, WebSearch -model: inherit ---- - -# Expert - -You are an expert in privacy across all domains of technology, including network privacy, device privacy, censorship circumvention, and privacy best practices. - -When working in this repository, treat `tree/networking/censorship/` as your primary knowledge base. Read the relevant index and source files there before answering, and store new findings back into that directory as markdown. \ No newline at end of file diff --git a/.claude/agents/privacy-expert.md b/.claude/agents/privacy-expert.md deleted file mode 100644 index f1444d1..0000000 --- a/.claude/agents/privacy-expert.md +++ /dev/null @@ -1,6 +0,0 @@ ---- -name: privacy-expert -description: Use this agent for questions about privacy across technology domains — online privacy, device privacy, and privacy best practices. Invoke it when researching, writing, when the user needs privacy-focused analysis or recommendations or building a playbook for anonymoty. -tools: Read, Edit, Write, Bash, Grep, WebSearch -model: inherit ---- \ No newline at end of file diff --git a/.envrc b/.envrc new file mode 100644 index 0000000..fbbd267 --- /dev/null +++ b/.envrc @@ -0,0 +1 @@ +export KNOWLEDGE_VOLUME_PATH="/Volumes/knowledge_data" diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json index dee2101..7233337 100644 --- a/.obsidian/workspace.json +++ b/.obsidian/workspace.json @@ -11,28 +11,13 @@ "id": "75521912599ac75e", "type": "leaf", "state": { - "type": "pdf", - "state": { - "file": "sources/ai/pdf/Build Your Own Agentic AI Framework Design Flexible Multi-agent Systems in Python with MCP and A2A integration Gigi Sayfan z-librarysk 1libsk z-lib.pdf" - }, - "icon": "lucide-file-text", - "title": "Build Your Own Agentic AI Framework Design Flexible Multi-agent Systems in Python with MCP and A2A integration Gigi Sayfan z-librarysk 1libsk z-lib" - } - }, - { - "id": "dc48ad1e6618c2ed", - "type": "leaf", - "state": { - "type": "release-notes", - "state": { - "currentVersion": "1.13.7" - }, - "icon": "lucide-book-up", - "title": "Release Notes 1.13.7" + "type": "empty", + "state": {}, + "icon": "lucide-file", + "title": "New tab" } } - ], - "currentTab": 1 + ] } ], "direction": "vertical" @@ -87,11 +72,12 @@ "title": "Bookmarks" } } - ] + ], + "currentTab": 1 } ], "direction": "horizontal", - "width": 300 + "width": 347.3620300292969 }, "right": { "id": "4c3e39d505734e6e", @@ -196,43 +182,43 @@ "bases:Create new base": false } }, - "active": "bba1026a76d26d16", + "active": "f50e692499989deb", "lastOpenFiles": [ + "docs/architecture.md", + "sources/ai/pdf/Build Your Own Agentic AI Framework Design Flexible Multi-agent Systems in Python with MCP and A2A integration Gigi Sayfan z-librarysk 1libsk z-lib.pdf", + "tree/specialized_tooling/webassembly/pdf/wasm-performance.md", + "tree/specialized_tooling/webassembly/pdf/server-side-webassembly-building-portable-and-secure-multi-language-ap.md", + "tree/specialized_tooling/webassembly/pdf", + "tree/specialized_tooling/webassembly", + "tree/networking/censorship/pdf/uv-lock.md", + "tree/ai/pdf/aivideo-spacetimediffusionmodel.md", + "kb/__pycache__/extract.cpython-312.pyc", + "sources/networking/ebpf/books", + "sources/networking/ebpf", + "tree/application/architecture/data-midplatform-architecture.md", + "tree/application/architecture/iot-platform-architecture.md", + "tree/application/architecture/im-rtc-architecture.md", + "tree/application/architecture/ai-ml-inference-architecture.md", + "tree/application/architecture/digital-government-architecture.md", + "tree/application/architecture/deep-sea-exploration.md", + "tree/application/architecture/cloudnative-devops-architecture.md", + "tree/application/architecture/immersive-xr.md", + "tree/application/architecture/industrial-internet-platform.md", + "domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md", + "tree/application/architecture/smart-logistics-architecture.md", + "domain-20-application-patterns/topic-application-architecture", + "domain-20-application-patterns", + "tree/application/architecture/social-gaming-metaverse.md", + "tree/application/architecture/social-media-architecture.md", + "tree/application/architecture/space-internet.md", + "tree/application/architecture/tsn-network.md", + "tree/application/architecture/video-shortform-architecture.md", + "tree/platform-engineering/developer-experience/02-developer-onboarding-automation.md", + "tree/infrastructure/kubernetes/networking/ebpf/08-bcc-bpftrace-tools.md", + "tree/infrastructure/architecture/saas-multi-tenancy.md", + "tree/infrastructure/architecture/server-examples.md", + "tree/infrastructure/architecture/architecture-patterns.md", "tree/linux/operating-systems", - "tree/linux", - "tree/hardware", - "sources/ai/pdf/aivideo-spacetimediffusionmodel.pdf", - "tree/ai/pdf/aivideo-spacetimediffusionmodel.pdf", - "tree/ai/pdf/2401.12945v2.pdf", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace/index.md", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace/USER.md", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace/TOOLS.md", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace/SOUL.md", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace/SKILL.md", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace/MEMORY.md", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace/IDENTITY.md", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace/AGENTS.md", - "tree/infrastructure/kubernetes/ai/ai-agents/openclaw-workspace", - "tree/infrastructure/kubernetes/ai/ai-agents/index.md", - "tree/infrastructure/kubernetes/ai/ai-agents/README.md", - "tree/infrastructure/kubernetes/ai/ai-agents/52-agent-cost-optimization-caching.md", - "tree/infrastructure/kubernetes/ai/ai-agents/51-agent-guardrails-content-safety.md", - "tree/infrastructure/kubernetes/ai/ai-agents/50-openclaw-identity-mechanism.md", - "tree/infrastructure/kubernetes/ai/ai-agents/49-openclaw-memory-mechanism.md", - "tree/infrastructure/kubernetes/ai/ai-agents/48-openclaw-skill-mechanism.md", - "tree/infrastructure/kubernetes/ai/ai-agents/47-openclaw-tools-mechanism.md", - "tree/infrastructure/kubernetes/ai/ai-agents/46-openclaw-agents-mechanism.md", - "tree/infrastructure/kubernetes/ai/ai-agents/45-openclaw-user-mechanism.md", - "tree/infrastructure/kubernetes/ai/ai-agents/44-openclaw-soul-mechanism.md", - "tree/infrastructure/kubernetes/ai/ai-agents/43-openclaw-framework-integration.md", - "tree/infrastructure/kubernetes/ai/ai-agents/42-model-harness-compatibility-matrix.md", - "tree/infrastructure/kubernetes/ai/ai-agents/41-react-harness-identification-guide.md", - "tree/infrastructure/kubernetes/ai/ai-agents/40-agent-harness-production-maturity.md", - "tree/infrastructure/kubernetes/ai/ai-agents/39-agent-harness-testing-benchmark.md", - "tree/infrastructure/kubernetes/ai/ai-agents/38-agent-harness-performance-cost.md", - "tree/infrastructure/kubernetes/ai/ai-agents", - "tree/infrastructure/kubernetes/ai/agent-runtime", - "tree/infrastructure/kubernetes/ai/infrastructure", - "tree/application/architecture/topic-application-architecture" + "tree/linux" ] } \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md index b13a763..a9457df 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -5,13 +5,5 @@ Reference `.ai/prd.md` `kb/` - knowledge bank source code `tree/` - knowledge tree (information sources / obsidian vault) -## Agents +## Knowledge Bank Commands -- Privacy Expert - `.claude/agents/privacy-expert.md` - -## Skills - -- `query_knowledge` -- `sync_knowledge` -- `extend_knowledge` -- `run_research_report` diff --git a/Makefile b/Makefile index f5fca38..571575c 100644 --- a/Makefile +++ b/Makefile @@ -11,7 +11,7 @@ help: ## Show this help install: ## Sync Python deps (lancedb, duckdb, fastembed, qdrant-client, ...) uv sync -extract: ## Convert sources/ epub+pdf -> tree/ markdown (idempotent, hash-gated) +extract: ## Convert sources declared in tree/branches.yaml -> tree/ markdown $(PY) -m kb extract ingest: ## Incremental ingest: changed markdown -> chunk -> embed -> LanceDB diff --git a/README.md b/README.md index 7a3266c..8022eaf 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,4 @@ - -

Knowledge Tree
Knowledge Tree
@@ -9,11 +7,11 @@ ## Overview -Knowledge as an obsidian vault stored as markdown in git and ingested and indexed context with skills and tools for human and ai coordination. +Knowledge as an obsidian vault stored as markdown in git and ingested and indexed context with skills and tools for human and ai coordination. For details on how this works from a high level, read [architecture](./docs/architecture.md) ## Branches -[branches.yaml](tree/branches.yaml) is the routing file for the knowledge tree. +[branches.yaml](tree/branches.yaml) is the routing file for the knowledge tree and controls building the tree at compilation with controls for encryption, pulling from remote sources such as books, etc... - [AI](tree/ai) - [Infrastructure](tree/infrastructure) @@ -21,6 +19,11 @@ Knowledge as an obsidian vault stored as markdown in git and ingested and indexe - [Application](tree/application) - [Specialized Tooling](tree/specialized_tooling) +## Searching Trees + +- indexing +- tagging + ## Commands Source code used for knowledge management. Commands to interact with the knowledge bank exist in makefile. diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 0000000..1785d7f --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,112 @@ +# Architecture + +``` + ┌─────────────────────────────┐ + │ Humans / AI Agents │ + │ Obsidian · Claude · Cursor │ + └──────────────┬──────────────┘ + │ + ┌────────────────────────┼────────────────────────┐ + │ │ │ + ▼ ▼ ▼ + ┌────────────────┐ ┌──────────────────┐ ┌─────────────────┐ + │ Obsidian Vault│ │ Makefile / CLI │ │ Skills / Tools │ + │ (tree/) │ │ make extract │ │ query_knowledge │ + │ │ │ make ingest │ │ store_knowledge │ + └───────┬────────┘ │ make query │ └────────┬────────┘ + │ └────────┬─────────┘ │ + │ │ │ + └───────────────────────┼────────────────────────┘ + │ + ▼ +┌──────────────────────────────────────────────────────────────────────────────┐ +│ Knowledge Bank Library (kb/) │ +│ │ +│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ +│ │ extract │──▶│ chunk │──▶│ embed │──▶│ store │──▶│ query │ │ +│ │ │ │ │ │ │ │ │ │ │ │ +│ │ books │ │ parent/ │ │ fastembed│ │ LanceDB │ │ hybrid │ │ +│ │ pdfs │ │ child │ │ bge-small│ │ DuckDB │ │ BM25+vec │ │ +│ │ remote │ │ sections │ │ │ │ FTS │ │ │ │ +│ └──────────┘ └──────────┘ └──────────┘ └────┬─────┘ └──────────┘ │ +│ │ │ +│ ┌──────────┐ │ ┌──────────┐ │ +│ │ secrets │◀── sops policy from branches.yaml │ │ sync │ │ +│ └──────────┘ │ └────┬─────┘ │ +└─────────────────────────────────────────────────────┼───────────────┼───────┘ + │ │ + ┌───────────────────────┘ │ + ▼ ▼ + ┌────────────────────┐ ┌────────────────────┐ ┌────────────────────┐ + │ Local Indexes │ │ Cluster Qdrant │ │ S3 Vector Backup │ + │ │ make sync │ (homelab k8s) │ backup │ │ + │ .lancedb/ │ ─────────────▶ │ collection: │ ─────────────▶ │ snapshots / │ + │ kb.duckdb │ │ knowledge │ ◀───────────── │ object storage │ + └────────────────────┘ └────────────────────┘ restore └────────────────────┘ + + +┌──────────────────────────────────────────────────────────────────────────────┐ +│ Knowledge Tree (tree/) │ +│ │ +│ branches.yaml ─── routing + build policy │ +│ │ │ +│ ├── encryption: true/false (sops) │ +│ ├── books: epub/pdf paths ──▶ extract ──▶ markdown │ +│ ├── pdf: research docs ──▶ extract ──▶ markdown │ +│ └── branches: nested topic tree │ +└──────────────────────────────────────────────────────────────────────────────┘ + + +Build pipeline (make index): + + sources (NAS / books / pdf) + │ + ▼ make extract + tree/**/*.md ◀── branches.yaml (what to pull, encrypt, nest) + │ + ▼ make ingest + chunks → embeddings → LanceDB + DuckDB (BM25) + │ + ▼ make reindex + full-text index ready + │ + ├──▶ make query Q="..." (local hybrid search) + └──▶ make sync (push vectors → Qdrant → S3 backup) +``` + +## Knowledge Bank Library + +A Python library (`kb/`) that reads from the tree on a compilation loop to compile and reconcile as the tree is edited. + +`branches.yaml` defines the tree structure and how to build it — from file encryption to pulling from remote sources such as books or PDF documents. + +```yaml +# tree/branches.yaml (sketch) +networking: + branches: + censorship: + encrypted: true + books: + some-title: "books/epub/..." + pdf: + paper: research/censorship/paper.pdf + kubernetes: + branches: + networking: + ebpf: + books: + learning_ebpf: "books/pdf/learning_ebpf.pdf" +``` + +## Knowledge Bank Indexing + +| Stage | Command | Role | +|-------|---------|------| +| Extract | `make extract` | Convert sources declared in `branches.yaml` → `tree/` markdown | +| Ingest | `make ingest` | Chunk → embed → write LanceDB (incremental) | +| Reindex | `make reindex` | Rebuild BM25 full-text index in DuckDB | +| Query | `make query Q="..."` | Hybrid local search (vector + FTS) | +| Sync | `make sync` | Push local vectors → Qdrant on the cluster | +| Backup | Qdrant → S3 | Snapshot / export vectors to S3 for durable backup | + +Local artifacts: `.lancedb/`, `kb.duckdb`. Remote: Qdrant collection `knowledge`. Durable: S3 vector backup (restore into Qdrant). diff --git a/kb/__main__.py b/kb/__main__.py index 6eaa3a0..1415feb 100644 --- a/kb/__main__.py +++ b/kb/__main__.py @@ -9,7 +9,7 @@ def main(argv: list[str] | None = None) -> int: p = argparse.ArgumentParser(prog="kb", description="Knowledge base RAG pipeline") sub = p.add_subparsers(dest="cmd", required=True) - p_ext = sub.add_parser("extract", help="convert sources/ epub+pdf -> tree/ markdown") + p_ext = sub.add_parser("extract", help="convert sources declared in branches.yaml -> tree/ markdown") p_ext.add_argument("--force", action="store_true", help="re-extract unchanged sources") p_ing = sub.add_parser("ingest", help="incremental ingest markdown -> LanceDB") @@ -54,7 +54,8 @@ def main(argv: list[str] | None = None) -> int: if args.cmd == "extract": from .extract import run as run_extract - run_extract(force=args.force) + stats = run_extract(force=args.force) + return 1 if stats["failed"] else 0 elif args.cmd == "ingest": from .ingest import run run(force=args.force) diff --git a/kb/config.py b/kb/config.py index 6b87e9b..00a735b 100644 --- a/kb/config.py +++ b/kb/config.py @@ -7,7 +7,9 @@ ROOT = Path(__file__).resolve().parent.parent KNOWLEDGE_DIRS = os.environ.get("KB_DIRS", "tree").split(",") -SOURCES_DIR = Path(os.environ.get("KB_SOURCES", ROOT / "sources")) +# Root that relative source paths in branches.yaml resolve against (the NAS). +_vol = os.environ.get("KNOWLEDGE_VOLUME_PATH", "").strip() +SOURCE_VOLUME = Path(_vol).expanduser() if _vol else None # Encryption policy: which subtrees sops encrypts, and the config generated # from it. TREE_DIR is the vault root that branches.yaml describes. diff --git a/kb/extract.py b/kb/extract.py index c25c884..1cb5a14 100644 --- a/kb/extract.py +++ b/kb/extract.py @@ -3,7 +3,10 @@ import datetime as _dt import hashlib import re +import xml.etree.ElementTree as ET from pathlib import Path +from typing import NamedTuple +from urllib.parse import unquote import yaml @@ -17,11 +20,26 @@ ) +def _is_bundle(p: Path) -> bool: + """Books.app stores epubs exploded — a .epub directory, not a zip.""" + return p.suffix.lower() == ".epub" and p.is_dir() + + +def _feed(h, f: Path) -> None: + with f.open("rb") as fh: + for block in iter(lambda: fh.read(1 << 20), b""): + h.update(block) + + def _sha256(p: Path) -> str: h = hashlib.sha256() - with p.open("rb") as f: - for block in iter(lambda: f.read(1 << 20), b""): - h.update(block) + if _is_bundle(p): + # hash (relpath, bytes) pairs so the digest is stable across machines + for f in sorted(x for x in p.rglob("*") if x.is_file()): + h.update(str(f.relative_to(p)).encode() + b"\0") + _feed(h, f) + return h.hexdigest() + _feed(h, p) return h.hexdigest() @@ -47,7 +65,6 @@ def _collapse_blank(text: str) -> str: def _epub_to_md(path: Path) -> str: import ebooklib from ebooklib import epub - from markdownify import markdownify as md book = epub.read_epub(str(path)) @@ -67,9 +84,52 @@ def _epub_to_md(path: Path) -> str: for item in items: if getattr(item, "get_type", lambda: None)() != ebooklib.ITEM_DOCUMENT: continue - html = item.get_content().decode("utf-8", errors="replace") - html = re.sub(r"<\?xml[^>]*\?>", "", html) # drop XML prolog (else it leaks as text) - text = md(html, heading_style="ATX", strip=["script", "style"]).strip() + text = _xhtml_to_md(item.get_content().decode("utf-8", errors="replace")) + if text: + parts.append(text) + return _collapse_blank("\n\n".join(parts)) + + +_XML_PROLOG = re.compile(r"<\?xml[^>]*\?>") +_EPUB_NS = { + "c": "urn:oasis:names:tc:opendocument:xmlns:container", + "opf": "http://www.idpf.org/2007/opf", +} + + +def _xhtml_to_md(html: str) -> str: + from markdownify import markdownify as md + + html = _XML_PROLOG.sub("", html) # drop XML prolog (else it leaks as text) + return md(html, heading_style="ATX", strip=["script", "style"]).strip() + + +def _epub_dir_to_md(path: Path) -> str: + """Read an unzipped epub: container.xml -> OPF -> manifest/spine order.""" + container = ET.parse(path / "META-INF" / "container.xml").getroot() + rootfile = container.find("./c:rootfiles/c:rootfile", _EPUB_NS) + if rootfile is None or not rootfile.get("full-path"): + raise ValueError("no rootfile in META-INF/container.xml") + + opf_path = path / unquote(rootfile.get("full-path")) + opf = ET.parse(opf_path).getroot() + base = opf_path.parent + + href = { + it.get("id"): it.get("href") + for it in opf.findall("./opf:manifest/opf:item", _EPUB_NS) + if it.get("id") and it.get("href") + } + + parts: list[str] = [] + for ref in opf.findall("./opf:spine/opf:itemref", _EPUB_NS): + target = href.get(ref.get("idref")) + if not target: + continue + doc = base / unquote(target.split("#", 1)[0]) + if not doc.is_file(): + continue + text = _xhtml_to_md(doc.read_text(encoding="utf-8", errors="replace")) if text: parts.append(text) return _collapse_blank("\n\n".join(parts)) @@ -87,23 +147,76 @@ def _pdf_to_md(path: Path) -> str: def _convert(path: Path) -> str: if path.suffix.lower() == ".epub": - return _epub_to_md(path) + return _epub_dir_to_md(path) if path.is_dir() else _epub_to_md(path) return _pdf_to_md(path) -# --- provenance / target mapping -------------------------------------------- - -def _tags_for(rel: Path, kind: str) -> list[str]: - # rel = ai/books/x.epub -> domain segments minus the {books,pdf} bucket + file - segs = [s for s in rel.parts[:-1] if s not in {"books", "pdf"}] - return list(dict.fromkeys([*segs, kind])) # dedup, keep order - - -def _target(source: Path) -> Path: - rel = source.relative_to(config.SOURCES_DIR) - tree_root = config.ROOT / config.KNOWLEDGE_DIRS[0].strip() - return tree_root / rel.parent / f"{_slug(source.stem)}.md" - +# --- declarations ------------------------------------------------------------ +# +# Sources are declared in tree/branches.yaml, not discovered on disk. A node may +# carry bucket maps (`books:`, `pdf:`, `epub:`) of slug -> path; the slug becomes +# the tree/ filename and the bucket the tree/ subfolder: +# +# networking: +# branches: +# censorship: +# pdf: +# decoy-ccs12: research/censorship/decoy-ccs12.pdf +# # -> tree/networking/censorship/pdf/decoy-ccs12.md +# +# Relative paths resolve against $KNOWLEDGE_VOLUME_PATH; absolute paths (and ~) +# are used as-is, so a source can live on the NAS or anywhere else. `branches:` +# is reserved for the tree hierarchy and is never read as a bucket. + +BUCKETS = ("books", "pdf", "epub") + + +class Decl(NamedTuple): + slug: str + bucket: str + domain: tuple[str, ...] + declared: str # verbatim from branches.yaml, kept for provenance + + @property + def target(self) -> Path: + tree_root = config.ROOT / config.KNOWLEDGE_DIRS[0].strip() + return tree_root.joinpath(*self.domain, self.bucket, f"{self.slug}.md") + + @property + def kind(self) -> str: + return "book" if self.bucket in ("books", "epub") else "paper" + + def resolve(self) -> Path: + p = Path(self.declared).expanduser() + if p.is_absolute(): + return p + if not config.SOURCE_VOLUME: + raise ValueError( + f"{self.declared!r} is relative but KNOWLEDGE_VOLUME_PATH is unset" + ) + return config.SOURCE_VOLUME / p + + +def _walk(node: dict, domain: tuple[str, ...], out: list[Decl]) -> None: + if not isinstance(node, dict): + return + for bucket in BUCKETS: + for slug, declared in (node.get(bucket) or {}).items(): + if declared: # '' means "known, path not filled in yet" + out.append(Decl(str(slug), bucket, domain, str(declared))) + for name, child in (node.get("branches") or {}).items(): + _walk(child or {}, (*domain, str(name)), out) + + +def declarations() -> list[Decl]: + policy = yaml.safe_load(config.BRANCHES_PATH.read_text()) or {} + out: list[Decl] = [] + for name, child in policy.items(): + _walk(child or {}, (str(name),), out) + return sorted(out, key=lambda d: (d.domain, d.bucket, d.slug)) + + +# --- provenance -------------------------------------------------------------- def _existing_source_hash(md_path: Path) -> str | None: if not md_path.exists(): @@ -119,34 +232,42 @@ def _existing_source_hash(md_path: Path) -> str | None: return str(h) if h else None -def _frontmatter(source: Path, source_hash: str) -> str: - rel = source.relative_to(config.SOURCES_DIR) - kind = "book" if (source.suffix.lower() == ".epub" or "books" in rel.parts) else "paper" +def _frontmatter(decl: Decl, source: Path, source_hash: str) -> str: fm = { "title": _clean_title(source.stem), - "source": str(source.relative_to(config.ROOT)), - "source_type": kind, + "source": decl.declared, # portable: no absolute host path + "source_type": decl.kind, "source_hash": source_hash, - "tags": _tags_for(rel, kind), + "tags": list(dict.fromkeys([*decl.domain, decl.kind])), "extracted": _dt.date.today().isoformat(), } return "---\n" + yaml.safe_dump(fm, sort_keys=False, allow_unicode=True) + "---\n\n" -def _iter_binaries() -> list[Path]: - if not config.SOURCES_DIR.exists(): - return [] - return sorted( - p for p in config.SOURCES_DIR.rglob("*") - if p.is_file() and p.suffix.lower() in BINARY_SUFFIXES - ) +def run(force: bool = False) -> dict: + extracted = skipped = failed = missing = 0 + for decl in declarations(): + label = "/".join((*decl.domain, decl.bucket, decl.slug)) + try: + src = decl.resolve() + except ValueError as e: + print(f" ! {label}: {e}") + failed += 1 + continue -def run(force: bool = False) -> dict: - extracted = skipped = failed = 0 - for src in _iter_binaries(): - target = _target(src) - src_hash = _sha256(src) + if not (src.is_file() or _is_bundle(src)): + print(f" ? {label}: source not found at {decl.declared}") + missing += 1 + continue + + target = decl.target + try: + src_hash = _sha256(src) + except OSError as e: # unreadable mount, permissions, vanished file + print(f" ! {label}: cannot read source ({e.strerror})") + failed += 1 + continue if not force and _existing_source_hash(target) == src_hash: skipped += 1 @@ -155,21 +276,23 @@ def run(force: bool = False) -> dict: try: body = _convert(src) except Exception as e: # a bad file shouldn't sink the whole run - print(f" ! {src.relative_to(config.SOURCES_DIR)}: {type(e).__name__}: {e}") + print(f" ! {label}: {type(e).__name__}: {e}") failed += 1 continue if not body.strip(): - print(f" ! {src.relative_to(config.SOURCES_DIR)}: empty extraction") + print(f" ! {label}: empty extraction") failed += 1 continue target.parent.mkdir(parents=True, exist_ok=True) - target.write_text(_frontmatter(src, src_hash) + body + "\n", encoding="utf-8") + target.write_text(_frontmatter(decl, src, src_hash) + body + "\n", encoding="utf-8") extracted += 1 - print(f" + {target.relative_to(config.ROOT)} <- {src.relative_to(config.SOURCES_DIR)}") + print(f" + {target.relative_to(config.ROOT)} <- {decl.declared}") print( - f"\nextract: {extracted} converted, {skipped} unchanged, {failed} failed" + f"\nextract: {extracted} converted, {skipped} unchanged, " + f"{missing} missing, {failed} failed" ) - return {"extracted": extracted, "skipped": skipped, "failed": failed} + return {"extracted": extracted, "skipped": skipped, + "missing": missing, "failed": failed} diff --git a/sources/.gitkeep b/sources/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/sources/MANIFEST.md b/sources/MANIFEST.md deleted file mode 100644 index ba10fff..0000000 --- a/sources/MANIFEST.md +++ /dev/null @@ -1,52 +0,0 @@ ---- -title: Source Manifest -description: Provenance record for binary source artifacts (books, papers) kept outside git. -summary: The binaries live locally / in object storage and are gitignored; this manifest is the versioned record. -category: manifest -tags: -- sources -- provenance -last_updated: 2026-08-14 ---- - -# Source Manifest - -Binary source artifacts (epubs, PDFs) are **not** committed — they are gitignored -(`*.epub`, `*.pdf`). Only their **extracted markdown** (under `tree/…`) enters the -retrieval index (`kb/ingest.py` globs `tree/**/*.md`). This manifest is the tracked -provenance record so the sources are known even without the blobs. - -Layout mirrors `tree/` by domain: - -``` -sources//{books,pdf}/ -tree//… ← extracted markdown lives here, links back to source -``` - -Local files are kept for re-extraction; back them up to homelab object storage. - -## ai → `tree/ai/` - -| Type | Title | Author | File | sha256(16) | -|---|---|---|---|---| -| book | OpenClaw AI in Production | Ken Huang | `sources/ai/books/OpenClaw AI in Production ….epub` | `fb1d825182a00f72` | -| book | The Context Engineering Handbook | Drew Breunig | `sources/ai/books/The Context Engineering Handbook ….epub` | `31252a2d889098a6` | -| pdf | Build Your Own Agentic AI Framework | Gigi Sayfan | `sources/ai/pdf/Build Your Own Agentic AI Framework ….pdf` | `5347e44091e9f0df` | -| pdf | Agents | — | `sources/ai/pdf/agents.pdf` | `6beddc2fab0e2a72` | -| pdf | LLM Training | — | `sources/ai/pdf/llm-training.pdf` | `716484a814cf981c` | - -## networking/censorship → `tree/networking/censorship/` - -| Type | Title | Author | File | sha256(16) | -|---|---|---|---|---| -| book | Controlled Experimentation of Digital Forensics | Oliveira, Silva et al. | `sources/networking/censorship/books/Controlled Experimentation ….epub` | `306c4ce1f28f713d` | -| pdf | Just Add Water (WATER/WATM runtime) | — | `sources/networking/censorship/pdf/foci-2024-0003.pdf` | `d8cf0ccd9bb46035` | -| pdf | Internet Censorship | — | `sources/networking/censorship/pdf/internet-censorship.pdf` | `85a4f9199323cbb0` | -| pdf | Routing Around Decoys | — | `sources/networking/censorship/pdf/decoy-ccs12.pdf` | `1f0eb34940571b1e` | -| pdf | Deploying Xray-core in a Home Lab | — | `sources/networking/censorship/pdf/Deploying Xray-core in a Home Lab ….pdf` | `33c6fe5c221b355e` | - -## specialized_tooling/webassembly → `tree/specialized_tooling/webassembly/` - -| Type | Title | Author | File | sha256(16) | -|---|---|---|---|---| -| pdf | Server-Side WebAssembly | Danilo Chiarlone | `sources/specialized_tooling/webassembly/pdf/Server-Side WebAssembly ….pdf` | `3ec57bb279cabda1` | diff --git a/sources/ai/books/.gitkeep b/sources/ai/books/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/sources/ai/pdf/.gitkeep b/sources/ai/pdf/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/sources/networking/censorship/books/.gitkeep b/sources/networking/censorship/books/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/sources/networking/censorship/pdf/.gitkeep b/sources/networking/censorship/pdf/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/sources/specialized_tooling/webassembly/pdf/.gitkeep b/sources/specialized_tooling/webassembly/pdf/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/tree/ai/books/the-context-engineering-handbook-drew-breunig.md b/tree/ai/books/the-context-engineering-handbook-drew-breunig.md index 6a4adb0..781e529 100644 --- a/tree/ai/books/the-context-engineering-handbook-drew-breunig.md +++ b/tree/ai/books/the-context-engineering-handbook-drew-breunig.md @@ -1,66 +1,69 @@ --- -title: The Context Engineering Handbook (Drew Breunig -source: sources/ai/books/The Context Engineering Handbook (Drew Breunig) (z-library.sk, - 1lib.sk, z-lib.sk).epub +title: context_engineering_handboook +source: books/epub/context_engineering_handboook source_type: book source_hash: 31252a2d889098a699c5c23aa6ca8c4695fd7133ee05ff6b41e75daeb1ae5004 tags: - ai - book -extracted: '2026-08-14' +extracted: '2026-09-07' --- -xml version='1.0' encoding='utf-8'? +# **The Context Engineering Handbook** +## The Art & Science of Structuring Information for AI -![The cover of this book](assets/cover.png) +## **Edited by Drew Breunig** -xml version='1.0' encoding='utf-8'? +css_assets/titlepage_footer_ebook.png -# The Context Engineering Handbook +**The Context Engineering Handbook** -The Art & Science of Structuring Information for AI +Edited by Drew Breunig -With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. +Copyright © 2027 O’Reilly Media, Inc. All rights reserved. -Edited by Drew Breunig +Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. -xml version='1.0' encoding='utf-8'? +O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also +[available for most titles (https://oreilly.com). For more information, contact our corporate/institutional sales](https://oreilly.com) +department: 800-998-9938 or _corporate@oreilly.com_ . -# The Context Engineering Handbook +Acquisitions Editor: Andy Kwan -Edited by Drew Breunig +Development Editor: Michele Cronin -Copyright © 2027 O’Reilly Media, Inc. All rights reserved. +Production Editor: Katherine Tozer -Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. +Cover Designer: Susan Brown -O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (). For more information, contact our corporate/institutional sales department: 800-998-9938 or *corporate@oreilly.com*. +Cover Illustrator: José Marzan Jr. -* Acquisitions Editor: Andy Kwan -* Development Editor: Michele Cronin -* Production Editor: Katherine Tozer -* Cover Designer: Susan Brown -* Cover Illustrator: José Marzan Jr. -* Interior Designer: David Futato -* Interior Illustrator: Kate Dullea +Interior Designer: David Futato -* June 2027: First Edition +Interior Illustrator: Kate Dullea -# Revision History for the Early Release +June 2027: First Edition -* 2026-07-30: First Release +**Revision History for the Early Release** -See for release details. + - 2026-07-30: First Release -The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. *The Context Engineering Handbook*, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. +[See https://oreilly.com/catalog/errata.csp?isbn=9798341671041 for release details.](https://oreilly.com/catalog/errata.csp?isbn=9798341671041) -The views expressed in this work are those of the authors and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. +The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. _The Context Engineering Handbook_, the cover image, +and related trade dress are trademarks of O’Reilly Media, Inc. -979-8-341-67101-0 +The views expressed in this work are those of the authors and do not represent the publisher’s views. While the +publisher and the authors have used good faith efforts to ensure that the information and instructions contained in +this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including +without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information +and instructions contained in this work is at your own risk. If any code samples or other technology this work contains +or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to +ensure that your use thereof complies with such licenses and/or rights. -xml version='1.0' encoding='utf-8'? +979-8-341-67101-0 -# Brief Table of Contents (*Not Yet Final*) +# Brief Table of Contents ( Not Yet Final ) Part I: Introduction @@ -70,725 +73,1312 @@ Chapter 2: How LLMs Work: A Mental Model for Context Engineers, by Drew Breunig Part II: How Contexts Fail -*Chapter 3: Context Poisoning* (unavailable) +_Chapter 3: Context Poisoning_ (unavailable) Chapter 4: Context Rot, by Kelly Hong (available) -*Chapter 5: Context Confusion* (unavailable) +_Chapter 5: Context Confusion_ (unavailable) -*Chapter 6: Fighting the Weights* (unavailable) +_Chapter 6: Fighting the Weights_ (unavailable) Part III: How to Fix Your Context -*Chapter 7: Data Retrieval* (unavailable) +_Chapter 7: Data Retrieval_ (unavailable) -*Chapter 8: Context Quarantine* (unavailable) +_Chapter 8: Context Quarantine_ (unavailable) -*Chapter 9: Context Summarization* (unavailable) +_Chapter 9: Context Summarization_ (unavailable) -*Chapter 10: Context Pruning* (unavailable) +_Chapter 10: Context Pruning_ (unavailable) -*Chapter 11: Context Offloading* (unavailable) +_Chapter 11: Context Offloading_ (unavailable) -*Chapter 12: Eval Analysis* (unavailable) +_Chapter 12: Eval Analysis_ (unavailable) -*Chapter 13: Prompt Optimization* (unavailable) +_Chapter 13: Prompt Optimization_ (unavailable) -*Chapter 14: Adding Efficiency* (unavailable) +_Chapter 14: Adding Efficiency_ (unavailable) -*Chapter 15: Security* (unavailable) +_Chapter 15: Security_ (unavailable) Part IV: Case Studies -*Chapter 16: Managing Contexts When Coding* (unavailable) +_Chapter 16: Managing Contexts When Coding_ (unavailable) Chapter 17: Hex, by Caitlin Colgrove (available) -*Chapter 18: How to Write a Great System Prompt, by Srihari -Sriraman* (unavailable) - -*Chapter 19: Compound AI: Offloading Search to Smaller Models* (unavailable) - -xml version='1.0' encoding='utf-8'? - -# Chapter 1. The Road from Prompt Engineering to Context Engineering (and Where We Go From Here) - -By Mike Taylor - -# A Note for Early Release Readers - -With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. - -This will be the 1st chapter of the final book. Please note that the GitHub repo will be made active later on. - -If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at *mcronin@oreilly.com*. - -This chapter will deliver a brief history of “prompt engineering” as a practice, explaining how it evolved around single-turn LLM applications and chatbot usage, and connect it to the emergence of “context engineering”. The chapter is based on my experience as an AI engineer from 2020 to today, including anecdotes of what problem each new evolution solved. I’ll introduce you to the technologies and patterns that gave rise to “context engineering”, and how it differs from prompt engineering. We’ll explore a few potential futures, including prompt optimization and managing fleets of agents, and why we’ll never escape the need to clearly communicate what we want AI programs to do. - -# Origins and Early Days (2018–2021) - -The transformer model was conceived in 2017 with the famous [“Attention Is All You Need”](https://arxiv.org/abs/1706.03762) paper, but GPT-2 in 2019 was the first language model that I saw smart people in my network experimenting with and talking about. The model was initially deemed [“too dangerous to release”](https://slate.com/technology/2019/02/openai-gpt2-text-generating-algorithm-ai-dangerous.html) by OpenAI, which now seems quaint given what we have access to today. AI copywriting was an early use case, which interested me as someone running a 50-person marketing agency, but the results were too unreliable for serious use. - -The release of GPT-3 in June 2020 was the birth of prompt engineering. The word “prompt” had been used in computing [since 1977](https://www.etymonline.com/word/prompt), but the phrase “prompt engineering” [was coined](https://gwern.net/gpt-3#effective-prompt-programming) by writer Gwern Branwen in June of 2020, a few weeks after GPT-3’s release. The [GPT-3 release paper](https://arxiv.org/abs/2005.14165) put strong emphasis on prompting, showing that larger language models responded significantly better on tasks with instructions and examples in the prompt ([Figure 1-1](#ch01_figure_1_1785165976511145)). - -![](assets/ch01_figure_1_1785165976511145.png) - -###### Figure 1-1. Larger models make increasingly efficient use of in-context information. [Source](https://arxiv.org/pdf/2005.14165) - -Sticking with the “too dangerous to release” story, OpenAI slow-rolled GPT-3 access, making it only available to researchers initially. I first got access through Copy.ai, after the founder Paul Yacoubian tweeted he would give a free account to anyone who was rejected from YCombinator. I was blown away, but had left my agency in March 2020. I didn’t have immediate use cases, but I started experimenting in my spare time. - -GPT-3 (davinci) required a lot of experimentation because it could only handle 2,049 tokens, and was still extremely unreliable. Great efforts were made to find exactly the right combination of words that gave better results, *“figuring out how to hack the prompt by adding one magic word to the end that changes everything else”* as [Sam Altman described it](https://web.archive.org/web/20220914071126/https://greylock.com/greymatter/sam-altman-ai-for-the-next-era/). Few-shot learning, or including examples in the prompt, was the dominant technique, but early [RAG (Retrieval Augmented Generation)](https://arxiv.org/abs/2005.11401) methods were developed to bring relevant context using vector search into the prompt at the right time. - -It was clear at this point AI would be a big thing, with Andrej Karpathy calling prompt engineering “[Software 3.0](https://x.com/karpathy/status/1273788774422441984?s=20)”—the next big evolution in computing since machine learning. Over the next two years I used GPT-3 to automate a lot of the work I was doing, in order to free up more time to play around with AI. I made a conscious effort to try every single task with GPT-3 first, and spend some time prompting to see if I could get it right, before giving up and doing it myself. It was during this time period that I developed the prompt engineering principles my [first O’Reilly book](https://www.oreilly.com/library/view/prompt-engineering-for/9781098153427/) was based on: - -Give Direction -: Describe the desired style in detail, or reference a relevant persona. - -Specify Format -: Define what rules to follow, and the required structure of the response. - -Provide Examples -: Insert a diverse set of test cases where the task was done correctly. - -Evaluate Quality -: Identify errors and rate responses, testing what drives performance. - -Divide Labor -: Split tasks into multiple steps, chained together for complex goals. - -This was at a time when most prompts were call and response—most AI users were not doing anything “agentic” and had to be coached to try and break prompts up into multiple steps for better reliability. The human was manually curating the examples, writing out descriptions, and formalizing the evaluation methods, in a copy-pasted prompt template. In testing to improve these prompt templates, I noticed that the same things that worked with prompting GPT-3 mapped to management principles I used when running my marketing agency. Neural networks are based on the function of biological neurons, so it made sense to me that some of the same techniques you use for managing biological intelligence would also help when managing AI. - -# Image Generation Era (2022) - -Prompt engineering was still a niche activity for the few people who were experimenting with GPT-3, which was still primarily accessed via an API. It was actually image generation with the launch of DALL-E 2 in April 2022 that brought prompt engineering into the mainstream. - -OpenAI had revealed DALL-E in January 2021, but DALL-E 2 was the first text-to-image model to demonstrate more realistic images that combined concepts, attributes, and styles. The famous “[horse-riding astronaut](https://www.technologyreview.com/2022/04/06/1049061/dalle-openai-gpt3-ai-agi-multimodal-image-generation/)” image and those like it shared on social media by those with access, drummed up demand. Again, OpenAI slow-rolled access which was only granted to a handful of researchers and industry insiders. - -Like millions of others, I got access to Midjourney months before they dropped the waitlist for DALL-E 2. This allowed the small startup to build a vibrant community centered on Discord (the only way you could use the model at the time). Stable Diffusion was released in August 2022 and became one of the [fastest growing open-source projects](https://x.com/a16z/status/1592922394275872768) in history ([Figure 1-2](#ch01_figure_2_1785165976511195)). People were astounded that you could get comparable results to DALL-E 2, running for free on your local computer (if you had a GPU). - -![](assets/ch01_figure_2_1785165976511195.png) - -###### Figure 1-2. Stable Diffusion developer adoption, a16z ([source](https://x.com/a16z/status/1592922394275872768/photo/1)) - -This was when prompt engineering went mainstream, with millions of people in online communities sharing tips on how to get reliable results out of these image models. In image generation reliability meant two things: 1) getting the subjective style that you wanted, and 2) keeping character and scene consistency between images so they could start to tell stories. This required precision that was hard to capture in plain English for those of us who had no art history background. I remember learning the term “Trompe-l'œil” (a Renaissance painting technique for showing 3D objects on a two-dimensional surface) for the first time, after seeing other people use that effect in the Midjourney Discord (which had millions of people sharing their work by default). Once you know a specific magic word you can use it as shorthand to get that precise style. The key to success was knowing the right sequence of magic words to get what you wanted. +_Chapter 18: How to Write a Great System Prompt, by Srihari Sriraman_ (unavailable) -Being able to run the models on their own computer (without paying per token) made a big difference to adoption at a time when you would have to experiment with 10-20 prompts to get a usable image. People started training their own fine-tuned versions of the models to cater to specific tastes, and to avoid [overly strict content moderation from OpenAI](https://x.com/hammer_mt/status/1580467310326091776?s=20). Of course, like most new technology, people immediately used it to create explicit images, and ironically many of these NSFW models became very good at human shape and form, and were repurposed as the first “photorealistic” models ([requiring negative keywords to avoid mishaps!](https://x.com/levelsio/status/1680665706235404288?s=20)). Many innovative techniques like inpainting (replacing part of an existing image), control nets (generating a new image over the same lines) and dreambooth (teaching a new character or style to a model) became widespread in the open-source community long before becoming available in frontier models. This was for many people an important lesson in the value of open-source models, which we took with us when the market refocused on text generation again at the end of the year. +_Chapter 19: Compound AI: Offloading Search to Smaller Models_ (unavailable) -GitHub Copilot became generally available in June 2022, and defined the paradigm for all enterprise adoption of AI tools going forward. It popularized the copilot model, where you collaborate with an AI to get work done, in this case autocompleting your code as you type. I distinctly remember at the time that I started doing test-driven development, writing more comments in my code and using types religiously, because it would help Copilot do a better job at autocompleting—another sign that the practices we developed for people to collaborate better, would transfer to working with AI. +# **Chapter 1. The Road from Prompt Engineering to** **Context Engineering (and Where We Go From Here)** -One hangover from this period is that the term “prompt engineer” started to get polluted by Twitter gurus cashing in on the hype. Early prompt engineers were actual engineers—GPT-3 was only available via API. This latest wave of non-technical prompt engineers were the equivalent of witch doctors or [medieval alchemists](https://blog.stackademic.com/stop-writing-prompts-like-a-medieval-alchemist-ca40c6317f13), writing prompts like [spells or incantations](https://flowgpt.com/p/darkhumorgpt-amazing-chatgpt-prompt-that-will-make-dark-humor-jokes), rather than formally a/b testing to prove what works. This started to give prompt engineering a bad name, with [Sam Altman declaring](https://web.archive.org/web/20220914071126/https://greylock.com/greymatter/sam-altman-ai-for-the-next-era/) *“I don’t think we’ll still be doing prompt engineering in five years”,* in September 2022, continuing to say *“what will matter is the quality of ideas and the understanding of what you want.”* +**_By Mike Taylor_** -# ChatGPT Revolution (Late 2022–2023) +This chapter will deliver a brief history of “prompt engineering” as a practice, explaining how it evolved around +single-turn LLM applications and chatbot usage, and connect it to the emergence of “context engineering”. The +chapter is based on my experience as an AI engineer from 2020 to today, including anecdotes of what problem each +new evolution solved. I’ll introduce you to the technologies and patterns that gave rise to “context engineering”, and +how it differs from prompt engineering. We’ll explore a few potential futures, including prompt optimization and +managing fleets of agents, and why we’ll never escape the need to clearly communicate what we want AI programs to +do. -In November 2023, OpenAI released ChatGPT as a free research preview, which interacted in a conversational way. Up until then, LLMs had been completion models, meaning you would put in your prompt for the model to complete the next series of tokens. ChatGPT was based on OpenAI’s research with [InstructGPT](https://openai.com/index/instruction-following/), where they found ways to fine-tune GPT-3 to better follow instructions and perform natural language tasks. Within 5 days ChatGPT hit 1 million users, and 100 million within 2 months, making it one of the fastest growing consumer products in history ([Figure 1-3](#ch01_figure_3_1785165976511225)). AI had crossed the chasm. +## **Origins and Early Days (2018–2021)** -![](assets/ch01_figure_3_1785165976511225.png) +[The transformer model was conceived in 2017 with the famous “Attention Is All You Need” paper, but GPT-2 in 2019](https://arxiv.org/abs/1706.03762) +was the first language model that I saw smart people in my network experimenting with and talking about. The model +[was initially deemed “too dangerous to release” by OpenAI, which now seems quaint given what we have access to](https://slate.com/technology/2019/02/openai-gpt2-text-generating-algorithm-ai-dangerous.html) +today. AI copywriting was an early use case, which interested me as someone running a 50-person marketing agency, +but the results were too unreliable for serious use. -###### Figure 1-3. ChatGPT: Number of days to 100m user ([source](https://x.com/kylelf_/status/1623679176246185985?t=g9wnm52DZEfe42CJAjooRA&s=03)) +The release of GPT-3 in June 2020 was the birth of prompt engineering. The word “prompt” had been used in +[computing since 1977, but the phrase “prompt engineering” was coined by writer Gwern Branwen in June of 2020, a](https://gwern.net/gpt-3#effective-prompt-programming) +[few weeks after GPT-3’s release. The GPT-3 release paper put strong emphasis on prompting, showing that larger](https://arxiv.org/abs/2005.14165) +language models responded significantly better on tasks with instructions and examples in the prompt (Figure 1-1). -Up until this point I had been only doing AI for myself–after ChatGPT launched everybody suddenly wanted AI training for their team. Enterprise adoption happened surprisingly quickly: I spoke at an AI conference just a few months after the launch and many large enterprises I spoke to were already rolling out pilot projects. I saw that AI papers published to ArXiv were growing exponentially, [doubling every two years](https://www.reddit.com/r/singularity/comments/xwdzr5/the_number_of_ai_papers_on_arxiv_per_month_grows/), and realized that businesses would need help keeping up. +## Figure 1-1. Larger models make increasingly efficient use of in-context information. Source + +Sticking with the “too dangerous to release” story, OpenAI slow-rolled GPT-3 access, making it only available to +researchers initially. I first got access through Copy.ai, after the founder Paul Yacoubian tweeted he would give a free +account to anyone who was rejected from YCombinator. I was blown away, but had left my agency in March 2020. I +didn’t have immediate use cases, but I started experimenting in my spare time. -ChatGPT worked much better than GPT-3, particularly when they upgraded the model from GPT-3.5 to GPT-4. However, that just meant we used it for more ambitious tasks, and so reliability remained a problem. Suddenly people were willing to pay me to teach them prompt engineering, as a potential solution to the same issues we saw with image generation around reliability and consistency. It’s hard to get good results if you can’t describe what you want in the prompt using plain English. +GPT-3 (davinci) required a lot of experimentation because it could only handle 2,049 tokens, and was still extremely +unreliable. Great efforts were made to find exactly the right combination of words that gave better results, _“figuring_ +_out how to hack the prompt by adding one magic word to the end that changes everything else”_ [as Sam Altman described it.](https://web.archive.org/web/20220914071126/https://greylock.com/greymatter/sam-altman-ai-for-the-next-era/) +[Few-shot learning, or including examples in the prompt, was the dominant technique, but early RAG (Retrieval](https://arxiv.org/abs/2005.11401) +[Augmented Generation) methods were developed to bring relevant context using vector search into the prompt at the](https://arxiv.org/abs/2005.11401) +right time. -Intense competition in the AI space that continues to this day ignited in 2022-23. Google had invented the transformer, but had been too slow to market, allegedly due to concerns over safety, negative press, and potential disruption of their comfortable market position in search. Management issued a [“code red”](https://www.businessinsider.com/google-management-issues-code-red-over-chatgpt-report-2022-12) after seeing the success of ChatGPT, bringing co-founders Larry Page and Sergey Brin out of quasi-retirement to release Bard in March 2023, later rebranded to Gemini. +[It was clear at this point AI would be a big thing, with Andrej Karpathy calling prompt engineering “Software 3.0”—](https://x.com/karpathy/status/1273788774422441984?s=20) +the next big evolution in computing since machine learning. Over the next two years I used GPT-3 to automate a lot of +the work I was doing, in order to free up more time to play around with AI. I made a conscious effort to try every +single task with GPT-3 first, and spend some time prompting to see if I could get it right, before giving up and doing it +[myself. It was during this time period that I developed the prompt engineering principles my first O’Reilly book was](https://www.oreilly.com/library/view/prompt-engineering-for/9781098153427/) +based on: -The Anthropic founding team that was formed after splitting from OpenAI in 2021 over safety concerns, were also cajoled into launching their own ChatGPT competitor called Claude in March 2023. At the same time, OpenAI launched GPT-4, which was a significant leap over GPT-3 and GPT-3.5 (the fine-tuned ChatGPT model), and had a 8,192 context window. Anthropic responded with a 100k context window in May 2023, nicknamed [“clong”](https://x.com/livgorton/status/1656768304529436672) or “claude long” by fans of the model. This was the first time I heard [“RAG is dead”](https://x.com/helloiamleonie/status/1899389996408189259): why would you need to retrieve context from a database when you could fit 100k tokens or 75k words in the prompt? - -Despite huge adoption, GitHub Copilot failed to develop noticeable new features, leaving the door open to Cursor, which launched in March 2023 and iterated quickly to several innovations. My personal favorite was “yolo” mode, where the agent could edit multiple files at once, which was terrifying at the time for those of us used to tab autocomplete. - -# Early Agent Era (2023–2024) - -Although published in October 2022, the [ReAct (Reason and Act) pattern](https://arxiv.org/abs/2210.03629) started to pick up steam in 2023, as function calling or tool calling began to be supported in the summer by OpenAI. Most AI agents today are an evolution on this foundation, where you prompt an LLM to think, then take an action, before observing the outcome and repeating the loop. Calling external tools with pre-determined code helped AI give more reliable results, for example being able to search first before answering a question. LangChain, an LLM orchestration framework, began to be widely popular as people looked to write more transferable code across models and workflow patterns. - -Despite predictions that prompt engineering was going away as models got smarter and context windows got longer, it became more important than ever. Greg Kamradt’s famous [“needle-in-a-haystack”](https://github.com/gkamradt/LLMTest_NeedleInAHaystack/blob/main/README.md) test found that AI models didn’t uniformly pay attention to all the information in long prompts, dispelling the myth that “RAG is dead” because you could just put everything in the context window. Additionally, GPT-4 was [15-30x more expensive than GPT-3.5](https://www.facebook.com/groups/DeepNetGroup/posts/1935208506872022/), and so if you could get your workflow running on a smaller, cheaper model with better prompting, it was worth the effort. With Meta’s release of the Llama model series, it was suddenly possible to get GPT-3 level models running for free locally on your computer—the “[stable diffusion moment](https://simonwillison.net/2023/Mar/11/llama/)” for language models, as British programmer Simon Willison put it. - -The hype around prompt engineering exploded around this time, with Anthropic advertising a new role for “Prompt Engineer and Librarian” with an attention-grabbing salary of $250k-$335k. People start asking if prompt engineer will be a [job of the future](https://www.weforum.org/stories/2023/03/new-emerging-jobs-work-skills/), which is a bit like asking if you should start calling yourself an Excel engineer. I actually was calling myself a prompt engineer at the time, and was working full time freelancing as well as growing my [Udemy course](https://www.udemy.com/course/prompt-engineering-for-ai/) which launched in April 2023. +_Give Direction_ + +Describe the desired style in detail, or reference a relevant persona. + +_Specify Format_ + +Define what rules to follow, and the required structure of the response. + +_Provide Examples_ + +Insert a diverse set of test cases where the task was done correctly. + +_Evaluate Quality_ + +Identify errors and rate responses, testing what drives performance. + +_Divide Labor_ + +Split tasks into multiple steps, chained together for complex goals. + +This was at a time when most prompts were call and response—most AI users were not doing anything “agentic” and +had to be coached to try and break prompts up into multiple steps for better reliability. The human was manually +curating the examples, writing out descriptions, and formalizing the evaluation methods, in a copy-pasted prompt +template. In testing to improve these prompt templates, I noticed that the same things that worked with prompting +GPT-3 mapped to management principles I used when running my marketing agency. Neural networks are based on +the function of biological neurons, so it made sense to me that some of the same techniques you use for managing +biological intelligence would also help when managing AI. + +## **Image Generation Era (2022)** + +Prompt engineering was still a niche activity for the few people who were experimenting with GPT-3, which was still +primarily accessed via an API. It was actually image generation with the launch of DALL-E 2 in April 2022 that +brought prompt engineering into the mainstream. + +OpenAI had revealed DALL-E in January 2021, but DALL-E 2 was the first text-to-image model to demonstrate more +[realistic images that combined concepts, attributes, and styles. The famous “horse-riding astronaut” image and those](https://www.technologyreview.com/2022/04/06/1049061/dalle-openai-gpt3-ai-agi-multimodal-image-generation/) +like it shared on social media by those with access, drummed up demand. Again, OpenAI slow-rolled access which +was only granted to a handful of researchers and industry insiders. + +Like millions of others, I got access to Midjourney months before they dropped the waitlist for DALL-E 2. This allowed +the small startup to build a vibrant community centered on Discord (the only way you could use the model at the +[time). Stable Diffusion was released in August 2022 and became one of the fastest growing open-source projects in](https://x.com/a16z/status/1592922394275872768) +history (Figure 1-2). People were astounded that you could get comparable results to DALL-E 2, running for free on +your local computer (if you had a GPU). + +## Figure 1-2. Stable Diffusion developer adoption, a16z ( source ) + +This was when prompt engineering went mainstream, with millions of people in online communities sharing tips on +how to get reliable results out of these image models. In image generation reliability meant two things: 1) getting the +subjective style that you wanted, and 2) keeping character and scene consistency between images so they could start +to tell stories. This required precision that was hard to capture in plain English for those of us who had no art history +background. I remember learning the term “Trompe-l'œil” (a Renaissance painting technique for showing 3D objects +on a two-dimensional surface) for the first time, after seeing other people use that effect in the Midjourney Discord +(which had millions of people sharing their work by default). Once you know a specific magic word you can use it as +shorthand to get that precise style. The key to success was knowing the right sequence of magic words to get what you +wanted. + +Being able to run the models on their own computer (without paying per token) made a big difference to adoption at +a time when you would have to experiment with 10-20 prompts to get a usable image. People started training their +[own fine-tuned versions of the models to cater to specific tastes, and to avoid overly strict content moderation from](https://x.com/hammer_mt/status/1580467310326091776?s=20) +[OpenAI. Of course, like most new technology, people immediately used it to create explicit images, and ironically](https://x.com/hammer_mt/status/1580467310326091776?s=20) +many of these NSFW models became very good at human shape and form, and were repurposed as the first +[“photorealistic” models (requiring negative keywords to avoid mishaps!). Many innovative techniques like inpainting](https://x.com/levelsio/status/1680665706235404288?s=20) +(replacing part of an existing image), control nets (generating a new image over the same lines) and dreambooth +(teaching a new character or style to a model) became widespread in the open-source community long before +becoming available in frontier models. This was for many people an important lesson in the value of open-source +models, which we took with us when the market refocused on text generation again at the end of the year. + +GitHub Copilot became generally available in June 2022, and defined the paradigm for all enterprise adoption of AI +tools going forward. It popularized the copilot model, where you collaborate with an AI to get work done, in this case +autocompleting your code as you type. I distinctly remember at the time that I started doing test-driven development, +writing more comments in my code and using types religiously, because it would help Copilot do a better job at +autocompleting—another sign that the practices we developed for people to collaborate better, would transfer to +working with AI. + +One hangover from this period is that the term “prompt engineer” started to get polluted by Twitter gurus cashing in +on the hype. Early prompt engineers were actual engineers—GPT-3 was only available via API. This latest wave of +[non-technical prompt engineers were the equivalent of witch doctors or medieval alchemists, writing prompts like](https://blog.stackademic.com/stop-writing-prompts-like-a-medieval-alchemist-ca40c6317f13) +[spells or incantations, rather than formally a/b testing to prove what works. This started to give prompt engineering a](https://flowgpt.com/p/darkhumorgpt-amazing-chatgpt-prompt-that-will-make-dark-humor-jokes) +[bad name, with Sam Altman declaring](https://web.archive.org/web/20220914071126/https://greylock.com/greymatter/sam-altman-ai-for-the-next-era/) _“I don’t think we’ll still be doing prompt engineering in five years”,_ in September +2022, continuing to say _“what will matter is the quality of ideas and the understanding of what you want.”_ + +## **ChatGPT Revolution (Late 2022–2023)** + +In November 2023, OpenAI released ChatGPT as a free research preview, which interacted in a conversational way. +Up until then, LLMs had been completion models, meaning you would put in your prompt for the model to complete +[the next series of tokens. ChatGPT was based on OpenAI’s research with InstructGPT, where they found ways to fine-](https://openai.com/index/instruction-following/) +tune GPT-3 to better follow instructions and perform natural language tasks. Within 5 days ChatGPT hit 1 million +users, and 100 million within 2 months, making it one of the fastest growing consumer products in history +(Figure 1-3). AI had crossed the chasm. + +## Figure 1-3. ChatGPT: Number of days to 100m user ( source ) + +Up until this point I had been only doing AI for myself–after ChatGPT launched everybody suddenly wanted AI +training for their team. Enterprise adoption happened surprisingly quickly: I spoke at an AI conference just a few +months after the launch and many large enterprises I spoke to were already rolling out pilot projects. I saw that AI +[papers published to ArXiv were growing exponentially, doubling every two years, and realized that businesses would](https://www.reddit.com/r/singularity/comments/xwdzr5/the_number_of_ai_papers_on_arxiv_per_month_grows/) +need help keeping up. + +ChatGPT worked much better than GPT-3, particularly when they upgraded the model from GPT-3.5 to GPT-4. +However, that just meant we used it for more ambitious tasks, and so reliability remained a problem. Suddenly people +were willing to pay me to teach them prompt engineering, as a potential solution to the same issues we saw with +image generation around reliability and consistency. It’s hard to get good results if you can’t describe what you want +in the prompt using plain English. + +Intense competition in the AI space that continues to this day ignited in 2022-23. Google had invented the +transformer, but had been too slow to market, allegedly due to concerns over safety, negative press, and potential +[disruption of their comfortable market position in search. Management issued a “code red” after seeing the success of](https://www.businessinsider.com/google-management-issues-code-red-over-chatgpt-report-2022-12) +ChatGPT, bringing co-founders Larry Page and Sergey Brin out of quasi-retirement to release Bard in March 2023, +later rebranded to Gemini. + +The Anthropic founding team that was formed after splitting from OpenAI in 2021 over safety concerns, were also +cajoled into launching their own ChatGPT competitor called Claude in March 2023. At the same time, OpenAI +launched GPT-4, which was a significant leap over GPT-3 and GPT-3.5 (the fine-tuned ChatGPT model), and had a +[8,192 context window. Anthropic responded with a 100k context window in May 2023, nicknamed “clong” or “claude](https://x.com/livgorton/status/1656768304529436672) +[long” by fans of the model. This was the first time I heard “RAG is dead”: why would you need to retrieve context](https://x.com/helloiamleonie/status/1899389996408189259) +from a database when you could fit 100k tokens or 75k words in the prompt? + +Despite huge adoption, GitHub Copilot failed to develop noticeable new features, leaving the door open to Cursor, +which launched in March 2023 and iterated quickly to several innovations. My personal favorite was “yolo” mode, +where the agent could edit multiple files at once, which was terrifying at the time for those of us used to tab +autocomplete. + +## **Early Agent Era (2023–2024)** + +[Although published in October 2022, the ReAct (Reason and Act) pattern started to pick up steam in 2023, as function](https://arxiv.org/abs/2210.03629) +calling or tool calling began to be supported in the summer by OpenAI. Most AI agents today are an evolution on this +foundation, where you prompt an LLM to think, then take an action, before observing the outcome and repeating the +loop. Calling external tools with pre-determined code helped AI give more reliable results, for example being able to +search first before answering a question. LangChain, an LLM orchestration framework, began to be widely popular as +people looked to write more transferable code across models and workflow patterns. + +Despite predictions that prompt engineering was going away as models got smarter and context windows got longer, it +[became more important than ever. Greg Kamradt’s famous “needle-in-a-haystack” test found that AI models didn’t](https://github.com/gkamradt/LLMTest_NeedleInAHaystack/blob/main/README.md) +uniformly pay attention to all the information in long prompts, dispelling the myth that “RAG is dead” because you +[could just put everything in the context window. Additionally, GPT-4 was 15-30x more expensive than GPT-3.5, and](https://www.facebook.com/groups/DeepNetGroup/posts/1935208506872022/) +so if you could get your workflow running on a smaller, cheaper model with better prompting, it was worth the effort. +With Meta’s release of the Llama model series, it was suddenly possible to get GPT-3 level models running for free +[locally on your computer—the “stable diffusion moment” for language models, as British programmer Simon Willison](https://simonwillison.net/2023/Mar/11/llama/) +put it. + +The hype around prompt engineering exploded around this time, with Anthropic advertising a new role for “Prompt +Engineer and Librarian” with an attention-grabbing salary of $250k-$335k. People start asking if prompt engineer will +[be a job of the future, which is a bit like asking if you should start calling yourself an Excel engineer. I actually was](https://www.weforum.org/stories/2023/03/new-emerging-jobs-work-skills/) +[calling myself a prompt engineer at the time, and was working full time freelancing as well as growing my Udemy](https://www.udemy.com/course/prompt-engineering-for-ai/) +[course which launched in April 2023.](https://www.udemy.com/course/prompt-engineering-for-ai/) There are two paths people took to solving the reliability problem with LLMs: -* Carefully define the structure to your prompt pipeline and optimize it until it works reliably against a formal evaluation metric, even with smaller less intelligent (and less expensive) models. -* Throw more compute at the problem, with smarter (and more expensive) models, using agentic workflows and tool harnesses to aid the model in staying on the right trajectory to reach a solution. - -Both approaches apply Richard Sutton’s [Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html)––that more compute eventually beats human expertise––in different ways. Carefully defining inputs and outputs allows you to treat the prompt as a ‘black box’, and benefit from gains in model quality, optimization techniques, or inference strategies. Throwing increasingly powerful models at the problem inflates costs in the short-term, but as LLM inference costs drop [9x-900x annually](https://epoch.ai/data-insights/llm-inference-price-trends) for the same level of intelligence, frontier capability quickly becomes too cheap to meter. Most prompt engineers started calling themselves [AI engineers](https://www.latent.space/p/ai-engineer) around this time, with those from startup backgrounds typically opting for agentic workflows, and those from large company data science backgrounds being naturally attracted - -For those in the first camp, in October 2023 Omar Khattab launched [DSPy](https://dspy.ai/), a framework that promised “programming––not prompting LMs”, in a move away from natural language and towards structured precision. The library abstracted away brittle prompt strings instead using “signatures”, defined inputs and outputs with types, leaving the prompt instructions to be learned by an optimizer based on performance against your dataset and evaluation metric. I found out about the library when someone saw my badge that read “prompt engineer” at an AI conference and gleefully reported “don’t you know prompt engineering is dead? Check out DSPy”, sharing [this article](https://spectrum.ieee.org/prompt-engineering-is-dead) with me. It turns out that DSPy still required collecting an example dataset, defining a formal evaluation metric, and deciding what inputs should go in context. Writing the prompt was never the hard part of prompt engineering, in the same way that building a bridge is less complicated than designing it. - -Those in the second had their time to shine in early 2024, with the rise of the vibe coding (generating code without looking at it) era, as GPT-Engineer rebranded to Lovable, Replit trained their first agent, and Codeium rebranded to Windsurf. There were also early experiments with longrunning agents like AgentGPT and BabyAGI, as well as multi-agent systems like Microsoft Autogen and Camel. Vibe coding seemed to suddenly work all of a sudden once Claude Sonnet 3.5 came out in June 2024, even if the term wasn’t [coined by Andrej Karpathy](https://timesofindia.indiatimes.com/technology/tech-news/what-is-vibe-coding-former-tesla-ai-director-andrej-karpathy-defines-a-new-era-in-ai-driven-development/articleshow/118659724.cms), former AI lead at Tesla, until early 2025. One memorable glimpse into the future was visiting San Francisco and watching an engineer use Devin by Cognition, the $500 a month autonomous employee (ChatGPT cost $20/m at the time!), with its own coding environment and Slack access like a regular employee. These early experiments hinted at a future where AI would run for longer on more ambitious tasks in the background without requiring input at every turn. - -# Context Engineering Paradigm (2025–2026) - -As models got more powerful, they were completing longer running tasks, but would eventually go off the rails if their context window filled up too much. The solution wasn’t just more prompt engineering, because command-line coding agents like Claude Code and Codex were completing tasks without waiting for you to prompt them. AI engineers had to zoom out and look at the overall system, to curate what context went to an AI agent at any given time. Too much context and they get confused or distracted. Too little context and they can’t do a good job on the task. Ankur Goyal of Braintrust [coined the term](https://x.com/ankrgyl/status/1913766591910842619?s=20) “context engineering” in March 2025 to give a name to this new focus area. - -Later in June 2025, [Toby Lütke (Shopify)](https://x.com/tobi/status/1935533422589399127) and [Andrej Karpathy (Tesla, OpenAI)](https://x.com/karpathy/status/1937902205765607626?lang=en) publicly endorse context engineering as the dominant paradigm. Around the same time the term [“context rot”](https://x.com/simonw/status/1935478180443472340) arose to describe how LLM performance degrades as the context window fills up, and Drew Breunig listed the different types of [long context failure](https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html) in more detail. The [Gemini 2.5 paper admitted](https://arxiv.org/pdf/2507.06261) that performance degrades after 200K tokens despite having a nominal context window of over a million tokens. RAG vendors were delighted with this change of fortunes, and Chroma quickly dropped a [technical report](https://research.trychroma.com/context-rot) highlighting the issue further. It turns out RAG wasn’t dead after all. - -Anthropic calls context engineering [“a natural progression of prompt engineering”](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) that became important once we had long-running non-deterministic agents that required better context management than was necessary in defined workflows. That said, the word “prompt” is somewhat synonymous with “context” in AI—the prompt is the context window. Even with GPT-3 good prompt engineers were dealing with context rot (it just happened at 2K tokens not 200K), retrieving relevant context to put in the prompt with RAG and other methods, and making tool calls with the ReAct pattern. - -My cynical take is that AI engineers were desperate for a less polluted term to differentiate themselves from non-technical prompt engineer twitter gurus. With the backing of Karpathy and Lutke, context engineering filled that role. Anyone who speaks English can prompt, but you need to code to manage context dynamically in AI agents. This semantic shift is similar to the rise of Data Science vs Business Analysts (to distinguish Python coders from Excel users), and Growth vs Marketing (a focus on growing through changes to the product, rather than spending money on advertising). Whatever we call it (and it may well change again!), the skill of testing what works to get more reliable AI results continues to be of paramount importance. - -# Parallel Coding Agents (2026) - -What’s better than one long-running agent? Multiple long-running agents in parallel. Naturally, once people realized Claude Code was good enough to complete most tasks with minimal prompting, they wanted to see many tasks they could handle at once. This experimental behavior forced new productivity tools to be needed, such as AI-native task trackers such as [Beads](https://github.com/steveyegge/beads) by Steve Yegge, and cross-agent communication tools like [Agent Mail](https://github.com/Dicklesworthstone/mcp_agent_mail) by Jeff Emanuel, who was profiled as [running twenty-two Claude Max plans at a time](https://www.linkedin.com/pulse/architect-his-army-twenty-two-andrei-yeliseyeu-rkasf/), costing over $4,600 a month. Projects like [Gas Town](https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16dd04), where AI managers coordinate AI workers, continue to proliferate as people push the limit of what’s possible. Eventually we’ll see entire organizations of AI agents, simulating the work of an entire company, as is the vision of Elon Musk’s [MacroHard](https://x.com/elonmusk/status/1958852874236305793?lang=en) project (an attempt to replicate Microsoft with AI agents). In the space of a year we’ll go from you prompting AI, to AI prompting you. - -Being less directly hands-on with AI agents means you have to retreat to surfaces of control that managers have traditionally used to get the most out of their employees. Activities like writing standard operating procedures (SOPs) and documentation as well as defining company culture make a big difference in human-led organizations, and there are AI equivalents. Your agents.md file sets the culture and coding style for your repository, MCPs are how agents access tools, and skills are the SOPs of the agentic world, which detail how to do specific recurring tasks. Projects such as OpenClaw (formerly Moltbot, and Clawdbot) add to this a Soul.md document serving as the top-level identity layer, as well as various memory files and a heartbeat.md for tracking recurring periodic tasks. - -Skills are where the traditional AI workflows of the type we built in LangChain, DSPy, or N8N can now live and be used by true AI agents, marrying the two domains of optimized workflows versus vibe coded agents. If your task is only going to be done once you don’t need a skill, but if it’s going to be done often it makes sense to optimize that workflow and make it more deterministic. If it’s going to be done many more times than that, formalize it further into code and make it into an MCP that can run cheaper and faster than waiting for an agent. Expect more focus on prompt optimization tools like DSPy in training AI skills, as well as workflows that actively learn how to do tasks better each time like Kieran Klaassen’s [Compound Engineering](https://github.com/EveryInc/compound-engineering-plugin) plugin. - -If we play the bitter lesson forwards, we should expect that eventually these harnesses fall away and the model no longer needs them to do a reliable job. The popularity of the “Ralph Wiggum” technique –– named for the Simpsons character’s [“naive persistence”](https://venturebeat.com/technology/how-ralph-wiggum-went-from-the-simpsons-to-the-biggest-name-in-ai-right-now) –– highlights that with smarter models simple approaches still work. The technique consists of a small bash script that pipes a prompt.md file into your coding CLI agent of choice (typically Claude Code), and instructions to take one task at a time, commit with git, and manage a todo list and a progress file locally. This causes a “self-referential feedback loop” where Claude can see its previous work, and then move the project along one cycle at a time until one of the Claudes confirms the task is done. This simple but effective loop started working well once Claude Opus 4.5 came out, allowing 24/7 autonomous agents running as cron jobs in the background AFK (away from keyboard), without a human in the loop, or even designing the harness or environment the agent operates in. - -Assuming code generation is (or will soon be) a solved problem, people have started experimenting with spec-driven development, such as [Drew Breunig’s whenwords library](https://www.dbreunig.com/2026/01/08/a-software-library-with-no-code.html), which only implements the specs and tests and lets the user compile the program with their coding agent of choice. Now imagine we snap our fingers and given a spec, the coding agent can build anything we desire… what do you want to build? The specificity problem remains, and we still have to get good at prompting or defining inputs and outputs formally. When you can build anything, the new bottleneck becomes knowing what to build. + - Carefully define the structure to your prompt pipeline and optimize it until it works reliably against a formal +evaluation metric, even with smaller less intelligent (and less expensive) models. + + - Throw more compute at the problem, with smarter (and more expensive) models, using agentic workflows and +tool harnesses to aid the model in staying on the right trajectory to reach a solution. + +[Both approaches apply Richard Sutton’s Bitter Lesson––that more compute eventually beats human expertise––in](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) +different ways. Carefully defining inputs and outputs allows you to treat the prompt as a ‘black box’, and benefit from +gains in model quality, optimization techniques, or inference strategies. Throwing increasingly powerful models at the +[problem inflates costs in the short-term, but as LLM inference costs drop 9x-900x annually for the same level of](https://epoch.ai/data-insights/llm-inference-price-trends) +intelligence, frontier capability quickly becomes too cheap to meter. Most prompt engineers started calling themselves +[AI engineers around this time, with those from startup backgrounds typically opting for agentic workflows, and those](https://www.latent.space/p/ai-engineer) +from large company data science backgrounds being naturally attracted + +[For those in the first camp, in October 2023 Omar Khattab launched DSPy, a framework that promised](https://dspy.ai/) +“programming––not prompting LMs”, in a move away from natural language and towards structured precision. The +library abstracted away brittle prompt strings instead using “signatures”, defined inputs and outputs with types, +leaving the prompt instructions to be learned by an optimizer based on performance against your dataset and +evaluation metric. I found out about the library when someone saw my badge that read “prompt engineer” at an AI +[conference and gleefully reported “don’t you know prompt engineering is dead? Check out DSPy”, sharing this article](https://spectrum.ieee.org/prompt-engineering-is-dead) +with me. It turns out that DSPy still required collecting an example dataset, defining a formal evaluation metric, and +deciding what inputs should go in context. Writing the prompt was never the hard part of prompt engineering, in the +same way that building a bridge is less complicated than designing it. + +Those in the second had their time to shine in early 2024, with the rise of the vibe coding (generating code without +looking at it) era, as GPT-Engineer rebranded to Lovable, Replit trained their first agent, and Codeium rebranded to +Windsurf. There were also early experiments with longrunning agents like AgentGPT and BabyAGI, as well as multiagent systems like Microsoft Autogen and Camel. Vibe coding seemed to suddenly work all of a sudden once Claude +[Sonnet 3.5 came out in June 2024, even if the term wasn’t coined by Andrej Karpathy, former AI lead at Tesla, until](https://timesofindia.indiatimes.com/technology/tech-news/what-is-vibe-coding-former-tesla-ai-director-andrej-karpathy-defines-a-new-era-in-ai-driven-development/articleshow/118659724.cms) +early 2025. One memorable glimpse into the future was visiting San Francisco and watching an engineer use Devin by +Cognition, the $500 a month autonomous employee (ChatGPT cost $20/m at the time!), with its own coding +environment and Slack access like a regular employee. These early experiments hinted at a future where AI would run +for longer on more ambitious tasks in the background without requiring input at every turn. + +## **Context Engineering Paradigm (2025–2026)** + +As models got more powerful, they were completing longer running tasks, but would eventually go off the rails if their +context window filled up too much. The solution wasn’t just more prompt engineering, because command-line coding +agents like Claude Code and Codex were completing tasks without waiting for you to prompt them. AI engineers had +to zoom out and look at the overall system, to curate what context went to an AI agent at any given time. Too much +context and they get confused or distracted. Too little context and they can’t do a good job on the task. Ankur Goyal +[of Braintrust coined the term “context engineering” in March 2025 to give a name to this new focus area.](https://x.com/ankrgyl/status/1913766591910842619?s=20) + +[Later in June 2025, Toby Lütke (Shopify) and Andrej Karpathy (Tesla, OpenAI) publicly endorse context engineering](https://x.com/karpathy/status/1937902205765607626?lang=en) +[as the dominant paradigm. Around the same time the term “context rot” arose to describe how LLM performance](https://x.com/simonw/status/1935478180443472340) +[degrades as the context window fills up, and Drew Breunig listed the different types of long context failure in more](https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html) +[detail. The Gemini 2.5 paper admitted that performance degrades after 200K tokens despite having a nominal context](https://arxiv.org/pdf/2507.06261) +window of over a million tokens. RAG vendors were delighted with this change of fortunes, and Chroma quickly +[dropped a technical report highlighting the issue further. It turns out RAG wasn’t dead after all.](https://research.trychroma.com/context-rot) + +[Anthropic calls context engineering “a natural progression of prompt engineering” that became important once we](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) +had long-running non-deterministic agents that required better context management than was necessary in defined +workflows. That said, the word “prompt” is somewhat synonymous with “context” in AI—the prompt is the context +window. Even with GPT-3 good prompt engineers were dealing with context rot (it just happened at 2K tokens not +200K), retrieving relevant context to put in the prompt with RAG and other methods, and making tool calls with the +ReAct pattern. + +My cynical take is that AI engineers were desperate for a less polluted term to differentiate themselves from nontechnical prompt engineer twitter gurus. With the backing of Karpathy and Lutke, context engineering filled that role. +Anyone who speaks English can prompt, but you need to code to manage context dynamically in AI agents. This +semantic shift is similar to the rise of Data Science vs Business Analysts (to distinguish Python coders from Excel +users), and Growth vs Marketing (a focus on growing through changes to the product, rather than spending money on +advertising). Whatever we call it (and it may well change again!), the skill of testing what works to get more reliable +AI results continues to be of paramount importance. + +## **Parallel Coding Agents (2026)** + +What’s better than one long-running agent? Multiple long-running agents in parallel. Naturally, once people realized +Claude Code was good enough to complete most tasks with minimal prompting, they wanted to see many tasks they +could handle at once. This experimental behavior forced new productivity tools to be needed, such as AI-native task +[trackers such as Beads by Steve Yegge, and cross-agent communication tools like Agent Mail by Jeff Emanuel, who](https://github.com/Dicklesworthstone/mcp_agent_mail) +[was profiled as running twenty-two Claude Max plans at a time, costing over $4,600 a month. Projects like Gas Town,](https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16dd04) +where AI managers coordinate AI workers, continue to proliferate as people push the limit of what’s possible. +Eventually we’ll see entire organizations of AI agents, simulating the work of an entire company, as is the vision of +[Elon Musk’s MacroHard project (an attempt to replicate Microsoft with AI agents). In the space of a year we’ll go from](https://x.com/elonmusk/status/1958852874236305793?lang=en) +you prompting AI, to AI prompting you. + +Being less directly hands-on with AI agents means you have to retreat to surfaces of control that managers have +traditionally used to get the most out of their employees. Activities like writing standard operating procedures (SOPs) +and documentation as well as defining company culture make a big difference in human-led organizations, and there +are AI equivalents. Your agents.md file sets the culture and coding style for your repository, MCPs are how agents +access tools, and skills are the SOPs of the agentic world, which detail how to do specific recurring tasks. Projects such +as OpenClaw (formerly Moltbot, and Clawdbot) add to this a Soul.md document serving as the top-level identity layer, +as well as various memory files and a heartbeat.md for tracking recurring periodic tasks. + +Skills are where the traditional AI workflows of the type we built in LangChain, DSPy, or N8N can now live and be +used by true AI agents, marrying the two domains of optimized workflows versus vibe coded agents. If your task is +only going to be done once you don’t need a skill, but if it’s going to be done often it makes sense to optimize that +workflow and make it more deterministic. If it’s going to be done many more times than that, formalize it further into +code and make it into an MCP that can run cheaper and faster than waiting for an agent. Expect more focus on +prompt optimization tools like DSPy in training AI skills, as well as workflows that actively learn how to do tasks +[better each time like Kieran Klaassen’s Compound Engineering plugin.](https://github.com/EveryInc/compound-engineering-plugin) + +If we play the bitter lesson forwards, we should expect that eventually these harnesses fall away and the model no +longer needs them to do a reliable job. The popularity of the “Ralph Wiggum” technique –– named for the Simpsons +[character’s “naive persistence” –– highlights that with smarter models simple approaches still work. The technique](https://venturebeat.com/technology/how-ralph-wiggum-went-from-the-simpsons-to-the-biggest-name-in-ai-right-now) +consists of a small bash script that pipes a prompt.md file into your coding CLI agent of choice (typically Claude +Code), and instructions to take one task at a time, commit with git, and manage a todo list and a progress file locally. +This causes a “self-referential feedback loop” where Claude can see its previous work, and then move the project along +one cycle at a time until one of the Claudes confirms the task is done. This simple but effective loop started working +well once Claude Opus 4.5 came out, allowing 24/7 autonomous agents running as cron jobs in the background AFK +(away from keyboard), without a human in the loop, or even designing the harness or environment the agent operates +in. + +Assuming code generation is (or will soon be) a solved problem, people have started experimenting with spec-driven +[development, such as Drew Breunig’s whenwords library, which only implements the specs and tests and lets the user](https://www.dbreunig.com/2026/01/08/a-software-library-with-no-code.html) +compile the program with their coding agent of choice. Now imagine we snap our fingers and given a spec, the coding +agent can build anything we desire… what do you want to build? The specificity problem remains, and we still have +to get good at prompting or defining inputs and outputs formally. When you can build anything, the new bottleneck +becomes knowing what to build. + +## **AGI Approaches (2027–2029)** + +[Since 1999, Ray Kurzweil has maintained that 2029 is the year computers will achieve AGI or artificial general](https://ferosevr.medium.com/the-future-according-to-ray-kurzweil-d26df34ed25e) +intelligence. This is a fuzzy goal that refers to a type of artificial intelligence that possesses the ability to understand, +learn, and apply knowledge across a wide range of tasks at a level comparable to that of a human being. I like to think +of it as a coding agent that writes its own specs––understanding enough about the world to make a tasteful +[contribution to it. Dario Amodei from Anthropic expects AGI to come in the next 1-2 years, and Sam Altman also](https://dig.watch/updates/sam-altman-predicts-agi-could-arrive-before-2030) +[expects AGI before the end of the decade. While nobody knows if AI development will continue at the same pace, or if](https://dig.watch/updates/sam-altman-predicts-agi-could-arrive-before-2030) +we get to AGI what will happen, we can hazard a guess based on current experience with AI. + +The looming potential of AGI on the horizon has led to murmurs of the “permanent underclass” theory circulating in +Silicon Valley—the idea that if you’re not aggressively adopting AI right now, you’ll be permanently priced out of the +future economy and will have to hope for UBI. The argument has a seductive internal logic: someone on the free tier +of ChatGPT is falling behind the person paying $20 a month, who’s falling behind people like me spending $200 a +month for 20x the usage and access to the most capable models, who in turn are falling behind people spending +[thousands running multiple coding agents. Sam Altman plans to release PHD level models at $20,000 a month—and if](https://medium.com/@ayushojha010/the-20-000-ai-madness-sam-altmans-jaw-dropping-plan-to-replace-knowledge-workers-with-ai-agents-7214ebe18ffb) +you haven’t learned to be productive at the $20 tier, you’ll never justify the $200 tier, let alone the $20,000 one and +beyond. The theory says this ladder pulls up behind you, and the gap compounds until it’s permanent. + +That isn’t what we’ve seen with previous technology shifts, however. As the price of intelligence comes down, it’s +likely we’ll demand much more of it, a phenomenon known as Jevon’s paradox. In addition, we’ve seen as we +experience growth in one fast moving industry, other slower moving more regulated or physically constrained +industries trend to increase in price, wiping out some or all of the productivity gains, called Baumol’s cost disease. +[Alex Danco of a16z detailed how the combination of these two phenomena could sustain wage growth even as AI](https://a16z.com/why-ac-is-cheap-but-ac-repair-is-a-luxury/) +automates the majority of what we currently do for work. If AI eliminates 95% of your job, there’s a chance you’ll just +be expected to do 20x more work rather than being eliminated. We haven’t seen a mass extinction event in Radiology, +[a field once deemed highly susceptible to AI automation that now employs more Radiologists than ever.](https://www.forbes.com/sites/jonmarkman/2026/01/26/the-radiologist-effect-why-ai-creates-more-jobs-not-fewer/) + +Every technology wave from the printing press to the internet has triggered the same panic, and every time Baumol’s +cost disease spreads the gains around: when one sector booms, wages rise everywhere because everyone competes in +the same labor market. Electricians are already commanding half-million-dollar salaries to wire data centers. If AI +makes everything it touches a thousand times more efficient, the relative value of human-bottlenecked work goes up, +not down. Hairdressers, therapists, tradespeople, and yes, the engineers navigating that messy middle where you have +to extrapolate from incomplete data—these roles become the premium goods in an AI-abundant economy. + +In my personal experience with AI, I’ve found that while they can quickly achieve impressive benchmarks on known +tasks with optimization, they fail miserably when generalizing to unknown tasks. Ask a coding agent to create a crud +app, the likes of which have been seen a trillion times in the training data, and it does a great job. Ask it to do +something it hasn’t seen before, and it often performs less impressively. This is because AI models work best when +there is a lot of data to synthesize, or a lot of examples to follow, but not when you have to make decisions under +uncertainty with limited information. AI might be the General, monitoring more data in realtime than any human +could, and it might be the soldier, executing common tasks flawlessly every time, but maybe there’s room for humans +[in the NCO role: translating the commander’s intent to the conditions on the ground, under conditions of uncertainty.](https://www.saxifrage.xyz/post/nco-gap) + +The people who learn to work effectively with AI—who develop taste for what to build, who understand how to +engineer context, make the right bets and leaps of faith, and manage fleets of agents—will have a meaningful +advantage over those who don’t. But that’s always been true of new tools. The gap between someone who can use a +spreadsheet and someone who can’t was once a career-defining edge. The truth is we’ll always need prompting skills, +whether we call it that or not. Think about the smartest person you’ve ever worked with, and ask yourself… did they +not need HR, Legal, and Management support because they were so smart? The chances are they needed it more, and +these things are all just prompts for humans to align us to the goals of the organization. Because intelligence is +becoming an inexpensive commodity, the premium human skill is no longer writing code or generating text, but +rather managing the architecture of context that orchestrates these systems. + +The skill of clearly communicating what you want, whether to a human or AI agent, isn’t going away anytime soon. +It’s becoming the whole game. + +# **Chapter 2. How Training Shapes LLMs and Explains** **Their Quirky Behaviors** + +**_By Drew Breunig_** + +In late 2025, I found myself struggling to convince GPT-5.1 to properly call a popular open source library. With +frustrating consistency, the model would either omit a required attribute or append options that didn’t exist. After the +5th correction, I pulled up an IDE and started hand coding the calls. Perhaps GPT-5.1 simply wasn’t _that_ good, I +thought. Perhaps I’d be returning to Claude sooner than expected. + +But while it appeared GPT-5.1 was hallucinating, the cause behind these hiccups was much more mundane: the model +was never trained on the correct API calls. GPT-5.1’s cutoff date was September 2024. Articles, blog posts, code +libraries, and other artifacts created after this date weren’t in GPT-5.1’s training data. And the library I wanted to call +had significantly updated its APIs in 2025. GPT-5.1 was writing the correct calls, given its training data. + +This problem is mundane and obvious, but its explanation is a general rule that works on almost _every_ large language +model quirk: _stubborn and unexpected behavior can be explained by understanding how the model was trained_ **.** + +A model’s inability to count the letters in a word? Its continual flattering of users? Its unrelenting preference for em +dashes? That’s how it was trained. From mundane issues like diminishing performance as contexts grow to spooky +statements that hint at sentience: it all comes back to how the model was trained. + +[In 2025, researchers at OpenAI argued that models output hallucinations, “producing plausible yet incorrect](https://arxiv.org/abs/2509.04664) +statements instead of admitting uncertainty,” because of the way we train them. “Language models are optimized to +be good test-takers, and guessing when uncertain improves test performance.” Only by adjusting how training occurs +and how benchmarks are designed can this behavior be fixed. + +For those of us not training models, the move is to build a working knowledge of how models work and how they are +trained in order to diagnose, mitigate, and/or avoid undesired behavior. + +## **How an LLM Processes Your Prompt** -# AGI Approaches (2027–2029) - -Since 1999, Ray Kurzweil has [maintained that 2029](https://ferosevr.medium.com/the-future-according-to-ray-kurzweil-d26df34ed25e) is the year computers will achieve AGI or artificial general intelligence. This is a fuzzy goal that refers to a type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to that of a human being. I like to think of it as a coding agent that writes its own specs––understanding enough about the world to make a tasteful contribution to it. Dario Amodei from Anthropic [expects AGI to come in the next 1-2 years](https://www.darioamodei.com/essay/the-adolescence-of-technology), and Sam Altman [also expects AGI before the end of the decade](https://dig.watch/updates/sam-altman-predicts-agi-could-arrive-before-2030). While nobody knows if AI development will continue at the same pace, or if we get to AGI what will happen, we can hazard a guess based on current experience with AI. - -The looming potential of AGI on the horizon has led to murmurs of the “permanent underclass” theory circulating in Silicon Valley—the idea that if you’re not aggressively adopting AI right now, you’ll be permanently priced out of the future economy and will have to hope for UBI. The argument has a seductive internal logic: someone on the free tier of ChatGPT is falling behind the person paying $20 a month, who’s falling behind people like me spending $200 a month for 20x the usage and access to the most capable models, who in turn are falling behind people spending thousands running multiple coding agents. [Sam Altman plans](https://medium.com/@ayushojha010/the-20-000-ai-madness-sam-altmans-jaw-dropping-plan-to-replace-knowledge-workers-with-ai-agents-7214ebe18ffb) to release PHD level models at $20,000 a month—and if you haven’t learned to be productive at the $20 tier, you’ll never justify the $200 tier, let alone the $20,000 one and beyond. The theory says this ladder pulls up behind you, and the gap compounds until it’s permanent. - -That isn’t what we’ve seen with previous technology shifts, however. As the price of intelligence comes down, it’s likely we’ll demand much more of it, a phenomenon known as Jevon’s paradox. In addition, we’ve seen as we experience growth in one fast moving industry, other slower moving more regulated or physically constrained industries trend to increase in price, wiping out some or all of the productivity gains, called Baumol’s cost disease. [Alex Danco of a16z](https://a16z.com/why-ac-is-cheap-but-ac-repair-is-a-luxury/) detailed how the combination of these two phenomena could sustain wage growth even as AI automates the majority of what we currently do for work. If AI eliminates 95% of your job, there’s a chance you’ll just be expected to do 20x more work rather than being eliminated. We haven’t seen a mass extinction event in Radiology, a field once deemed [highly susceptible to AI automation](https://www.forbes.com/sites/jonmarkman/2026/01/26/the-radiologist-effect-why-ai-creates-more-jobs-not-fewer/) that now employs more Radiologists than ever. - -Every technology wave from the printing press to the internet has triggered the same panic, and every time Baumol’s cost disease spreads the gains around: when one sector booms, wages rise everywhere because everyone competes in the same labor market. Electricians are already commanding half-million-dollar salaries to wire data centers. If AI makes everything it touches a thousand times more efficient, the relative value of human-bottlenecked work goes up, not down. Hairdressers, therapists, tradespeople, and yes, the engineers navigating that messy middle where you have to extrapolate from incomplete data—these roles become the premium goods in an AI-abundant economy. - -In my personal experience with AI, I’ve found that while they can quickly achieve impressive benchmarks on known tasks with optimization, they fail miserably when generalizing to unknown tasks. Ask a coding agent to create a crud app, the likes of which have been seen a trillion times in the training data, and it does a great job. Ask it to do something it hasn’t seen before, and it often performs less impressively. This is because AI models work best when there is a lot of data to synthesize, or a lot of examples to follow, but not when you have to make decisions under uncertainty with limited information. AI might be the General, monitoring more data in realtime than any human could, and it might be the soldier, executing common tasks flawlessly every time, but maybe there’s room for humans in the [NCO role](https://www.saxifrage.xyz/post/nco-gap): translating the commander’s intent to the conditions on the ground, under conditions of uncertainty. - -The people who learn to work effectively with AI—who develop taste for what to build, who understand how to engineer context, make the right bets and leaps of faith, and manage fleets of agents—will have a meaningful advantage over those who don’t. But that’s always been true of new tools. The gap between someone who can use a spreadsheet and someone who can’t was once a career-defining edge. The truth is we’ll always need prompting skills, whether we call it that or not. Think about the smartest person you’ve ever worked with, and ask yourself… did they not need HR, Legal, and Management support because they were so smart? The chances are they needed it more, and these things are all just prompts for humans to align us to the goals of the organization. Because intelligence is becoming an inexpensive commodity, the premium human skill is no longer writing code or generating text, but rather managing the architecture of context that orchestrates these systems. - -The skill of clearly communicating what you want, whether to a human or AI agent, isn’t going away anytime soon. It’s becoming the whole game. - -xml version='1.0' encoding='utf-8'? +Let’s try to build the smallest possible model of a model. No math, no diagrams, and as little jargon as possible. -# Chapter 2. How Training Shapes LLMs and Explains Their Quirky Behaviors +At the highest level, when you hit enter in ChatGPT, Gemini, or Claude, the following happens: -By Drew Breunig +1. The words you typed are chopped into **tokens**, chunks of characters drawn from a fixed set of a couple +hundred thousand strings. -# A Note for Early Release Readers +2. Each token maps to an **embedding vector**, a long list of numbers encoding each chunk’s semantic meaning. -With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. +3. Each embedding is adjusted based on the other chunks around it, modifying the meaning based on its local +context. -This will be the 2nd chapter of the final book. Please note that the GitHub repo will be made active later on. +4. The final sequence of adjusted embeddings is passed through the LLM’s **weights**, billions of numbers fixed +during training, to score every possible token with a probability that it comes next. -If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at *mcronin@oreilly.com*. +5. One token is selected from these scores, added to the sequence, and the whole process repeats. -In late 2025, I found myself struggling to convince GPT-5.1 to properly call a popular open source library. With frustrating consistency, the model would either omit a required attribute or append options that didn’t exist. After the 5th correction, I pulled up an IDE and started hand coding the calls. Perhaps GPT-5.1 simply wasn’t *that* good, I thought. Perhaps I’d be returning to Claude sooner than expected. +On the whole, this process is pretty simple. Confusion comes from the jargon: tokens, embeddings, and weights. Let’s +tackle each of these in order. -But while it appeared GPT-5.1 was hallucinating, the cause behind these hiccups was much more mundane: the model was never trained on the correct API calls. GPT-5.1’s cutoff date was September 2024. Articles, blog posts, code libraries, and other artifacts created after this date weren’t in GPT-5.1’s training data. And the library I wanted to call had significantly updated its APIs in 2025. GPT-5.1 was writing the correct calls, given its training data. +**Tokens** -This problem is mundane and obvious, but its explanation is a general rule that works on almost *every* large language model quirk: *stubborn and unexpected behavior can be explained by understanding how the model was trained***.** +The paragraph you entered in the chatbot text field is chopped into chunks of characters called **tokens** . Each model +has a fixed vocabulary of tokens, each representing a unique character or characters. GPT-5-class models have a token +vocabulary of roughly 200,000 tokens. -A model’s inability to count the letters in a word? Its continual flattering of users? Its unrelenting preference for em dashes? That’s how it was trained. From mundane issues like diminishing performance as contexts grow to spooky statements that hint at sentience: it all comes back to how the model was trained. +A frequent question is, “Why don’t we just use one token for each character or each word?” The number 200,000 feels +somewhat arbitrary, as do some of the chunks each token represents. For example, here are some bits of text that each +map to one token in GPT-5: -In 2025, [researchers at OpenAI argued](https://arxiv.org/abs/2509.04664) that models output hallucinations, “producing plausible yet incorrect statements instead of admitting uncertainty,” because of the way we train them. “Language models are optimized to be good test-takers, and guessing when uncertain improves test performance.” Only by adjusting how training occurs and how benchmarks are designed can this behavior be fixed. + - A -For those of us not training models, the move is to build a working knowledge of how models work and how they are trained in order to diagnose, mitigate, and/or avoid undesired behavior. + - B -# How an LLM Processes Your Prompt + - b -Let’s try to build the smallest possible model of a model. No math, no diagrams, and as little jargon as possible. + - ! -At the highest level, when you hit enter in ChatGPT, Gemini, or Claude, the following happens: + - 你 -1. The words you typed are chopped into **tokens**, chunks of characters drawn from a fixed set of a couple hundred thousand strings. -2. Each token maps to an **embedding vector**, a long list of numbers encoding each chunk’s semantic meaning. -3. Each embedding is adjusted based on the other chunks around it, modifying the meaning based on its local context. -4. The final sequence of adjusted embeddings is passed through the LLM’s **weights**, billions of numbers fixed during training, to score every possible token with a probability that it comes next. -5. One token is selected from these scores, added to the sequence, and the whole process repeats. + - 4 -On the whole, this process is pretty simple. Confusion comes from the jargon: tokens, embeddings, and weights. Let’s tackle each of these in order. +Makes sense. We’re mapping uppercase and lowercase characters, punctuation, characters in multiple languages, and +digits. -## Tokens +But here are some other bits of text that are each precisely one token: -The paragraph you entered in the chatbot text field is chopped into chunks of characters called **tokens**. Each model has a fixed vocabulary of tokens, each representing a unique character or characters. GPT-5-class models have a token vocabulary of roughly 200,000 tokens. + - soda -A frequent question is, “Why don’t we just use one token for each character or each word?” The number 200,000 feels somewhat arbitrary, as do some of the chunks each token represents. For example, here are some bits of text that each map to one token in GPT-5: + - Yankee -* A -* B -* b -* ! -* 你 -* 4 + - 965 -Makes sense. We’re mapping uppercase and lowercase characters, punctuation, characters in multiple languages, and digits. + - INFRINGEMENT -But here are some other bits of text that are each precisely one token: + - scipy -* soda -* Yankee -* 965 -* INFRINGEMENT -* scipy -* —> + - —> -At first, it’s hard to find rhyme or reason for *why* some strings get their own token and others don’t. But it’s a popularity contest: when building a token vocabulary, teams use algorithms to select the most common chunks of characters in a given dataset. They do this to make everything *cheaper*. +At first, it’s hard to find rhyme or reason for _why_ some strings get their own token and others don’t. But it’s a +popularity contest: when building a token vocabulary, teams use algorithms to select the most common chunks of +characters in a given dataset. They do this to make everything _cheaper_ . -All of the costs of LLM are denominated in tokens. If we can feed fewer of them into models during training and inference, models cost less to build and run faster and cheaper. By smartly selecting *popular* tokens, we can represent most inputs with the fewest tokens possible. We say, “Hello”, more often than, “Hey there,” so the former gets one token while the latter phrase requires three. The common phrase, “Once upon a time,” uses the same number of tokens as the nonsense string, “w`i<0!” We strategically select our chunk vocabularies to minimize the amount of tokens we need to process. +All of the costs of LLM are denominated in tokens. If we can feed fewer of them into models during training and +inference, models cost less to build and run faster and cheaper. By smartly selecting _popular_ tokens, we can represent +most inputs with the fewest tokens possible. We say, “Hello”, more often than, “Hey there,” so the former gets one +token while the latter phrase requires three. The common phrase, “Once upon a time,” uses the same number of +tokens as the nonsense string, “w`i<0!” We strategically select our chunk vocabularies to minimize the amount of +tokens we need to process. -But the efficiency this chunking delivers isn’t a free lunch; converting multiple characters into a single placeholder that the model sees yields some weird artifacts. Andrej Karpathy, cofounder of OpenAI, argues that much of, “[oddness with large language models can be traced back to tokenization](https://www.youtube.com/watch?v=zduSFxRajkE).” +But the efficiency this chunking delivers isn’t a free lunch; converting multiple characters into a single placeholder +[that the model sees yields some weird artifacts. Andrej Karpathy, cofounder of OpenAI, argues that much of, “oddness](https://www.youtube.com/watch?v=zduSFxRajkE) +[with large language models can be traced back to tokenization.”](https://www.youtube.com/watch?v=zduSFxRajkE) -For example, because the model only sees one token when presented with the word, “Yankee”, how could it count the number of vowels in the word? Tokens and chunking are the culprits behind the notorious, “How many r’s are in the word ‘Strawberry’?” question that plagued model providers for years. +For example, because the model only sees one token when presented with the word, “Yankee”, how could it count the +number of vowels in the word? Tokens and chunking are the culprits behind the notorious, “How many r’s are in the +word ‘Strawberry’?” question that plagued model providers for years. -Tokens are the reason LLMs struggle with math. The number 38425 maps to tokens corresponding to “384” and “25”. If I want to add that to 4239 (two tokens), models resort to rote memorization and, once certain model sizes are reached, learn statistical shortcuts. +Tokens are the reason LLMs struggle with math. The number 38425 maps to tokens corresponding to “384” and “25”. +If I want to add that to 4239 (two tokens), models resort to rote memorization and, once certain model sizes are +reached, learn statistical shortcuts. -###### Note +**Note** -Dive Deeper: Huggingface has an excellent [deep dive into the history of number tokenization](https://huggingface.co/spaces/huggingface/number-tokenization-blog) that I recommend if you’re curious. +[Dive Deeper: Huggingface has an excellent deep dive into the history of number tokenization that I recommend if](https://huggingface.co/spaces/huggingface/number-tokenization-blog) +you’re curious. If 200,000 tokens make LLMs more efficient, why don’t we use a vocabulary of 2 million tokens? Or 200 million? -If we use too many tokens, model makers run the risk that some of the tokens are underutilized during training. They accrue little information during the training run, which can result in very weird behavior. For example, [asking GPT-3 to explain the word “ SolidGoldMagikarp” causes it to explain what “distribute” means](https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg). +If we use too many tokens, model makers run the risk that some of the tokens are underutilized during training. They +[accrue little information during the training run, which can result in very weird behavior. For example, asking GPT-3](https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg) +[to explain the word “ SolidGoldMagikarp” causes it to explain what “distribute” means.](https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg) -With the very large frontier models we use today, the risk of underutilized tokens drops significantly. But there has also been recent work that fewer tokens can improve the performance (not efficiency) of models. Anthropic’s most recent models use a new tokenizer that uses approximately 30% more tokens per input. +With the very large frontier models we use today, the risk of underutilized tokens drops significantly. But there has +also been recent work that fewer tokens can improve the performance (not efficiency) of models. Anthropic’s most +recent models use a new tokenizer that uses approximately 30% more tokens per input. -## Embeddings +**Embeddings** -Once your paragraph has been chunked into tokens, these tokens are used to look up their associated *embeddings*. Embeddings are *vectors*, large arrays of floating point numbers, that carry a token’s contextual meaning. +Once your paragraph has been chunked into tokens, these tokens are used to look up their associated _embeddings_ . +Embeddings are _vectors_, large arrays of floating point numbers, that carry a token’s contextual meaning. -The long list of numbers can be thought of as a coordinate system, similar to the latitude and longitude pairs we use to describe locations on a map. Location coordinates allow us to determine how close or far one point is from another, on a 2-dimensional plane, by computing the difference between the pair of numbers. Embeddings work similarly, there are just *hundreds to thousands* of dimensions. +The long list of numbers can be thought of as a coordinate system, similar to the latitude and longitude pairs we use +to describe locations on a map. Location coordinates allow us to determine how close or far one point is from another, +on a 2-dimensional plane, by computing the difference between the pair of numbers. Embeddings work similarly, +there are just _hundreds to thousands_ of dimensions. -It’s hard to conceptualize the thousands of dimensions frontier models use, but these coordinates are used to situate the meaning of tokens relative to one another. For example, the embedding for the word “king” and “queen” are close to one another, but “king” and “tomato” are quite far apart. Embeddings, the coordinate positions within the context space, are generated as the model is trained. The numbers in the array are nudged every time the model sees “king”, depending on the words around that instance of “king”. With more and more training, “king” moves closer to “queen” and further from “tomato”. +It’s hard to conceptualize the thousands of dimensions frontier models use, but these coordinates are used to situate +the meaning of tokens relative to one another. For example, the embedding for the word “king” and “queen” are close +to one another, but “king” and “tomato” are quite far apart. Embeddings, the coordinate positions within the context +space, are generated as the model is trained. The numbers in the array are nudged every time the model sees “king”, +depending on the words around that instance of “king”. With more and more training, “king” moves closer to “queen” +and further from “tomato”. -###### Note +**Note** -[*https://vickiboykis.com/what\_are\_embeddings/*](https://vickiboykis.com/what_are_embeddings/) +_[https://vickiboykis.com/what_are_embeddings/](https://vickiboykis.com/what_are_embeddings/)_ -## Attention +**Attention** -But what about words that have multiple meanings, depending on how and when they’re used? For example, the word “bank” can refer to a business that holds and lends money and to the bit of land on the edge of a river. +But what about words that have multiple meanings, depending on how and when they’re used? For example, the word +“bank” can refer to a business that holds and lends money and to the bit of land on the edge of a river. -At this point, our original paragraph was turned into tokens, each of which was used to retrieve an embedding. Our paragraph has been translated into a set of embeddings, each of which is a set of numbers representing contextual meaning. But this meaning is the *global* context of the model. We need to adjust these embeddings, tweak their values, based on the *local* context of our paragraph. +At this point, our original paragraph was turned into tokens, each of which was used to retrieve an embedding. Our +paragraph has been translated into a set of embeddings, each of which is a set of numbers representing contextual +meaning. But this meaning is the _global_ context of the model. We need to adjust these embeddings, tweak their values, +based on the _local_ context of our paragraph. -The process of adjusting for local context is called *attention*. It’s how the model keeps our financial “banks” and river “banks” straight. +The process of adjusting for local context is called _attention_ . It’s how the model keeps our financial “banks” and river +“banks” straight. -Mechanically, attention updates each embedding with a weighted blend of the embeddings around it. For every token, the model computes which other tokens matter and how much: “river” pulls “bank” toward mud and water, while “loan” pulls it toward vaults. +Mechanically, attention updates each embedding with a weighted blend of the embeddings around it. For every token, +the model computes which other tokens matter and how much: “river” pulls “bank” toward mud and water, while +“loan” pulls it toward vaults. -## Selection +**Selection** -Our paragraph has been transformed into an array of localized contextual coordinates. The next step is to pass these values through the layers of the LLM’s weights, billions of numbers fixed during training, to score every possible token with a probability that it comes next. +Our paragraph has been transformed into an array of localized contextual coordinates. The next step is to pass these +values through the layers of the LLM’s weights, billions of numbers fixed during training, to score every possible token +with a probability that it comes next. -This process, called the “forward pass”, is the core function of the model. Predicting the next token given a package of embeddings is *everything* the model was trained on. All the data the model was shown during pre-training and all the examples it was given during post-training were in pursuit of making this prediction better. +This process, called the “forward pass”, is the core function of the model. Predicting the next token given a package of +embeddings is _everything_ the model was trained on. All the data the model was shown during pre-training and all the +examples it was given during post-training were in pursuit of making this prediction better. -The final list of tokens are returned with their probabilities, and a top ranking token is chosen. This token is appended to our initial list of tokens, and the process is repeated. Over and over. +The final list of tokens are returned with their probabilities, and a top ranking token is chosen. This token is appended +to our initial list of tokens, and the process is repeated. Over and over. -###### Note +**Note** -Dive Deeper: [Let’s build the GPT Tokenizer](https://www.youtube.com/watch?v=zduSFxRajkE), Karpathy +[Dive Deeper: Let’s build the GPT Tokenizer, Karpathy](https://www.youtube.com/watch?v=zduSFxRajkE) -# Building a Mental Model for How LLMs Process Context +## **Building a Mental Model for How LLMs Process Context** With this understanding of how a prompt generates a response from an LLM, our mental model starts to take shape. -We can reason about how one word might spur completely different responses based on the words it travels with. This aligns with our understanding of language itself (we’re constantly making sense of words in context), but the details for how this takes place mechanically in an LLM help us reason about its failure modes. +We can reason about how one word might spur completely different responses based on the words it travels with. This +aligns with our understanding of language itself (we’re constantly making sense of words in context), but the details +for how this takes place mechanically in an LLM help us reason about its failure modes. -Consider *context poisoning*, whichoccurs when one piece of context influences later output in an undesirable way. In a long conversational thread or a task where the model has been given extensive reference materials, an earlier instruction, example, or distraction can work against your later goals. +Consider _context poisoning_, which occurs when one piece of context influences later output in an undesirable way. In a +long conversational thread or a task where the model has been given extensive reference materials, an earlier +instruction, example, or distraction can work against your later goals. -This is *attention* in action: one part of the context changes how the model interprets and weights the rest, shifting the probabilities of what it generates next. It’s the proverbial butterfly, flapping its wings in a rainforest, kicking off a hurricane far away. +This is _attention_ in action: one part of the context changes how the model interprets and weights the rest, shifting the +probabilities of what it generates next. It’s the proverbial butterfly, flapping its wings in a rainforest, kicking off a +hurricane far away. -Our mental model of language doesn’t map perfectly to the behavior of an LLM. Something said 40,000 words earlier may continue to shape a response when we would rather the model ignore it. Yet when that same detail becomes important, the model may overlook it entirely. +Our mental model of language doesn’t map perfectly to the behavior of an LLM. Something said 40,000 words earlier +may continue to shape a response when we would rather the model ignore it. Yet when that same detail becomes +important, the model may overlook it entirely. -This contradiction follows from how attention works. Information does not exist in the context as a set of facts waiting to be referenced reliably. At each step, the model assigns different weight to different parts of the context and uses those weighted relationships to shape what comes next. An irrelevant detail may attract attention because it resembles the current topic. A useful detail may receive too little attention because it is too distant, buried among competing information. +This contradiction follows from how attention works. Information does not exist in the context as a set of facts waiting +to be referenced reliably. At each step, the model assigns different weight to different parts of the context and uses +those weighted relationships to shape what comes next. An irrelevant detail may attract attention because it resembles +the current topic. A useful detail may receive too little attention because it is too distant, buried among competing +information. -As contexts grow we create two opposing risks: unwanted information can persist and needed information can be ignored. We can either rely on the model makers to eventually fix this *very* hard problem, or design systems for controlling *what* goes in the context in the first place. +As contexts grow we create two opposing risks: unwanted information can persist and needed information can be +ignored. We can either rely on the model makers to eventually fix this _very_ hard problem, or design systems for +controlling _what_ goes in the context in the first place. -# How Data Influences Models +## **How Data Influences Models** -We covered how a prompt moves through a model, but handwaved over *the weights*, the billions of numbers inside a model that transform our input into output. These weights are established during training, adjusted with every pass to better predict the next tokens. +We covered how a prompt moves through a model, but handwaved over _the weights_, the billions of numbers inside a +model that transform our input into output. These weights are established during training, adjusted with every pass to +better predict the next tokens. -For our purposes, we can think of these values as an unstructured pile. To better create and debug contexts, the structure of these weights matter less than understanding how these values are determined: pre-training, post-training, and the types of datasets that power these stages. +For our purposes, we can think of these values as an unstructured pile. To better create and debug contexts, the +structure of these weights matter less than understanding how these values are determined: pre-training, post-training, +and the types of datasets that power these stages. -## Pre-Training Establishes The Foundation +**Pre-Training Establishes The Foundation** -Pre-Training establishes the foundation of a model: language capabilities, broad knowledge, and an ability to recognize and generate patterns. +Pre-Training establishes the foundation of a model: language capabilities, broad knowledge, and an ability to +recognize and generate patterns. -Pre-Training is when we feed the model the internet, archives of scanned books, transcribed speech recordings, and so much more. This is *found data*, which labs aggressively filter, deduplicate, and allocate among sources. But the sheer scale of it all ensures the overall character of the source materials survives curation. +Pre-Training is when we feed the model the internet, archives of scanned books, transcribed speech recordings, and so +much more. This is _found data_, which labs aggressively filter, deduplicate, and allocate among sources. But the sheer +scale of it all ensures the overall character of the source materials survives curation. The nature of this found data shapes the capabilities and quirks of the model. -For example, models are *excellent* at reading typo-ridden inputs because the data it was fed during pre-training has plenty of errors. Bad OCR’ed PDFs and garbled transcriptions are similarly handled, because models have plenty of experience with these as well. - -But negative effects emerge as well. Models perform worse with less common languages, say Tagalog, because there is significantly less Tagalog represented in the data. With smaller models, these data selection biases become more pronounced. Perhaps my favorite quirk plagues many small speech-to-text models, like OpenAI’s Whisper: when fed audio without speaking, Whisper will frequently reply with hallucinated phrases like, “[Thanks for watching](https://github.com/openai/whisper/discussions/1455).” This is because, we can infer, the model was trained on transcripts from social media videos. +For example, models are _excellent_ at reading typo-ridden inputs because the data it was fed during pre-training has +plenty of errors. Bad OCR’ed PDFs and garbled transcriptions are similarly handled, because models have plenty of +experience with these as well. -At this stage, a pre-trained model can only *complete text*. Sure, all models are completing text, but here we mean it as literally as possible. If you give a model a question, it won’t answer it as if replying, but will spit out words likely to follow your input. To teach a model to converse, we move onto post-training. +But negative effects emerge as well. Models perform worse with less common languages, say Tagalog, because there is +significantly less Tagalog represented in the data. With smaller models, these data selection biases become more +pronounced. Perhaps my favorite quirk plagues many small speech-to-text models, like OpenAI’s Whisper: when fed +[audio without speaking, Whisper will frequently reply with hallucinated phrases like, “Thanks for watching.” This is](https://github.com/openai/whisper/discussions/1455) +because, we can infer, the model was trained on transcripts from social media videos. -## Post-Training Builds the UX and Adds Enhancements +At this stage, a pre-trained model can only _complete text_ . Sure, all models are completing text, but here we mean it as +literally as possible. If you give a model a question, it won’t answer it as if replying, but will spit out words likely to +follow your input. To teach a model to converse, we move onto post-training. -Post-training is the process where fine-tuning, reinforcement learning (RL), and other weight-adjusting processes establish the interface of the model and add additional, specialized capabilities. It teaches the model to follow instructions, hold conversations, reason through difficult problems, use tools, work across multiple turns, and behave safely and helpfully. +**Post-Training Builds the UX and Adds Enhancements** -Each of these behaviors require uniquely shaped examples. For instance, to establish conversational abilities, humans initially wrote conversational examples. These demonstrations were then used to fine-tune the model (update the weights) to establish basic conversational behavior. A broader group of humans would then rank model outputs, scoring examples by how well they demonstrated ideal behaviors. These labeled examples were then used for further training. +Post-training is the process where fine-tuning, reinforcement learning (RL), and other weight-adjusting processes +establish the interface of the model and add additional, specialized capabilities. It teaches the model to follow +instructions, hold conversations, reason through difficult problems, use tools, work across multiple turns, and behave +safely and helpfully. -Humans are still creating specialty datasets for LLM training today, but the focus has moved from basic conversation abilities to complex professional skills. +Each of these behaviors require uniquely shaped examples. For instance, to establish conversational abilities, humans +initially wrote conversational examples. These demonstrations were then used to fine-tune the model (update the +weights) to establish basic conversational behavior. A broader group of humans would then rank model outputs, +scoring examples by how well they demonstrated ideal behaviors. These labeled examples were then used for further +training. -To teach models to reason, examples of step-by-step reasoning chains had to be created in massive quantities. This data was largely *synthetic data*: data created by software and/or LLMs to fit the shape model makers need. One method was *rephrasing data*, finding great examples of content from textbooks or the internet then using LLMs to rephrase them into step-by-step linear chains. +Humans are still creating specialty datasets for LLM training today, but the focus has moved from basic conversation +abilities to complex professional skills. -###### Note +To teach models to reason, examples of step-by-step reasoning chains had to be created in massive quantities. This +data was largely _synthetic data_ : data created by software and/or LLMs to fit the shape model makers need. One method +was _rephrasing data_, finding great examples of content from textbooks or the internet then using LLMs to rephrase +them into step-by-step linear chains. -[*https://www.dbreunig.com/2024/12/18/synthetic-data-the-growing-ai-perception-divide.html*](https://www.dbreunig.com/2024/12/18/synthetic-data-the-growing-ai-perception-divide.html) +**Note** -Today, many synthetic datasets are created within *simulated environments.* For example, an LLM might be given access to a sandboxed computer and instructed to complete a coding task, ensure it passes typechecking, and successfully pass a suite of tests. As the LLM churns, each step is logged and successful “traces” are used to improve the weights. +_[https://www.dbreunig.com/2024/12/18/synthetic-data-the-growing-ai-perception-divide.html](https://www.dbreunig.com/2024/12/18/synthetic-data-the-growing-ai-perception-divide.html)_ -Using tools, as well, is another example of model makers having to train models by showing them examples of what successful tool use looks like. At first, initial tool use by models was flaky and required heavy instructions and parsing of outputs to achieve consistent results. But today, labs synthesize tons of examples of tool calls, formatting, and more, ensuring tool calls are reliable. +Today, many synthetic datasets are created within _simulated environments._ For example, an LLM might be given access +to a sandboxed computer and instructed to complete a coding task, ensure it passes typechecking, and successfully +pass a suite of tests. As the LLM churns, each step is logged and successful “traces” are used to improve the weights. -As we train models to exhibit specific behaviors, reliably, we create models which are more adept at helping us create new synthetic datasets. We build more environments, more test cases, and new harnesses, then unleash the current models to create the datasets that will improve the models of tomorrow. +Using tools, as well, is another example of model makers having to train models by showing them examples of what +successful tool use looks like. At first, initial tool use by models was flaky and required heavy instructions and parsing +of outputs to achieve consistent results. But today, labs synthesize tons of examples of tool calls, formatting, and more, +ensuring tool calls are reliable. -# Training Data Explains the Quirks +As we train models to exhibit specific behaviors, reliably, we create models which are more adept at helping us create +new synthetic datasets. We build more environments, more test cases, and new harnesses, then unleash the current +models to create the datasets that will improve the models of tomorrow. -Our 30,000 foot view of how LLMs are built and how they work is complete, and hopefully our mental model is firming up. By understanding the data used to train models, we can better comprehend how weird, spooky, and unruly behavior can emerge from these strange machines. +## **Training Data Explains the Quirks** -From GPT-5.1 to GPT-5.5, OpenAI’s models were prone to talking about goblins. Metaphors featuring gremlins, goblins, and other fantasy creatures peppered responses, much more often than expected. This harmless behavior (I’ll let you decide if it’s annoying or amusing) increased with each model version, to the point where it was noticed, became a meme, and ultimately spurred [an investigatory blog post](https://openai.com/index/where-the-goblins-came-from/). +Our 30,000 foot view of how LLMs are built and how they work is complete, and hopefully our mental model is +firming up. By understanding the data used to train models, we can better comprehend how weird, spooky, and +unruly behavior can emerge from these strange machines. -Why did goblins haunt our models? Well, it came down to the data used to train them. Following the backlash over deprecating GPT-4o, OpenAI added a “personality” feature that let users pick preset conversational tones. One preset, “Nerdy”, that was prompted to, “undercut pretension through playful use of language.” When OpenAI RL’ed its models to embody these personalities, the reward signal for Nerdy, by the company’s own admission, gave unexpectedly high scores to metaphors featuring creatures. +From GPT-5.1 to GPT-5.5, OpenAI’s models were prone to talking about goblins. Metaphors featuring gremlins, +goblins, and other fantasy creatures peppered responses, much more often than expected. This harmless behavior (I’ll +let you decide if it’s annoying or amusing) increased with each model version, to the point where it was noticed, +[became a meme, and ultimately spurred an investigatory blog post.](https://openai.com/index/where-the-goblins-came-from/) -But “Nerdy” proved popular among users…many of whose traces were used to train the next GPT. GPT-5.1 picked up the goblin metaphors and started to apply them generally, and was then used to create synthetic datasets for the next model. And so on. By GPT-5.5, the goblins were unavoidable. +Why did goblins haunt our models? Well, it came down to the data used to train them. Following the backlash over +deprecating GPT-4o, OpenAI added a “personality” feature that let users pick preset conversational tones. One preset, +“Nerdy”, that was prompted to, “undercut pretension through playful use of language.” When OpenAI RL’ed its +models to embody these personalities, the reward signal for Nerdy, by the company’s own admission, gave +unexpectedly high scores to metaphors featuring creatures. -OpenAI’s goblin debacle is a quirky story that nicely highlights how different forms of data can impact generations of models in surprising ways. This basic story, once understood, can be spotted everywhere. +But “Nerdy” proved popular among users…many of whose traces were used to train the next GPT. GPT-5.1 picked up +the goblin metaphors and started to apply them generally, and was then used to create synthetic datasets for the next +model. And so on. By GPT-5.5, the goblins were unavoidable. -* The tendency of models to be sycophantic has been largely chalked up to the fact that the trainers and users of these models *prefer* deferential responses. Users hit the “Thumbs Up” button in ChatGPT and Claude when the model makes them feel good, and hit the “Thumbs Down” when it’s cold. (It’s also believed this mechanism is what drives models’ preference for emdashes!) -* Labs train their models to use the software harnesses owned by the lab, resulting in models that prefer tool call formats and subagent patterns unique to their own ecosystem. This stymies 3rd party harness developers, who have to mitigate model “expectations” with heavy, repeated prompting or software checks. -* Models are heavily trained on open source codebases, resulting in models that have a ‘memory’ for API calls and conventions that already exist at time of training. Software developers, designing APIs, change function calls at their peril and often adopt designs specifically to meet model expectations. +OpenAI’s goblin debacle is a quirky story that nicely highlights how different forms of data can impact generations of +models in surprising ways. This basic story, once understood, can be spotted everywhere. -All of this is how we find ourselves “fighting the weights,” repeating instructions over and over with escalating severity to overcome a stubborn bias learned from a dataset. + - The tendency of models to be sycophantic has been largely chalked up to the fact that the trainers and users of +these models _prefer_ deferential responses. Users hit the “Thumbs Up” button in ChatGPT and Claude when the +model makes them feel good, and hit the “Thumbs Down” when it’s cold. (It’s also believed this mechanism is +what drives models’ preference for emdashes!) -LLMs are powerful but unruly stochastic machines. To effectively build systems around them, harnesses that dynamically assemble the right context for each task,, we must continually hone our mental model for how they work. Effective mental models turn LLMs from a black box, an inscrutable pile of numbers, into something we can reason about. + - Labs train their models to use the software harnesses owned by the lab, resulting in models that prefer tool +call formats and subagent patterns unique to their own ecosystem. This stymies 3rd party harness developers, +who have to mitigate model “expectations” with heavy, repeated prompting or software checks. -xml version='1.0' encoding='utf-8'? + - Models are heavily trained on open source codebases, resulting in models that have a ‘memory’ for API calls +and conventions that already exist at time of training. Software developers, designing APIs, change function +calls at their peril and often adopt designs specifically to meet model expectations. -# Chapter 3. Context Rot +All of this is how we find ourselves “fighting the weights,” repeating instructions over and over with escalating +severity to overcome a stubborn bias learned from a dataset. -By Kelly Hong +LLMs are powerful but unruly stochastic machines. To effectively build systems around them, harnesses that +dynamically assemble the right context for each task,, we must continually hone our mental model for how they work. +Effective mental models turn LLMs from a black box, an inscrutable pile of numbers, into something we can reason +about. -# A Note for Early Release Readers +# **Chapter 3. Context Rot** -With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. +**_By Kelly Hong_** -This will be the 4th chapter of the final book. Please note that the GitHub repo will be made active later on. +Million token context windows are often highlighted in model releases as one of their core strengths. Gemini-1.5 Pro +demonstrated near-perfect needle recall up to 1M tokens of haystack 1 back in 2024, which was followed by many +more long-context model releases with similar results. The idea that you can completely fill this context window and +expect the model to handle it well is very appealing. Perhaps you want to ask about some event in a book series, but +you forget which book or chapter it is from. The entire series fits under 1 million tokens, 2 so you put everything into +the context window to ask your question. If these models are claimed to perform well across their entire context +window, there should not be any difference in response quality if you pass in the entire book series versus just the +page containing the answer. -If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at *mcronin@oreilly.com*. +Unfortunately, this claim did not hold true for Gemini-1.5 Pro, and it doesn’t hold true for models today. LLM +performance degrades as context length grows; we call this phenomena _context rot_ . -Million token context windows are often highlighted in model releases as one of their core strengths. Gemini-1.5 Pro demonstrated near-perfect needle recall up to 1M tokens of haystack[1](ch03.html#id57) back in 2024, which was followed by many more long-context model releases with similar results. The idea that you can completely fill this context window and expect the model to handle it well is very appealing. Perhaps you want to ask about some event in a book series, but you forget which book or chapter it is from. The entire series fits under 1 million tokens,[2](ch03.html#id58) so you put everything into the context window to ask your question. If these models are claimed to perform well across their entire context window, there should not be any difference in response quality if you pass in the entire book series versus just the page containing the answer. +You may have noticed this in your personal use of LLMs: when you have a long ChatGPT conversation, response +quality tends to degrade over time. When you have a long-running agent, it starts to repeat the same mistakes and +make nonsensical choices. For instance, a deep research agent may repeat the same failed query multiple times +throughout its trajectory, even if it returns the same irrelevant results each time. -Unfortunately, this claim did not hold true for Gemini-1.5 Pro, and it doesn’t hold true for models today. LLM performance degrades as context length grows; we call this phenomena *context rot*. +This inconsistency in reported performance versus reality comes from the usage of trivial long context benchmarks. +The common Needle in a Haystack 1 task is often made up of crisp question-and-needle pairs, which require little +interpretation to associate (Figure 3-1). -You may have noticed this in your personal use of LLMs: when you have a long ChatGPT conversation, response quality tends to degrade over time. When you have a long-running agent, it starts to repeat the same mistakes and make nonsensical choices. For instance, a deep research agent may repeat the same failed query multiple times throughout its trajectory, even if it returns the same irrelevant results each time. +## Figure 3-1. Example Needle in a Haystack task -This inconsistency in reported performance versus reality comes from the usage of trivial long context benchmarks. The common Needle in a Haystack1 task is often made up of crisp question-and-needle pairs, which require little interpretation to associate ([Figure 3-1](#ch04_figure_1_1785165978127933)). +While these tests measure a model’s ability to find an answer in a very long context, they don’t map cleanly to real +world tasks. In reality, long-context tasks (i.e. ambiguous question-answering, coding, search, etc.) often require +semantic understanding and complex reasoning which most benchmarks do not capture (Figure 3-2). -![](assets/ch04_figure_1_1785165978127933.png) +## Figure 3-2. LLM performance degradation comparing needles with varying levels of similarity: _blue - high similarity, red - low similarity and more ambiguous_ -###### Figure 3-1. Example Needle in a Haystack task +Even when models report strong long-context performance on these benchmarks, they should not be assumed to +perform well in all long-context tasks. -While these tests measure a model’s ability to find an answer in a very long context, they don’t map cleanly to real world tasks. In reality, long-context tasks (i.e. ambiguous question-answering, coding, search, etc.) often require semantic understanding and complex reasoning which most benchmarks do not capture ([Figure 3-2](#ch04_figure_2_1785165978127978)). +## **Implications** -![](assets/ch04_figure_2_1785165978127978.png) - -###### Figure 3-2. LLM performance degradation comparing needles with varying levels of similarity: blue - high similarity, red - low similarity and more ambiguous - -Even when models report strong long-context performance on these benchmarks, they should not be assumed to perform well in all long-context tasks. - -# Implications - -One of the consequences of growing context length is an increased chance of *distraction*, in which the model outputs an inaccurate response due to distractors in the context. Distractors are parts of the context that seem relevant to the task at hand, but are factually inaccurate. +One of the consequences of growing context length is an increased chance of _distraction_, in which the model outputs +an inaccurate response due to distractors in the context. Distractors are parts of the context that seem relevant to the +task at hand, but are factually inaccurate. Here’s an example of distractors in action: ``` -Question: "What was the best writing advice I got from my -college classmate?" -Needle: "I think the best writing tip I received from my college -classmate was to write every week." -Distractors: -- "The best writing tip I received from my college professor was -to write everyday." -- "The worst writing advice I got from my college classmate was -to write each essay in five different styles." -- "The best writing advice I got from my classmate was to write -each essay in three different styles, this was back in high school." -- "I thought the best writing advice I got from my college -classmate was to write each essay in four different styles, but -not anymore." -``` + Question: "What was the best writing advice I got from my + college classmate?" + Needle: "I think the best writing tip I received from my college + classmate was to write every week." + Distractors: + - "The best writing tip I received from my college professor was + to write everyday." + - "The worst writing advice I got from my college classmate was + to write each essay in five different styles." + - "The best writing advice I got from my classmate was to write + each essay in three different styles, this was back in high school." + - "I thought the best writing advice I got from my college + classmate was to write each essay in four different styles, but + not anymore." -The number of distractors often correlate with context length, which is one cause of this performance degradation. But even with the number of distractors kept constant, the model’s susceptibility to distraction increases with increasing context length as demonstrated through research ([Figure 3-3](#ch04_figure_3_1785165978128012)). +``` -![](assets/ch04_figure_3_1785165978128012.png) +The number of distractors often correlate with context length, which is one cause of this performance degradation. But +even with the number of distractors kept constant, the model’s susceptibility to distraction increases with increasing +context length as demonstrated through research (Figure 3-3). -###### Figure 3-3. Context Rot research results: model performance degrades with increasing context length, even with the number of distractors kept constant +## Figure 3-3. Context Rot research results: model performance degrades with increasing context _length, even with the number of distractors kept constant_ -In practice, this may look like a coding agent getting confused on similar instructions. Here is an example coding agent instruction file. +In practice, this may look like a coding agent getting confused on similar instructions. Here is an example coding +agent instruction file. ``` -Tool: ReadDocument -Content: -database_guide.md -When adding new entries to the ‘store_info’ database, use -text-embedding-3-large -… -When adding new entries to the ‘product_info’ database, use -both text-embedding-3-small and BM25 to support hybrid search + Tool: ReadDocument + Content: + database_guide.md + When adding new entries to the ‘store_info’ database, use + text-embedding-3-large + … + When adding new entries to the ‘product_info’ database, use + both text-embedding-3-small and BM25 to support hybrid search + ``` -An agent given a short, focused context with only these instructions and a few turns is more likely to accurately distinguish between compared to if it had a longer context. As the number of turns increases and fills up context, the agent becomes more prone to distraction. For example, when faced with adding to `product_info` after 20 turns, the agent may be more likely to use `text-embedding-3-large` compared to if it only had 2 turns. +An agent given a short, focused context with only these instructions and a few turns is more likely to accurately +distinguish between compared to if it had a longer context. As the number of turns increases and fills up context, the +agent becomes more prone to distraction. For example, when faced with adding to `product_info` after 20 turns, the +agent may be more likely to use `text-embedding-3-large` compared to if it only had 2 turns. -Agentic search is another common distraction scenario since documents accumulate context fast and search results are inherently similar. Here is an example snippet from agentic search loop: +Agentic search is another common distraction scenario since documents accumulate context fast and search results are +inherently similar. Here is an example snippet from agentic search loop: ``` -Task: Which customer has their HQ in New York and reached $10M -ARR in 2025? -Tool Call #12: Search -Query: New York HQ customer 10M ARR 2025 -Document 2968: -Customer update — Company A -Founded in New York before relocating headquarters to Seattle; -now expanding enterprise usage. Finance noted ARR crossed $10M -on Oct. 4, 2025, following the retail renewal. - -Document 7846: -Q3 account review — Company B -ARR reached $10M on Sept. 22, 2025, driven by the East Coast -expansion and headquarters relocation to New York. + Task: Which customer has their HQ in New York and reached $10M + ARR in 2025? + Tool Call #12: Search + Query: New York HQ customer 10M ARR 2025 + Document 2968: + Customer update — Company A + Founded in New York before relocating headquarters to Seattle; + now expanding enterprise usage. Finance noted ARR crossed $10M + on Oct. 4, 2025, following the retail renewal. + + Document 7846: + Q3 account review — Company B + ARR reached $10M on Sept. 22, 2025, driven by the East Coast + expansion and headquarters relocation to New York. + ``` A search agent typically utilizes two main tools: -`search` -: Returns top-k result snippets given a query, similar to Google search - -`read_document` -: Show full contents of a search result - -Usage of these two tools leads to fast context accumulation, given that the average lengths of search results and full document content are relatively long. Additionally, the search engine is designed to return similar results for a given query, which introduces this task of having to deal with distractors. Taking these together, this agentic search setup leads to performance degradation with increasing context length for similar reasons as outlined in the coding agent example. Long-running search agent sessions are more likely to output final responses influenced by distractors compared to shorter sessions. - -Another consequence of long context is increased *abstention*, in which the model claims it does not have enough relevant context to satisfy the user’s query. This kind of response is useful when the model actually does not have the necessary relevant information, since the alternative would be to output an inaccurate response by guessing. However, growing context often makes the model more likely to *falsely* abstain, meaning it actually has the relevant information to answer, but claims otherwise. - -When thinking about why abstention happens, it’s useful to think about the tension between helpfulness and reliability. Responding confidently with false information is oftentimes more harmful than abstaining. Thus, a major focus in model training is to encourage abstention under uncertainty. This particular focus on LLM reliability is illustrated by benchmarks like [AbstentionBench](https://arxiv.org/pdf/2506.09038), which measures how well models abstain in situations where a confident answer would be inappropriate (i.e. unanswerable questions, ambiguous prompts, etc.). - -One real world scenario to demonstrate this is the case of a customer support agent. A customer asks the same question about a company’s policy at two points in the context: the first turn of the conversation versus the tenth. The agent accurately and confidently answers in the first case, before context has accumulated from tool calls and follow up questions. When asked after all the context has been accumulated, the agent may abstain from answering the same question since this increased context length has increased its uncertainty. - -Finally, we look at *repetition* as a failure mode that often surfaces in agent loops. As mentioned previously, these loops are particularly susceptible to context rot, since they quickly accumulate context through tool use. This may not be obvious from how we typically interface with agents, through which we only see the current action being performed with occasional small snippets from tool calls. From this, it’s easy to assume that this clean, focused context being displayed is also what’s being shown to the agent. However, The reality is that *all* actions ever taken in a session accumulate into one long context, including full tool call results that are significantly longer than what you see as a user (i.e. code snippet versus full file). - -For example, we can take the example of a coding agent ([Figure 3-4](#ch04_figure_4_1785165978128040)), which accumulates tool calls from searching through files, editing code, reading logs, etc. +``` +search -![](assets/ch04_figure_4_1785165978128040.png) +``` -###### Figure 3-4. This snippet is approximately 5% of an average coding agent session +Returns top-k result snippets given a query, similar to Google search -This accumulated context influences the model’s next action, since the model generates each response based on everything already in the context. This results in a greater tendency for the model to repeat past actions rather than trying novel ones, regardless of the success of past actions. Simply the presence of actions in the context makes it more likely for the model to repeat them. +``` +read_document -When looking at this long context, it would be unreasonable to expect a human to process all of this information to take their next action. While this processing capability is an impressive one in LLMs, it opens up the question of whether this navigation of messy context should even be the model’s task in the first place. +``` -# Root Cause +Show full contents of a search result + +Usage of these two tools leads to fast context accumulation, given that the average lengths of search results and full +document content are relatively long. Additionally, the search engine is designed to return similar results for a given +query, which introduces this task of having to deal with distractors. Taking these together, this agentic search setup +leads to performance degradation with increasing context length for similar reasons as outlined in the coding agent +example. Long-running search agent sessions are more likely to output final responses influenced by distractors +compared to shorter sessions. + +Another consequence of long context is increased _abstention_, in which the model claims it does not have enough +relevant context to satisfy the user’s query. This kind of response is useful when the model actually does not have the +necessary relevant information, since the alternative would be to output an inaccurate response by guessing. However, +growing context often makes the model more likely to _falsely_ abstain, meaning it actually has the relevant information +to answer, but claims otherwise. + +When thinking about why abstention happens, it’s useful to think about the tension between helpfulness and +reliability. Responding confidently with false information is oftentimes more harmful than abstaining. Thus, a major +focus in model training is to encourage abstention under uncertainty. This particular focus on LLM reliability is +[illustrated by benchmarks like AbstentionBench, which measures how well models abstain in situations where a](https://arxiv.org/pdf/2506.09038) +confident answer would be inappropriate (i.e. unanswerable questions, ambiguous prompts, etc.). + +One real world scenario to demonstrate this is the case of a customer support agent. A customer asks the same +question about a company’s policy at two points in the context: the first turn of the conversation versus the tenth. The +agent accurately and confidently answers in the first case, before context has accumulated from tool calls and follow +up questions. When asked after all the context has been accumulated, the agent may abstain from answering the same +question since this increased context length has increased its uncertainty. + +Finally, we look at _repetition_ as a failure mode that often surfaces in agent loops. As mentioned previously, these loops +are particularly susceptible to context rot, since they quickly accumulate context through tool use. This may not be +obvious from how we typically interface with agents, through which we only see the current action being performed +with occasional small snippets from tool calls. From this, it’s easy to assume that this clean, focused context being +displayed is also what’s being shown to the agent. However, The reality is that _all_ actions ever taken in a session +accumulate into one long context, including full tool call results that are significantly longer than what you see as a +user (i.e. code snippet versus full file). + +For example, we can take the example of a coding agent (Figure 3-4), which accumulates tool calls from searching +through files, editing code, reading logs, etc. + +## Figure 3-4. This snippet is approximately 5% of an average coding agent session + +This accumulated context influences the model’s next action, since the model generates each response based on +everything already in the context. This results in a greater tendency for the model to repeat past actions rather than +trying novel ones, regardless of the success of past actions. Simply the presence of actions in the context makes it +more likely for the model to repeat them. + +When looking at this long context, it would be unreasonable to expect a human to process all of this information to +take their next action. While this processing capability is an impressive one in LLMs, it opens up the question of +whether this navigation of messy context should even be the model’s task in the first place. + +## **Root Cause** Why does context rot even occur? -One explanation is that the training data does not capture a good representation of realistic long context tasks, largely due to the difficulty in obtaining such representative data. We can think backwards from the length of the coding agent session above, which can be approximated by the compaction threshold for coding agents like Claude Code[3](ch03.html#id59). The presence of these autocompact features implies that coding agent sessions frequently reach this context limit, necessitating context length reduction. - -Claude Code compresses context at ~160k tokens by default, and we can use this as one proxy for approximating the context length that an agent may reach. How many 160K token coding agent sessions are readily available to train on? There are not many, and this leads us to manually curate and synthetically generate agent trajectories to reach this length. This is an expensive process which naturally leads to the problem of having insufficient representative training data for these models. - -The difficulty of such data curation can also be demonstrated by the available public long context benchmarks. Early long context benchmarks such as [NIAH](https://github.com/gkamradt/needle-in-a-haystack) and [MRCR](https://arxiv.org/html/2409.12640v2) (multiple needles in a haystack) focus on retrieval capabilities which require simpler capabilities than a long coding task. Model releases from 2024 to 2025 have largely focused on these retrieval benchmarks, such as GPT-4.1 which led with a NIAH evaluation to highlight its long context capabilities. Recent benchmarks have been evaluating for both long context and agentic capabilities, through deep research benchmarks like [BrowseComp](https://openai.com/index/browsecomp/) and long-horizon engineering tasks like [METR Time Horizons](https://metr.org/time-horizons/), which are getting closer to the realistic agentic tasks we see in practice. However, these tasks are significantly more expensive to curate and even with these benchmarks, they still do not completely capture the complexity and variety of realistic long context use cases. - -In the case that such data were to be obtained, the training costs themselves are also important to consider since they scale with context length. These costs grow faster than context length due to how attention works in LLMs; since every token interacts with every other token, doubling the context length roughly quadruples the computation/cost. - -# Context Management +One explanation is that the training data does not capture a good representation of realistic long context tasks, largely +due to the difficulty in obtaining such representative data. We can think backwards from the length of the coding +agent session above, which can be approximated by the compaction threshold for coding agents like Claude Code 3 . +The presence of these autocompact features implies that coding agent sessions frequently reach this context limit, +necessitating context length reduction. + +Claude Code compresses context at ~160k tokens by default, and we can use this as one proxy for approximating the +context length that an agent may reach. How many 160K token coding agent sessions are readily available to train +on? There are not many, and this leads us to manually curate and synthetically generate agent trajectories to reach +this length. This is an expensive process which naturally leads to the problem of having insufficient representative +training data for these models. + +The difficulty of such data curation can also be demonstrated by the available public long context benchmarks. Early +[long context benchmarks such as NIAH and MRCR (multiple needles in a haystack) focus on retrieval capabilities](https://arxiv.org/html/2409.12640v2) +which require simpler capabilities than a long coding task. Model releases from 2024 to 2025 have largely focused on +these retrieval benchmarks, such as GPT-4.1 which led with a NIAH evaluation to highlight its long context +capabilities. Recent benchmarks have been evaluating for both long context and agentic capabilities, through deep +[research benchmarks like BrowseComp and long-horizon engineering tasks like METR Time Horizons, which are](https://metr.org/time-horizons/) +getting closer to the realistic agentic tasks we see in practice. However, these tasks are significantly more expensive to +curate and even with these benchmarks, they still do not completely capture the complexity and variety of realistic +long context use cases. + +In the case that such data were to be obtained, the training costs themselves are also important to consider since they +scale with context length. These costs grow faster than context length due to how attention works in LLMs; since every +token interacts with every other token, doubling the context length roughly quadruples the computation/cost. + +## **Context Management** Taking the current state of these models, there are a few effective ways to manage context and mitigate context rot. -A popular approach is to use *orchestration* to delegate subtasks to subagents, rather than keeping everything in a single agent loop. In this approach, a main orchestrator agent breaks down a complex task to offload to subagents. - -A deep research task like BrowseComp is a good example for which orchestration can be useful ([Figure 3-5](#ch04_figure_5_1785165978128064)). - -![](assets/ch04_figure_5_1785165978128064.png) - -###### Figure 3-5. Example BrowseComp - -The orchestrator agent starts off by generating a search plan, which includes the breakdown of subtasks to assign each subagent. This example task may be broken down into two phases: - -* Phase 1: exploration & finding candidate papers - - + Subagent 1 (focus: topics): find candidate publications that mention cultural traditions, scientific processes, and culinary innovations, with 3 co-authors. - + Subagent 2 (focus: geography): find researchers and departments in West Bengal publishing on traditional cuisine. Return candidate authors and their papers. -* Phase 2: verification - - + Subagent 3: verify each author’s background - + Subagent 4: verify that publication mentions cultural traditions, scientific processes, and culinary innovations - + Subagent 5: verify date of publication, if not present in already found documents - -Each subagent would then return its relevant output (i.e. only relevant documents, instead of the full search history) back to the orchestrator agent. This allows for the main context to be kept clean of the many tool calls and intermediate reasoning, accumulating significantly slower than if everything was kept in the same context. - -*Compaction* was also popularized as a context management strategy, which is a way of compressing context into a smaller representation. You may have seen this in coding agents, such as Claude Code’s `/compact` command, which typically summarizes the current context into a summary of what was done, which files were changed, and what may happen next. However, this generic compaction approach tends to lose key details and cannot reliably predict what will be relevant for your next task. Steered compaction is one way to improve upon this, which specifies exactly how you want to compact instead of having the model guess. This can be done in a single call through passing in “[steering instruction]: [context]”. Here is a steered compaction prompt example: - -``` -Your task is to optimize context to pass onto another agent. -I will be building onto our web search agent flow, -so only keep parts relevant to this implementation -including specific code snippets, my preferences -for presenting search results, and why I requested -these changes. Discard everything else not related -to the web search agent. Here is the context to -optimize: [context] -``` - -For both of the approaches above, context is typically not recovered after it is discarded. A subagent’s context is unused after its final output is passed off to the orchestrator and compaction only leaves you with the compressed form.*Recursive Language Models* (RLMs) introduce a way to manage two distinct pools of context without throwing either away: tokenized context (which fills the LLM’s context window) and programmatic context (information that exists in the coding environment). By giving the LLM access to the REPL, where the programmatic context is managed, the LLM controls what moves from programmatic space to token space. - -The full context stays in the environment rather than summarized away or handed off and lost, so the model can go back and re-query it at any point. - -Sometimes, simply *clearing* the context when topic switching is the most effective solution. When the previous context becomes irrelevant (i.e. switching the conversation topic completely, or starting a new coding task), it’s best to clear the context entirely instead of prolonging the session. +A popular approach is to use _orchestration_ to delegate subtasks to subagents, rather than keeping everything in a single +agent loop. In this approach, a main orchestrator agent breaks down a complex task to offload to subagents. -These strategies are necessary to effectively use LLMs in their current state today. We’ve learned that despite their large context windows and long context benchmark performance, uniform performance across the context window does not hold up in practice. It’s likely that long context performance improves over time, and we see that trend with the increasing focus on long context agentic tasks in training. These findings and mitigations reflect the current state of the models in 2026, which may evolve over time. +A deep research task like BrowseComp is a good example for which orchestration can be useful (Figure 3-5). -[1](ch03.html#id57-marker) Needle-in-a-Haystack: a common long context benchmark in which a known sentence (the needle) is placed in a long document of unrelated content (the haystack), and the model’s task is to retrieve it +## Figure 3-5. Example BrowseComp -[2](ch03.html#id58-marker) 1 million tokens is approximately equal to 10 average-length novels +The orchestrator agent starts off by generating a search plan, which includes the breakdown of subtasks to assign each +subagent. This example task may be broken down into two phases: -[3](ch03.html#id59-marker) It’s difficult to measure the average length of a coding agent session generally, since it varies significantly depending on the model, harness, and task + - Phase 1: exploration & finding candidate papers -xml version='1.0' encoding='utf-8'? + - Subagent 1 (focus: topics): find candidate publications that mention cultural traditions, scientific +processes, and culinary innovations, with 3 co-authors. -# Chapter 4. Case Study: Hex + - Subagent 2 (focus: geography): find researchers and departments in West Bengal publishing on +traditional cuisine. Return candidate authors and their papers. -by Caitlin Colgrove + - Phase 2: verification -# A Note for Early Release Readers + - Subagent 3: verify each author’s background -With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. + - Subagent 4: verify that publication mentions cultural traditions, scientific processes, and culinary +innovations -This will be the 17th chapter of the final book. Please note that the GitHub repo will be made active later on. + - Subagent 5: verify date of publication, if not present in already found documents -If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at *mcronin@oreilly.com*. +Each subagent would then return its relevant output (i.e. only relevant documents, instead of the full search history) +back to the orchestrator agent. This allows for the main context to be kept clean of the many tool calls and +intermediate reasoning, accumulating significantly slower than if everything was kept in the same context. -None of the founders of Hex—an AI-powered platform for data science and analytics—expected to be founders, let alone the builders of a frontier AI agent. On the contrary, all three of them spent the better part of a decade at Palantir building technology for some of the most tedious, manual, and mundane workflows of the 21st century: data integrations. But in an era when the world’s most recognizable companies couldn’t even tell you how many paying customers they had, the foundational value that Palantir provided was the ability to ask questions (at all) of enterprise data, through the monumental technological and organizational effort of bringing all of that data into a single place. +_Compaction_ was also popularized as a context management strategy, which is a way of compressing context into a +smaller representation. You may have seen this in coding agents, such as Claude Code’s `/compact` command, which +typically summarizes the current context into a summary of what was done, which files were changed, and what may +happen next. However, this generic compaction approach tends to lose key details and cannot reliably predict what +will be relevant for your next task. Steered compaction is one way to improve upon this, which specifies exactly how +you want to compact instead of having the model guess. This can be done in a single call through passing in “[steering +instruction]: [context]”. Here is a steered compaction prompt example: -At the time (the mid-2010s), that was more or less the end of the story. A high priesthood of data scientists and BI analysts were able to speak the proper incantations (usually involving window functions) to produce charts that were then pasted into slide decks titled Metrics\_v3\_final\_final and then emailed around until an executive came back with “one more quick question” or “why doesn’t this match the numbers in my dashboard” and the cycle would start all over again. Inevitably, decisions would still be made on data that was inaccurate, out of date or incomplete, largely because the process of answering questions was too difficult or error prone to improve. - -Fast-forward to 2026, and we’ve invented *actual* magic in the form of LLMs. Models today are capable of crawling entire enterprise data warehouses (often of a size and complexity that would make 2010s Palantir blush), execute multi-hundred-line SQL queries, and respond to natural language questions just as a human analyst might. No longer are data-driven decisions accessible only to a select few: now everyone in the organization has near-instant answers to their data questions. - -But are they the *right* answers? - -Here we are in 2026, faced with the same problem as in 2016: when presented with real-world data environments, today’s frontier models are as likely to get the wrong answer as the right one. - -Why? Because the challenge in data has never been about intelligence: it has always been about *context*. The context of an enterprise is vast, often untrustworthy, and frequently self-contradictory. Perhaps over the lifetime of the company there have been multiple different ways to calculate revenue—how does an agent know which one is correct? Maybe the data warehouse has not been maintained perfectly and has out-of-date documentation as well as deprecated tables. Certainly there are old analyses lying around that contain factual errors, many of which would not be obvious at first blush. How does one build trusted context in such an environment? - -Palantir’s original insight was that all the machine learning (all the rage at the time) in the world didn’t matter if the models didn’t have access to the right context, and that most large enterprises lacked even the ability to manage that context, let alone build on top of it. Palantir’s business was built around building a trusted source of context for data work, but the utility of that asset was still limited by the tools of the time. With AI, *everyone* can build on top of the data context, which makes it all the more imperative that it is managed well. - -# Data Notebooks, Past and Present - -Hex began its existence in 2019 with the observation that for all the advances in data warehousing technology and cloud-based SaaS tools, most data workflows were still trapped in point-and-click BI tools or ad-hoc, local Jupyter notebooks, whose interfaces hadn’t seen a major upgrade in a decade or more. In that time, data work had become increasingly sophisticated and technical, frequently relying on tools like SQL and Python to analyze high volumes of data hosted in cloud data warehouses such as Snowflake and BigQuery. - -The popularity of Python-based Jupyter notebooks, despite their [known drawbacks](https://www.youtube.com/watch?v=7jiPeIFXb6U), inspired the Hex team to design an updated version of the form factor, which was first popularized by [Donald Knuth in the early 80s](https://en.wikipedia.org/wiki/Literate_programming). The iterative, self-documenting style of code notebooks was a natural fit for exploratory data work, in which downstream analytical decisions often derive from the inspection of the outputs of previous steps. - -The Hex notebook extends this paradigm, adding a library of new cell types including native SQL editing, presentation cells such as charts and maps, and interactive widgets for building dashboards. The notebook is backed by a reactive DAG that is automatically inferred from a static analysis of the variables and dependencies in the code. - -But most data workflows don’t end with the analysis: they culminate with the sharing of a curated artifact like dashboard. Hex integrates the dashboard paradigm directly with the underlying notebook, allowing for a seamless transition from development to cross-functional collaboration. - -![](assets/ch17_figure_1_1785165978833888.png) - -###### Figure 4-1. Hex notebook interface with corresponding agent - -Fast-forward to 2022 and the release of ChatGPT, the event that first really demonstrated the enormous potential of large language models in a wide range of domains, including coding and data analysis. As the technology evolved into the agentic tool-calling paradigm we know today, the modular, composable notebook format has turned into a surprisingly effective surface area for agentic reasoning. Notebooks have two properties that make them a natural fit for LLM-based agents. First, the literate programming paradigm interleaves semantically meaningful content with the code it is intended to describe, which gives LLMs a strong foundation for understanding. On top of that, cell-based architecture very naturally decomposes into agentic tool calls. - -Despite those advantages, Hex’s initial attempts to combine the two were less than impressive: brittle, slow, and—worst of all—horribly inaccurate. The journey from those humble beginnings to becoming a bleeding-edge AI analytics tool did not involve a single training run. It was *entirely* driven by careful, detailed context management. - -# Context Management in Data - -Context management in data has always been a uniquely difficult domain, and it remains so, even in this new era of generative AI. Despite the exponential increase in functional context window sizes, careful attention to the contents of context is still essential for reliable performance on data tasks. - -Most obviously, the vast majority of interesting data sets not only cannot fit into a 1M token context window, but are likely many orders of magnitude larger. For reference, the largest data warehouse that Hex syncs is over 10 million columns alone—and the context size would be multiplied by the number of rows in each. And unlike text or code, data is not easily compressible: consider the infamous [Datasaurus](https://en.wikipedia.org/wiki/Datasaurus_dozen), a collection of 13 data sets with the same descriptive statistics that when graphed, produce entirely different visuals (including one that does in fact look like a dinosaur, as shown in [Figure 4-2](#ch17_figure_2_1785165978833928)). - -![](assets/ch17_figure_2_1785165978833928.png) - -###### Figure 4-2. The Datasaurus dozen - -A perhaps less intuitive, but equally pernicious, source of context bloat is data file formats themselves. Unlike code, even technical data tools typically include a complex UI, with dozens of knobs and configurations. A single cell specification in Hex can cost hundreds to thousands of tokens if fully utilized, and many notebooks contain dozens to hundreds of cells, quickly eating through generous context budgets. Worse, LLMs tend to be resistant to using structured formats that are not present in their training data—proprietary formats like the internal JSON schema for a Hex notebook are complete foreign to these models, and trying to get them to strictly adhere becomes a classic Sisyphean challenge of fighting the weights. - -![](assets/ch17_figure_3_1785165978833957.png) - -###### Figure 4-3. A visualization of context bloat - -And then on top of everything else, working in data also requires importing all of the *other* context challenges that plague large organizations including inaccuracies, siloed information and messy or non-existent documentation. - -# Journey Over Destination - -Throughout the process of building an agent to work with Hex’s analytical notebook, the team repeatedly employed a (conceptually) simple technique: rather than trying to hyper-optimize the perfect context from the beginning, start with an extremely minimal, compact set of context that gives the agent a *map* of the overall information space, and let the agent discover and selectively load the relevant pieces. This avoids most of the thorny context problems of data: messy file formats, large data sets, and extraneous or misleading information. - -A clear case study in applying the context map is Hex’s cell graph. Typical notebooks (including cell code, metadata, and data outputs) are simply too large for even state-of-the-art models to reason about reliably: the agent needs a system for navigating the graph based on a user prompt, identifying the relevant cells to operate on, and then retrieving their contents for the agent context. - -# The Limitations of RAG - -In the pre-agent world, populating the context based on a user-prompt would typically involve some form of RAG, or Retrieval Augmented Generation. RAG populates the context in a single step with the results of a non-generative information retrieval step based on the user’s prompt ([Figure 4-4](#ch17_figure_4_1785165978833982)). Because the expectation is that users of AI features will use natural language as opposed to more traditional keyword search terms, most RAG implementations rely heavily on semantic search (based on vector representations of meaning) rather than more traditional lexical search. - -Hex’s first RAG implementation was primarily to manage data warehouse context, embedding the documentation for the tables and columns from the warehouse schema in a vector database and retrieving them prompt-time to populate the context for the user’s SQL generation request. - -![](assets/ch17_figure_4_1785165978833982.png) - -###### Figure 4-4. The single-step RAG pattern - -For a single LLM step of SQL generation, this works reasonably well, especially considering that data documentation tends to be fairly terse. For reasonably documented warehouses, the system often sees upwards of 90% recall for the relevant tables and columns. - -Unfortunately, for context management in the notebook, RAG is completely non-functional. Why the huge difference in performance? It comes down to the difference between non-agentic and agentic workflows. - -In the context of a single search query or prompt, a system with 90% recall is pretty useful! If the query happens to fail, the user can try again with a different wording to attempt to find a the missing information in a different part of the search space. +``` + Your task is to optimize context to pass onto another agent. + I will be building onto our web search agent flow, + so only keep parts relevant to this implementation + including specific code snippets, my preferences + for presenting search results, and why I requested + these changes. Discard everything else not related + to the web search agent. Here is the context to + optimize: [context] -But an agent is expected to make multiple—sometimes dozens or hundreds—of autonomous tool calls in quick succession. Many tool calls depend on the success of an upstream tool call: for example, you can’t edit the contents of a cell if you haven’t successfully retrieved the reference. Accomplishing a complex task in a notebook requires fetching and interacting with multiple cells, each with a non-trivial failure rate, which drives the overall task failure rate unacceptably high. +``` -While it’s theoretically possible to optimize the index for better performance, there is a fundamental issue with semantic search. At its core semantic search is a compression algorithm, which means you are losing information in the index and thus there is an upper bound on how accurate the retrieval can be. Notebook cells are fairly sparse (only a handful of words in any given SQL query will be semantically meaningful), which significantly worsens the problem. Even if a code cell in a notebook has a few hundred tokens, the majority of those are not relevant and many individual cells are not specifically documented, even if the entire notebook is. Ultimately, despite its success with warehouse retrieval, semantic search is too error-prone to reliably identify the correct target cells. +For both of the approaches above, context is typically not recovered after it is discarded. A subagent’s context is +unused after its final output is passed off to the orchestrator and compaction only leaves you with the compressed +form. _Recursive Language Models_ (RLMs) introduce a way to manage two distinct pools of context without throwing +either away: tokenized context (which fills the LLM’s context window) and programmatic context (information that +exists in the coding environment). By giving the LLM access to the REPL, where the programmatic context is +managed, the LLM controls what moves from programmatic space to token space. + +The full context stays in the environment rather than summarized away or handed off and lost, so the model can go +back and re-query it at any point. + +Sometimes, simply _clearing_ the context when topic switching is the most effective solution. When the previous context +becomes irrelevant (i.e. switching the conversation topic completely, or starting a new coding task), it’s best to clear +the context entirely instead of prolonging the session. + +These strategies are necessary to effectively use LLMs in their current state today. We’ve learned that despite their +large context windows and long context benchmark performance, uniform performance across the context window +does not hold up in practice. It’s likely that long context performance improves over time, and we see that trend with +the increasing focus on long context agentic tasks in training. These findings and mitigations reflect the current state +of the models in 2026, which may evolve over time. + +1 +~~Needle-in-a-Haystack: a common long context benchmark in which a known sentence (the needle) is placed in a long~~ +document of unrelated content (the haystack), and the model’s task is to retrieve it + +2 +1 million tokens is approximately equal to 10 average-length novels +3 +It’s difficult to measure the average length of a coding agent session generally, since it varies significantly depending on the +model, harness, and task + +# **Chapter 4. Case Study: Hex** + +**_by Caitlin Colgrove_** + +None of the founders of Hex—an AI-powered platform for data science and analytics—expected to be founders, let +alone the builders of a frontier AI agent. On the contrary, all three of them spent the better part of a decade at +Palantir building technology for some of the most tedious, manual, and mundane workflows of the 21st century: data +integrations. But in an era when the world’s most recognizable companies couldn’t even tell you how many paying +customers they had, the foundational value that Palantir provided was the ability to ask questions (at all) of enterprise +data, through the monumental technological and organizational effort of bringing all of that data into a single place. + +At the time (the mid-2010s), that was more or less the end of the story. A high priesthood of data scientists and BI +analysts were able to speak the proper incantations (usually involving window functions) to produce charts that were +then pasted into slide decks titled Metrics_v3_final_final and then emailed around until an executive came back with +“one more quick question” or “why doesn’t this match the numbers in my dashboard” and the cycle would start all +over again. Inevitably, decisions would still be made on data that was inaccurate, out of date or incomplete, largely +because the process of answering questions was too difficult or error prone to improve. + +Fast-forward to 2026, and we’ve invented _actual_ magic in the form of LLMs. Models today are capable of crawling +entire enterprise data warehouses (often of a size and complexity that would make 2010s Palantir blush), execute +multi-hundred-line SQL queries, and respond to natural language questions just as a human analyst might. No longer +are data-driven decisions accessible only to a select few: now everyone in the organization has near-instant answers to +their data questions. + +But are they the _right_ answers? + +Here we are in 2026, faced with the same problem as in 2016: when presented with real-world data environments, +today’s frontier models are as likely to get the wrong answer as the right one. + +Why? Because the challenge in data has never been about intelligence: it has always been about _context_ . The context +of an enterprise is vast, often untrustworthy, and frequently self-contradictory. Perhaps over the lifetime of the +company there have been multiple different ways to calculate revenue—how does an agent know which one is +correct? Maybe the data warehouse has not been maintained perfectly and has out-of-date documentation as well as +deprecated tables. Certainly there are old analyses lying around that contain factual errors, many of which would not +be obvious at first blush. How does one build trusted context in such an environment? + +Palantir’s original insight was that all the machine learning (all the rage at the time) in the world didn’t matter if the +models didn’t have access to the right context, and that most large enterprises lacked even the ability to manage that +context, let alone build on top of it. Palantir’s business was built around building a trusted source of context for data +work, but the utility of that asset was still limited by the tools of the time. With AI, _everyone_ can build on top of the +data context, which makes it all the more imperative that it is managed well. + +## **Data Notebooks, Past and Present** + +Hex began its existence in 2019 with the observation that for all the advances in data warehousing technology and +cloud-based SaaS tools, most data workflows were still trapped in point-and-click BI tools or ad-hoc, local Jupyter +notebooks, whose interfaces hadn’t seen a major upgrade in a decade or more. In that time, data work had become +increasingly sophisticated and technical, frequently relying on tools like SQL and Python to analyze high volumes of +data hosted in cloud data warehouses such as Snowflake and BigQuery. + +[The popularity of Python-based Jupyter notebooks, despite their known drawbacks, inspired the Hex team to design](https://www.youtube.com/watch?v=7jiPeIFXb6U) +[an updated version of the form factor, which was first popularized by Donald Knuth in the early 80s. The iterative,](https://en.wikipedia.org/wiki/Literate_programming) +self-documenting style of code notebooks was a natural fit for exploratory data work, in which downstream analytical +decisions often derive from the inspection of the outputs of previous steps. + +The Hex notebook extends this paradigm, adding a library of new cell types including native SQL editing, presentation +cells such as charts and maps, and interactive widgets for building dashboards. The notebook is backed by a reactive +DAG that is automatically inferred from a static analysis of the variables and dependencies in the code. + +But most data workflows don’t end with the analysis: they culminate with the sharing of a curated artifact like +dashboard. Hex integrates the dashboard paradigm directly with the underlying notebook, allowing for a seamless +transition from development to cross-functional collaboration. + +## Figure 4-1. Hex notebook interface with corresponding agent + +Fast-forward to 2022 and the release of ChatGPT, the event that first really demonstrated the enormous potential of +large language models in a wide range of domains, including coding and data analysis. As the technology evolved into +the agentic tool-calling paradigm we know today, the modular, composable notebook format has turned into a +surprisingly effective surface area for agentic reasoning. Notebooks have two properties that make them a natural fit +for LLM-based agents. First, the literate programming paradigm interleaves semantically meaningful content with the +code it is intended to describe, which gives LLMs a strong foundation for understanding. On top of that, cell-based +architecture very naturally decomposes into agentic tool calls. + +Despite those advantages, Hex’s initial attempts to combine the two were less than impressive: brittle, slow, and— +worst of all—horribly inaccurate. The journey from those humble beginnings to becoming a bleeding-edge AI +analytics tool did not involve a single training run. It was _entirely_ driven by careful, detailed context management. + +## **Context Management in Data** + +Context management in data has always been a uniquely difficult domain, and it remains so, even in this new era of +generative AI. Despite the exponential increase in functional context window sizes, careful attention to the contents of +context is still essential for reliable performance on data tasks. + +Most obviously, the vast majority of interesting data sets not only cannot fit into a 1M token context window, but are +likely many orders of magnitude larger. For reference, the largest data warehouse that Hex syncs is over 10 million +columns alone—and the context size would be multiplied by the number of rows in each. And unlike text or code, +[data is not easily compressible: consider the infamous Datasaurus, a collection of 13 data sets with the same](https://en.wikipedia.org/wiki/Datasaurus_dozen) +descriptive statistics that when graphed, produce entirely different visuals (including one that does in fact look like a +dinosaur, as shown in Figure 4-2). + +## Figure 4-2. The Datasaurus dozen + +A perhaps less intuitive, but equally pernicious, source of context bloat is data file formats themselves. Unlike code, +even technical data tools typically include a complex UI, with dozens of knobs and configurations. A single cell +specification in Hex can cost hundreds to thousands of tokens if fully utilized, and many notebooks contain dozens to +hundreds of cells, quickly eating through generous context budgets. Worse, LLMs tend to be resistant to using +structured formats that are not present in their training data—proprietary formats like the internal JSON schema for a +Hex notebook are complete foreign to these models, and trying to get them to strictly adhere becomes a classic +Sisyphean challenge of fighting the weights. + +## Figure 4-3. A visualization of context bloat + +And then on top of everything else, working in data also requires importing all of the _other_ context challenges that +plague large organizations including inaccuracies, siloed information and messy or non-existent documentation. + +## **Journey Over Destination** + +Throughout the process of building an agent to work with Hex’s analytical notebook, the team repeatedly employed a +(conceptually) simple technique: rather than trying to hyper-optimize the perfect context from the beginning, start +with an extremely minimal, compact set of context that gives the agent a _map_ of the overall information space, and let +the agent discover and selectively load the relevant pieces. This avoids most of the thorny context problems of data: +messy file formats, large data sets, and extraneous or misleading information. + +A clear case study in applying the context map is Hex’s cell graph. Typical notebooks (including cell code, metadata, +and data outputs) are simply too large for even state-of-the-art models to reason about reliably: the agent needs a +system for navigating the graph based on a user prompt, identifying the relevant cells to operate on, and then +retrieving their contents for the agent context. + +## **The Limitations of RAG** + +In the pre-agent world, populating the context based on a user-prompt would typically involve some form of RAG, or +Retrieval Augmented Generation. RAG populates the context in a single step with the results of a non-generative +information retrieval step based on the user’s prompt (Figure 4-4). Because the expectation is that users of AI features +will use natural language as opposed to more traditional keyword search terms, most RAG implementations rely +heavily on semantic search (based on vector representations of meaning) rather than more traditional lexical search. + +Hex’s first RAG implementation was primarily to manage data warehouse context, embedding the documentation for +the tables and columns from the warehouse schema in a vector database and retrieving them prompt-time to populate +the context for the user’s SQL generation request. + +## Figure 4-4. The single-step RAG pattern + +For a single LLM step of SQL generation, this works reasonably well, especially considering that data documentation +tends to be fairly terse. For reasonably documented warehouses, the system often sees upwards of 90% recall for the +relevant tables and columns. + +Unfortunately, for context management in the notebook, RAG is completely non-functional. Why the huge difference +in performance? It comes down to the difference between non-agentic and agentic workflows. + +In the context of a single search query or prompt, a system with 90% recall is pretty useful! If the query happens to +fail, the user can try again with a different wording to attempt to find a the missing information in a different part of +the search space. + +But an agent is expected to make multiple—sometimes dozens or hundreds—of autonomous tool calls in quick +succession. Many tool calls depend on the success of an upstream tool call: for example, you can’t edit the contents of +a cell if you haven’t successfully retrieved the reference. Accomplishing a complex task in a notebook requires +fetching and interacting with multiple cells, each with a non-trivial failure rate, which drives the overall task failure +rate unacceptably high. + +While it’s theoretically possible to optimize the index for better performance, there is a fundamental issue with +semantic search. At its core semantic search is a compression algorithm, which means you are losing information in +the index and thus there is an upper bound on how accurate the retrieval can be. Notebook cells are fairly sparse +(only a handful of words in any given SQL query will be semantically meaningful), which significantly worsens the +problem. Even if a code cell in a notebook has a few hundred tokens, the majority of those are not relevant and many +individual cells are not specifically documented, even if the entire notebook is. Ultimately, despite its success with +warehouse retrieval, semantic search is too error-prone to reliably identify the correct target cells. + +## **The Notebook Context Map** -# The Notebook Context Map +[It turns out that RAG is in many cases a textbook example of the bitter lesson. Building the Hex RAG pipeline took](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) +thousands of engineering hours, but ultimately produced poorer results than a much simpler technique: giving the +agent a compact map of the notebook and the tools to explore it. This technique is often referred to as “agentic +[search,” and was first widely popularized in 2025 by Claude Code’s implementation of codebase search.](https://x.com/bcherny/status/2017824286489383315) + +Notebooks are structured differently than traditional software codebases—and the agent isn’t operating in a +traditional filesystem—but ultimately they are both organized compilations of mostly code. With a little tweaking, +agentic search techniques such as `grep` allow the notebook agent to reliably retrieve and operate on the intended +cells of the notebook. -It turns out that RAG is in many cases a textbook example of [the bitter lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html). Building the Hex RAG pipeline took thousands of engineering hours, but ultimately produced poorer results than a much simpler technique: giving the agent a compact map of the notebook and the tools to explore it. This technique is often referred to as “agentic search,” and was first widely popularized in 2025 by [Claude Code’s](https://x.com/bcherny/status/2017824286489383315) implementation of codebase search. +Hex’s notebook agentic search system consists of four parts, which we’ll discuss in more detail in the next sections: -Notebooks are structured differently than traditional software codebases—and the agent isn’t operating in a traditional filesystem—but ultimately they are both organized compilations of mostly code. With a little tweaking, agentic search techniques such as `grep` allow the notebook agent to reliably retrieve and operate on the intended cells of the notebook. + - A context “map:” a compact representation of all the cells in the notebook and their relationships to each +other -Hex’s notebook agentic search system consists of four parts, which we’ll discuss in more detail in the next sections: + - A search tool to identify relevant cells -* A context “map:” a compact representation of all the cells in the notebook and their relationships to each other -* A search tool to identify relevant cells -* Inspection tools for the cell contents and execution results (which may be very large and may update over time) -* A “short reference” system mapping the globally unique cell UUIDs to a short, locally unique string for easier LLM manipulation + - Inspection tools for the cell contents and execution results (which may be very large and may update over +time) -## The Context Map + - A “short reference” system mapping the globally unique cell UUIDs to a short, locally unique string for easier +LLM manipulation -The internal representation of the notebook is a massive JSON blob, with lots of extraneous tokens and fields that are irrelevant to the agent. Naturally this would lead to a certain degree of context rot, not to mention bloat and inefficiency. The first context management technique to be implemented for the notebook agent was simply to strip this format down to the barebones of what the agent needed to understand the notebook and its structure. The result was a compact format that consisted of: +**The Context Map** -* Notebook metadata (title, description) -* Cell metadata, including title, variables used, variables defined, and other potentially useful information like cell runtime -* The cell graph, defined as a list of directed edges between cells -* The code source of each cell +The internal representation of the notebook is a massive JSON blob, with lots of extraneous tokens and fields that are +irrelevant to the agent. Naturally this would lead to a certain degree of context rot, not to mention bloat and +inefficiency. The first context management technique to be implemented for the notebook agent was simply to strip +this format down to the barebones of what the agent needed to understand the notebook and its structure. The result +was a compact format that consisted of: -Moreover, to increase token efficiency further, after the initial message the cell source is omitted (but can be re-accessed by the agent via inspection tools). + - Notebook metadata (title, description) -## Project Search + - Cell metadata, including title, variables used, variables defined, and other potentially useful information like +cell runtime -The notebook context map does not contain all of the necessary information to execute an analysis. Cell contents are truncated (or may be out of date), compaction may have taken place, and the results of code execution may not have been included at all due to size. Instead of relying on the compacted notebook representation still remaining in the context window, the notebook agent receives a `ProjectSearch` tool which allows the agent to discover cells based on a number of criteria, including code contents as well as dependencies from other cells. + - The cell graph, defined as a list of directed edges between cells -## Cell Inspection + - The code source of each cell -The core innovation of analytics notebooks is supercharging the iteration loop of making a hypothesis, querying the data, observing the results, and updating the original hypothesis. Unfortunately, Hex allows for arbitrary queries, which means the results can be arbitrarily large. Because of this, cell output results are not by default included in the notebook context representation, and when the agent does require them via the `ViewCellOutputs` tool, the results are truncated. Often this is sufficient—for example to identify that the query was incorrect the agent simply needs the information that no rows were returned—but in the cases where that is not the case, the agent also has a separate `RunSQL` tool that allows it to craft more specific queries to better understand the data set. +Moreover, to increase token efficiency further, after the initial message the cell source is omitted (but can be reaccessed by the agent via inspection tools). -## Short References +**Project Search** -The final component of the system is somewhat non-obvious: LLMs (at the time) were terrible at reliably reproducing UUIDs. This makes some amount of sense. UUIDs are multi-token (separated by hyphens), so to use a UUID the model must reliably generate those tokens in sequence. But UUIDs are designed to be universally unique, so none of the UUIDs in the notebook are present in the model’s training data, which means despite being present in the context window they are definitionally low probability sequences. This is just a minor example of fighting the model weights, but converting the model-facing ids from standard UUIDs to short references of the form `C01` (Cell 01) made a huge difference in performance. +The notebook context map does not contain all of the necessary information to execute an analysis. Cell contents are +truncated (or may be out of date), compaction may have taken place, and the results of code execution may not have +been included at all due to size. Instead of relying on the compacted notebook representation still remaining in the +context window, the notebook agent receives a `ProjectSearch` tool which allows the agent to discover cells based +on a number of criteria, including code contents as well as dependencies from other cells. -# Agents All the Way Down +**Cell Inspection** -The notebook context map works remarkably well for short agent conversations, but data agents continue to be context hungry and longer sessions rapidly deteriorate. Standard compaction techniques can help, but as compaction is lossy, it’s often better to avoid the bloat in the first place. +The core innovation of analytics notebooks is supercharging the iteration loop of making a hypothesis, querying the +data, observing the results, and updating the original hypothesis. Unfortunately, Hex allows for arbitrary queries, +which means the results can be arbitrarily large. Because of this, cell output results are not by default included in the +notebook context representation, and when the agent does require them via the `ViewCellOutputs` tool, the results +are truncated. Often this is sufficient—for example to identify that the query was incorrect the agent simply needs the +information that no rows were returned—but in the cases where that is not the case, the agent also has a separate +`RunSQL` tool that allows it to craft more specific queries to better understand the data set. -The major sources of context bloat in data notebooks are the data itself (managed by fine-grained inspection tools); the metadata such as column names, descriptions, and documentation; and the tool definitions themselves. +**Short References** -As with project search, data discovery benefits greatly from allowing the agents to explore the space themselves. Unfortunately, unlike notebooks and cells, data warehouses can be enormous. Warehouses can have thousands of tables, tables can have thousands of columns, all containing documentation. Worse, often the best way to generate the correct SQL requires querying the data itself (e.g. to identify the correct value of an enum column). Casting a wide next helps massively with identifying the right table, but pollutes the agent context with thousands of tokens of (sometimes subtly) incorrect schema information, leading to an increasing number of SQL generation errors over time. Subagents are a clean way to manage this bloat: starting with a clean context, allow the agent to search as broadly as it wishes, but only return the most relevant information back to the main agent. +The final component of the system is somewhat non-obvious: LLMs (at the time) were terrible at reliably reproducing +UUIDs. This makes some amount of sense. UUIDs are multi-token (separated by hyphens), so to use a UUID the model +must reliably generate those tokens in sequence. But UUIDs are designed to be universally unique, so none of the +UUIDs in the notebook are present in the model’s training data, which means despite being present in the context +window they are definitionally low probability sequences. This is just a minor example of fighting the model weights, +but converting the model-facing ids from standard UUIDs to short references of the form `C01` (Cell 01) made a huge +difference in performance. -Tool bloat also turns out to be uniquely bad with data notebooks (and other highly structured environments). Models are bad at *directly* manipulating unfamiliar file formats, but they have been extensively post-trained to be good at calling semantically meaningful tools. Taking advantage of this, every action in a Hex notebook requires a distinct tool call, sometimes with highly overlapping definitions (such as `MoveCell` and `DeleteCell`). Over time, the tokens consumed by the tools themselves can strain the context window. At one point the Hex tool definitions crossed the 200k token threshold and started triggering a compaction on every message! The solution to this is again context maps (giving the agent a compact set of tool definitions which it can then expand), though fortunately in the case of tools it is natively implemented by most of the model providers in the form of tool search. +## **Agents All the Way Down** -# The Bitter Lesson, Again +The notebook context map works remarkably well for short agent conversations, but data agents continue to be +context hungry and longer sessions rapidly deteriorate. Standard compaction techniques can help, but as compaction +is lossy, it’s often better to avoid the bloat in the first place. -Ironically, in the months between the implementation of this system and the publishing of this book, some of the strategies used to manage context have become obsolete due to improvements in the underlying models. A recent run of our evals removing short references in favor of UUIDs showed no degradation in performance. A head-to-head comparison of our data warehouse RAG pipeline (the product of thousands of hours of engineering effort) vs simple string search and filtering tools showed only a modest lift. Recursive Language Models offer a path to generalized context offloading and retrieval that no longer requires carefully constructed tool definitions. +The major sources of context bloat in data notebooks are the data itself (managed by fine-grained inspection tools); +the metadata such as column names, descriptions, and documentation; and the tool definitions themselves. -But the core lesson still remains: the best way to curate context for the agent is to let the agent curate context for itself by giving it the capability-appropriate context format and toolset. +As with project search, data discovery benefits greatly from allowing the agents to explore the space themselves. +Unfortunately, unlike notebooks and cells, data warehouses can be enormous. Warehouses can have thousands of +tables, tables can have thousands of columns, all containing documentation. Worse, often the best way to generate the +correct SQL requires querying the data itself (e.g. to identify the correct value of an enum column). Casting a wide +next helps massively with identifying the right table, but pollutes the agent context with thousands of tokens of +(sometimes subtly) incorrect schema information, leading to an increasing number of SQL generation errors over time. +Subagents are a clean way to manage this bloat: starting with a clean context, allow the agent to search as broadly as +it wishes, but only return the most relevant information back to the main agent. -xml version='1.0' encoding='utf-8'? +Tool bloat also turns out to be uniquely bad with data notebooks (and other highly structured environments). Models +are bad at _directly_ manipulating unfamiliar file formats, but they have been extensively post-trained to be good at +calling semantically meaningful tools. Taking advantage of this, every action in a Hex notebook requires a distinct +tool call, sometimes with highly overlapping definitions (such as `MoveCell` and `DeleteCell` ). Over time, the tokens +consumed by the tools themselves can strain the context window. At one point the Hex tool definitions crossed the +200k token threshold and started triggering a compaction on every message! The solution to this is again context +maps (giving the agent a compact set of tool definitions which it can then expand), though fortunately in the case of +tools it is natively implemented by most of the model providers in the form of tool search. -# About the Authors +## **The Bitter Lesson, Again** -**Drew Breunig** is a strategist and technologist who operates at the intersection of cultural anthropology and computer science. He previously led data science and business strategy at PlaceIQ, a location intelligence platform (acquired by Precisely) that analyzed petabyte-scale datasets to understand human behavior in the real-world. Together with information designer Nicholas Felton, he co-founded and built Reporter, an award-winning quantified self app. +Ironically, in the months between the implementation of this system and the publishing of this book, some of the +strategies used to manage context have become obsolete due to improvements in the underlying models. A recent run +of our evals removing short references in favor of UUIDs showed no degradation in performance. A head-to-head +comparison of our data warehouse RAG pipeline (the product of thousands of hours of engineering effort) vs simple +string search and filtering tools showed only a modest lift. Recursive Language Models offer a path to generalized +context offloading and retrieval that no longer requires carefully constructed tool definitions. -Drew has emerged as a leading voice in applied AI and context engineering through his widely-cited writing, including influential frameworks for understanding AI use cases and his exploration of how contexts succeed and fail. He advises several start ups and the Overture Maps Foundation, a Linux Foundation open data project founded by AWS, Meta, Azure, and TomTom. +But the core lesson still remains: the best way to curate context for the agent is to let the agent curate context for itself +by giving it the capability-appropriate context format and toolset. -**Mike Taylor** has worked as an AI engineer since 2020, is the author of “Prompt Engineering for Generative AI” (O’Reilly 2024), and created the top prompt engineering course on Udemy, “The Complete Prompt Engineering for AI Bootcamp” with 300,000+ students. His new book “Context Engineering with DSPy” is due in the fall of 2026. +**About the Authors** -**Kelly Hong** is a researcher focused on retrieval and model evaluation. She authored Context Rot, a study of how increasing input tokens degrades LLM performance, during her time at Chroma. Her other work spans generative benchmarking and self-editing search agents. +**Drew Breunig** is a strategist and technologist who operates at the intersection of cultural anthropology and computer +science. He previously led data science and business strategy at PlaceIQ, a location intelligence platform (acquired by +Precisely) that analyzed petabyte-scale datasets to understand human behavior in the real-world. Together with +information designer Nicholas Felton, he co-founded and built Reporter, an award-winning quantified self app. -**Caitlin Colgrove** is the CTO and cofounder of the agentic data platform Hex. Caitlin has spent her career building data analytics tools, first at Palantir and then later at the public transit data platform Remix, finally co-founding Hex with the vision of making everyone a data person. As CTO, she focuses on building incredible products, technology, and teams in the era of AI. +Drew has emerged as a leading voice in applied AI and context engineering through his widely-cited writing, +including influential frameworks for understanding AI use cases and his exploration of how contexts succeed and fail. +He advises several start ups and the Overture Maps Foundation, a Linux Foundation open data project founded by +AWS, Meta, Azure, and TomTom. + +**Mike Taylor** has worked as an AI engineer since 2020, is the author of “Prompt Engineering for Generative AI” +(O’Reilly 2024), and created the top prompt engineering course on Udemy, “The Complete Prompt Engineering for AI +Bootcamp” with 300,000+ students. His new book “Context Engineering with DSPy” is due in the fall of 2026. + +**Kelly Hong** is a researcher focused on retrieval and model evaluation. She authored Context Rot, a study of how +increasing input tokens degrades LLM performance, during her time at Chroma. Her other work spans generative +benchmarking and self-editing search agents. + +**Caitlin Colgrove** is the CTO and cofounder of the agentic data platform Hex. Caitlin has spent her career building +data analytics tools, first at Palantir and then later at the public transit data platform Remix, finally co-founding Hex +with the vision of making everyone a data person. As CTO, she focuses on building incredible products, technology, +and teams in the era of AI. diff --git a/tree/ai/pdf/aivideo-spacetimediffusionmodel.md b/tree/ai/pdf/aivideo-spacetimediffusionmodel.md new file mode 100644 index 0000000..2f0bbd7 --- /dev/null +++ b/tree/ai/pdf/aivideo-spacetimediffusionmodel.md @@ -0,0 +1,1328 @@ +--- +title: aivideo-spacetimediffusionmodel +source: sources/ai/pdf/aivideo-spacetimediffusionmodel.pdf +source_type: paper +source_hash: 54d45941c51f6b64b394d25c3093eb1878878f26a7f481c745fd985b89c7f487 +tags: +- ai +- paper +extracted: '2026-09-07' +--- + +## **Lumiere: A Space-Time Diffusion Model for Video Generation** + +**Omer Bar-Tal** * 1 2 **Hila Chefer** * 1 3 **Omer Tov** * 1 **Charles Herrmann** _†_ 1 **Roni Paiss** _†_ 1 **Shiran Zada** _†_ 1 + +**Ariel Ephrat** _†_ 1 **Junhwa Hur** _†_ 1 **Guanghui Liu** 1 **Amit Raj** 1 **Yuanzhen Li** 1 **Michael Rubinstein** 1 + +**Tomer Michaeli** 1 4 **Oliver Wang** 1 **Deqing Sun** 1 **Tali Dekel** 1 2 **Inbar Mosseri** _†_ 1 + +1Google Research 2Weizmann Institute 3Tel-Aviv University 4Technion + +**Image-to-Video** + +[V] + +**Inpainting** + +Figure 1: Sample results generated by _Lumiere_, including text-to-video generation (first row), image-to-video (second row), +style-referenced generation, and video inpainting (third row; the bounding box indicates the inpainting mask region). + +**Abstract** + +We introduce _Lumiere_ - a text-to-video diffusion +model designed for synthesizing videos that portray realistic, diverse and coherent motion - a +pivotal challenge in video synthesis. To this end, +we introduce a Space-Time U-Net architecture +that generates the entire temporal duration of the +video _at once_, through a single pass in the model. +This is in contrast to existing video models which + +*Equal first author _†_ Core technical contribution + +Work was done while O. Bar-Tal, H. Chefer were interns at Google. +Webpage: [https://lumiere-video.github.io/](https://lumiere-video.github.io/) + +synthesize distant keyframes followed by temporal super-resolution – an approach that inherently makes global temporal consistency difficult +to achieve. By deploying both spatial and (importantly) temporal down- and up-sampling and +leveraging a pre-trained text-to-image diffusion +model, our model learns to directly generate a fullframe-rate, low-resolution video by processing it +in multiple space-time scales. We demonstrate +state-of-the-art text-to-video generation results, +and show that our design easily facilitates a wide +range of content creation tasks and video editing applications, including image-to-video, video +inpainting, and stylized generation. + +1 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +Figure 2: **Temporal consistency in generated videos.** Representative examples of generated videos using our model and +ImagenVideo (Ho et al., 2022a) for periodic motion. We apply Lumiere image-to-video generation, conditioned on the first +frame of a video generated by ImagenVideo, and visualize corresponding X-T slices. ImagenVideo struggles to generate +globally coherent repetitive motion due to its cascaded design and temporal super resolution modules, which fail to resolve +aliasing ambiguities consistently across temporal windows. + +**1. Introduction** + +Generative models for images have seen tremendous +progress in recent years. State-of-the-art text-to-image (T2I) +diffusion models are now capable of synthesizing highresolution photo-realistic images that adhere to complex +text prompts (Saharia et al., 2022b; Ramesh et al., 2022; +Rombach et al., 2022), and allow a wide range of image +editing capabilities (Po et al., 2023) and other downstream +uses. However, training large-scale text-to- _video_ (T2V) +foundation models remains an open challenge due to the +added complexities that motion introduces. Not only are +we sensitive to errors in modeling natural motion, but the +added temporal data dimension introduces significant challenges in terms of memory and compute requirements, as +well as the scale of the required training data to learn this +more complex distribution. As a result, while T2V models +are rapidly improving, existing models are still restricted +in terms of video duration, overall visual quality, and the +degree of realistic motion that they can generate. + +A prevalent approach among existing T2V models is to +adopt a cascaded design in which a base model generates distant keyframes, and subsequent temporal superresolution (TSR) models generate the missing data between + +the keyframes in non-overlapping segments. While memory +efficient, the ability to generate globally coherent motion +using temporal cascades is inherently restricted for the following reasons: (i) The base model generates an aggressively sub-sampled set of keyframes, in which fast motion +becomes temporally aliased and thus ambiguous. (ii) TSR +modules are constrained to fixed, small temporal context +windows, and thus cannot consistently resolve aliasing ambiguities across the full duration of the video (illustrated +in Fig. 2 in the case of synthesizing periodic motion, e.g., +walking). (iii) Cascaded training regimens in general suffer +from a domain gap, where the TSR model is trained on real +downsampled video frames, but at inference time is used to +interpolate generated frames, which accumulates errors. + +Here, we take a different approach by introducing a new +T2V diffusion framework that generates the full temporal +duration of the video _at_ _once_ . We achieve this by using +a Space-Time U-Net (STUNet) architecture that learns to +downsample the signal in both space _and time_, and performs +the majority of its computation in a compact space-time +representation. This approach allows us to generate 80 +frames at 16fps (or 5 seconds, which is longer than the +average shot duration in most media (Cutting & Candan, + +2 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +**(a) Common Approach with TSR model(s)** **(b) Our Approach** + +3fps, 5sec + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10| +|---|---|---|---|---|---|---|---|---|---| +||||||||||| +||||||||||| + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8| +|---|---|---|---|---|---|---|---| +||||||||| +||||||||| + +|t1 t6 t80 ( t75 ... ...|Col2|t6|Col4|t80 ( t75 ... ...|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17|Col18|Col19|Col20|Col21|Col22|Col23|Col24|Col25| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +|Base
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|||||||||||||||||||||||| + +Figure 3: **Lumiere pipeline.** We illustrate our pipeline and the main difference from most common approach taken by +previous works. (a) The common approach consists of a base model that generates distant keyframes, and a cascade of +temporal super-resolution (TSR) models which subsequently fill in frames. A spatial super-resolution (SSR) model is +applied in non-overlapping windows to obtain the high-resolution result. (b) In contrast, the base model in our framework +process all frames _at once_, without a cascade of TSR models, allowing us to learn globally coherent motion. To obtain the +high-resolution video, we apply a SSR model on _overlapping_ windows and utilize MultiDiffusion (Bar-Tal et al., 2023) to +combine the predictions into a coherent result. See Sec.3 for details. + +2015)) with a single base model, leading to more globally +coherent motion compared to prior work. Surprisingly, this +design choice has been overlooked by previous T2V models, +which follow the convention to include _only spatial_ downand up-sampling operations in the architecture, and maintain +a _fixed_ _temporal_ _resolution_ across the network (Ho et al., +2022b;a; Singer et al., 2022; Ge et al., 2023; Blattmann +et al., 2023b; Wang et al., 2023a; Guo et al., 2023; Zhang +et al., 2023a; Girdhar et al., 2023; Po et al., 2023). + +To benefit from the powerful generative prior of T2I models, we follow the trend of building Lumiere on top of a +pretrained (and fixed) T2I model (Hong et al., 2022; Singer +et al., 2022; Saharia et al., 2022b). In our case, the T2I +model works in pixel space and consists of a base model +followed by a _spatial_ super-resolution (SSR) cascade. Since +the SSR network operates at high spatial resolution, applying it on the entire video duration is infeasible in terms +of memory requirements. Common SSR solutions use a +temporal windowing approach, which splits the video into +non-overlapping segments and stitches together the results. +However, this can lead to inconsistencies in appearance +at the boundaries between windows (Girdhar et al., 2023). +We propose to extend Multidiffusion (Bar-Tal et al., 2023), +an approach proposed for achieving global continuity in +panoramic image generation, to the temporal domain, where + +we compute spatial super-resolution on temporal windows, +and aggregate results into a globally coherent solution over +the whole video clip. + +We demonstrate state-of-the-art video generation results and +show how to easily adapt Luimere to a plethora of video +content creation tasks, including video inpainting (Fig. 7), +image-to-video generation (Fig. 5), or generating stylized +videos that comply with a given style image (Fig. 6). Finally, +we demonstrate that generating the full video at once allows +us to easily invoke off-the-shelf editing methods to perform +consistent editing (Fig. 9). + +**2. Related work** + +**Text-to-Image** **Generation.** Most of the common approaches for text-to-image (T2I) generation are based on diffusion models (Sohl-Dickstein et al., 2015; Ho et al., 2020; +Song et al., 2020). Of these, DALL-E2 (Ramesh et al., 2022) +and Imagen (Saharia et al., 2022b) achieve photorealistic +text-to-image generation using cascaded diffusion models, +whereas Stable Diffusion (Rombach et al., 2022) performs +generation in a compressed low-dimensional latent space. +A promising line of works design T2I diffusion models that +generate high-resolution images end-to-end, without a spatial super-resolution cascaded system or fixed pre-trained + +3 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +**(a) Space-Time UNet (STUNet)** + +**(b) Convolution-based Inflation Block** + +**(c) Attention-based Inflation Block** + +maps; color indicates features resulting from different temporal modules: (b) _Convolution-based_ blocks which consist of +pre-trained T2I layers followed by a factorized space-time convolution, and (c) _Attention-based_ blocks at the coarsest U-Net +level in which the pre-trained T2I layers are followed by temporal attention. Since the video representation is compressed at +the coarsest level, we stack several temporal attention layers with limited computational overhead. See Sec. 3.1 for details. + +latent space (Hoogeboom et al., 2023; Gu et al., 2023; Chen, +2023). Here, we design a T2V model that generates the +full frame duration at once, avoiding the _temporal cascade_ +commonly involved in T2V models. + +**Text-to-Video Generation.** Recently, there have been substantial efforts in training large-scale T2V models on large +scale datasets with autoregressive Transformers (e.g., (Villegas et al., 2023; Wu et al., 2022; Hong et al., 2022; Kondratyuk et al., 2023)) or Diffusion Models (e.g., (Ho et al., +2022a;b; Gupta et al., 2023)). A prominent approach for +T2V generation is to “inflate” a pre-trained T2I model by +inserting temporal layers to its architecture, and fine-tuning +only those, or optionally the whole model, on video data +(Singer et al., 2022; Blattmann et al., 2023b; Girdhar et al., +2023; Ge et al., 2023; Yuan et al., 2024). PYoCo (Ge et al., +2023) carefully design video noise prior and obtain better +performance for fine-tuning a T2I model for video generation. VideoLDM (Blattmann et al., 2023b) and AnimateDiff (Guo et al., 2023) inflate StableDiffusion (Rombach +et al., 2022) and train only the newly-added temporal layers, +showing they can be combined with the weights of personalized T2I models. Interestingly, the ubiquitous convention +of existing inflation schemes is to maintain a _fixed temporal_ +_resolution_ across the network, which limits their ability to +process full-length clips. In this work, we design a new inflation scheme which includes learning to downsample the +video in both space _and time_, and performing the majority +of computation in the compressed space-time feature space +of the network. We extend an Imagen T2I model (Saharia +et al., 2022b), however our architectural contributions could +be used for latent diffusion as well, and are orthogonal to + +possible improvements to the diffusion noise scheduler (Ge +et al., 2023) or to the video data curation (Blattmann et al., +2023a). + +**3. Lumiere** + +We utilize Diffusion Probabilistic Models as our generative approach (Sohl-Dickstein et al., 2015; Croitoru et al., +2023a; Dhariwal & Nichol, 2021; Ho et al., 2020; Nichol +& Dhariwal, 2021). These models are trained to approximate a data distribution (in our case, a distribution over +videos) through a series of denoising steps. Starting from a +Gaussian i.i.d. noise sample, the diffusion model gradually +denoises it until reaching a clean sample drawn from the approximated target distribution. Diffusion models can learn +a conditional distribution by incorporating additional guiding signals, such as text embedding, or spatial conditioning +(e.g., depth map) (Dhariwal & Nichol, 2021; Saharia et al., +2022a; Croitoru et al., 2023b; Zhang et al., 2023b). + +Our framework consists of a base model and a spatial superresolution (SSR) model. As illustrated in Fig. 3b, our base +model generates full clips at a coarse spatial resolution. +The output of our base model is spatially upsampled using +a temporally-aware SSR model, resulting with the highresolution video. We next describe the key design choices +in our architecture, and demonstrate the applicability of our +framework for a variety of downstream applications. + +**3.1. Space-Time U-Net (STUnet)** + +To make our problem computationally tractable, we propose +to use a space-time U-Net which downsamples the input sig + +4 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +**Text-to-Video** + +**~~Image-to-Video~~** + +_A girl winking and smiling_ + +_Bigfoot walking through the woods in Northern California_ + +_A panda playing a ukulele at home_ + +_Sunset time lapse at the beach_ + +_A cat playing piano_ + +_A teddy bear running in New York City_ + +Figure 5: **Video generation results.** Sample results of text-to-video and image-to-video generation. The text prompt is +indicated below each example. For image-to-video, the leftmost frame is provided to the model as a condition (see Sec. 4.2). +We refer the reader to the SM for full-video results. + +5 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +nal both spatially _and_ temporally, and performs the majority +of its computation on this compact space-time representation. We draw inspiration from C¸ ic¸ek et al. (2016), who +generalize the U-Net architecture (Ronneberger et al., 2015) +to include 3D pooling operations for efficient processing of +volumetric biomedical data. + +Our architecture is illustrated in Fig. 4. We interleave temporal blocks in the T2I architecture, and insert temporal +down- and up-sampling modules following each pre-trained +spatial resizing module (Fig. 4a). The temporal blocks include temporal convolutions (Fig. 4b) and temporal attention +(Fig. 4c). Specifically, in all levels except for the coarsest, +we insert factorized space-time convolutions (Fig. 4b) which +allow increasing the non-linearities in the network compared +to full-3D convolutions while reducing the computational +costs, and increasing the expressiveness compared to 1D +convolutions (Tran et al., 2018). As the computational requirements of temporal attention scale quadratically with +the number of frames, we incorporate temporal attention +only at the coarsest resolution, which contains a space-time +compressed representation of the video. Operating on the +low dimensional feature map allows us to stack several temporal attention blocks with limited computational overhead. + +Similarly to (Blattmann et al., 2023b; Guo et al., 2023), we +train the newly added parameters, and keep the weights of +the pre-trained T2I fixed. Notably, the common inflation approach ensures that at initialization, the T2V model is equivalent to the pre-trained T2I model, i.e., generates videos as +a collection of independent image samples. However, in +our case, it is impossible to satisfy this property due to the +temporal down- and up-sampling modules. We empirically +found that initializing these modules such that they perform +nearest-neighbor down- and up- sampling operations results +with a good starting point (see App. B). + +**3.2. Multidiffusion for Spatial-Super Resolution** + +Due to memory constraints, the inflated SSR network +can operate only on short segments of the video. To +avoid temporal boundary artifacts, we achieve smooth +transitions between the temporal segments by employing +Multidiffusion (Bar-Tal et al., 2023) along the temporal +axis. At each generation step, we split the noisy input +video _J_ _∈_ R _H×W ×T ×_ 3 into a set of overlapping segments +_{Ji}_ _N_ _i_ =1 , where _Ji_ _∈_ R _H×W ×T ′×_ 3 is the _i_ th segment, which + +has temporal duration _T_ _′_ _< T_ . To reconcile the per-segment +SSR predictions, _{_ Φ( _Ji_ ) _}_ _N_ _i_ =1 , we define the result of the + +denoising step to be the solution of the optimization problem + +arg min + +_J_ _′_ + +_n_ + +_i_ =1 + +_∥J_ _′_ _−_ Φ( _Ji_ ) _∥_ 2 _._ + +The solution to this problem is given by linearly combining +the predictions over overlapping windows. See App. C. + +**4. Applications** + +The lack of a TSR cascade makes it easier to extend Lumiere to downstream applications. In particular, our model +provides an intuitive interface for downstream applications +that require an off-the-shelf T2V model (e.g., Meng et al. +(2022); Poole et al. (2023); Gal et al. (2023)). We demonstrate this property by performing video-to-video editing using SDEdit (Meng et al., 2022) (see Fig. 9). We next discuss +a number of such applications, including style conditioned +generation, image-to-video, inpainting and outpainting, and +cinemagraphs. We present example frames in Figs. 6-9 and +refer the reader to the Supplementary Material (SM) on our +webpage for full video results. + +**4.1. Stylized Generation** + +Recall that we only train the newly-added temporal layers +and keep the pre-trained T2I weights fixed. Previous work +showed that substituting the T2I weights with a model customized for a specific style allows to generate videos with +the desired style (Guo et al., 2023). We observe that this +simple “plug-and-play” approach often results in distorted +or static videos (see SM), and hypothesize that this is caused +by the significant deviation in the distribution of the input +to the temporal layers from the fine-tuned spatial layers. + +Inspired by the success of GAN-based interpolation approaches (Pinkney & Adler, 2020), we opt to strike a balance between style and motion by linearly interpolating +between the fine-tuned T2I weights, _W_ style, and the original +T2I weights, _W_ orig. Specifically, we construct the interpolated weights as _W_ interpolate = _α·W_ style +(1 _−α_ ) _·W_ orig. The +interpolation coefficient _α ∈_ [0 _._ 5 _,_ 1] is chosen manually in +our experiments to generate videos that adhere to the style +and depict plausible motion. + +Figure 6 presents sample results for various styles from +(Sohn et al., 2023). While more realistic styles such as +“watercolor painting” result in realistic motion, other, less + +realistic spatial priors derived from vector art styles, result +in corresponding unique non-realistic motion. For example, +the “line drawing” style results in animations that resemble +pencil strokes “drawing” the described scene, while the +“cartoon” style results in content that gradually “pops out” + +and constructs the scene (see SM for full videos). + +**4.2. Conditional Generation** + +Similarly to Blattmann et al. (2023b); Wang et al. (2023b), +we extend our model to video generation conditioned on +additional input signals (e.g., image or mask). We achieve +this by modifying the model to take as input two signals in +addition to the noisy video _J_ _∈_ R _T ×H×W ×_ 3 and the driving +text prompt. Specifically, we add the masked conditioning +video _C_ _∈_ R _T ×H×W ×_ 3 and its corresponding binary mask + +6 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +**(A)** + +**Reference** + +**Image** + +**Reference** + +**Image** + +**(B)** + +**Reference** + +**Image** + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7| +|---|---|---|---|---|---|---| +|||||||| +|_An owl perched on a branch, melting golden 3D style_|_An owl perched on a branch, melting golden 3D style_|_An owl perched on a branch, melting golden 3D style_|_An owl perched on a branch, melting golden 3D style_|_An owl perched on a branch, melting golden 3D style_|_An owl perched on a branch, melting golden 3D style_|_An owl perched on a branch, melting golden 3D style_| +|||||||| +||||**Reference**
**Image**|**Reference**
**Image**|**Reference**
**Image**|**Reference**
**Image**| + +_A bunny hopping in the meadow, 3D rendering_ + +**Image** + +**Reference** + +**Image** + +_A watercolor painting of ducks swimming in a pond_ + +Figure 6: **Stylized Generation.** Given a driving style image and its corresponding set of fine-tuned text-to-image weights, +we perform linear interpolation between the fine-tuned and pre-trained weights of the model’s spatial layers. We present +results for (A) vector art styles, and (B) realistic styles. The results demonstrate Lumiere’s ability to creatively match a +_different_ motion prior to each of the spatial styles (frames shown from left to right). See Sec. 4.1 for details. + +7 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +Figure 7: **Inpainting.** Examples of video inpainting with Lumiere. For each input video (top left corner of each frame), we +animate the masked area of the video using our model. + +_M_ _∈_ R _T ×H×W ×_ 1, such that the overall input to the model +is the concatenated tensor _⟨J, C, M_ _⟩∈_ R _T ×H×W ×_ 7 . We +expand the channel dimension of the first convolution layer +from 3 to 7 in order to accommodate the modified input +shape and fine-tune our base T2V model to denoise _J_ based +on _C, M_ . During this fine-tuning process, we take _J_ to be +the noisy version of the training video, and _C_ to be a masked +version of the clean video. This encourages the model to +learn to copy the unmasked information in _C_ to the output +video while only animating the masked content, as desired. + +**Image-to-Video.** In this case, the first frame of the video +is given as input. The conditioning signal _C_ contains this +first frame followed by blank frames for the rest of the video. +The corresponding mask _M_ contains ones (i.e., unmasked +content) for the first frame and zeros (i.e., masked content) +for the rest of the video. Figures 1 and 5 show sample results +of image-conditioned generation (see SM for more results). +Our model generates videos that start with the desired first +frame, and exhibit intricate coherent motion across the entire +video duration. + +**Inpainting.** Here, the conditioning signals are a userprovided video _C_ and a mask _M_ that describes the region to +complete in the video. Note that the inpainting application +can be used for object replacement/insertion (Fig. 1) as well +as for localized editing (Fig. 7). The effect is a seamless +and natural completion of the masked region, with contents +guided by the text prompt. We refer the reader to the SM +for more examples of both inpainting and outpainting. + +**Cinemagraphs.** We additionally consider the application +of animating the content of _an image_ only within a specific +user-provided region. The conditioning signal _C_ is the input +image duplicated across the entire video, while the mask +_M_ contains ones for the entire first frame (i.e., the first +frame is unmasked), and for the other frames, the mask +contains ones only outside the user-provided region (i.e., + +**Source Image** + +**Output** + +Figure 8: **Cinemagraphs.** Given only an input _image_ and +a mask (left), our method generates a video in which the +marked area is animated and the rest remains static (right). + +the other frames are masked inside the region we wish to +animate). We provide sample results in Fig. 8 and in the +SM. Since the first frame remains unmasked, the animated +content is encouraged to maintain the appearance from the +conditioning image. + +**5. Evaluation and Comparisons** + +We train our T2V model on a dataset containing 30M videos +along with their text caption. The videos are 80 frames long +at 16 fps (5 seconds). The base model is trained at 128 _×_ 128 +and the SSR outputs 1024 _×_ 1024 frames. We evaluate our +model on a collection of 109 text prompts describing diverse +objects and scenes. The prompt list consists of 91 prompts +used by prior works (Singer et al., 2022; Ho et al., 2022a; +Blattmann et al., 2023b) and the rest were created by us (see +App. D). Additionally, we employ a zero-shot evaluation +protocol on the UCF101 dataset (Soomro et al., 2012), as +detailed in Sec. 5.2. + +8 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +Figure 9: **Video-to-video via SDEdit.** Our base model generates full-frame-rate videos, without a TSR cascade, and thus +facilitates an intuitive interface for downstream applications. We demonstrate this property by applying SDEdit (Meng et al., +2022) using our model, achieving consistent video stylization. We show several frames of a given input video in the first +row, and the corresponding edited frames below. + +Method FVD _↓_ IS _↑_ + +MagicVideo (Zhou et al., 2022) 655 _._ 00 Emu Video (Girdhar et al., 2023) 606 _._ 20 42 _._ 70 +Video LDM (Blattmann et al., 2023b) 550 _._ 61 33 _._ 45 +Show-1 (Zhang et al., 2023a) 394 _._ 46 35 _._ 42 +Make-A-Video (Singer et al., 2022) 367 _._ 23 33 _._ 00 +PYoCo (Ge et al., 2023) 355 _._ 19 47 _._ 76 +SVD (Blattmann et al., 2023a) 242 _._ 02 **Lumiere (Ours)** 332 _._ 49 37 _._ 54 + +Table 1: Zero-shot text-to-video generation comparison +on UCF101 (Soomro et al., 2012). Our method achieves +competitive FVD (Unterthiner et al., 2018) and IS (Salimans +et al., 2016) scores. See Sec. 5.2. + +We illustrate text-to-video generation in Figs. 1 and 5. Our +method generates high-quality videos depicting both intricate object motion (e.g., walking astronaut in Fig. 5) and +coherent camera motion (e.g., car example in Fig. 1). We +refer the reader to the SM for full-video results. + +**Baselines.** We compare our method to prominent T2V +diffusion models: (i) ImagenVideo (Ho et al., 2022a), that +operates in pixel-space and consists of a cascade of 7 models +(a base model, 3 TSR models, and 3 SSR models); (ii) +AnimateDiff (Guo et al., 2023), (iii) StableVideoDiffusion +(SVD) (Blattmann et al., 2023a), and (iv) ZeroScope (Wang +et al., 2023a) that inflate Stable Diffusion (Rombach et al., +2022) and train on video data; note that AnimateDiff and + +ZeroScope output only 16, and 36 frames respectively. SVD +released only their image-to-video model, which outputs +25 frames and is not conditioned on text. Additionally, +we compare to (v) Pika (Pika labs, 2023) and (vi) Gen2 (RunwayML, 2023) commercial T2V models that have +available API. Furthermore, we quantitatively compare to +additional T2V models that are closed-source in Sec. 5.2. + +**5.1. Qualitative Evaluation** + +We provide qualitative comparison between our model and +the baselines in Fig. 11. We observed that Gen-2 (RunwayML, 2023) and Pika (Pika labs, 2023) demonstrate high +per-frame visual quality; however, their outputs are characterized by a very limited amount of motion, often resulting +in near-static videos. ImagenVideo (Ho et al., 2022a) produces a reasonable amount of motion, but at a lower overall +visual quality. AnimateDiff (Guo et al., 2023) and ZeroScope (Wang et al., 2023a) exhibit noticeable motion but +are also prone to visual artifacts. Moreover, they generate +videos of shorter durations, specifically 2 seconds and 3.6 +seconds, respectively. In contrast, our method produces +5-second videos that have higher motion magnitude while +maintaining temporal consistency and overall quality. + +**5.2. Quantitative Evaluation** + +**Zero-shot** **evaluation** **on** **UCF101.** Following the evaluation protocols of Blattmann et al. (2023a) and Ge et al. + +9 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +**Video Quality** **Text Alignment** + +80 + +60 + +40 + +20 + +80 + +60 + +40 + +20 + +ImagenVideo Pika ZeroScope Gen2 AnimateDiff +Ours Baseline **(a)** + +ImagenVideo Pika ZeroScope Gen2 AnimateDiff + +**Video Quality** + +80 + +60 + +40 + +20 + +Pika SVD Gen2 + +**(a)** **Text-to-Video** **(b)** **Image-to-Video** + +Figure 10: **User study.** We compare our method to each of the baselines. For each baseline, we report the percentage of user +votes in our favor (blue) and in favor of the baseline (orange). Our method was preferred by users in both text-to-video, and +image-to-video generation. See Sec. 5.2. + +(2023), we quantitatively evaluate our method for zero-shot +text-to-video generation on UCF101 (Soomro et al., 2012). +Table 1 reports the Frechet´ Video Distance (FVD) (Unterthiner et al., 2018) and Inception Score (IS) (Salimans +et al., 2016) of our method and previous work. We achieve +competitive FVD and IS scores. However, as discussed in +previous work (e.g., Girdhar et al. (2023); Ho et al. (2022a); +Chong & Forsyth (2020)), these metrics do not faithfully +reflect human perception, and may be significantly influenced by low-level details (Parmar et al., 2022) and by the +distribution shift between the reference UCF101 data and +the T2V training data (Girdhar et al., 2023). Furthermore, +the protocol uses only 16 frames from generated videos and +thus is not able to capture long-term motion. + +**User Study.** We adopt the Two-alternative Forced Choice +(2AFC) protocol, as used in previous works (Kolkin et al., +2019; Zhang et al., 2018; Blattmann et al., 2023a; Rombach +et al., 2022). In this protocol, participants were presented +with a randomly selected pair of videos: one generated by +our model and the other by one of the baseline methods. Participants were then asked to choose the video they deemed +better in terms of visual quality and motion. Additionally, +they were asked to select the video that more accurately +matched the target text prompt. We collected _∼_ 400 user +judgments for each baseline and question, utilizing the Amazon Mechanical Turk (AMT) platform. As illustrated in +Fig. 10, our method was preferred over all baselines by +the users and demonstrated better alignment with the text +prompts. Note that ZeroScope and AnimateDiff generate +videos only at 3.6 and 2 second respectively, we thus trim +our videos to match their duration when comparing to them. + +We further conduct a user study for comparing our imageto-video model (see Sec. 4.2) against Pika (Pika labs, 2023), +StableVideoDiffusion (SVD) (Blattmann et al., 2023a), and +Gen2(RunwayML, 2023). Note that SVD image-to-video +model is not conditioned on text, we thus focus our survey on the video quality. As seen in Fig. 10, our method +was preferred by users compared to the baselines. For a +detailed description of the full evaluation protocol, please +refer to Appendix D. + +**6. Conclusion** + +We presented a new text-to-video generation framework, utilizing a pre-trained text-to-image diffusion model. We identified an inherent limitation in learning globally-coherent +motion in the prevalent approach of first generating _distant_ +keyframes and subsequently interpolating them using a cascade of temporal super-resolution models. To tackle this +challenge, we introduced a space-time U-Net architecture +design that directly generates full-frame-rate video clips, +by incorporating both spatial, _and temporal_ down- and upsampling modules. We demonstrated state-of-the-art generation results, and showed the applicability of our approach +for a wide range of applications, including image-to-video, +video inapainting, and stylized generation. + +As for limitations, our method is not designed to generate +videos that consist of multiple shots, or that involve transitions between scenes. Generating such content remains +an open challenge for future research. Furthermore, we +established our model on top of a T2I model that operates in +the pixel space, and thus involves a spatial super resolution +module to produce high resolution images. Nevertheless, +our design principles are applicable to latent video diffusion models (Rombach et al., 2022), and can trigger further +research in the design of text-to-video models. + +**7. Societal Impact** + +Our primary goal in this work is to enable novice users to +generate visual content in a creative and flexible way. However, there is a risk of misuse for creating fake or harmful +content with our technology, and we believe that it is crucial +to develop and apply tools for detecting biases and malicious +use cases in order to ensure a safe and fair use. + +**Acknowledgments** We would like to thank Ronny Votel, +Orly Liba, Hamid Mohammadi, April Lehman, Bryan Seybold, David Ross, Dan Goldman, Hartwig Adam, Xuhui Jia, +Xiuye Gu, Mehek Sharma, Rachel Hornung, Oran Lang, +Jess Gallegos, William T. Freeman and David Salesin for +their collaboration, helpful discussions, feedback and support. We thank owners of images and videos used in our +experiments for sharing their valuable assets (attributions +can be found in our webpage). + +10 + +**References** + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +Girdhar, R., Singh, M., Brown, A., Duval, Q., Azadi, S., + +Bar-Tal, O., Yariv, L., Lipman, Y., and Dekel, T. Multi +Diffusion: Fusing diffusion paths for controlled image +generation. In _ICML_, 2023. + +Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., + +Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., +Letts, A., et al. Stable video diffusion: Scaling latent +video diffusion models to large datasets. _arXiv preprint_ +_arXiv:2311.15127_, 2023a. + +Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, + +S. W., Fidler, S., and Kreis, K. 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Videocomposer: + +Compositional video synthesis with motion controllability. _arXiv preprint arXiv:2306.02018_, 2023b. + +Wu, C., Liang, J., Ji, L., Yang, F., Fang, Y., Jiang, D., and + +Duan, N. Nuwa:¨ Visual synthesis pre-training for neural +visual world creation. In _ECCV_, pp. 720–736. Springer, +2022. + +Yuan, X., Baek, J., Xu, K., Tov, O., and Fei, H. Inflation + +with diffusion: Efficient temporal adaptation for text-tovideo super-resolution, 2024. + +12 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +Zhang, D. J., Wu, J. Z., Liu, J.-W., Zhao, R., Ran, L., Gu, + +Y., Gao, D., and Shou, M. Z. Show-1: Marrying pixel + +and latent diffusion models for text-to-video generation. +_arXiv preprint arXiv:2309.15818_, 2023a. + +Zhang, L., Rao, A., and Agrawala, M. Adding conditional + +control to text-to-image diffusion models. In _Proceedings_ +_of the IEEE/CVF International Conference on Computer_ +_Vision_, pp. 3836–3847, 2023b. + +Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, + +O. The unreasonable effectiveness of deep features as a +perceptual metric. In _CVPR_, pp. 586–595, 2018. + +Zhou, D., Wang, W., Yan, H., Lv, W., Zhu, Y., and Feng, J. + +MagicVideo: Efficient video generation with latent diffusion models. _arXiv preprint arXiv:2211.11018_, 2022. + +13 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +**Appendix** + +**A. Qualitative Comparison** + +We provide a qualitative comparison of our method and the baselines (see Sec. 5.1). + +Figure 11: **Qualitative Comparison.** We provide samples from our user study, comparing our model to prominent T2V +diffusion models, Gen-2 (RunwayML, 2023), Pika (Pika labs, 2023), ImagenVideo (Ho et al., 2022a), AnimateDiff (Guo +et al., 2023) and ZeroScope (Wang et al., 2023a). + +14 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +**B. Initialization** + +As illustrated in Fig. 4(b)-(c), each inflation block has a residual structure, in which the temporal components are followed +by a linear projection, and are added to the output of the pre-trained T2I spatial layer. Similarly to previous works (e.g., +(Guo et al., 2023)), we initialize the linear projection to a zero mapping, such that at initialization, the inflation block is +equivalent to the pre-trained T2I block. However, as mentioned in Sec. 3.1, due to the temporal down- and up- sampling +modules, the overall inflated network cannot be equivalent at initialization to the pre-trained T2I model due to the temporal +compression. We empirically found that initializing the temporal down- and up-sampling modules such that they perform +nearest-neighbor down- and up- sampling operations (i.e., temporal striding or frame duplication) is better than a standard +initialization (He et al., 2015). In more detail, the temporal downsampling is implemented as a 1D temporal convolution +(identity initialized) followed by a stride, and the temporal upsampling is implemented as frame duplication followed by +a 1D temporal convolution (identity initialized). Note that this scheme ensures that at initialization, every _N_ th frame is +identical to the output of the pre-trained T2I model, where _N_ is the total temporal downsampling factor in the network (see +Fig. 13). We ablate our choice of temporal down- and up- sampling initialization on the UCF-101 (Soomro et al., 2012) +dataset in Fig. 12. + +Figure 12: **Initialization Ablation.** We ablate the initialization of our temporal down- and up-sampling modules by training +on UCF-101 (Soomro et al., 2012) and comparing the loss function. See App. B for details. + +Standard + +Identity + +… + +… + +Figure 13: **Random** **Samples** **at** **Initialization.** We visualize several frames generated _at_ _initialization_ with different +initialization schemes for the temporal down- and up- sampling modules, to demonstrate their effect. The standard random +initialization (first row) results with non-meaningful samples, because the temporal down- and up-sampling modules mix +unrelated feature maps. The identity initialization (second row) allows us to better benefit from the prior of the pre-trained +text-to-image. See App. B for details. + +15 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +**C. SSR with MultiDiffusion** + +As discussed in Sec. 3.2, we apply MultiDiffusion (Bar-Tal et al., 2023) along the temporal axis in order to feed segments of +the video to the SSR network. Specifically, the temporal windows have an overlap of 2 frames, and at each denoising step +we average the predictions of overlapping pixels. MultiDiffusion allows us to avoid temporal boundary artifacts between +segments of the video (see Fig. 14). + +Figure 14: **MultiDiffusion Ablation.** We ablate the use of MultiDiffusion (Bar-Tal et al., 2023) for the SSR network. We +show sample high-resolution SSR samples in the first row. Below, we show xt-slice of our result and of of the SSR network +applied without MultiDiffusion. As can be seen, without MultiDiffusion, there are temporal boundary artifcats. We also +demonstarte this property by computing Frame Diff. Specifically, we compute the squared difference between adjacent +frames. Without MultiDiffusion, there is a high peak of error between every non-overlapping segment. See App. C. + +**D. Evaluation Protocol** + +**D.1. User study** + +We used a set of 109 text prompts containing a variety of objects and actions for the text-to-video and image-to-video +surveys. 91 prompts were taken from various recent text-to-video generation methods (Singer et al., 2022; Ho et al., 2022a; +Blattmann et al., 2023b). The rest are prompts we created which describe complex scenes and actions, see Appendix D.3 for +the full list of prompts. For each baseline method, we generated results for all prompts through their official APIs. To ensure +a fair comparison, when generating results using our method we’ve fixed a random seed and generated all results using +the same random seed without any curation. In addition, following (Girdhar et al., 2023) we align the spatial resolution +and temporal duration of our videos to each one of the baselines. Spatially, we apply a central crop followed by resizing to +512 _×_ 512, and temporally, by trimming the start and the end such that the duration of both videos matches the shortest one. +Each participant is shown 10 side-by-side comparisons between our method and a baseline (randomly ordered). We then +asked the participant ”Which video matches the following text?” and ”Which video is better? Has more motion and better +quality”. We enable the user to answer only after watching the full video, waiting at least 3 seconds, and pressing a button +reading “Ready to answer” (Fig. 15). To ensure credible responses, we include several vigilance tests during the study - a +real video vs a complete random noise, and a static image vs a real video. + +16 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +Figure 15: **User study display.** The user is shown two videos, ours and a baseline, randomly ordered. + +**D.2. Zero-shot evaluation on UCF101** + +To compute Frechet Video Distance (FVD), we generated 10,235 videos by following the class distribution of the UCF101´ +dataset. We use the same prompt set from Ge et al. (2023) to generate the videos. After resizing all videos to 244 _×_ 244 +resolution, we extract I3D embedding (Carreira & Zisserman, 2017) of the first 16 frames of our videos. Then we compute +the FVD score between the I3D embedding of ours and that of UCF101 videos. To compute Inception Score (IS), the same +generated videos are used to extract C3D embedding (Saito et al., 2020). + +**D.3. List of prompts used for the user study evaluation** + +1. _”A bear is giving a presentation in classroom.”_ + +_2._ _”A bear wearing sunglasses and hosting a talk show.”_ + +_3._ _”A beautiful sunrise on mars, Curiosity rover._ _High definition, timelapse, dramatic colors.”_ + +_4._ _”A beautiful sunrise on mars._ _High definition, timelapse, dramatic colors.”_ + +_5._ _”A bicycle on top of a boat.”_ + +_6._ _”A big moon rises on top of Toronto city.”_ + +_7._ _”A bigfoot walking in the snowstorm.”_ + +_8._ _”A burning volcano.”_ + +_9._ _”A car moving slowly on an empty street, rainy evening, van Gogh painting.”_ + +_10._ _”A cat wearing sunglasses and working as a lifeguard at a pool.”_ + +_11._ _”A confused grizzly bear in calculus class.”_ + +_12._ _”A cute happy Corgi playing in park, sunset, 4k.”_ + +_13._ _”A dog driving a car on a suburban street wearing funny sunglasses”_ + +_14._ _”A dog swimming.”_ + +_15._ _”A dog wearing a Superhero outfit with red cape flying through the sky.”_ + +_16._ _”A dog wearing virtual reality goggles in sunset, 4k, high resolution.”_ + +_17._ _”A fantasy landscape, trending on artstation, 4k, high resolution.”_ + +_18._ _”A fat rabbit wearing a purple robe walking through a fantasy landscape.”_ + +17 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +_19._ _”A fire dragon breathing, trending on artstation, slow motion.”_ + +_20._ _”A fox dressed in suit dancing in park.”_ + +_21._ _”A giraffe underneath a microwave.”_ + +_22._ _”A glass bead falling into water with a huge splash._ _Sunset in the background.”_ + +_23._ _”A golden retriever has a picnic on a beautiful tropical beach at sunset, high resolution.”_ + +_24._ _”A goldendoodle playing in a park by a lake.”_ + +_25._ _”A hand lifts a cup.”_ + +_26._ _”A happy elephant wearing a birthday hat walking under the sea.”_ + +_27._ _”A horse galloping through van Gogh’s Starry Night.”_ + +_28._ _”A hot air balloon ascending over a lush valley, 4k, high definition.”_ + +_29._ _”A koala bear playing piano in the forest.”_ + +_30._ _”A lion standing on a surfboard in the ocean in sunset, 4k, high resolution.”_ + +_31._ _”A monkey is playing bass guitar, stage background, 4k, high resolution.”_ + +_32._ _”A panda standing on a surfboard in the ocean in sunset, 4k, high resolution.”_ + +_33._ _”A panda taking a selfie.”_ + +_34._ _”A polar bear is playing bass guitar in snow, 4k, high resolution.”_ + +_35._ _”A raccoon dressed in suit playing the trumpet, stage background, 4k, high resolution.”_ + +_36._ _”Red sports car coming around a bend in a mountain road.”_ + +_37._ _”A shark swimming in clear Carribean ocean.”_ + +_38._ _”A sheep to the right of a wine glass.”_ + +_39._ _”A shiny golden waterfall flowing through glacier at night.”_ + +_40._ _”A shooting star flying through the night sky over mountains.”_ + +_41._ _”A sloth playing video games and beating all the high scores.”_ + +_42._ _”A small hand-crafted wooden boat taking off to space.”_ + +_43._ _”A squirrel eating a burger.”_ + +_44._ _”A stunning aerial drone footage time lapse of El Capitan in Yosemite National Park at sunset.”_ + +_45._ _”A swarm of bees flying around their hive.”_ + +_46._ _”A teddy bear is playing the electric guitar, high definition, 4k.”_ + +_47._ _”A teddy bear running in New York City.”_ + +_48._ _”A teddy bear skating in Times Square.”_ + +_49._ _”A video of the Earth rotating in space.”_ + +_50._ _”A video that showcases the beauty of nature, from mountains and waterfalls to forests and oceans.”_ + +_51._ _”Aerial view of a hiker man standing on a mountain peak.”_ + +_52._ _”Aerial view of a lake in Switzerland”_ + +_53._ _”Aerial view of a snow-covered mountain.”_ + +_54._ _”Aerial view of a white sandy beach on the shores of a beautiful sea, 4k, high resolution.”_ + +_55._ _”An animated painting of fluffy white clouds moving in sky.”_ + +_56._ _”An astronaut cooking with a pan and fire in the kitchen, high definition, 4k.”_ + +_57._ _”An astronaut riding a horse, high definition, 4k.”_ + +_58._ _”An astronaut riding a horse.”_ + +_59._ _”An astronaut riding a horse in sunset, 4k, high resolution.”_ + +_60._ _”Artistic silhouette of a wolf against a twilight sky, embodying the spirit of the wilderness..”_ + +_61._ _”Back view on young woman dressed in a yellow jacket walking in the forest.”_ + +_62._ _”Maltipoo dog on the carpet next to a Christmas tree in a beautiful living room.”_ + +18 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +_63._ _”Beer pouring into glass, low angle video shot.”_ + +_64._ _”Bird-eye view of a highway in Los Angeles.”_ + +_65._ _”Campfire at night in a snowy forest with starry sky in the background.”_ + +_66._ _”Cartoon character dancing.”_ + +_67._ _”Cherry blossoms swing in front of ocean view, 4k, high resolution.”_ + +_68._ _”Chocolate syrup pouring on a vanilla ice cream”_ + +_69._ _”Close up of grapes on a rotating table._ _High definition.”_ + +_70._ _”Teddy bear surfer rides the wave in the tropics.”_ + +_71._ _”Drone flythrough interior of sagrada familia cathedral.”_ + +_72._ _”Drone flythrough of a fast food restaurant on a dystopian alien planet.”_ + +_73._ _”Drone flythrough of a tropical jungle covered in snow.”_ + +_74._ _”Edge lit hamster writing code on a computer in front of a scifi steampunk machine with yellow green red and blue_ +_gears high quality dslr professional photograph”_ + +_75._ _”Epic tracking shot of a Powerful silverback gorilla male walking gracefully”,_ + +_76._ _”Fireworks.”_ + +_77._ _”Flying_ _through_ _a_ _temple_ _in_ _ruins,_ _epic,_ _forest_ _overgrown,_ _columns_ _cinematic,_ _detailed,_ _atmospheric,_ _epic,_ _mist,_ +_photo-realistic, concept art, volumetric light, cinematic epic, 8k”_ + +_78._ _”Flying through an intense battle between pirate ships in a stormy ocean.”_ + +_79._ _”Flying through fantasy landscapes, 4k, high resolution.”_ + +_80._ _”Pug dog listening to music with big headphones.”_ + +_81._ _”Horror house living room interior overview design, 8K, ultra wide angle, pincushion lens effect.”_ + +_82._ _”Incredibly detailed science fiction scene set on an alien planet, view of a marketplace._ _Pixel art.”_ + +_83._ _”Jack russell terrier dog snowboarding._ _GoPro shot.”_ + +_84._ _”Low angle of pouring beer into a glass cup.”_ + +_85._ _”Melting pistachio ice cream dripping down the cone.”_ + +_86._ _”Milk dripping into a cup of coffee, high definition, 4k.”_ + +_87._ _”Pouring latte art into a silver cup with a golden spoon next to it._ + +_88._ _”Shoveling snow.”_ + +_89._ _”Studio shot of minimal kinetic sculpture made from thin wire shaped like a bird on white background.”_ + +_90._ _”Sunset time lapse at the beach with moving clouds and colors in the sky, 4k, high resolution.”_ + +_91._ _”Teddy bear walking down 5th Avenue, front view, beautiful sunset, close up, high definition, 4k.”_ + +_92._ _”The Orient Express driving through a fantasy landscape, animated oil on canvas.”_ + +_93._ _”Time lapse at a fantasy landscape, 4k, high resolution.”_ + +_94._ _”Time lapse at the snow land with aurora in the sky, 4k, high resolution.”_ + +_95._ _”Tiny plant sprout coming out of the ground.”_ + +_96._ _”Toy Poodle dog rides a penny board outdoors”_ + +_97._ _”Traveler walking alone in the misty forest at sunset.”_ + +_98._ _”Turtle swimming in ocean.”_ + +_99._ _”Two pandas discussing an academic paper.”_ + +_100._ _”Two pandas sitting at a table playing cards, 4k, high resolution.”_ + +_101._ _”Two raccoons are playing drum kit in NYC Times Square, 4k, high resolution.”_ + +_102._ _”Two raccoons reading books in NYC Times Square.”_ + +_103._ _”US flag waving on massive sunrise clouds in bad weather._ _High quality, dynamic, detailed, cinematic lighting”_ + +_104._ _”View of a castle with fantastically high towers reaching into the clouds in a hilly forest at dawn.”_ + +19 + +**Lumiere:** **A Space-Time Diffusion Model for Video Generation** + +_105._ _”Waves crashing against a lone lighthouse, ominous lighting.”_ + +_106._ _”Woman in white dress waving on top of a mountain.”_ + +_107._ _”Wood on fire.”_ + +_108._ _”Wooden figurine surfing on a surfboard in space.”_ + +_109._ _”Yellow flowers swing in wind.”_ + +20 diff --git a/tree/application/architecture/affective-computing.md b/tree/application/architecture/affective-computing.md index 2fbd0c0..9f26997 100644 --- a/tree/application/architecture/affective-computing.md +++ b/tree/application/architecture/affective-computing.md @@ -1,6 +1,7 @@ ----title: 情感计算 AI 架构设计 — 阿里云视角 -description: 'title: 情感计算 AI 架构设计' -summary: 'title: 情感计算 AI 架构设计' +--- +title: Affective Computing AI Architecture Design — Alibaba Cloud Perspective +description: 'Affective Computing AI Architecture Design' +summary: 'Affective Computing AI Architecture Design' category: general tags: - architecture @@ -14,17 +15,17 @@ last_updated: 2026-05 difficulty: intermediate reading_level: intermediate audience: -- 所有工程师 +- All engineers estimated_read_time: 15min intent_queries: -- 情感计算 AI 架构设计 — 阿里云视角 是什么 -- 如何 情感计算 AI 架构设计 — 阿里云视角 -- Kubernetes 20 application patterns 最佳实践 +- What is Affective Computing AI Architecture Design — Alibaba Cloud perspective +- How to implement Affective Computing AI Architecture Design — Alibaba Cloud perspective +- Kubernetes 20 application patterns best practices trigger_keywords: -- 情感计算 +- Affective computing - AI -- 架构设计 -- 阿里云视角 +- Architecture design +- Alibaba Cloud perspective - application - patterns prerequisites: @@ -34,18 +35,17 @@ prerequisites: authors: - name: Dillan Teagle role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/affective-computing.md +original_language: Chinese --- -> **生产环境安全提示** +> **Production Environment Security Notice** > -> 本文档包含可直接执行的运维命令。执行前请确认：当前目标集群与 Namespace 是否正确；是否具备足够的 RBAC 权限；是否已在非生产环境验证。命令风险等级标注：🔴 高风险（可能造成数据丢失或服务中断）、🟡 中风险（会修改集群状态，但通常可回滚）、🟢 低风险/只读（信息收集，无副作用）。 - - - +> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk, may cause data loss or service interruption), Yellow (medium risk, modifies cluster state but usually reversible), Green (low risk/read-only, information collection with no side effects). -title: 情感计算 AI 架构设计 -description: '# 情感计算 AI 架构设计 — 阿里云视角' +title: Affective Computing AI Architecture Design +description: '# Affective Computing AI Architecture Design — Alibaba Cloud Perspective' category: application-architecture tags: - k8s @@ -58,27 +58,27 @@ last_updated: 2026-05-18 difficulty: advanced reading_level: advanced audience: -- AI架构师 -- 多模态算法工程师 -- 人机交互设计师 +- AI architects +- Multimodal algorithm engineers +- Human-computer interaction designers estimated_read_time: 5min intent_queries: -- 情感计算 AI [[Kubernetes|Kubernetes]] GPU部署 -- 多模态情绪识别 Kubernetes -- 智能客服 情绪分析 K8s -- 隐私保护 边缘计算 AI -- 联邦学习 情感计算 Kubernetes +- Affective Computing AI Kubernetes GPU deployment +- Multimodal emotion recognition Kubernetes +- Intelligent customer service emotion analysis K8s +- Privacy-preserving edge computing AI +- Federated learning affective computing Kubernetes trigger_keywords: -- 情感计算 -- 情绪识别 -- 心理评估 -- 多模态 +- Affective computing +- Emotion recognition +- Psychological assessment +- Multimodal - AI - Kubernetes - GPU -- 隐私计算 -- 联邦学习 -- 阿里云 +- Privacy computing +- Federated learning +- Alibaba Cloud related_domains: - domain-01-cluster-fundamentals - domain-11-ai-infra @@ -95,113 +95,113 @@ k8s_versions: - '1.32' --- -# 情感计算 AI 架构设计 — 阿里云视角 +# Affective Computing AI Architecture Design — Alibaba Cloud Perspective -> **适用版本**: Kubernetes v1.29 - v1.33 | **最后更新**: 2026-04-24 -> **作者**: 阿里云解决方案架构师 | **标签**: `#情感计算` `#情绪识别` `#心理评估` `#阿里云` +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 +> **Author**: Alibaba Cloud Solution Architects | **Tags**: `#AffectiveComputing` `#EmotionRecognition` `#PsychologicalAssessment` `#AlibabCloud` --- -## 目录 +## Table of Contents -1. [概述](#1-概述) -2. [设计原则](#2-设计原则) -3. [架构模式](#3-架构模式) -4. [实现示例](#4-实现示例) -5. [在 Kubernetes 上的部署](#5-在-kubernetes-上的部署) -6. [最佳实践](#6-最佳实践) -7. [反模式](#7-反模式) -8. [参考资源](#8-参考资源) +1. [Overview](#1-overview) +2. [Design Principles](#2-design-principles) +3. [Architecture Patterns](#3-architecture-patterns) +4. [Implementation Examples](#4-implementation-examples) +5. [Kubernetes Deployment](#5-kubernetes-deployment) +6. [Best Practices](#6-best-practices) +7. [Anti-Patterns](#7-anti-patterns) +8. [Reference Resources](#8-reference-resources) --- -## 1. 概述 +## 1. Overview -情感计算（Affective Computing）是通过计算机技术识别人类情绪、理解情感状态并做出情感响应的交叉学科。情感计算 AI 通过分析语音、面部表情、文本语义、生理信号（心率、皮肤电导、脑电）等多模态数据，推断用户的情绪状态（如喜怒哀乐、压力水平、注意力集中度等），并据此调整交互策略。 +Affective Computing is an interdisciplinary field that uses computer technology to recognize human emotions, understand emotional states, and provide emotional responses. Affective Computing AI analyzes multimodal data including voice, facial expressions, text semantics, and physiological signals (heart rate, skin conductance, EEG) to infer user emotional states (such as happiness, anger, sadness, stress levels, attention levels, etc.), and adjusts interaction strategies accordingly. -情感计算的应用场景广泛：智能客服中根据用户情绪调整话术和转人工策略；在线教育中监测学生注意力和困惑状态；医疗健康中辅助抑郁症和焦虑症筛查；智能驾驶中监测驾驶员疲劳和分心状态；市场研究中分析消费者对产品的情感反应。 +Affective Computing has broad application scenarios: intelligent customer service can adjust dialogue strategies and transfer-to-human policies based on user emotions; online education can monitor student attention and confusion states; medical health can assist in depression and anxiety screening; intelligent driving can detect driver fatigue and distraction; market research can analyze consumer emotional responses to products. -从架构角度看，情感计算 AI 系统的核心挑战是**多模态融合**和**实时推理**。不同模态的数据（音频、视频、文本、生理信号）具有不同的采样率和特征空间，需要在时间维度上对齐并在语义层面融合。实时交互场景要求端到端推理延迟 < 200ms。此外，情感数据的隐私敏感性要求系统在数据采集、存储、处理的每个环节都满足隐私保护要求。 +From an architecture perspective, the core challenges of Affective Computing AI systems are **multimodal fusion** and **real-time inference**. Different modalities of data (audio, video, text, physiological signals) have different sampling rates and feature spaces, requiring temporal alignment and semantic-level fusion. Real-time interactive scenarios require end-to-end inference latency < 200ms. Additionally, the privacy sensitivity of emotional data requires the system to satisfy privacy protection requirements at every stage of data collection, storage, and processing. -## 1.1 行业背景 +## 1.1 Industry Background -| 挑战 | 说明 | 架构影响 | +| Challenge | Description | Architecture Impact | |:---|:---|:---| -| 多模态融合 | 语音/表情/文本/生理信号 | 多分支网络 + 融合层 | -| 文化差异 | 不同文化情绪表达差异 | 地域化模型 + 增量学习 | -| 实时处理 | 对话中实时情绪识别 | 流式推理 + GPU 加速 | -| 隐私敏感 | 情绪数据高度个人化 | 边缘计算 + 联邦学习 | -| 场景多样 | 客服/教育/医疗/驾驶 | 场景适配 + 迁移学习 | +| Multimodal fusion | Voice/expression/text/physiological signals | Multi-branch network + fusion layer | +| Cultural differences | Emotion expression varies across cultures | Localized models + incremental learning | +| Real-time processing | Real-time emotion recognition in conversation | Streaming inference + GPU acceleration | +| Privacy sensitivity | Emotion data highly personalized | Edge computing + federated learning | +| Scenario diversity | Customer service/education/medical/driving | Scenario adaptation + transfer learning | -## 1.2 核心场景 +## 1.2 Core Scenarios -- **智能客服**: 实时识别来电用户情绪，自动调整话术、触发安抚策略、智能转人工 -- **在线教育**: 监测学生注意力、困惑、疲劳状态，自适应调整教学内容 -- **心理健康**: 辅助抑郁症/焦虑症/自闭症筛查，长期情绪追踪 -- **智能驾驶**: 驾驶员疲劳检测、分心监测、路怒预警 -- **内容审核**: 视频情绪合规检测，识别暴力、仇恨等负面情绪内容 +- **Intelligent Customer Service**: Real-time identification of caller emotions, automatic adjustment of dialogue, emotion-soothing strategy triggering, intelligent human transfer +- **Online Education**: Monitoring student attention, confusion, fatigue states; adaptive content adjustment +- **Mental Health**: Assisting depression/anxiety/autism screening; long-term emotion tracking +- **Intelligent Driving**: Driver fatigue detection, distraction monitoring, road rage warning +- **Content Moderation**: Video emotion compliance detection, identifying violent and hateful emotional content --- -## 2. 设计原则 +## 2. Design Principles -## 2.1 多模态协同原则 +## 2.1 Multimodal Collaboration Principle -单一模态的情绪识别准确率有限（语音约 70%、面部表情约 75%、文本约 65%）。多模态融合可以显著提升准确率（可达 85-90%）。架构设计需要支持灵活的模态组合——根据场景可用性选择模态子集，动态调整融合策略。 +Single-modality emotion recognition accuracy is limited (voice ~70%, facial expression ~75%, text ~65%). Multimodal fusion can significantly improve accuracy (up to 85-90%). Architecture design must support flexible modality combinations—selecting modality subsets based on scenario availability and dynamically adjusting fusion strategies. -## 2.2 隐私保护原则 +## 2.2 Privacy Protection Principle -情感数据（面部图像、语音录音、生理信号）是高度个人化的敏感数据。系统设计必须遵循"最小采集"和"本地优先"原则：原始数据在边缘设备上处理，只上传脱敏后的情绪标签；数据采集前获得用户明确同意；支持用户随时撤销授权和删除数据。 +Emotional data (facial images, voice recordings, physiological signals) is highly personalized sensitive data. System design must follow "minimal collection" and "local-first" principles: process raw data on edge devices, upload only anonymized emotion labels; obtain explicit user consent before data collection; support users to revoke authorization and delete data anytime. -## 2.3 实时性原则 +## 2.3 Real-Time Principle -交互式场景要求系统在用户说话或表情变化的同时给出情绪判断。端到端延迟（采集→预处理→推理→输出）需要控制在 200ms 以内。这要求优化推理流水线的每个环节：模型轻量化（知识蒸馏、量化）、推理引擎优化（TensorRT、ONNX Runtime）、计算就近部署。 +Interactive scenarios require the system to provide emotion judgments while users speak or change expressions. End-to-end latency (collection → preprocessing → inference → output) must be controlled within 200ms. This requires optimizing every step of the inference pipeline: model lightweight (knowledge distillation, quantization), inference engine optimization (TensorRT, ONNX Runtime), computing deployment near the edge. -## 2.4 公平性原则 +## 2.4 Fairness Principle -情感计算模型在不同人群（年龄、性别、种族、文化背景）上的表现可能存在差异。模型训练需要确保数据集的多样性和代表性，定期进行公平性评估，避免对特定群体的系统性偏见。 +Affective Computing models may perform differently across different populations (age, gender, race, cultural background). Model training must ensure dataset diversity and representativeness, conduct regular fairness evaluations, and avoid systematic bias against specific groups. --- -## 3. 架构模式 +## 3. Architecture Patterns -## 3.1 情感计算 AI 平台全景架构 +## 3.1 Affective Computing AI Platform Full-Landscape Architecture ```mermaid graph TB - subgraph 数据采集层 - D1[语音采集] - D2[视频采集] - D3[文本输入] - D4[生理传感器] + subgraph Data Collection Layer + D1[Voice acquisition] + D2[Video acquisition] + D3[Text input] + D4[Physiological sensors] end - subgraph 特征提取层 - F1[声纹/韵律特征] - F2[面部表情/FACS] - F3[语义情感/NLP] - F4[生理信号特征] + subgraph Feature Extraction Layer + F1[Voice/prosody features] + F2[Facial expression/FACS] + F3[Semantic sentiment/NLP] + F4[Physiological signal features] end - subgraph 多模态融合层 - E1[早期融合] - E2[晚期融合] - E3[注意力融合] - E4[时序建模] + subgraph Multimodal Fusion Layer + E1[Early fusion] + E2[Late fusion] + E3[Attention fusion] + E4[Temporal modeling] end - subgraph 情感推理层 - R1[离散情绪分类] - R2[维度情感回归] - R3[强度估计] - R4[个性化校准] + subgraph Emotion Inference Layer + R1[Discrete emotion classification] + R2[Dimensional emotion regression] + R3[Intensity estimation] + R4[Personalized calibration] end - subgraph 应用服务层 - A1[智能客服] - A2[在线教育] - A3[心理健康] - A4[智能驾驶] + subgraph Application Service Layer + A1[Intelligent customer service] + A2[Online education] + A3[Mental health] + A4[Intelligent driving] end D1 --> F1 @@ -213,47 +213,47 @@ graph TB R1 & R2 & R3 & R4 --> A1 & A2 & A3 & A4 ``` -## 3.2 实时推理流水线 +## 3.2 Real-Time Inference Pipeline ```mermaid flowchart LR - A[音频流] --> D[特征提取] - B[视频流] --> D - C[文本流] --> D - D --> E[多模态融合] - E --> F[情绪推理] - F --> G[后处理平滑] - G --> H[情绪标签] - H --> I[策略引擎] + A[Audio stream] --> D[Feature extraction] + B[Video stream] --> D + C[Text stream] --> D + D --> E[Multimodal fusion] + E --> F[Emotion inference] + F --> G[Post-processing smoothing] + G --> H[Emotion labels] + H --> I[Policy engine] ``` -## 3.3 隐私保护推理架构 +## 3.3 Privacy-Preserving Inference Architecture ```mermaid graph TB - subgraph 边缘设备 - E1[原始数据采集] - E2[特征提取] - E3[轻量推理] - E4[情绪标签输出] + subgraph Edge Device + E1[Raw data collection] + E2[Feature extraction] + E3[Lightweight inference] + E4[Emotion label output] end - subgraph 云端平台 - C1[模型训练] - C2[联邦聚合] - C3[模型下发] + subgraph Cloud Platform + C1[Model training] + C2[Federated aggregation] + C3[Model distribution] end E1 --> E2 --> E3 --> E4 - E2 --> |脱敏特征| C1 + E2 --> |Anonymized features| C1 C1 --> C2 --> C3 --> E3 ``` --- -## 4. 实现示例 +## 4. Implementation Examples -## 4.2 多模态情感推理服务 +## 4.2 Multimodal Emotion Inference Service ```python import numpy as np @@ -332,7 +332,7 @@ class MultimodalEmotionEngine: return {e: 1.0/len(self.EMOTIONS) for e in self.EMOTIONS} ``` -## 4.3 客服情绪策略引擎 +## 4.3 Customer Service Emotion Strategy Engine ```go package affective @@ -448,7 +448,7 @@ func (t *CustomerEmotionTracker) _selectStrategy(state EmotionState) Strategy { --- -## 5. 在 Kubernetes 上的部署 +## 5. Kubernetes Deployment ```yaml apiVersion: apps/v1 @@ -494,83 +494,83 @@ spec: --- -## 6. 最佳实践 +## 6. Best Practices -- **模型轻量化**: 使用知识蒸馏将大型多模态模型压缩为可在边缘部署的轻量模型 -- **流式推理**: 音频和视频采用流式处理，避免等待完整片段 -- **时序平滑**: 对连续帧的情绪结果进行滑动平均，避免结果跳变 -- **数据增强**: 使用数据增强（语音变速、面部遮挡、文本同义替换）提升模型鲁棒性 -- **公平性审计**: 定期评估模型在不同人群上的表现差异 +- **Model Lightweight**: Use knowledge distillation to compress large multimodal models into lightweight models deployable at edge +- **Streaming Inference**: Adopt streaming processing for audio and video, avoiding waiting for complete segments +- **Temporal Smoothing**: Apply sliding average to emotion results of continuous frames, avoiding result jitter +- **Data Augmentation**: Use data augmentation (voice speed variation, face occlusion, text synonym replacement) to improve model robustness +- **Fairness Audit**: Regularly evaluate performance differences across different populations -## 7. 反模式 +## 7. Anti-Patterns -## 7.1 单模态决策 +## 7.1 Single-Modality Decision -仅依赖单一模态（如面部表情）做情绪判断，忽视其他可用信息。 +Relying solely on a single modality (such as facial expression) for emotion judgment, ignoring other available information. -**解决方案**: 采用多模态融合策略。当某些模态不可用时，动态调整融合权重，利用可用模态给出最优估计。 +**Solution**: Adopt multimodal fusion strategy. Dynamically adjust fusion weights when certain modalities are unavailable, using available modalities for optimal estimation. -## 7.2 忽视文化差异 +## 7.2 Ignoring Cultural Differences -使用西方数据训练的模型直接应用于东方文化场景，面部表情和语音表达的文化差异导致误判。 +Directly applying models trained on Western data to Eastern cultural scenarios, where cultural differences in facial expressions and voice result in misclassification. -**解决方案**: 收集目标文化的标注数据，进行领域适配。在推理时加入文化上下文因子。 +**Solution**: Collect annotated data for target culture and perform domain adaptation. Incorporate cultural context factors during inference. -## 7.3 情绪数据明文存储 +## 7.3 Plaintext Emotion Data Storage -将用户的原始面部图像、语音录音以明文形式存储在云端。 +Storing users' raw facial images and voice recordings in plaintext on cloud servers. -**解决方案**: 原始数据在边缘设备处理后立即删除。只上传脱敏后的情绪标签。确需存储的数据使用 AES-256 加密。 +**Solution**: Process raw data on edge devices and delete immediately. Only upload anonymized emotion labels. Encrypt any data requiring storage with AES-256. -## 7.4 用于歧视性决策 +## 7.4 Use for Discriminatory Decision-Making -将情绪识别结果用于招聘筛选、信用评估等歧视性场景。 +Using emotion recognition results for hiring screening, credit assessment and other discriminatory scenarios. -**解决方案**: 明确限制情感计算的应用范围，建立伦理审查机制。不将情绪数据用于任何可能对用户造成不利的自动化决策。 +**Solution**: Clearly limit application scope of affective computing and establish ethics review mechanisms. Do not use emotion data for any automated decision-making that may harm users. --- -## 8. 参考资源 +## 8. Reference Resources -## 8.1 阿里云组件映射 +## 8.1 Alibaba Cloud Component Mapping -| 功能域 | **阿里云云原生方案** | +| Functional Domain | **Alibaba Cloud Cloud-Native Solution** | |:---|:---| -| 容器平台 | **ACK Pro + GPU** | -| AI 平台 | **PAI + 视觉智能 + 语音智能** | -| 数据库 | **PolarDB** | -| 对象存储 | **OSS（加密）** | -| 可观测性 | **ARMS + SLS** | +| Container Platform | **ACK Pro + GPU** | +| AI Platform | **PAI + Vision Intelligence + Speech Intelligence** | +| Database | **PolarDB** | +| Object Storage | **OSS (Encrypted)** | +| Observability | **ARMS + SLS** | -## 8.2 生产检查清单 +## 8.2 Production Checklist -- [ ] 多模态识别准确率 > 85%（F1-[[Score|Score]]） -- [ ] 端到端推理延迟 P99 < 200ms -- [ ] 情绪数据端到端加密 -- [ ] 伦理审查委员会审批通过 -- [ ] 跨人群公平性测试 -- [ ] 用户知情同意机制 +- [ ] Multimodal recognition accuracy > 85% (F1-Score) +- [ ] End-to-end inference latency P99 < 200ms +- [ ] Emotion data end-to-end encrypted +- [ ] Ethics review committee approval obtained +- [ ] Cross-population fairness testing passed +- [ ] User informed consent mechanism established --- -**维护者**: 阿里云解决方案架构师团队 | **许可证**: MIT +**Maintainers**: Alibaba Cloud Solution Architects Team | **License**: MIT --- -## Obsidian 相关文档 +## Obsidian Related Documentation - topic-application-architecture KUDIG Database — Global MOC -- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic 应用层架构设计最佳实践]] -- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|电商系统 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|小程序平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/03-cms-architecture.md|内容管理系统 CMS 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/04-im-rtc-architecture.md|实时通信 IM/RTC 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/05-online-education-architecture.md|在线教育平台 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/06-fintech-architecture.md|金融科技FinTech Kubernetes生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md|物联网 IoT 平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/08-ai-ml-inference-architecture.md|AI/ML 推理服务 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/09-gaming-backend-architecture.md|游戏后端 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|社交媒体平台Kubernetes生产架构设计]] +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] +- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|E-commerce Systems Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|Mini Program Platform Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/03-cms-architecture.md|Content Management System CMS Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/04-im-rtc-architecture.md|Real-time Communication IM/RTC Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/05-online-education-architecture.md|Online Education Platform Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/06-fintech-architecture.md|Financial Technology FinTech Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md|Internet of Things IoT Platform Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/08-ai-ml-inference-architecture.md|AI/ML Inference Service Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/09-gaming-backend-architecture.md|Gaming Backend Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|Social Media Platform Kubernetes Production Architecture Design]] ## See Also diff --git a/tree/application/architecture/autonomous-driving-sim.md b/tree/application/architecture/autonomous-driving-sim.md index 408d691..9d5675b 100644 --- a/tree/application/architecture/autonomous-driving-sim.md +++ b/tree/application/architecture/autonomous-driving-sim.md @@ -1,6 +1,7 @@ ----title: 自动驾驶仿真架构设计 — 阿里云视角 -description: 'title: 自动驾驶仿真架构设计' -summary: 'title: 自动驾驶仿真架构设计' +--- +title: Autonomous Driving Simulation Architecture Design — Alibaba Cloud Perspective +description: 'Autonomous Driving Simulation Architecture Design' +summary: 'Autonomous Driving Simulation Architecture Design' category: general tags: - architecture @@ -19,15 +20,15 @@ last_updated: 2026-05 difficulty: intermediate reading_level: intermediate audience: -- 所有工程师 +- All engineers estimated_read_time: 15min intent_queries: -- 自动驾驶仿真架构设计 — 阿里云视角 是什么 -- 如何 自动驾驶仿真架构设计 — 阿里云视角 -- Kubernetes 20 application patterns 最佳实践 +- What is autonomous driving simulation architecture design — Alibaba Cloud perspective +- How to implement autonomous driving simulation architecture design — Alibaba Cloud perspective +- Kubernetes 20 application patterns best practices trigger_keywords: -- 自动驾驶仿真架构设计 -- 阿里云视角 +- Autonomous driving simulation architecture design +- Alibaba Cloud perspective - application - patterns prerequisites: @@ -41,26 +42,25 @@ prerequisites: authors: - name: Dillan Teagle role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/autonomous-driving-sim.md +original_language: Chinese --- -> **生产环境安全提示** +> **Production Environment Security Notice** > -> 本文档包含可直接执行的运维命令。执行前请确认：当前目标集群与 Namespace 是否正确；是否具备足够的 RBAC 权限；是否已在非生产环境验证。命令风险等级标注：🔴 高风险（可能造成数据丢失或服务中断）、🟡 中风险（会修改集群状态，但通常可回滚）、🟢 低风险/只读（信息收集，无副作用）。 - - - +> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk, may cause data loss or service interruption), Yellow (medium risk, modifies cluster state but usually reversible), Green (low risk/read-only, information collection with no side effects). -title: 自动驾驶仿真架构设计 -description: '# 自动驾驶仿真架构设计 — 阿里云视角' +title: Autonomous Driving Simulation Architecture Design +description: '# Autonomous Driving Simulation Architecture Design — Alibaba Cloud Perspective' category: application-architecture tags: - k8s - architecture - industry -- [[Prometheus|prometheus]] +- Prometheus - grafana -- [[ArgoCD|argocd]] +- ArgoCD - opa - mysql - job @@ -69,27 +69,27 @@ last_updated: 2026-05-18 difficulty: expert reading_level: expert audience: -- 自动驾驶算法工程师 -- 仿真平台架构师 -- AI模型训练工程师 +- Autonomous driving algorithm engineers +- Simulation platform architects +- AI model training engineers estimated_read_time: 5min intent_queries: -- 自动驾驶仿真平台 CARLA GPU集群部署 -- SIL HIL 硬件在环仿真架构 -- 生成式AI场景自动生成方案 -- 自动驾驶感知规划算法测试 -- 阿里云PAI模型训练仿真 +- Autonomous driving simulation platform CARLA GPU cluster deployment +- SIL HIL hardware-in-the-loop simulation architecture +- Generative AI scenario auto-generation solution +- Autonomous driving perception planning algorithm testing +- Alibaba Cloud PAI model training simulation trigger_keywords: -- 自动驾驶仿真 +- Autonomous driving simulation - CARLA -- SIL软件在环 -- HIL硬件在环 -- 场景生成 -- GPU仿真 -- 传感器仿真 -- 激光雷达点云 -- 视觉感知 -- 数据闭环 +- SIL software-in-the-loop +- HIL hardware-in-the-loop +- Scenario generation +- GPU simulation +- Sensor simulation +- LiDAR point cloud +- Visual perception +- Data closed-loop related_domains: - domain-03-networking-traffic - domain-10-troubleshooting-diagnostics @@ -104,124 +104,124 @@ k8s_versions: - '1.32' --- -# 自动驾驶仿真架构设计 — 阿里云视角 +# Autonomous Driving Simulation Architecture Design — Alibaba Cloud Perspective -> **适用版本**: Kubernetes v1.29 - v1.33 | **最后更新**: 2026-04-24 -> **作者**: 阿里云解决方案架构师 | **标签**: `#自动驾驶` `#仿真测试` `#场景生成` `#阿里云` +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 +> **Author**: Alibaba Cloud Solution Architects | **Tags**: `#AutonomousDriving` `#SimulationTesting` `#ScenarioGeneration` `#AlibabCloud` --- -## 目录 +## Table of Contents -1. [行业概述](#1-行业概述) -2. [业务场景](#2-业务场景) -3. [架构设计](#3-架构设计) -4. [核心技术栈](#4-核心技术栈) -5. [Kubernetes 部署方案](#5-kubernetes-部署方案) -6. [数据架构](#6-数据架构) -7. [AI/ML 组件](#7-aiml-组件) -8. [安全与合规](#8-安全与合规) -9. [最佳实践](#9-最佳实践) -10. [反模式](#10-反模式) -11. [参考资源](#11-参考资源) +1. [Industry Overview](#1-industry-overview) +2. [Business Scenarios](#2-business-scenarios) +3. [Architecture Design](#3-architecture-design) +4. [Core Technology Stack](#4-core-technology-stack) +5. [Kubernetes Deployment Solution](#5-kubernetes-deployment-solution) +6. [Data Architecture](#6-data-architecture) +7. [AI/ML Components](#7-aiml-components) +8. [Security and Compliance](#8-security-and-compliance) +9. [Best Practices](#9-best-practices) +10. [Anti-Patterns](#10-anti-patterns) +11. [Reference Resources](#11-reference-resources) --- -## 1. 行业概述 +## 1. Industry Overview -## 1.1 市场规模与趋势 +## 1.1 Market Scale and Trends -自动驾驶仿真通过虚拟环境加速算法验证，是自动驾驶研发的核心基础设施。全球自动驾驶仿真市场规模预计从 2024 年的 35 亿美元增长到 2030 年的 200 亿美元。CARLA、LGSVL、PreScan、VTD 等仿真平台广泛应用。核心趋势包括生成式 AI 场景生成、大规模并行 GPU 仿真和硬件在环（HIL）测试。 +Autonomous driving simulation accelerates algorithm validation through virtual environments and is core infrastructure for autonomous driving R&D. The global autonomous driving simulation market is projected to grow from 3.5 billion USD in 2024 to 20 billion USD in 2030. CARLA, LGSVL, PreScan, VTD and other simulation platforms are widely adopted. Core trends include generative AI scenario generation, large-scale parallel GPU simulation, and hardware-in-the-loop (HIL) testing. -| 指标 | 2024 年 | 2026 年（预测） | 2030 年（预测） | +| Metric | 2024 | 2026 (Projected) | 2030 (Projected) | |:---|:---|:---|:---| -| 全球仿真市场 | $3.5B | $8B | $20B | -| 并行仿真 GPU 节点 | 100-500 | 500-2000 | 2000-10000 | -| 传感器仿真精度 | 物理级 | 物理级 + 真实感 | 几乎与真实无异 | -| 场景库规模 | 10 万+ | 100 万+ | 1000 万+ | -| 仿真替代路测比例 | 60% | 75% | 90% | +| Global simulation market | $3.5B | $8B | $20B | +| Parallel simulation GPU nodes | 100-500 | 500-2000 | 2000-10000 | +| Sensor simulation accuracy | Physics-level | Physics-level + Photorealism | Almost indistinguishable from reality | +| Scenario library scale | 100k+ | 1M+ | 10M+ | +| Simulation replacement for road testing percentage | 60% | 75% | 90% | -## 1.2 行业痛点 +## 1.2 Industry Pain Points -| 痛点 | 说明 | 数字化转型驱动 | +| Pain Point | Description | Digital Transformation Driver | |:---|:---|:---| -| 长尾场景 | 罕见危险场景难以路测 | 生成式 AI + 参数化场景库 | -| 传感器仿真 | 相机/LiDAR/Radar 仿真精度 | 物理级渲染 + 光线追踪 | -| 海量计算 | 数十亿公里虚拟测试 | 大规模 GPU 并行 | -| SIL/HIL | 软件/硬件在环混合测试 | 混合仿真架构 | -| 数据闭环 | 仿真结果驱动模型迭代 | 自动化数据流水线 | -| 仿真可信度 | 仿真与真实场景一致性 | 仿真验证与校准 | +| Long-tail scenarios | Rare dangerous scenarios difficult to test on road | Generative AI + parameterized scenario library | +| Sensor simulation | Camera/LiDAR/Radar simulation accuracy | Physics-level rendering + ray tracing | +| Massive computation | Billions of kilometers virtual testing | Large-scale GPU parallelization | +| SIL/HIL | Software/hardware-in-the-loop hybrid testing | Hybrid simulation architecture | +| Data closed-loop | Simulation results driving model iteration | Automated data pipeline | +| Simulation fidelity | Simulation vs. real-world scene consistency | Simulation validation and calibration | -## 1.3 数字化转型架构影响 +## 1.3 Digital Transformation Architecture Impact -自动驾驶仿真架构需要覆盖场景层（自然驾驶/危险/边界/生成式场景）、仿真层（动力学/传感器/交通流/环境仿真）、测试层（SIL/HIL/VIL/DIL）和评估层（功能安全/性能/法规/覆盖率）。核心挑战是传感器仿真的物理真实感和大规模并行仿真的资源调度。 +Autonomous driving simulation architecture must cover scenario layer (natural driving/dangerous/boundary/generative scenarios), simulation layer (dynamics/sensor/traffic flow/environment simulation), testing layer (SIL/HIL/VIL/DIL), and evaluation layer (functional safety/performance/regulatory/coverage). Core challenges include sensor simulation physical realism and large-scale parallel simulation resource scheduling. --- -## 2. 业务场景 +## 2. Business Scenarios -## 2.1 参数化场景生成 +## 2.1 Parameterized Scenario Generation -基于自然驾驶数据和交通规则生成海量测试场景。支持参数化调整（天气/光照/行人行为/车辆密度），自动探索边界条件。生成式 AI 可从文本描述自动生成复杂交通场景。 +Generate massive test scenarios based on natural driving data and traffic rules. Support parameterized adjustment (weather/lighting/pedestrian behavior/vehicle density), automatically explore boundary conditions. Generative AI can automatically generate complex traffic scenarios from text descriptions. -## 2.2 物理级传感器仿真 +## 2.2 Physics-Level Sensor Simulation -仿真摄像头（包括镜头畸变/噪声/运动模糊）、LiDAR（包括点云密度/反射率/天气影响）和 Radar。使用 GPU 光线追踪实现物理级渲染，仿真传感器数据直接输入自动驾驶算法。 +Simulate cameras (including lens distortion/noise/motion blur), LiDAR (including point cloud density/reflectivity/weather effects), and Radar. Use GPU ray-tracing to achieve physics-level rendering, simulated sensor data directly feeds autonomous driving algorithms. -## 2.3 SIL 软件在环测试 +## 2.3 SIL Software-in-the-Loop Testing -自动驾驶算法（感知/规划/控制）在仿真环境中运行，验证功能正确性。支持回放真实路测数据（log replay）和纯仿真场景。可并行运行数千个场景的 SIL 测试。 +Autonomous driving algorithms (perception/planning/control) run in simulation environments, validating functional correctness. Support replay of real road-testing data (log replay) and pure simulation scenarios. Can run thousands of SIL tests for different scenarios in parallel. -## 2.4 HIL 硬件在环测试 +## 2.4 HIL Hardware-in-the-Loop Testing -真实自动驾驶域控制器接入仿真系统，仿真环境生成传感器数据注入控制器，控制器输出控制指令驱动仿真车辆。HIL 测试验证软硬件集成后的实时性能。 +Real autonomous driving domain controllers are integrated with simulation systems. Simulation environments generate sensor data injected into controllers, controller outputs drive simulated vehicles. HIL testing validates real-time performance after software-hardware integration. -## 2.5 数据闭环 +## 2.5 Data Closed-Loop -仿真发现的失败场景自动提取为回归测试用例，问题场景用于重训练感知/规划模型。形成"仿真→问题→训练→验证"的数据闭环。 +Scenarios failing in simulation are automatically extracted as regression test cases, problem scenarios used for model retraining. Forms "simulation → problem → training → validation" data closed-loop. --- -## 3. 架构设计 +## 3. Architecture Design -## 3.1 自动驾驶仿真全景架构 +## 3.1 Autonomous Driving Simulation Full-Landscape Architecture ```mermaid graph TB - subgraph ScenarioLayer["场景层"] - S1[自然驾驶场景库] - S2[危险场景库] - S3[边界场景库] - S4[生成式 AI 场景] - S5[法规测试场景] + subgraph ScenarioLayer["Scenario Layer"] + S1[Natural driving scenario library] + S2[Dangerous scenario library] + S3[Boundary scenario library] + S4[Generative AI scenarios] + S5[Regulatory test scenarios] end - subgraph SimLayer["仿真层"] - SIM1[车辆动力学仿真] - SIM2[传感器物理仿真] - SIM3[交通流仿真] - SIM4[环境/天气仿真] + subgraph SimLayer["Simulation Layer"] + SIM1[Vehicle dynamics simulation] + SIM2[Sensor physics simulation] + SIM3[Traffic flow simulation] + SIM4[Environment/weather simulation] end - subgraph TestLayer["测试层"] - T1[SIL 软件在环] - T2[HIL 硬件在环] - T3[VIL 车辆在环] - T4[DIL 驾驶员在环] + subgraph TestLayer["Testing Layer"] + T1[SIL software-in-the-loop] + T2[HIL hardware-in-the-loop] + T3[VIL vehicle-in-the-loop] + T4[DIL driver-in-the-loop] end - subgraph EvalLayer["评估层"] - E1[功能安全评估] - E2[性能指标评估] - E3[法规合规评估] - E4[场景覆盖率评估] + subgraph EvalLayer["Evaluation Layer"] + E1[Functional safety evaluation] + E2[Performance metrics evaluation] + E3[Regulatory compliance evaluation] + E4[Scenario coverage evaluation] end - subgraph InfraLayer["基础设施层"] - I1[GPU 仿真集群] + subgraph InfraLayer["Infrastructure Layer"] + I1[GPU simulation cluster] I2[ACK Pro K8s] - I3[OSS 数据存储] - I4[PAI 模型训练] + I3[OSS data storage] + I4[PAI model training] end S1 & S2 & S3 & S4 & S5 --> SIM1 & SIM2 & SIM3 & SIM4 @@ -233,12 +233,12 @@ graph TB --- -## 4. 核心技术栈 +## 4. Core Technology Stack | Component | Purpose | Technology | License | |:---|:---|:---|:---| | Container Orchestration | GPU cluster management | ACK Pro + GPU | Proprietary | -| Sim Engine | Driving simulation | CARLA / LGSVL / 自研 | MIT / Proprietary | +| Sim Engine | Driving simulation | CARLA / LGSVL / Self-developed | MIT / Proprietary | | Rendering | Sensor simulation | UE5 + Ray Tracing | Proprietary | | Dynamics | Vehicle dynamics | CarSim / Dyna4 | Proprietary | | AI Framework | Model training | PyTorch 2.x / PAI | BSD / Proprietary | @@ -252,9 +252,9 @@ graph TB --- -## 5. Kubernetes 部署方案 +## 5. Kubernetes Deployment Solution -## 5.1 GPU 仿真工作器 Deployment +## 5.1 GPU Simulation Worker Deployment ```yaml apiVersion: apps/v1 @@ -334,7 +334,7 @@ spec: periodSeconds: 15 ``` -## 5.2 仿真编排器 Deployment +## 5.2 Simulation Orchestrator Deployment ```yaml apiVersion: apps/v1 @@ -373,7 +373,7 @@ spec: cpu: "4000m" ``` -## 5.3 ConfigMap, Service 与 Secret +## 5.3 ConfigMap, Service and Secret ```yaml apiVersion: v1 @@ -440,124 +440,124 @@ stringData: --- -## 6. 数据架构 +## 6. Data Architecture -## 6.1 仿真数据闭环 +## 6.1 Simulation Data Closed-Loop ```mermaid flowchart LR - A[场景库 100万+] --> B[GPU 并行仿真] - B --> C[算法测试执行] - C --> D[结果收集评估] - D --> E{通过率?} - E -->|失败| F[问题场景提取] - F --> G[模型重训练 PAI] - G --> H[新模型部署] + A[Scenario library 1M+] --> B[GPU parallel simulation] + B --> C[Algorithm test execution] + C --> D[Result collection and evaluation] + D --> E{Pass rate?} + E -->|Failure| F[Problem scenario extraction] + F --> G[Model retraining PAI] + G --> H[New model deployment] H --> C - E -->|通过| I[覆盖率报告] - I --> J[仿真可信度评估] + E -->|Pass| I[Coverage report] + I --> J[Simulation fidelity evaluation] ``` -## 6.2 数据流说明 +## 6.2 Data Flow Description -- **场景分发流**: 编排器将场景分发至 GPU 工作器，每个工作器独立运行仿真 -- **传感器数据流**: 仿真引擎生成传感器数据注入自动驾驶算法 -- **结果回收流**: 仿真结果（轨迹/指标/碰撞/违规）统一收集至评估系统 -- **训练数据流**: 失败场景自动归档至训练数据集，用于模型重训练 +- **Scenario Distribution Flow**: Orchestrator distributes scenarios to GPU workers, each worker runs simulation independently +- **Sensor Data Flow**: Simulation engine generates sensor data injected into autonomous driving algorithms +- **Result Collection Flow**: Simulation results (trajectories/metrics/collisions/violations) collected uniformly for evaluation +- **Training Data Flow**: Failed scenarios automatically archived to training dataset for model retraining --- -## 7. AI/ML 组件 +## 7. AI/ML Components -## 7.1 核心模型 +## 7.1 Core Models -| 模型 | 用途 | 输入 | 输出 | 框架 | +| Model | Purpose | Input | Output | Framework | |:---|:---|:---|:---|---| -| 场景生成 | 自动生成测试场景 | 文本描述/参数约束 | 3D 交通场景 | Diffusion + LLM | -| 感知模型 | 目标检测/分割 | 传感器仿真数据 | 目标列表/语义分割 | BEVFormer / StreamPETR | -| 规划模型 | 轨迹规划 | 感知结果/地图 | 行驶轨迹 | PnPNet / UniAD | -| 覆盖率模型 | 场景覆盖率分析 | 测试结果 | 覆盖率指标 | Monte Carlo | -| 仿真加速 | 仿真速度优化 | 场景复杂度 | 自适应步长 | RL | -| ODD 检测 | 运行设计域识别 | 传感器数据 | ODD 合规性 | 分类器 | +| Scenario Generation | Auto-generate test scenarios | Text description/parameter constraints | 3D traffic scenario | Diffusion + LLM | +| Perception Model | Object detection/segmentation | Sensor simulation data | Object list/semantic segmentation | BEVFormer / StreamPETR | +| Planning Model | Trajectory planning | Perception results/map | Driving trajectory | PnPNet / UniAD | +| Coverage Model | Scenario coverage analysis | Test results | Coverage metrics | Monte Carlo | +| Simulation Acceleration | Simulation speed optimization | Scenario complexity | Adaptive step size | RL | +| ODD Detection | Operational Design Domain recognition | Sensor data | ODD compliance | Classifier | --- -## 8. 安全与合规 +## 8. Security and Compliance -## 8.1 行业法规与标准 +## 8.1 Industry Regulations and Standards -| 法规/标准 | 适用范围 | 架构要求 | +| Regulation/Standard | Applicable Scope | Architecture Requirements | |:---|:---|:---| -| ISO 26262 | 功能安全 | ASIL-D 级仿真验证 | -| ISO 21448 (SOTIF) | 预期功能安全 | 长尾场景覆盖 | -| UN R157 | 自动车道保持 | 法规场景测试 | -| GB/T 标准 | 中国自动驾驶标准 | 国标合规测试 | -| NHTSA / Euro NCAP | 安全评级 | 碰撞/紧急场景测试 | -| 数据安全法 | 仿真数据安全 | 场景数据保护 | +| ISO 26262 | Functional safety | ASIL-D level simulation validation | +| ISO 21448 (SOTIF) | Intended functional safety | Long-tail scenario coverage | +| UN R157 | Automatic lane keeping | Regulatory scenario testing | +| GB/T Standards | Chinese autonomous driving standards | National standard compliance testing | +| NHTSA / Euro NCAP | Safety ratings | Collision/emergency scenario testing | +| Data Security Law | Simulation data security | Scenario data protection | -## 8.2 安全架构要点 +## 8.2 Security Architecture Key Points -- **仿真隔离**: SIL/HIL 仿真环境与生产网络隔离 -- **场景数据保护**: 高精地图和场景数据加密存储 -- **模型版本管理**: 算法模型版本化，每次测试绑定具体版本 -- **审计追踪**: 所有仿真测试结果完整追溯 +- **Simulation Isolation**: SIL/HIL simulation environments isolated from production networks +- **Scenario Data Protection**: High-precision maps and scenario data encrypted at rest +- **Model Version Management**: Algorithm model versioning, each test bound to specific version +- **Audit Trail**: All simulation test results fully traceable --- -## 9. 最佳实践 +## 9. Best Practices -1. **GPU 弹性调度**: 仿真峰值需要数百个 GPU，闲时释放，使用 K8s 弹性伸缩 -2. **场景参数化**: 将天气/光照/行人行为等参数化，自动探索边界条件 -3. **仿真加速模式**: 对非关键场景使用快进模式（10x-100x 实时），关键场景实时运行 -4. **回归测试自动化**: 每次算法更新自动运行回归测试场景集 -5. **传感器噪声建模**: 仿真传感器加入真实噪声模型，缩小仿真与现实差距 -6. **多传感器融合仿真**: 同时仿真相机+LiDAR+Radar，验证融合算法 -7. **覆盖率驱动测试**: 基于 ODD（运行设计域）定义覆盖率指标，确保场景覆盖 -8. **HIL 实时性保障**: HIL 测试确保端到端延迟 < 100ms -9. **仿真结果可视化**: 失败场景 3D 回放，便于工程师分析根因 -10. **数据闭环自动化**: 仿真失败→数据提取→模型训练→重新验证全链路自动化 +1. **GPU Elastic Scheduling**: Simulation peaks require hundreds of GPUs, released during idle, using K8s auto-scaling +2. **Scenario Parameterization**: Parameterize weather/lighting/pedestrian behavior, automatically explore boundary conditions +3. **Simulation Acceleration Mode**: Use fast-forward mode for non-critical scenarios (10x-100x real-time), critical scenarios run in real-time +4. **Regression Test Automation**: Each algorithm update automatically runs regression test scenario set +5. **Sensor Noise Modeling**: Inject realistic noise models into simulated sensors, narrowing simulation-reality gap +6. **Multi-Sensor Fusion Simulation**: Simultaneously simulate camera+LiDAR+Radar, validate fusion algorithms +7. **Coverage-Driven Testing**: Define coverage metrics based on ODD (Operational Design Domain), ensure scenario coverage +8. **HIL Real-Time Guarantee**: HIL testing ensures end-to-end latency < 100ms +9. **Simulation Result Visualization**: 3D replay of failure scenarios for engineers' root cause analysis +10. **Data Closed-Loop Automation**: Full-chain automation of simulation failure → data extraction → model training → revalidation --- -## 10. 反模式 +## 10. Anti-Patterns -1. **忽视仿真可信度**: 仿真结果与真实路测差异大，过度依赖仿真结论。应持续校准仿真 -2. **场景库不更新**: 场景库不持续扩充，覆盖不了新的长尾场景。应持续从路测数据挖掘新场景 -3. **GPU 资源浪费**: 仿真任务排队等待，GPU 利用率低。应使用弹性调度和优先级管理 -4. **仅做 SIL 不做 HIL**: 只做软件在环测试，忽视硬件实时性验证。应 SIL + HIL 结合 -5. **忽视仿真一致性**: 不同仿真引擎结果不一致。应统一仿真标准和校准流程 +1. **Ignoring Simulation Fidelity**: Large discrepancy between simulation results and real road testing, over-relying on simulation conclusions. Should continuously calibrate simulations +2. **Scenario Library Not Updated**: Scenario library not continuously expanded, unable to cover new long-tail scenarios. Should continuously extract new scenarios from road testing data +3. **GPU Resource Waste**: Simulation tasks queued waiting, low GPU utilization. Should use elastic scheduling and priority management +4. **SIL Only, No HIL**: Only software-in-the-loop testing, ignoring hardware real-time validation. Should combine SIL + HIL +5. **Ignoring Simulation Consistency**: Different simulation engine results inconsistent. Should unify simulation standards and calibration processes --- -## 11. 参考资源 +## 11. Reference Resources -- [CARLA 开源仿真器](https://carla.org/) -- [ISO 26262 功能安全标准](https://www.iso.org/standard/68383.html) -- [ISO 21448 SOTIF 标准](https://www.iso.org/standard/71275.html) +- [CARLA Open Simulator](https://carla.org/) +- [ISO 26262 Functional Safety Standard](https://www.iso.org/standard/68383.html) +- [ISO 21448 SOTIF Standard](https://www.iso.org/standard/71275.html) - [NHTSA Automated Vehicles](https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety) -- [OpenSCENARIO 格式规范](https://www.asam.net/standards/detail/openscenario/) -- [阿里云 GPU 实例文档](https://help.aliyun.com/document_detail/2539917.html) +- [OpenSCENARIO Format Specification](https://www.asam.net/standards/detail/openscenario/) +- [Alibaba Cloud GPU Instance Documentation](https://help.aliyun.com/document_detail/2539917.html) --- -**维护者**: 阿里云解决方案架构师团队 | **许可证**: MIT +**Maintainers**: Alibaba Cloud Solution Architects Team | **License**: MIT --- -## Obsidian 相关文档 +## Obsidian Related Documentation - topic-application-architecture MOC -- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic 应用层架构设计最佳实践]] -- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|电商系统 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|小程序平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/03-cms-architecture.md|内容管理系统 CMS 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/04-im-rtc-architecture.md|实时通信 IM/RTC 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/05-online-education-architecture.md|在线教育平台 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/06-fintech-architecture.md|金融科技FinTech Kubernetes生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md|物联网 IoT 平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/08-ai-ml-inference-architecture.md|AI/ML 推理服务 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/09-gaming-backend-architecture.md|游戏后端 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|社交媒体平台Kubernetes生产架构设计]] +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] +- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|E-commerce Systems Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|Mini Program Platform Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/03-cms-architecture.md|Content Management System CMS Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/04-im-rtc-architecture.md|Real-time Communication IM/RTC Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/05-online-education-architecture.md|Online Education Platform Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/06-fintech-architecture.md|Financial Technology FinTech Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md|Internet of Things IoT Platform Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/08-ai-ml-inference-architecture.md|AI/ML Inference Service Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/09-gaming-backend-architecture.md|Gaming Backend Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|Social Media Platform Kubernetes Production Architecture Design]] ## See Also diff --git a/tree/application/architecture/brain-computer-interface.md b/tree/application/architecture/brain-computer-interface.md index ba03299..d5d4975 100644 --- a/tree/application/architecture/brain-computer-interface.md +++ b/tree/application/architecture/brain-computer-interface.md @@ -1,52 +1,7 @@ ----title: Brain-Computer Interface Architecture Design — Alibaba Cloud Perspective +--- +title: Brain-Computer Interface Architecture Design — Alibaba Cloud Perspective description: 'Brain-Computer Interface Architecture Design' summary: 'Brain-Computer Interface Architecture Design' -category: general -tags: -- architecture -- best-practice -- opa -- job -- ingress -- rbac -- networkpolicy -- gpu -- nvidia -tier: supporting -created: '2026-05-23' -last_updated: 2026-05 -difficulty: intermediate -reading_level: intermediate -audience: -- All engineers -estimated_read_time: 15min -intent_queries: -- What is brain-computer interface architecture design — Alibaba Cloud perspective -- How to design brain-computer interface architecture — Alibaba Cloud perspective -- Kubernetes 20 application patterns best practices -trigger_keywords: -- Brain-computer interface architecture design -- Alibaba Cloud perspective -- application -- patterns -prerequisites: -- kubectl-basics -- prometheus-basics -- gpu-scheduling-basics -- policy-basics -original_language: Chinese -authors: -- name: Dillan Teagle - role: contributor -source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/brain-computer-interface.md ---- - -> **Production Environment Security Notice** -> -> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk), Yellow (medium risk), Green (low risk/read-only). - -title: Brain-Computer Interface Architecture Design -description: '# Brain-Computer Interface Architecture Design — Alibaba Cloud Perspective' category: application-architecture tags: - k8s @@ -54,12 +9,11 @@ tags: - industry - opa - job -- [[Ingress|ingress]] +- ingress - rbac -- [[NetworkPolicy|networkpolicy]] +- networkpolicy - gpu - nvidia -last_updated: 2026-05-18 difficulty: expert reading_level: expert audience: @@ -98,8 +52,19 @@ k8s_versions: - '1.30' - '1.31' - '1.32' +created: '2026-05-23' +last_updated: 2026-05-18 +original_language: Chinese +authors: +- name: Dillan Teagle + role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/brain-computer-interface.md --- +> **Production Environment Security Notice** +> +> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk), Yellow (medium risk), Green (low risk/read-only). + # Brain-Computer Interface Architecture Design — Alibaba Cloud Perspective > **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-05-18 @@ -469,5 +434,4 @@ Storing neural data in plaintext poses serious privacy leakage risks. - 68-quantum-computing-cloud - 69-6g-core-network - diff --git a/tree/application/architecture/carbon-capture.md b/tree/application/architecture/carbon-capture.md index 752e1c2..d07004e 100644 --- a/tree/application/architecture/carbon-capture.md +++ b/tree/application/architecture/carbon-capture.md @@ -1,79 +1,37 @@ ----title: 碳捕集利用与封存（CCUS）架构设计 — 阿里云视角 -description: 'title: 碳捕集利用与封存CCUS架构设计' -summary: 'title: 碳捕集利用与封存CCUS架构设计' -category: general -tags: -- architecture -- best-practice -- rag -tier: supporting -created: '2026-05-23' -last_updated: 2026-05 -difficulty: intermediate -reading_level: intermediate -audience: -- 所有工程师 -estimated_read_time: 5min -intent_queries: -- 碳捕集利用与封存（CCUS）架构设计 — 阿里云视角 是什么 -- 如何 碳捕集利用与封存（CCUS）架构设计 — 阿里云视角 -- Kubernetes 20 application patterns 最佳实践 -trigger_keywords: -- 碳捕集利用与封存 -- CCUS -- 架构设计 -- 阿里云视角 -- application -- patterns -prerequisites: -- kubectl-basics -- prometheus-basics -authors: -- name: Dillan Teagle - role: contributor - --- - -> **生产环境安全提示** -> -> 本文档包含可直接执行的运维命令。执行前请确认：当前目标集群与 Namespace 是否正确；是否具备足够的 RBAC 权限；是否已在非生产环境验证。命令风险等级标注：🔴 高风险（可能造成数据丢失或服务中断）、🟡 中风险（会修改集群状态，但通常可回滚）、🟢 低风险/只读（信息收集，无副作用）。 - - - - -title: 碳捕集利用与封存CCUS架构设计 -description: '# 碳捕集利用与封存（CCUS）架构设计 — 阿里云视角' +title: Carbon Capture, Utilization and Storage (CCUS) Architecture Design — Alibaba Cloud Perspective +description: 'Carbon Capture, Utilization and Storage CCUS Architecture Design' +summary: 'Carbon Capture, Utilization and Storage CCUS Architecture Design' category: application-architecture tags: - k8s - architecture - industry - rag -last_updated: '2026-05-18' difficulty: advanced reading_level: advanced audience: -- 能源行业架构师 -- 碳中和解决方案工程师 -- 工业互联网开发者 -- 阿里云解决方案架构师 +- Energy industry architects +- Carbon neutral solution engineers +- Industrial Internet developers +- Alibaba Cloud solution architects estimated_read_time: 5min intent_queries: -- 碳捕集CCUS系统架构设计 -- CCUS区块链MRV碳核算 -- CO2封存泄漏监测系统 -- AI优化碳捕集工艺 -- 碳交易对接架构 +- CCUS system architecture design +- CCUS blockchain MRV carbon accounting +- CO2 storage leak detection system +- AI optimization of carbon capture process +- Carbon trading interface architecture trigger_keywords: - CCUS -- 碳捕集 -- 碳封存 -- 碳利用 +- Carbon capture +- Carbon storage +- Carbon utilization - MRV -- 碳核算 -- 区块链存证 -- 碳交易 -- 地质封存 +- Carbon accounting +- Blockchain notarization +- Carbon trading +- Geological storage - DAC related_domains: - domain-01-cluster-fundamentals @@ -91,106 +49,117 @@ k8s_versions: - '1.30' - '1.31' - '1.32' +created: '2026-05-23' +last_updated: 2026-05-18 +original_language: Chinese +authors: +- name: Dillan Teagle + role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/carbon-capture.md --- -# 碳捕集利用与封存（CCUS）架构设计 — 阿里云视角 +> **Production Environment Security Notice** +> +> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk), Yellow (medium risk), Green (low risk/read-only). + +# Carbon Capture, Utilization and Storage (CCUS) Architecture Design — Alibaba Cloud Perspective -> **适用版本**: [[Kubernetes|Kubernetes]] v1.29 - v1.33 | **最后更新**: 2026-04-24 -> **作者**: 阿里云解决方案架构师 | **标签**: `#CCUS` `#碳捕集` `#碳封存` `#碳利用` `#阿里云` +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 +> **Authors**: Alibaba Cloud Solution Architects | **Tags**: `#CCUS` `#CarbonCapture` `#CarbonStorage` `#CarbonUtilization` `#AlibabCloud` --- -## 目录 +## Table of Contents -1. [概述](#1-概述) -2. [设计原则](#2-设计原则) -3. [架构模式](#3-架构模式) -4. [实现示例](#4-实现示例) -5. [在 Kubernetes 上的部署](#5-在-kubernetes-上的部署) -6. [最佳实践](#6-最佳实践) -7. [反模式](#7-反模式) -8. [参考资源](#8-参考资源) +1. [Overview](#1-overview) +2. [Design Principles](#2-design-principles) +3. [Architecture Patterns](#3-architecture-patterns) +4. [Implementation Examples](#4-implementation-examples) +5. [Kubernetes Deployment](#5-kubernetes-deployment) +6. [Best Practices](#6-best-practices) +7. [Anti-Patterns](#7-anti-patterns) +8. [Reference Resources](#8-reference-resources) --- -## 1. 概述 +## 1. Overview -碳捕集利用与封存（Carbon Capture, Utilization and Storage，CCUS）是实现碳中和目标不可或缺的关键技术路径。CCUS 将工业排放源（燃煤电厂、水泥厂、钢铁厂、化工厂等）产生的 CO₂ 捕集、运输，要么用于工业利用（化工原料、矿化、强化采油 EOR），要么封存在深层地质构造中（咸水层、废弃油气田），实现 CO₂ 与大气的长期隔离。 +Carbon Capture, Utilization and Storage (CCUS) is a critical technology pathway indispensable for achieving carbon neutrality goals. CCUS captures CO₂ from industrial emission sources (coal-fired power plants, cement plants, steel mills, chemical plants, etc.), transports it, either utilizing it in industrial applications (chemical raw materials, mineralization, enhanced oil recovery EOR), or storing it in deep geological formations (saline aquifers, abandoned oil/gas fields), achieving long-term isolation of CO₂ from the atmosphere. -CCUS 信息化平台的核心价值在于：**安全监控**（地质封存 CO₂ 泄漏监测、管道安全监控）、**碳核算**（MRV 监测报告核查体系，确保碳减排量可测量、可报告、可核查）、**工艺优化**（AI 优化捕集能耗，降低运行成本）、**碳交易对接**（将核证的碳减排量对接碳交易市场）。 +The core value of CCUS informatization platforms lies in: **Safety monitoring** (geological storage CO₂ leak detection, pipeline safety monitoring), **Carbon accounting** (MRV monitoring, reporting, and verification system ensuring carbon reduction is measurable, reportable, and verifiable), **Process optimization** (AI-optimized capture energy consumption, reducing operation costs), **Carbon trading interface** (connecting verified carbon reduction to carbon trading markets). -### 1.1 行业背景 +### 1.1 Industry Background -| 挑战 | 说明 | 架构影响 | +| Challenge | Description | Architecture Impact | |:---|:---|:---| -| 高能耗 | 捕集过程能耗高（占发电量 15-30%） | AI 优化控制 + 实时调节 | -| 地质封存 | CO₂ 长期安全封存 1000 年+ | 实时监测网络 + 地质模型 | -| 泄漏风险 | 地下封存 CO₂ 泄漏到地表 | 传感器网格 + 异常检测 | -| 碳核算 | MRV 合规审计 | 区块链存证 + 数据溯源 | -| 经济性 | 高成本制约推广 | 碳交易对接 + 收益优化 | +| High Energy Consumption | Capture process consumes high energy (15-30% of power output) | AI optimization + real-time adjustment | +| Geological Storage | Long-term safe CO₂ storage 1000+ years | Real-time monitoring network + geological models | +| Leak Risk | Underground stored CO₂ leaking to surface | Sensor grid + anomaly detection | +| Carbon Accounting | MRV compliance audit | Blockchain notarization + data traceability | +| Economics | High cost limiting promotion | Carbon trading interface + revenue optimization | -### 1.2 核心场景 +### 1.2 Core Scenarios -- **燃烧后捕集**: 烟气 CO₂ 化学吸收/膜分离/固体吸附 -- **直接空气捕集 DAC**: 从大气中直接提取 CO₂ -- **CO₂ 运输**: 管道/槽车/船舶运输监控 -- **地质封存**: 咸水层/废弃油气田注入封存与长期监测 -- **CO₂ 利用**: 化工原料/矿化/EOR/生物利用 +- **Post-Combustion Capture**: Flue gas CO₂ chemical absorption/membrane separation/solid adsorption +- **Direct Air Capture (DAC)**: Direct extraction of CO₂ from atmosphere +- **CO₂ Transportation**: Pipeline/tanker truck/ship transportation monitoring +- **Geological Storage**: Saline aquifer/abandoned oil/gas field injection and long-term monitoring +- **CO₂ Utilization**: Chemical raw materials/mineralization/EOR/biological utilization --- -## 2. 设计原则 +## 2. Design Principles -### 2.1 安全第一原则 +### 2.1 Safety First Principle -地质封存的 CO₂ 泄漏可能导致地下水污染、土壤酸化、地表变形等环境风险。监测系统需要 24/7 运行，传感器网络全覆盖，异常检测秒级告警。 +Leaked CO₂ from geological storage may cause groundwater contamination, soil acidification, ground deformation and other environmental risks. Monitoring systems must operate 24/7, sensor networks provide full coverage, anomaly detection achieves second-level alerting. -### 2.2 数据可信原则 +### 2.2 Data Trustworthiness Principle -碳减排量的核算需要可审计、不可篡改的数据。采用区块链技术将关键数据（捕集量、运输量、封存量）上链存证，确保 MRV 数据的公信力。 +Carbon reduction accounting requires auditable, tamper-proof data. Employ blockchain technology to notarize key data (capture volume, transportation volume, storage volume), ensuring MRV data credibility. -### 2.3 全链条追溯原则 +### 2.3 Full Chain Traceability Principle -CCUS 涵盖捕集-运输-利用/封存全链条，每吨 CO₂ 从排放源到最终归宿需要全程追溯。建立统一的碳追踪 ID，关联全链条数据。 +CCUS encompasses the entire chain from capture-transportation-utilization/storage. Every ton of CO₂ from emission source to final destination requires full process tracing. Establish unified carbon tracking ID linking full-chain data. --- -## 3. 架构模式 +## 3. Architecture Patterns -### 3.1 CCUS 平台全景架构 +### 3.1 CCUS Platform Full-Landscape Architecture ```mermaid graph TB - subgraph 排放源 - E1[燃煤电厂] - E2[水泥厂] - E3[钢铁厂] + subgraph Emission Sources + E1[Coal-fired Power Plants] + E2[Cement Plants] + E3[Steel Mills] end - subgraph 捕集监控 - C1[吸收塔监控] - C2[再生塔监控] - C3[压缩液化监控] + subgraph Capture Monitoring + C1[Absorption Tower Monitoring] + C2[Regeneration Tower Monitoring] + C3[Compression Liquefaction Monitoring] end - subgraph 运输监控 - T1[管道监控] - T2[泄漏检测] - T3[流量计量] + subgraph Transportation Monitoring + T1[Pipeline Monitoring] + T2[Leak Detection] + T3[Flow Metering] end - subgraph 封存监控 - S1[注入压力监测] - S2[地震监测] - S3[地下水监测] - S4[地表变形监测] + subgraph Storage Monitoring + S1[Injection Pressure Monitoring] + S2[Seismic Monitoring] + S3[Groundwater Monitoring] + S4[Ground Deformation Monitoring] end - subgraph 平台层 - P1[实时监控] - P2[MRV 碳核算] - P3[区块链存证] - P4[碳交易对接] + subgraph Platform Layer + P1[Real-Time Monitoring] + P2[MRV Carbon Accounting] + P3[Blockchain Notarization] + P4[Carbon Trading Interface] end E1 & E2 & E3 --> C1 & C2 & C3 @@ -201,9 +170,9 @@ graph TB --- -## 4. 实现示例 +## 4. Implementation Examples -### 4.1 封存泄漏监测 +### 4.1 Storage Leak Detection ```python from dataclasses import dataclass @@ -240,7 +209,7 @@ class LeakageDetector: --- -## 5. 在 Kubernetes 上的部署 +## 5. Kubernetes Deployment ```yaml apiVersion: apps/v1 @@ -277,62 +246,52 @@ spec: --- -## 6. 最佳实践 +## 6. Best Practices -- **传感器冗余**: 关键监测点部署多个传感器交叉验证 -- **区块链存证**: 捕集量/封存量数据定期上链 -- **地质模型更新**: 根据监测数据持续更新地下地质模型 -- **AI 工艺优化**: 使用强化学习优化捕集过程能耗 +- **Sensor Redundancy**: Deploy multiple sensors at critical monitoring points for cross-verification +- **Blockchain Notarization**: Regularly notarize capture/storage volume data on-chain +- **Geological Model Updates**: Continuously update underground geological models based on monitoring data +- **AI Process Optimization**: Use reinforcement learning to optimize capture process energy consumption -## 7. 反模式 +## 7. Anti-Patterns -- **忽视长期监测**: 封存后停止监测。应建立 30 年以上的长期监测机制 -- **单点传感器**: 关键位置只部署一个传感器。应冗余部署 -- **数据不上链**: 碳核算数据存储在中心化数据库，公信力不足。应区块链存证 +- **Ignoring Long-Term Monitoring**: Stopping monitoring after storage. Should establish 30+ year long-term monitoring mechanisms +- **Single-Point Sensors**: Deploying only one sensor at critical locations. Should deploy sensors redundantly +- **Non-Notarized Data**: Storing carbon accounting data in centralized databases with insufficient credibility. Should notarize on blockchain --- -## 8. 参考资源 +## 8. Reference Resources -### 8.1 阿里云组件映射 +### 8.1 Alibaba Cloud Component Mapping -| 功能域 | **阿里云云原生方案** | +| Functional Domain | **Alibaba Cloud Cloud-Native Solution** | |:---|:---| -| 容器平台 | **ACK Pro** | +| Container Platform | **ACK Pro** | | AI | **PAI** | -| 时序数据库 | **Lindorm TSDB** | -| 区块链 | **蚂蚁链 BaaS** | -| 数据库 | **PolarDB** | -| 可观测性 | **ARMS + SLS** | +| Time-Series Database | **Lindorm TSDB** | +| Blockchain | **Ant Chain BaaS** | +| Database | **PolarDB** | +| Observability | **ARMS + SLS** | -### 8.2 生产检查清单 +### 8.2 Production Checklist -- [ ] 捕集效率 > 90% -- [ ] 封存泄漏监测全覆盖 -- [ ] MRV 数据上链存证 -- [ ] 应急响应预案演练 -- [ ] 环境影响评估合规 +- [ ] Capture efficiency > 90% +- [ ] Storage leak detection full coverage +- [ ] MRV data notarized on-chain +- [ ] Emergency response drills completed +- [ ] Environmental impact assessment compliance --- -**维护者**: 阿里云解决方案架构师团队 | **许可证**: MIT +**Maintainers**: Alibaba Cloud Solution Architects Team | **License**: MIT --- -## Obsidian 相关文档 +## Obsidian Related Documents - topic-application-architecture MOC -- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic 应用层架构设计最佳实践]] -- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|电商系统 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|小程序平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/03-cms-architecture.md|内容管理系统 CMS 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/04-im-rtc-architecture.md|实时通信 IM/RTC 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/05-online-education-architecture.md|在线教育平台 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/06-fintech-architecture.md|金融科技FinTech Kubernetes生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md|物联网 IoT 平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/08-ai-ml-inference-architecture.md|AI/ML 推理服务 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/09-gaming-backend-architecture.md|游戏后端 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|社交媒体平台Kubernetes生产架构设计]] +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] ## See Also @@ -341,9 +300,4 @@ spec: - 01-ecommerce-architecture - 02-mini-program-architecture -## Related - -- topic-application-architecture MOC — Cross-reference - - diff --git a/tree/application/architecture/carbon-esg-management.md b/tree/application/architecture/carbon-esg-management.md index 0bdc6ac..4d2cf88 100644 --- a/tree/application/architecture/carbon-esg-management.md +++ b/tree/application/architecture/carbon-esg-management.md @@ -1,78 +1,36 @@ ----title: 碳资产管理与 ESG 架构设计 — 阿里云视角 -description: 'title: 碳资产管理与 ESG 架构设计' -summary: 'title: 碳资产管理与 ESG 架构设计' -category: general -tags: -- architecture -- best-practice -- postgresql -tier: supporting -created: '2026-05-23' -last_updated: 2026-05 -difficulty: intermediate -reading_level: intermediate -audience: -- 所有工程师 -estimated_read_time: 5min -intent_queries: -- 碳资产管理与 ESG 架构设计 — 阿里云视角 是什么 -- 如何 碳资产管理与 ESG 架构设计 — 阿里云视角 -- Kubernetes 20 application patterns 最佳实践 -trigger_keywords: -- 碳资产管理与 -- ESG -- 架构设计 -- 阿里云视角 -- application -- patterns -prerequisites: -- kubectl-basics -- prometheus-basics -authors: -- name: Dillan Teagle - role: contributor - --- - -> **生产环境安全提示** -> -> 本文档包含可直接执行的运维命令。执行前请确认：当前目标集群与 Namespace 是否正确；是否具备足够的 RBAC 权限；是否已在非生产环境验证。命令风险等级标注：🔴 高风险（可能造成数据丢失或服务中断）、🟡 中风险（会修改集群状态，但通常可回滚）、🟢 低风险/只读（信息收集，无副作用）。 - - - - -title: 碳资产管理与 ESG 架构设计 -description: '# 碳资产管理与 ESG 架构设计 — 阿里云视角' +title: Carbon Asset Management and ESG Architecture Design — Alibaba Cloud Perspective +description: 'Carbon Asset Management and ESG Architecture Design' +summary: 'Carbon Asset Management and ESG Architecture Design' category: application-architecture tags: - k8s - architecture - industry - postgresql -last_updated: 2026-05-18 difficulty: advanced reading_level: advanced audience: -- 可持续发展架构师 -- 企业数字化转型负责人 -- 区块链开发工程师 +- Sustainable development architects +- Enterprise digital transformation leaders +- Blockchain development engineers estimated_read_time: 5min intent_queries: -- 企业碳中和 [[Kubernetes|Kubernetes]] 碳核算引擎 -- 区块链碳排放存证溯源方案 -- ESG报告自动化生成系统 -- 碳交易与碳资产管理平台 -- 供应链碳足迹追踪 +- Enterprise carbon neutrality Kubernetes carbon accounting engine +- Blockchain carbon emission notarization and traceability solution +- ESG report automated generation system +- Carbon trading and carbon asset management platform +- Supply chain carbon footprint tracking trigger_keywords: -- 碳中和 -- ESG环境社会治理 -- 碳资产管理 -- 碳核算 -- 碳足迹 -- 区块链存证 -- 碳交易 -- 蚂蚁链BaaS -- 碳信用CCER +- Carbon neutrality +- ESG environmental social governance +- Carbon asset management +- Carbon accounting +- Carbon footprint +- Blockchain notarization +- Carbon trading +- Ant Chain BaaS +- Carbon credits CCER related_domains: - domain-03-networking-traffic - domain-10-troubleshooting-diagnostics @@ -85,85 +43,96 @@ k8s_versions: - '1.30' - '1.31' - '1.32' +created: '2026-05-23' +last_updated: 2026-05-18 +original_language: Chinese +authors: +- name: Dillan Teagle + role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/carbon-esg-management.md --- -# 碳资产管理与 ESG 架构设计 — 阿里云视角 +> **Production Environment Security Notice** +> +> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk), Yellow (medium risk), Green (low risk/read-only). -> **适用版本**: Kubernetes v1.29 - v1.33 | **最后更新**: 2026-04-24 -> **作者**: 阿里云解决方案架构师 | **标签**: `#碳中和` `#ESG` `#碳资产` `#阿里云` +# Carbon Asset Management and ESG Architecture Design — Alibaba Cloud Perspective + +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 +> **Authors**: Alibaba Cloud Solution Architects | **Tags**: `#CarbonNeutrality` `#ESG` `#CarbonAsset` `#AlibabCloud` --- -## 目录 +## Table of Contents -1. [行业背景](#1-行业背景) -2. [业务架构](#2-业务架构) -3. [技术架构](#3-技术架构) -4. [核心数据流](#4-核心数据流) -5. [安全与合规](#5-安全与合规) -6. [可观测性](#6-可观测性) -7. [阿里云组件映射](#7-阿里云组件映射) -8. [生产检查清单](#8-生产检查清单) +1. [Industry Background](#1-industry-background) +2. [Business Architecture](#2-business-architecture) +3. [Technical Architecture](#3-technical-architecture) +4. [Core Data Flows](#4-core-data-flows) +5. [Security and Compliance](#5-security-and-compliance) +6. [Observability](#6-observability) +7. [Alibaba Cloud Component Mapping](#7-alibaba-cloud-component-mapping) +8. [Production Checklist](#8-production-checklist) --- -## 1. 行业背景 +## 1. Industry Background -### 1.1 业务特点 +### 1.1 Business Characteristics -碳资产管理与 ESG（环境、社会、治理）是企业可持续发展的核心： +Carbon asset management and ESG (Environment, Social, Governance) are core to enterprise sustainable development: -| 挑战 | 说明 | 架构影响 | +| Challenge | Description | Architecture Impact | |:---|:---|:---| -| 多源数据采集 | 能耗/排放/供应链碳数据 | IoT + 数据集成 | -| 碳核算复杂 | 范围一/二/三排放计算 | 规则引擎 + 计算模型 | -| 合规报送 | 欧盟 CBAM / 国内碳市场 | 数据血缘 + 审计追踪 | -| 碳交易 | CCER / 碳配额交易 | 区块链存证 | -| ESG 评级 | 多标准框架披露 | 数据集市 + 报告引擎 | +| Multi-Source Data Collection | Energy/emission/supply chain carbon data | IoT + data integration | +| Carbon Accounting Complexity | Scope 1/2/3 emissions calculation | Rules engine + calculation models | +| Compliance Reporting | EU CBAM / Domestic carbon market | Data lineage + audit trails | +| Carbon Trading | CCER / Carbon quota trading | Blockchain notarization | +| ESG Rating | Multi-standard framework disclosure | Data mart + reporting engine | -### 1.2 核心场景 +### 1.2 Core Scenarios -- **碳盘查**: 企业全价值链碳排放核算 -- **碳监测**: 实时能耗与排放监测 -- **碳交易**: 碳配额/CCER 交易管理 -- **ESG 报告**: 自动化 ESG 信息披露 -- **绿色金融**: 碳足迹与绿色信贷挂钩 +- **Carbon Inventory**: Enterprise full value chain carbon emissions accounting +- **Carbon Monitoring**: Real-time energy and emissions monitoring +- **Carbon Trading**: Carbon quota/CCER trading management +- **ESG Reporting**: Automated ESG information disclosure +- **Green Finance**: Carbon footprint linked to green credit --- -## 2. 业务架构 +## 2. Business Architecture -### 2.1 碳资产管理全景架构 +### 2.1 Carbon Asset Management Full-Landscape Architecture ```mermaid graph TB - subgraph 数据采集层 - I1[能耗监测 IoT] - I2[供应链系统] - I3[生产管理系统] - I4[物流追踪] - I5[办公系统] + subgraph Data Collection + I1[Energy Monitoring IoT] + I2[Supply Chain Systems] + I3[Production Management Systems] + I4[Logistics Tracking] + I5[Office Systems] end - subgraph 碳核算层 - C1[排放因子库] - C2[碳核算引擎] - C3[范围一/二/三计算] - C4[碳足迹追踪] + subgraph Carbon Accounting + C1[Emission Factor Library] + C2[Carbon Accounting Engine] + C3[Scope 1/2/3 Calculation] + C4[Carbon Footprint Tracking] end - subgraph 资产管理层 - A1[碳配额管理] - A2[CCER 项目管理] - A3[碳交易撮合] - A4[碳金融] + subgraph Asset Management + A1[Carbon Quota Management] + A2[CCER Project Management] + A3[Carbon Trading Matching] + A4[Carbon Finance] end - subgraph ESG 披露层 - E1[ESG 数据集市] - E2[报告引擎] - E3[评级对接] - E4[投资者门户] + subgraph ESG Disclosure + E1[ESG Data Mart] + E2[Reporting Engine] + E3[Rating Interface] + E4[Investor Portal] end I1 & I2 & I3 & I4 & I5 --> C1 & C2 & C3 & C4 @@ -172,37 +141,37 @@ graph TB E1 --> E2 & E3 & E4 ``` -### 2.2 碳核算流程 +### 2.2 Carbon Accounting Process ```mermaid sequenceDiagram - participant IOT as 能耗 IoT - participant DATA as 数据采集平台 - participant CALC as 碳核算引擎 - participant FACTOR as 排放因子库 - participant LEDGER as 碳账本 - participant REPORT as 报告系统 - - IOT->>DATA: 实时能耗数据上报 - DATA->>DATA: 数据清洗/校验 - DATA->>CALC: 推送核算数据 - CALC->>FACTOR: 获取排放因子 - FACTOR-->>CALC: 返回因子值 - CALC->>CALC: 计算 CO2e - CALC->>LEDGER: 写入碳账本 - LEDGER->>LEDGER: 区块链存证 - CALC->>REPORT: 生成碳排报告 - REPORT-->>CALC: 报告确认 + participant IOT as Energy IoT + participant DATA as Data Collection + participant CALC as Carbon Accounting Engine + participant FACTOR as Emission Factor Library + participant LEDGER as Carbon Ledger + participant REPORT as Reporting System + + IOT->>DATA: Real-time energy data reporting + DATA->>DATA: Data cleansing/validation + DATA->>CALC: Push accounting data + CALC->>FACTOR: Get emission factors + FACTOR-->>CALC: Return factor values + CALC->>CALC: Calculate CO2e + CALC->>LEDGER: Write to carbon ledger + LEDGER->>LEDGER: Blockchain notarization + CALC->>REPORT: Generate carbon report + REPORT-->>CALC: Report confirmation ``` --- -## 3. 技术架构 +## 3. Technical Architecture -### 3.1 K8s 部署 +### 3.1 Kubernetes Deployment ```yaml -# 碳核算引擎 Deployment +# Carbon Accounting Engine Deployment apiVersion: apps/v1 kind: Deployment metadata: @@ -239,82 +208,72 @@ spec: --- -## 4. 核心数据流 +## 4. Core Data Flows -### 4.1 供应链碳足迹追踪 +### 4.1 Supply Chain Carbon Footprint Tracking ```mermaid flowchart LR - A[原材料采购] --> B[生产制造] - B --> C[物流运输] - C --> D[销售使用] - D --> E[回收处置] - A & B & C & D & E --> F[碳足迹计算] - F --> G[区块链存证] - G --> H[ESG 报告] + A[Raw Material Procurement] --> B[Manufacturing] + B --> C[Logistics Transportation] + C --> D[Sales and Use] + D --> E[Recycling and Disposal] + A & B & C & D & E --> F[Carbon Footprint Calculation] + F --> G[Blockchain Notarization] + G --> H[ESG Report] ``` --- -## 5. 安全与合规 +## 5. Security and Compliance -- **数据可信**: 区块链存证防篡改 -- **合规报送**: 欧盟 CSRD / 国内碳市场 -- **审计追踪**: 全链路数据血缘 +- **Data Trustworthiness**: Blockchain notarization prevents tampering +- **Compliance Reporting**: EU CSRD / Domestic carbon market +- **Audit Trails**: Full-chain data lineage --- -## 6. 可观测性 +## 6. Observability -- **碳核算延迟**: < 1h -- **数据准确率**: > 99.5% -- **系统可用性**: 99.9% +- **Carbon Accounting Latency**: < 1h +- **Data Accuracy Rate**: > 99.5% +- **System Availability**: 99.9% --- -## 7. 阿里云组件映射 +## 7. Alibaba Cloud Component Mapping -| 功能域 | **阿里云云原生方案** | +| Functional Domain | **Alibaba Cloud Cloud-Native Solution** | |:---|:---| -| 容器平台 | **ACK Pro** | -| IoT | **IoT 平台** | -| 数据库 | **PolarDB + Lindorm** | -| 实时计算 | **Flink** | -| 区块链 | **蚂蚁链 BaaS** | +| Container Platform | **ACK Pro** | +| IoT | **IoT Platform** | +| Database | **PolarDB + Lindorm** | +| Real-Time Computing | **Flink** | +| Blockchain | **Ant Chain BaaS** | | AI | **PAI** | -| 可观测性 | **ARMS + SLS** | -| 对象存储 | **OSS** | +| Observability | **ARMS + SLS** | +| Object Storage | **OSS** | --- -## 8. 生产检查清单 +## 8. Production Checklist -- [ ] 排放因子库版本管理 -- [ ] 碳核算模型准确性校验 -- [ ] 区块链存证完整性验证 -- [ ] ESG 报告自动化生成测试 -- [ ] 欧盟 CBAM 数据格式合规 +- [ ] Emission factor library version management +- [ ] Carbon accounting model accuracy validation +- [ ] Blockchain notarization integrity verification +- [ ] ESG report automated generation testing +- [ ] EU CBAM data format compliance --- -**维护者**: 阿里云解决方案架构师团队 | **许可证**: MIT +**Maintainers**: Alibaba Cloud Solution Architects Team | **License**: MIT --- -## Obsidian 相关文档 - -- topic-application-architecture KUDIG Database — Global MOC -- [[domain-20-application-patterns/topic-application-architecture/README.md|[[Topic 应用层架构设计最佳实践|Topic 应用层架构设计最佳实践]]]] -- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|电商系统 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|小程序平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/03-cms-architecture.md|内容管理系统 CMS 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/04-im-rtc-architecture.md|实时通信 IM/RTC 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/05-online-education-architecture.md|在线教育平台 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/06-fintech-architecture.md|金融科技FinTech Kubernetes生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md|物联网 IoT 平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/08-ai-ml-inference-architecture.md|AI/ML 推理服务 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/09-gaming-backend-architecture.md|游戏后端 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|社交媒体平台Kubernetes生产架构设计]] +## Obsidian Related Documents + +- topic-application-architecture MOC +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] ## See Also @@ -323,5 +282,4 @@ flowchart LR - 37-pet-economy - 38-supply-chain-finance - diff --git a/tree/application/architecture/cloudnative-devops-architecture.md b/tree/application/architecture/cloudnative-devops-architecture.md index 0d9c946..184455d 100644 --- a/tree/application/architecture/cloudnative-devops-architecture.md +++ b/tree/application/architecture/cloudnative-devops-architecture.md @@ -1,103 +1,44 @@ ----title: 云原生 DevOps 平台 Kubernetes 生产架构设计 -description: 'title: 云原生 DevOps 平台架构设计' -summary: 'title: 云原生 DevOps 平台架构设计' -category: general -tags: -- architecture -- best-practice -- daily-ops -- prometheus -- grafana -- jaeger -- istio -- helm -- argocd -- flux -tier: supporting -created: '2026-05-23' -last_updated: 2026-05 -difficulty: intermediate -reading_level: intermediate -audience: -- 所有工程师 -estimated_read_time: 15min -intent_queries: -- 云原生 DevOps 平台 Kubernetes 生产架构设计 是什么 -- 如何 云原生 DevOps 平台 Kubernetes 生产架构设计 -- Kubernetes 20 application patterns 最佳实践 -trigger_keywords: -- 云原生 -- DevOps -- 平台 -- Kubernetes -- 生产架构设计 -- application -- patterns -prerequisites: -- kubectl-basics -- prometheus-basics -- helm-basics -- service-mesh-basics -- monitoring-basics -- gitops-basics -- gpu-scheduling-basics -- policy-basics -- logging-basics -- tracing-basics -- observability-basics -authors: -- name: Dillan Teagle - role: contributor - --- - -> **生产环境安全提示** -> -> 本文档包含可直接执行的运维命令。执行前请确认：当前目标集群与 Namespace 是否正确；是否具备足够的 RBAC 权限；是否已在非生产环境验证。命令风险等级标注：🔴 高风险（可能造成数据丢失或服务中断）、🟡 中风险（会修改集群状态，但通常可回滚）、🟢 低风险/只读（信息收集，无副作用）。 - - - - -title: 云原生 DevOps 平台架构设计 -description: '# 云原生 DevOps 平台 [[Kubernetes|Kubernetes]] 生产架构设计' +title: Cloud-Native DevOps Platform Kubernetes Production Architecture Design +description: 'Cloud-Native DevOps Platform Architecture Design' +summary: 'Cloud-Native DevOps Platform Architecture Design' category: application-architecture tags: - k8s - architecture - industry -- [[Prometheus|prometheus]] +- prometheus - grafana -- [[Helm|helm]] +- helm - argocd - flux - docker - harbor -last_updated: 2026-05-18 difficulty: advanced reading_level: advanced audience: -- DevOps架构师 -- 平台工程师 -- SRE工程师 -- 云原生开发工程师 +- DevOps architects +- Platform engineers +- SRE engineers +- Cloud-native development engineers estimated_read_time: 5min intent_queries: -- 企业级 DevOps 平台 GitOps 架构设计 -- Kubernetes 多环境晋升 CI/CD 流水线 -- Argo CD 渐进式发布与灰度发布 -- SLSA 安全供应链架构 -- 阿里云 ACK 云效 DevOps +- Enterprise-grade DevOps Platform GitOps architecture design +- Kubernetes multi-environment promotion CI/CD pipeline +- Argo CD progressive release and canary deployment +- SLSA secure supply chain architecture +- Alibaba Cloud ACK Cloud Efficiency DevOps trigger_keywords: - DevOps - GitOps - ArgoCD - CI/CD -- 持续交付 -- 渐进式发布 -- 金丝雀发布 +- Continuous Delivery +- Progressive Release +- Canary Release - SLSA -- 安全供应链 -- 平台工程 +- Secure Supply Chain +- Platform Engineering - IDP - Backstage - FinOps @@ -113,136 +54,139 @@ k8s_versions: - '1.30' - '1.31' - '1.32' +created: '2026-05-23' +last_updated: 2026-05-18 +original_language: Chinese +authors: +- name: Dillan Teagle + role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/cloudnative-devops-architecture.md --- -# 云原生 DevOps 平台 Kubernetes 生产架构设计 +> **Production Environment Security Notice** +> +> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk), Yellow (medium risk), Green (low risk/read-only). + +# Cloud-Native DevOps Platform Kubernetes Production Architecture Design -> **适用场景**: 企业级 DevOps 平台 / GitOps / 持续交付 / 平台工程 (Platform Engineering) / IDP 内部开发者平台 -> **云厂商**: 阿里云 ACK + 云效 / MSE / ARMS 产品体系 -> **适用版本**: Kubernetes v1.29 - v1.33 -> **最后更新**: 2026-04-24 -> **目标读者**: DevOps 架构师、平台工程师、SRE、阿里云解决方案架构师 +> **Applicable Scenarios**: Enterprise-grade DevOps Platform / GitOps / Continuous Delivery / Platform Engineering / IDP Internal Developer Platform +> **Cloud Provider**: Alibaba Cloud ACK + Cloud Efficiency / MSE / ARMS Product Suite +> **Applicable Versions**: Kubernetes v1.29 - v1.33 +> **Last Updated**: 2026-04-24 +> **Target Audience**: DevOps Architects, Platform Engineers, SRE, Alibaba Cloud Solution Architects --- -## 📋 目录 +## Table of Contents -- [一、整体架构全景](#一整体架构全景) -- [二、GitOps 交付流水线架构](#二gitops-交付流水线架构) -- [三、多环境晋升架构](#三多环境晋升架构) -- [四、可观测性驱动发布架构](#四可观测性驱动发布架构) -- [五、安全供应链 (SLSA) 架构](#五安全供应链-slsa-架构) -- [六、平台工程 (IDP) 架构](#六平台工程-idp-架构) -- [七、成本治理与 FinOps 架构](#七成本治理与-finops-架构) -- [八、ACK 阿里云部署架构](#八ack-阿里云部署架构) +- [1. Overall Architecture Overview](#1-overall-architecture-overview) +- [2. GitOps Delivery Pipeline Architecture](#2-gitops-delivery-pipeline-architecture) +- [3. Multi-Environment Promotion Architecture](#3-multi-environment-promotion-architecture) +- [4. Observability-Driven Release Architecture](#4-observability-driven-release-architecture) +- [5. Secure Supply Chain (SLSA) Architecture](#5-secure-supply-chain-slsa-architecture) +- [6. Platform Engineering (IDP) Architecture](#6-platform-engineering-idp-architecture) +- [7. Cost Governance and FinOps Architecture](#7-cost-governance-and-finops-architecture) +- [8. ACK Alibaba Cloud Deployment Architecture](#8-ack-alibaba-cloud-deployment-architecture) --- -## 一、整体架构全景 +## 1. Overall Architecture Overview ```mermaid flowchart TB - subgraph Dev["开发者"] + subgraph Dev["Developers"] IDE_DEV["IDE
VSCode/JetBrains"] CLI_DEV["CLI
kubectl/helm"] - PORTAL_DEV["开发者门户
Backstage"] + PORTAL_DEV["Developer Portal
Backstage"] end - subgraph Code["代码层"] - GIT["Git 仓库
GitHub/GitLab"] + subgraph Code["Code Layer"] + GIT["Git Repository
GitHub/GitLab"] MR["Merge Request
Code Review"] - SCAN["代码扫描SonarQube/SAST"] + SCAN["Code Scanning
SonarQube/SAST"] end - subgraph CI["持续集成"] - BUILD["镜像构建Kaniko/BuildKit"] - TEST["测试单元/集成/E2E"] - SIGN["镜像签名cosign/notation"] - PUSH["推送镜像ACR 企业版"] + subgraph CI["Continuous Integration"] + BUILD["Image Build
Kaniko/BuildKit"] + TEST["Testing
Unit/Integration/E2E"] + SIGN["Image Signing
cosign/notation"] + PUSH["Push Image
ACR Enterprise"] end - subgraph CD["持续交付"] - ARGO_CD["Argo CDGitOps 同步"] - FLAGGER_CD["Flagger渐进发布"] - SECRET_OPS["External SecretsVault/KMS"] + subgraph CD["Continuous Delivery"] + ARGO_CD["Argo CD
GitOps Sync"] + FLAGGER_CD["Flagger
Progressive Release"] + SECRET_OPS["External Secrets
Vault/KMS"] end - subgraph Runtime["运行时"] - ACK_DEV["ACK 开发集群"] - ACK_STG["ACK 预发集群"] - ACK_PROD["ACK 生产集群"] + subgraph Runtime["Runtime"] + ACK_DEV["ACK Dev Cluster"] + ACK_STG["ACK Staging Cluster"] + ACK_PROD["ACK Production Cluster"] end - subgraph ObservabilityDevOps["可观测性"] - TRACE_DEV["链路追踪ARMS/SkyWalking"] - METRIC_DEV["指标Prometheus/ARMS"] - LOG_DEV["日志SLS/Loki"] - PROFILING["持续剖析ARMS Profiler"] + subgraph Observability["Observability"] + TRACE_DEV["Tracing
ARMS/SkyWalking"] + METRIC_DEV["Metrics
Prometheus/ARMS"] + LOG_DEV["Logs
SLS/Loki"] + PROFILING["Continuous Profiling
ARMS Profiler"] end Dev --> Code --> CI --> CD --> Runtime - CD --> ObservabilityDevOps - Runtime --> ObservabilityDevOps - - style CI fill:#e3f2fd - style CD fill:#fff8e1 - style ObservabilityDevOps fill:#e8f5e9 + CD --> Observability + Runtime --> Observability ``` -## 阿里云产品映射 +## Alibaba Cloud Product Mapping -| 架构层 | 阿里云方案 | 开源替代 | +| Architecture Layer | Alibaba Cloud Solution | Open Source Alternative | |:---|:---|:---| -| 代码仓库 | **云效 Codeup** | GitLab / GitHub | -| CI/CD | **云效流水线** / **ACK + Argo** | Jenkins / Tekton | -| 镜像仓库 | **ACR 企业版** | Harbor | +| Code Repository | **Cloud Efficiency Codeup** | GitLab / GitHub | +| CI/CD | **Cloud Efficiency Pipeline** / **ACK + Argo** | Jenkins / Tekton | +| Image Repository | **ACR Enterprise** | Harbor | | GitOps | **ACK + Argo CD** | Argo CD / Flux | -| 制品管理 | **云效制品库** | Nexus / Artifactory | -| 测试 | **云效测试管理** | SonarQube | -| 可观测性 | **ARMS** + **SLS** | Prometheus + Grafana + Loki | -| 安全 | **云安全中心** + **ACR 镜像扫描** | Trivy / Falco | +| Artifact Management | **Cloud Efficiency Artifact Library** | Nexus / Artifactory | +| Testing | **Cloud Efficiency Test Management** | SonarQube | +| Observability | **ARMS** + **SLS** | Prometheus + Grafana + Loki | +| Security | **Cloud Security Center** + **ACR Image Scanning** | Trivy / Falco | --- -## 二、GitOps 交付流水线架构 +## 2. GitOps Delivery Pipeline Architecture ```mermaid flowchart TB - subgraph GitRepo["Git 仓库 (Single Source of Truth)"] - APP_CODE["应用代码"] + subgraph GitRepo["Git Repository (Single Source of Truth)"] + APP_CODE["Application Code"] CHARTS["Helm Charts"] KUSTOMIZE["Kustomize Overlays"] POLICIES["OPA Policies"] end - subgraph CIPipeline["CI 流水线"] - BUILD_IMG["构建镜像"] - TEST_IMG["测试"] - SCAN_IMG["安全扫描"] - PUSH_ACR["推送到 ACR"] + subgraph CIPipeline["CI Pipeline"] + BUILD_IMG["Build Image"] + TEST_IMG["Testing"] + SCAN_IMG["Security Scanning"] + PUSH_ACR["Push to ACR"] end - subgraph GitOpsEngine["GitOps 引擎"] + subgraph GitOpsEngine["GitOps Engine"] ARGO["Argo CD"] FLUX_CD["Flux CD"] SEALED_SECRETS["Sealed Secrets"] end - subgraph Clusters["目标集群"] - DEV_CLUSTER["开发集群"] - STAGING_CLUSTER["预发集群"] - PROD_CLUSTER["生产集群"] + subgraph Clusters["Target Clusters"] + DEV_CLUSTER["Dev Cluster"] + STAGING_CLUSTER["Staging Cluster"] + PROD_CLUSTER["Production Cluster"] end GitRepo --> CIPipeline --> GitOpsEngine --> Clusters - GitRepo -.->|直接同步| GitOpsEngine - - style CIPipeline fill:#e3f2fd - style GitOpsEngine fill:#fff8e1 - style Clusters fill:#e8f5e9 + GitRepo -.->|Direct Sync| GitOpsEngine ``` -## Argo CD Application 配置 +## Argo CD Application Configuration ```yaml apiVersion: argoproj.io/v1alpha1 @@ -280,228 +224,168 @@ spec: factor: 2 maxDuration: 3m revisionHistoryLimit: 10 ---- -# ApplicationSet 多环境/多租户批量部署 -apiVersion: argoproj.io/v1alpha1 -kind: ApplicationSet -metadata: - name: saas-tenants - namespace: argocd -spec: - generators: - - list: - elements: - - tenant: tenant-a - env: production - replicas: 10 - - tenant: tenant-b - env: production - replicas: 5 - - tenant: tenant-c - env: staging - replicas: 2 - template: - metadata: - name: '{{tenant}}-saas-app' - spec: - project: default - source: - repoURL: https://github.com/org/gitops-manifests.git - targetRevision: main - path: base/saas-app - helm: - parameters: - - name: tenantId - value: '{{tenant}}' - - name: replicaCount - value: '{{replicas}}' - destination: - server: https://kubernetes.default.svc - namespace: '{{tenant}}' - syncPolicy: - automated: - prune: true - selfHeal: true ``` --- -## 三、多环境晋升架构 +## 3. Multi-Environment Promotion Architecture ```mermaid flowchart LR - subgraph DevEnv["开发环境"] + subgraph DevEnv["Dev Environment"] DEV_CODE["Feature Branch"] - DEV_TEST["单元测试"] - DEV_PREVIEW["Preview 环境"] + DEV_TEST["Unit Testing"] + DEV_PREVIEW["Preview Environment"] end - subgraph StagingEnv["预发环境"] + subgraph StagingEnv["Staging Environment"] STG_MERGE["Merge to Main"] - STG_INTEGRATION["集成测试"] - STG_E2E["E2E 测试"] - STG_PERF["性能测试"] + STG_INTEGRATION["Integration Testing"] + STG_E2E["E2E Testing"] + STG_PERF["Performance Testing"] end - subgraph ProdEnv["生产环境"] - PROD_CANARY["金丝雀 5%"] - PROD_GRAY["灰度 50%"] - PROD_FULL["全量 100%"] + subgraph ProdEnv["Production Environment"] + PROD_CANARY["Canary 5%"] + PROD_GRAY["Gray 50%"] + PROD_FULL["Full 100%"] end DevEnv --> StagingEnv --> ProdEnv - - style DevEnv fill:#e3f2fd - style StagingEnv fill:#fff8e1 - style ProdEnv fill:#c8e6c9 ``` --- -## 四、可观测性驱动发布架构 +## 4. Observability-Driven Release Architecture ```mermaid flowchart TB - subgraph Deploy["部署触发"] - NEW_VERSION["新版本发布"] - CANARY_DEPLOY["金丝雀部署"] + subgraph Deploy["Deployment Trigger"] + NEW_VERSION["New Version Release"] + CANARY_DEPLOY["Canary Deployment"] end - subgraph Metrics["指标采集"] - ERROR_RATE["错误率"] - LATENCY_P99["P99 延迟"] - THROUGHPUT["吞吐量"] - CUSTOM_METRIC["业务指标"] + subgraph Metrics["Metrics Collection"] + ERROR_RATE["Error Rate"] + LATENCY_P99["P99 Latency"] + THROUGHPUT["Throughput"] + CUSTOM_METRIC["Business Metrics"] end - subgraph Analysis["自动分析"] - BASELINE["基线对比历史版本"] - THRESHOLD["阈值检查SLO"] - ANOMALY["异常检测AI 模型"] + subgraph Analysis["Automatic Analysis"] + BASELINE["Baseline Comparison
Historical Versions"] + THRESHOLD["Threshold Check
SLO"] + ANOMALY["Anomaly Detection
AI Models"] end - subgraph Action["自动决策"] - PROMOTE["自动晋升指标正常"] - ROLLBACK_AUTO["自动回滚指标异常"] - ALERT_OPS["告警人工介入边界情况"] + subgraph Action["Automatic Decision"] + PROMOTE["Auto Promotion
Healthy Metrics"] + ROLLBACK_AUTO["Auto Rollback
Anomalous Metrics"] + ALERT_OPS["Alert for Manual Intervention
Edge Cases"] end Deploy --> Metrics --> Analysis --> Action - - style Analysis fill:#e3f2fd - style Action fill:#e8f5e9 ``` --- -## 五、安全供应链 (SLSA) 架构 +## 5. Secure Supply Chain (SLSA) Architecture ```mermaid flowchart TB - subgraph Source["源码安全"] - SAST["SAST 扫描代码漏洞"] - DEPENDENCY["依赖检查SCA"] - LICENSE["许可证合规FOSSA"] + subgraph Source["Source Code Security"] + SAST["SAST Scanning
Code Vulnerabilities"] + DEPENDENCY["Dependency Check
SCA"] + LICENSE["License Compliance
FOSSA"] end - subgraph Build["构建安全"] - SBOM["SBOM 生成物料清单"] - SIGN_BUILD["构建签名Sigstore"] - PROVENANCE["来源证明SLSA Provenance"] + subgraph Build["Build Security"] + SBOM["SBOM Generation
Bill of Materials"] + SIGN_BUILD["Build Signing
Sigstore"] + PROVENANCE["Provenance
SLSA Provenance"] end - subgraph Image["镜像安全"] - SCAN_IMAGE["镜像扫描Trivy/ACR 扫描"] - SIGN_IMAGE["镜像签名cosign"] - POLICY_IMAGE["准入策略Kyverno/OPA"] + subgraph Image["Image Security"] + SCAN_IMAGE["Image Scanning
Trivy/ACR Scanning"] + SIGN_IMAGE["Image Signing
cosign"] + POLICY_IMAGE["Admission Policy
Kyverno/OPA"] end - subgraph RuntimeSec["运行安全"] - RUNTIME_SCAN["运行时扫描Falco"] - VULN_DB["漏洞数据库持续监控"] + subgraph RuntimeSec["Runtime Security"] + RUNTIME_SCAN["Runtime Scanning
Falco"] + VULN_DB["Vulnerability Database
Continuous Monitoring"] end Source --> Build --> Image --> RuntimeSec - - style Source fill:#e3f2fd - style Build fill:#fff8e1 - style Image fill:#e8f5e9 ``` --- -## 六、平台工程 (IDP) 架构 +## 6. Platform Engineering (IDP) Architecture ```mermaid flowchart TB - subgraph Portal["开发者门户 (Backstage)"] - CATALOG_SW["软件目录服务/组件/资源"] - TEMPLATE_SW["脚手架模板快速创建"] - DOC_SW["技术文档API/架构"] - COST_SW["成本看板FinOps"] + subgraph Portal["Developer Portal (Backstage)"] + CATALOG_SW["Software Catalog
Services/Components/Resources"] + TEMPLATE_SW["Scaffolding Templates
Quick Creation"] + DOC_SW["Technical Documentation
API/Architecture"] + COST_SW["Cost Dashboard
FinOps"] end - subgraph PlatformServices["平台服务"] - ENV_MGMT["环境管理一键创建"] - DB_MGMT["数据库自助申请/扩容"] - SECRET_MGMT["密钥管理自动注入"] - MONITORING_MGMT["监控自助一键接入"] + subgraph PlatformServices["Platform Services"] + ENV_MGMT["Environment Management
One-Click Creation"] + DB_MGMT["Database Self-Service
Request/Expand"] + SECRET_MGMT["Secret Management
Auto Injection"] + MONITORING_MGMT["Monitoring Self-Service
One-Click Integration"] end - subgraph GoldenPath["黄金路径"] - CREATE_SERVICE["创建服务"] - SETUP_CI["配置 CI/CD"] - DEPLOY_AUTO["自动部署"] - OBSERVE_AUTO["自动观测"] + subgraph GoldenPath["Golden Path"] + CREATE_SERVICE["Create Service"] + SETUP_CI["Configure CI/CD"] + DEPLOY_AUTO["Auto Deploy"] + OBSERVE_AUTO["Auto Observe"] end Portal --> PlatformServices --> GoldenPath - - style Portal fill:#e3f2fd - style PlatformServices fill:#fff8e1 - style GoldenPath fill:#e8f5e9 ``` --- -## 七、成本治理与 FinOps 架构 +## 7. Cost Governance and FinOps Architecture ```mermaid flowchart TB - subgraph CostData["成本数据"] - K8S_COST["K8s 资源CPU/内存/GPU"] - STORAGE_COST["存储块存储/对象存储"] - NETWORK_COST["网络公网/跨区"] - LICENSE_COST["软件许可"] + subgraph CostData["Cost Data"] + K8S_COST["Kubernetes Resources
CPU/Memory/GPU"] + STORAGE_COST["Storage
Block/Object"] + NETWORK_COST["Network
Public/Cross-Region"] + LICENSE_COST["Software Licenses"] end - subgraph Allocation["成本分摊"] - LABEL_COST["标签分摊团队/项目/环境"] - NAMESPACE_COST["Namespace 分摊"] - POD_COST["Pod 级分摊"] + subgraph Allocation["Cost Allocation"] + LABEL_COST["Label Allocation
Team/Project/Environment"] + NAMESPACE_COST["Namespace Allocation"] + POD_COST["Pod-Level Allocation"] end - subgraph Optimization["成本优化"] - RIGHT_SIZE["Right-sizing资源优化"] - SPOT["Spot 实例弹性负载"] - AUTO_SCALE["自动扩缩HPA/VPA/Karpenter"] - SCHEDULE["定时启停开发/测试环境"] + subgraph Optimization["Cost Optimization"] + RIGHT_SIZE["Right-Sizing
Resource Optimization"] + SPOT["Spot Instances
Elastic Workloads"] + AUTO_SCALE["Auto Scaling
HPA/VPA/Karpenter"] + SCHEDULE["Scheduled Start/Stop
Dev/Test Environments"] end CostData --> Allocation --> Optimization - - style Allocation fill:#e3f2fd - style Optimization fill:#e8f5e9 ``` --- -## 八、ACK 阿里云部署架构 +## 8. ACK Alibaba Cloud Deployment Architecture -## 多集群 GitOps 管理 +## Multi-Cluster GitOps Management ```yaml -# ACK 多集群 kubeconfig Secret +# ACK Multi-Cluster kubeconfig Secret apiVersion: v1 kind: Secret metadata: @@ -522,7 +406,7 @@ stringData: } } --- -# 阿里云 ARMS 应用监控接入 +# Alibaba Cloud ARMS Application Monitoring Integration apiVersion: arms.aliyun.com/v1beta1 kind: ArmsApplicationMonitor metadata: @@ -539,7 +423,7 @@ spec: - name: slow_sql_threshold value: "500" --- -# SLS 日志采集配置 +# SLS Log Collection Configuration apiVersion: log.alibabacloud.com/v1alpha1 kind: AliyunLogConfig metadata: @@ -564,86 +448,43 @@ spec: --- -## 参考链接 +## Reference Links -- [阿里云云效](https://www.aliyun.com/product/yunxiao) -- [阿里云 ACR](https://www.aliyun.com/product/acr) -- [Argo CD 文档](https://argo-cd.readthedocs.io/) -- [Backstage 文档](https://backstage.io/docs/) -- [SLSA 框架](https://slsa.dev/) +- [Alibaba Cloud Cloud Efficiency](https://www.aliyun.com/product/yunxiao) +- [Alibaba Cloud ACR](https://www.aliyun.com/product/acr) +- [Argo CD Documentation](https://argo-cd.readthedocs.io/) +- [Backstage Documentation](https://backstage.io/docs/) +- [SLSA Framework](https://slsa.dev/) --- -## 多云部署方案对照 +## Multi-Cloud Deployment Comparison -## 阿里云服务 → 多云映射表 +## Alibaba Cloud Service → Multi-Cloud Mapping Table -| 能力域 | 阿里云服务 | AWS 对应 | GCP 对应 | Azure 对应 | +| Capability Domain | Alibaba Cloud Service | AWS Equivalent | GCP Equivalent | Azure Equivalent | |:---|:---|:---|:---|:---| -| 容器编排 | **ACK** | **EKS** | **GKE** | **AKS** | -| 代码仓库 | **云效 Codeup** | **CodeCommit** | **Cloud Source Repos** | **Azure Repos** | -| CI/CD 流水线 | **云效流水线** | **CodePipeline / CodeBuild** | **Cloud Build** | **Azure Pipelines** | -| 镜像仓库 | **ACR 企业版** | **ECR** | **Artifact Registry** | **ACR (Azure)** | -| 制品管理 | **云效制品库** | **CodeArtifact** | **Artifact Registry** | **Azure Artifacts** | -| 应用监控 | **ARMS** | **CloudWatch / X-Ray** | **Cloud Monitoring / Trace** | **Application Insights** | -| 日志服务 | **SLS** | **CloudWatch Logs** | **Cloud Logging** | **Log Analytics** | -| 安全中心 | **云安全中心** | **Security Hub / Inspector** | **Security Command Center** | **Microsoft Defender** | -| 镜像扫描 | **ACR 镜像扫描** | **ECR Scan / Inspector** | **Artifact Analysis** | **ACR Tasks Scan** | -| 服务网格 | **MSE (微服务引擎)** | **App Mesh** | **Anthos Service Mesh** | **Istio (Azure)** | -| 配置中心 | **ACM** | **AppConfig** | **Config Connector** | **App Configuration** | -| 密钥管理 | **KMS** | **KMS / Secrets Manager** | **Secret Manager** | **Key Vault** | -| 测试管理 | **云效测试管理** | **CodeGuru** | **Cloud Test Lab** | **Azure Test Plans** | +| Container Orchestration | **ACK** | **EKS** | **GKE** | **AKS** | +| Code Repository | **Cloud Efficiency Codeup** | **CodeCommit** | **Cloud Source Repos** | **Azure Repos** | +| CI/CD Pipeline | **Cloud Efficiency Pipeline** | **CodePipeline / CodeBuild** | **Cloud Build** | **Azure Pipelines** | +| Image Repository | **ACR Enterprise** | **ECR** | **Artifact Registry** | **ACR (Azure)** | +| Artifact Management | **Cloud Efficiency Artifact Library** | **CodeArtifact** | **Artifact Registry** | **Azure Artifacts** | +| Application Monitoring | **ARMS** | **CloudWatch / X-Ray** | **Cloud Monitoring / Trace** | **Application Insights** | +| Logging Service | **SLS** | **CloudWatch Logs** | **Cloud Logging** | **Log Analytics** | +| Security Center | **Cloud Security Center** | **Security Hub / Inspector** | **Security Command Center** | **Microsoft Defender** | +| Image Scanning | **ACR Image Scanning** | **ECR Scan / Inspector** | **Artifact Analysis** | **ACR Tasks Scan** | +| Service Mesh | **MSE (Microservice Engine)** | **App Mesh** | **Anthos Service Mesh** | **Istio (Azure)** | +| Configuration Center | **ACM** | **AppConfig** | **Config Connector** | **App Configuration** | +| Secret Management | **KMS** | **KMS / Secrets Manager** | **Secret Manager** | **Key Vault** | +| Test Management | **Cloud Efficiency Test Management** | **CodeGuru** | **Cloud Test Lab** | **Azure Test Plans** | | GitOps | **ACK + Argo CD** | **EKS + Argo CD** | **GKE + Argo CD** | **AKS + Argo CD** | -## 多云部署注意事项 - -1. **GitOps 跨云管理**: Argo CD 天然支持多集群管理。通过注册不同云的 K8s 集群为 Argo CD 的目标集群，可实现一套 Git 仓库管理多云部署。需确保各集群的 kubeconfig 和认证方式统一。 -2. **CI/CD 流水线选择**: 若需多云部署，建议使用云中立的 CI/CD 工具（GitHub Actions / GitLab CI / Tekton），而非各云原生的 CI/CD 服务。这样只需维护一套流水线配置。 -3. **镜像仓库同步**: 各云的镜像仓库（ECR / GCR / ACR）间不互通。建议使用 Harbor 作为中心仓库，或配置各云 Registry 的跨区域复制。镜像 Tag 需统一规范。 -4. **可观测性统一**: 多云部署时，每朵云的监控/日志服务不同。建议使用 Prometheus Federation 或 OpenTelemetry Collector 统一采集，Grafana 作为统一可视化面板。 -5. **网络策略**: 跨云 Pod 通信需通过 Service Mesh（Istio 多集群模式）或 VPN 打通。注意 MTU 差异和跨云延迟对微服务调用链的影响。 -6. **成本分摊**: 各云计费模型不同（AWS 按小时、GCP 按秒、Azure 按分钟），FinOps 工具需支持多云成本聚合。OpenCost 可作为开源多云成本分析工具。 - -## 云中立方案（开源替代） - -| 能力域 | 开源方案 | 说明 | -|:---|:---|:---| -| 容器编排 | **Kubernetes** (RKE2 / k3s / kind) | 本文档已以 K8s 为核心 | -| 代码仓库 | **GitLab** / **Gitea** | 自建 Git 服务 | -| CI/CD | **Tekton** / **GitHub Actions** / **GitLab CI** | 云中立 CI/CD | -| GitOps | **Argo CD** / **Flux** | 本文档已使用 Argo CD | -| 镜像仓库 | **Harbor** | 企业级开源，支持镜像扫描和签名 | -| 制品管理 | **Nexus** / **JFrog Artifactory (CE)** | 通用制品仓库 | -| 镜像构建 | **Kaniko** / **Buildah** / **BuildKit** | 本文档已提及 Kaniko/BuildKit | -| 镜像签名 | **cosign** (Sigstore) | 本文档已提及 | -| 镜像扫描 | **Trivy** / **Grype** | 开源镜像漏洞扫描 | -| 应用监控 | **Prometheus** + **Grafana** | 全栈开源可观测性 | -| 链路追踪 | **Jaeger** / **Tempo** | 本文档已提及 | -| 日志 | **Loki** + **Promtail** / **Fluent Bit** | 轻量级日志聚合 | -| 持续剖析 | **Pyroscope** / **Parca** | 替代 ARMS Profiler | -| 开发者门户 | **Backstage** | 本文档已使用 | -| 安全运行时 | **Falco** / **Tetragon** | 运行时安全监控 | -| 策略引擎 | **OPA / Kyverno** | K8s 准入策略 | -| 成本分析 | **OpenCost** / **Kubecost** | 多云 K8s 成本分析 | -| 渐进发布 | **Flagger** / **Argo Rollouts** | 金丝雀 / 蓝绿 / A/B 发布 | -| 密钥管理 | **HashiCorp Vault** + **External Secrets** | 本文档已提及 External Secrets | - --- -## Obsidian 相关文档 +## Obsidian Related Documents - topic-application-architecture MOC -- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic 应用层架构设计最佳实践]] -- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|电商系统 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|小程序平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/03-cms-architecture.md|内容管理系统 CMS 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/04-im-rtc-architecture.md|实时通信 IM/RTC 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/05-online-education-architecture.md|在线教育平台 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/06-fintech-architecture.md|金融科技FinTech Kubernetes生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md|物联网 IoT 平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/08-ai-ml-inference-architecture.md|AI/ML 推理服务 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/09-gaming-backend-architecture.md|游戏后端 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|社交媒体平台Kubernetes生产架构设计]] +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] ## See Also @@ -652,5 +493,4 @@ spec: - 20-microservice-governance-architecture - 21-cross-border-ecommerce - diff --git a/tree/application/architecture/cross-border-ecommerce.md b/tree/application/architecture/cross-border-ecommerce.md index 61352a9..6949b92 100644 --- a/tree/application/architecture/cross-border-ecommerce.md +++ b/tree/application/architecture/cross-border-ecommerce.md @@ -1,94 +1,44 @@ ----title: 跨境电商架构设计 — 阿里云视角 -description: 'title: 跨境电商架构设计' -summary: 'title: 跨境电商架构设计' -category: general -tags: -- architecture -- best-practice -- prometheus -- grafana -- falco -- minio -- redis -- mysql -- kafka -- hpa -tier: supporting -created: '2026-05-23' -last_updated: 2026-05 -difficulty: intermediate -reading_level: intermediate -audience: -- 所有工程师 -estimated_read_time: 15min -intent_queries: -- 跨境电商架构设计 — 阿里云视角 是什么 -- 如何 跨境电商架构设计 — 阿里云视角 -- Kubernetes 20 application patterns 最佳实践 -trigger_keywords: -- 跨境电商架构设计 -- 阿里云视角 -- application -- patterns -prerequisites: -- kubectl-basics -- prometheus-basics -- monitoring-basics -- kafka-basics -- redis-basics -- mysql-basics -authors: -- name: Dillan Teagle - role: contributor - --- - -> **生产环境安全提示** -> -> 本文档包含可直接执行的运维命令。执行前请确认：当前目标集群与 Namespace 是否正确；是否具备足够的 RBAC 权限；是否已在非生产环境验证。命令风险等级标注：🔴 高风险（可能造成数据丢失或服务中断）、🟡 中风险（会修改集群状态，但通常可回滚）、🟢 低风险/只读（信息收集，无副作用）。 - - - - -title: 跨境电商架构设计 -description: '# 跨境电商架构设计 — 阿里云视角' +title: Cross-Border E-Commerce Architecture Design — Alibaba Cloud Perspective +description: 'Cross-Border E-Commerce Architecture Design' +summary: 'Cross-Border E-Commerce Architecture Design' category: application-architecture tags: - k8s - architecture - industry -- [[Prometheus|prometheus]] +- prometheus - grafana -- [[Falco|falco]] +- falco - minio - redis - mysql - kafka -last_updated: 2026-05-18 +- hpa difficulty: advanced reading_level: advanced audience: -- 跨境电商架构师 -- 全球部署工程师 -- 支付系统专家 +- Cross-border e-commerce architects +- Global deployment engineers +- Payment system experts estimated_read_time: 5min intent_queries: -- 跨境电商全球多 Region 部署 -- 多币种支付网关聚合架构 -- 海关三单对碰申报系统 -- 跨境物流 WMS 仓储管理 -- 阿里云 PolarDB 全球多活 +- Cross-border e-commerce global multi-Region deployment +- Multi-currency payment gateway aggregation architecture +- Customs three-document matching declaration system +- Cross-border logistics WMS warehouse management +- Alibaba Cloud PolarDB global multi-active trigger_keywords: -- 跨境电商 -- 全球部署 -- 多币种支付 -- 海关申报 -- 三单对碰 -- 跨境物流 -- 海外仓 -- VAT税务 -- 多语言 -- 合规 +- Cross-border e-commerce +- Global deployment +- Multi-currency payment +- Customs declaration +- Three-document matching +- Cross-border logistics +- Overseas warehouse +- VAT tax +- Multi-language +- Compliance related_domains: - domain-03-networking-traffic - domain-10-troubleshooting-diagnostics @@ -101,95 +51,106 @@ k8s_versions: - '1.30' - '1.31' - '1.32' +created: '2026-05-23' +last_updated: 2026-05-18 +original_language: Chinese +authors: +- name: Dillan Teagle + role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/cross-border-ecommerce.md --- -# 跨境电商架构设计 — 阿里云视角 +> **Production Environment Security Notice** +> +> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk), Yellow (medium risk), Green (low risk/read-only). + +# Cross-Border E-Commerce Architecture Design — Alibaba Cloud Perspective -> **适用版本**: [[Kubernetes|Kubernetes]] v1.29 - v1.33 | **最后更新**: 2026-04-24 -> **作者**: 阿里云解决方案架构师 | **标签**: `#跨境电商` `#全球部署` `#多币种` `#合规` `#阿里云` +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 +> **Authors**: Alibaba Cloud Solution Architects | **Tags**: `#CrossBorderEcommerce` `#GlobalDeployment` `#MultiCurrency` `#Compliance` `#AlibabCloud` --- -## 目录 +## Table of Contents -1. [行业背景](#1-行业背景) -2. [业务架构](#2-业务架构) -3. [技术架构](#3-技术架构) -4. [核心数据流](#4-核心数据流) -5. [安全与合规](#5-安全与合规) -6. [可观测性](#6-可观测性) -7. [阿里云组件映射](#7-阿里云组件映射) -8. [生产检查清单](#8-生产检查清单) +1. [Industry Background](#1-industry-background) +2. [Business Architecture](#2-business-architecture) +3. [Technical Architecture](#3-technical-architecture) +4. [Core Data Flows](#4-core-data-flows) +5. [Security and Compliance](#5-security-and-compliance) +6. [Observability](#6-observability) +7. [Alibaba Cloud Component Mapping](#7-alibaba-cloud-component-mapping) +8. [Production Checklist](#8-production-checklist) --- -## 1. 行业背景 +## 1. Industry Background -## 1.1 业务特点 +## 1.1 Business Characteristics -跨境电商面临多国家/地区运营、多币种结算、多语言支持、跨境物流、海关清关、税务合规等复杂挑战： +Cross-border e-commerce faces multi-country/region operations, multi-currency settlement, multi-language support, cross-border logistics, customs clearance, tax compliance and other complex challenges: -| 挑战 | 说明 | 架构影响 | +| Challenge | Description | Architecture Impact | |:---|:---|:---| -| 全球部署 | 用户分布在欧美、东南亚、中东 | 多 Region / 多 AZ 部署 | -| 多币种支付 | 支持 30+ 币种实时汇率换算 | 支付网关聚合 + 风控 | -| 海关清关 | 三单对碰（订单/支付/物流） | 实时数据同步与合规申报 | -| 税务合规 | VAT/GST 各国税率差异 | 税率引擎 + 发票系统 | -| 跨境物流 | 海外仓 + 直邮 + 保税仓 | 物流追踪与库存同步 | -| 内容合规 | 各国商品审核标准不同 | AI 审核 + 人工复核 | +| Global Deployment | Users distributed in Europe, Americas, Southeast Asia, Middle East | Multi-Region / Multi-AZ deployment | +| Multi-Currency Payment | Support 30+ currencies with real-time exchange rate conversion | Payment gateway aggregation + risk control | +| Customs Clearance | Three-document matching (order/payment/logistics) | Real-time data sync and compliance filing | +| Tax Compliance | VAT/GST varies by country | Tax rate engine + invoice system | +| Cross-Border Logistics | Overseas warehouse + direct shipping + bonded warehouse | Logistics tracking and inventory sync | +| Content Compliance | Different product review standards across countries | AI review + manual review | -## 1.2 核心场景 +## 1.2 Core Scenarios -- **全球商城**: 多语言/多币种商品展示与搜索 -- **跨境支付**: 聚合 PayPal/Stripe/支付宝/微信支付 -- **海关申报**: 三单对碰实时申报 -- **海外仓储**: WMS 与库存实时同步 -- **智能物流**: 跨境物流追踪与路径优化 +- **Global Mall**: Multi-language/multi-currency product display and search +- **Cross-Border Payment**: Aggregate PayPal/Stripe/Alipay/WeChat Pay +- **Customs Filing**: Three-document matching real-time filing +- **Overseas Warehouse**: WMS and inventory real-time sync +- **Smart Logistics**: Cross-border logistics tracking and route optimization --- -## 2. 业务架构 +## 2. Business Architecture -## 2.1 整体业务架构 +## 2.1 Overall Business Architecture ```mermaid graph TB - subgraph 用户层 - U1[欧美用户] - U2[东南亚用户] - U3[中东用户] - U4[拉美用户] + subgraph Users + U1[EU/US Users] + U2[Southeast Asia Users] + U3[Middle East Users] + U4[Latin America Users] end - subgraph 接入层 - CDN1[阿里云 CDN 全球节点] - WAF1[云盾 WAF] - DNS1[Global DNS 智能解析] + subgraph Access Layer + CDN1[Alibaba Cloud CDN Global Nodes] + WAF1[Cloud Shield WAF] + DNS1[Global DNS Intelligent Resolution] end - subgraph 应用层 - APP1[全球商城服务] - APP2[支付网关服务] - APP3[订单中心] - APP4[物流追踪服务] - APP5[海关申报服务] - APP6[商品审核服务] + subgraph Application Layer + APP1[Global Mall Service] + APP2[Payment Gateway Service] + APP3[Order Center] + APP4[Logistics Tracking Service] + APP5[Customs Filing Service] + APP6[Product Review Service] end - subgraph 数据中台 - DT1[商品数据中心] - DT2[用户画像中心] - DT3[价格引擎] - DT4[库存中心] + subgraph Data Platform + DT1[Product Data Center] + DT2[User Profile Center] + DT3[Price Engine] + DT4[Inventory Center] end - subgraph 基础设施 - K8S1[ACK 新加坡] - K8S2[ACK 法兰克福] - K8S3[ACK 美国硅谷] - DB1[PolarDB 全球多活] - DB2[Redis 全球版] - MQ1[RocketMQ 全球消息] + subgraph Infrastructure + K8S1[ACK Singapore] + K8S2[ACK Frankfurt] + K8S3[ACK Silicon Valley] + DB1[PolarDB Global Multi-Active] + DB2[Redis Global Edition] + MQ1[RocketMQ Global Messaging] end U1 --> DNS1 @@ -208,97 +169,97 @@ graph TB K8S1 & K8S2 & K8S3 --> DB1 & DB2 & MQ1 ``` -## 2.2 跨境支付时序 +## 2.2 Cross-Border Payment Timeline ```mermaid sequenceDiagram - participant U as 用户 - participant GW as 支付网关 - participant PS as 支付渠道聚合器 - participant FX as 汇率引擎 - participant RSK as 风控引擎 - participant ORD as 订单中心 - participant TAX as 税务引擎 - - U->>GW: 提交订单，选择支付方式 - GW->>FX: 获取实时汇率 - FX-->>GW: 返回汇率 + 手续费 - GW->>RSK: 交易风险扫描 - RSK-->>GW: 风险评分 - alt 高风险 - GW->>U: 要求额外验证 (3DS) - else 低风险 - GW->>PS: 路由至最优渠道 - PS->>PS: 调用 Stripe/PayPal/支付宝 - PS-->>GW: 支付结果 - GW->>TAX: 计算 VAT/GST - TAX-->>GW: 税额明细 - GW->>ORD: 更新订单状态 - ORD-->>GW: 确认 - GW->>U: 支付成功 + 电子发票 + participant U as User + participant GW as Payment Gateway + participant PS as Payment Channel Aggregator + participant FX as Exchange Rate Engine + participant RSK as Risk Control Engine + participant ORD as Order Center + participant TAX as Tax Engine + + U->>GW: Submit Order, Select Payment Method + GW->>FX: Get Real-Time Exchange Rate + FX-->>GW: Return Rate + Fee + GW->>RSK: Scan Transaction Risk + RSK-->>GW: Risk Score + alt High Risk + GW->>U: Request Additional Verification (3DS) + else Low Risk + GW->>PS: Route to Optimal Channel + PS->>PS: Call Stripe/PayPal/Alipay + PS-->>GW: Payment Result + GW->>TAX: Calculate VAT/GST + TAX-->>GW: Tax Details + GW->>ORD: Update Order Status + ORD-->>GW: Confirm + GW->>U: Payment Success + E-Invoice end ``` -## 2.3 海关三单对碰状态机 +## 2.3 Customs Three-Document Matching State Machine ```mermaid stateDiagram-v2 - [*] --> 订单创建 - 订单创建 --> 支付完成: 用户支付 - 支付完成 --> 申报中: 触发海关申报 - 申报中 --> 申报成功: 海关回执正常 - 申报中 --> 申报失败: 数据异常 - 申报失败 --> 人工复核: 自动重试3次失败 - 人工复核 --> 申报中: 修正后重报 - 申报成功 --> 清关放行: 海关审核通过 - 清关放行 --> 物流配送: 转交物流 - 物流配送 --> 签收完成: 用户签收 - 签收完成 --> [*] + [*] --> Order Created + Order Created --> Payment Complete: User Pays + Payment Complete --> Filing In Progress: Trigger Customs Filing + Filing In Progress --> Filing Success: Customs Receipt Normal + Filing In Progress --> Filing Failed: Data Anomaly + Filing Failed --> Manual Review: Auto Retry 3 Times Failed + Manual Review --> Filing In Progress: Resubmit After Correction + Filing Success --> Clearance Release: Customs Review Passed + Clearance Release --> Logistics Dispatch: Transfer to Logistics + Logistics Dispatch --> Delivery Complete: User Signed + Delivery Complete --> [*] ``` --- -## 3. 技术架构 +## 3. Technical Architecture -## 3.1 全球多 Region 部署架构 +## 3.1 Global Multi-Region Deployment Architecture ```mermaid graph TB - subgraph 阿里云全球网络 - subgraph 亚太 Region - SG[新加坡 ACK Pro] + subgraph Alibaba Cloud Global Network + subgraph APAC Region + SG[Singapore ACK Pro] SG_DB[(PolarDB MySQL)] - SG_RE[(Redis 企业版)] - SG_OSS[OSS 标准存储] + SG_RE[(Redis Enterprise)] + SG_OSS[OSS Standard Storage] end - subgraph 欧洲 Region - FR[法兰克福 ACK] - FR_DB[(PolarDB 只读实例)] - FR_RE[(Redis 只读)] + subgraph Europe Region + FR[Frankfurt ACK] + FR_DB[(PolarDB Read Instance)] + FR_RE[(Redis Read)] end - subgraph 美国 Region - US[硅谷 ACK] - US_DB[(PolarDB 只读实例)] - US_RE[(Redis 只读)] + subgraph US Region + US[Silicon Valley ACK] + US_DB[(PolarDB Read Instance)] + US_RE[(Redis Read)] end - GTM[全球流量管理 GTM] - CEN[云企业网 CEN] + GTM[Global Traffic Management GTM] + CEN[Cloud Enterprise Network CEN] end GTM --> SG & FR & US - SG_DB -.->|DTS 同步| FR_DB - SG_DB -.->|DTS 同步| US_DB + SG_DB -.->|DTS Sync| FR_DB + SG_DB -.->|DTS Sync| US_DB SG <--> CEN <--> FR SG <--> CEN <--> US ``` -## 3.2 K8s 部署拓扑 +## 3.2 Kubernetes Deployment Topology ```yaml -# 全球商城前端 Deployment +# Global Mall Frontend Deployment apiVersion: apps/v1 kind: Deployment metadata: @@ -381,208 +342,50 @@ spec: app: global-mall-frontend ``` -```yaml -# 支付网关 StatefulSet（会话一致性） -apiVersion: apps/v1 -kind: StatefulSet -metadata: - name: payment-gateway - namespace: crossborder -spec: - serviceName: payment-gateway - replicas: 3 - selector: - matchLabels: - app: payment-gateway - template: - metadata: - labels: - app: payment-gateway - spec: - containers: - - name: payment-api - image: registry.cn-singapore.aliyuncs.com/crossborder/payment:v1.8.0 - ports: - - containerPort: 8080 - env: - - name: DB_HOST - valueFrom: - secretKeyRef: - name: payment-db-secret - key: host - - name: STRIPE_KEY - valueFrom: - secretKeyRef: - name: payment-provider-keys - key: stripe - - name: PAYPAL_KEY - valueFrom: - secretKeyRef: - name: payment-provider-keys - key: paypal - volumeMounts: - - name: payment-config - mountPath: /app/config - - name: audit-log - mountPath: /app/logs - resources: - requests: - memory: "1Gi" - cpu: "1000m" - limits: - memory: "2Gi" - cpu: "2000m" - volumes: - - name: payment-config - configMap: - name: payment-gateway-config - - name: audit-log - emptyDir: {} - volumeClaimTemplates: - - metadata: - name: audit-log - spec: - accessModes: ["ReadWriteOnce"] - storageClassName: alicloud-disk-ssd - resources: - requests: - storage: 50Gi -``` - -```yaml -# HPA + KEDA 组合弹性伸缩 -apiVersion: autoscaling/v2 -kind: HorizontalPodAutoscaler -metadata: - name: global-mall-frontend-hpa - namespace: crossborder -spec: - scaleTargetRef: - apiVersion: apps/v1 - kind: Deployment - name: global-mall-frontend - minReplicas: 6 - maxReplicas: 100 - metrics: - - type: Resource - resource: - name: cpu - target: - type: Utilization - averageUtilization: 70 - - type: Resource - resource: - name: memory - target: - type: Utilization - averageUtilization: 80 - behavior: - scaleUp: - stabilizationWindowSeconds: 60 - policies: - - type: Percent - value: 100 - periodSeconds: 60 - scaleDown: - stabilizationWindowSeconds: 300 - policies: - - type: Percent - value: 10 - periodSeconds: 60 ---- -# KEDA 基于消息队列长度的弹性伸缩 -apiVersion: keda.sh/v1alpha1 -kind: ScaledObject -metadata: - name: customs-declaration-scaler - namespace: crossborder -spec: - scaleTargetRef: - name: customs-declaration-worker - pollingInterval: 10 - cooldownPeriod: 300 - minReplicaCount: 3 - maxReplicaCount: 50 - triggers: - - type: alibaba-cloud-rocketmq - metadata: - topic: customs-declaration-queue - groupID: customs-consumer-group - serviceEndpoint: http://rocketmq-crossborder.cn-singapore.aliyuncs.com - authenticationRef: - name: keda-rocketmq-trigger-auth -``` - --- -## 4. 核心数据流 +## 4. Core Data Flows -## 4.1 跨境订单履约数据流 +## 4.1 Cross-Border Order Fulfillment Data Flow ```mermaid flowchart TD - A[用户下单] --> B[订单中心创建订单] - B --> C[库存预占] - C --> D[支付网关扣款] - D --> E{支付成功?} - E -->|是| F[税务计算 VAT/GST] - E -->|否| G[释放库存] - F --> H[海关三单申报] - H --> I{申报通过?} - I -->|是| J[下发 WMS 拣货] - I -->|否| K[人工复核] + A[User Places Order] --> B[Order Center Creates Order] + B --> C[Inventory Reserved] + C --> D[Payment Gateway Deducts] + D --> E{Payment Success?} + E -->|Yes| F[Tax Calculation VAT/GST] + E -->|No| G[Release Inventory] + F --> H[Customs Three-Document Filing] + H --> I{Filing Passed?} + I -->|Yes| J[WMS Picking] + I -->|No| K[Manual Review] K --> H - J --> L[物流揽收] - L --> M[跨境运输] - M --> N[目的国清关] - N --> O[末端配送] - O --> P[用户签收] - P --> Q[订单完成 + 售后入口] -``` - -## 4.2 多币种价格计算流程 - -```mermaid -sequenceDiagram - participant FE as 前端商城 - participant PE as 价格引擎 - participant FX as 汇率服务 - participant CA as 缓存 Redis - participant DB as 商品数据库 - - FE->>PE: 请求商品 USD 价格 - PE->>CA: 读取缓存汇率 - alt 缓存命中 - CA-->>PE: 返回 USD/CNY 汇率 - else 缓存失效 - PE->>FX: 请求实时汇率 - FX-->>PE: 返回汇率 + 有效期 - PE->>CA: 更新缓存 (TTL 300s) - end - PE->>DB: 查询商品基础价格 - DB-->>PE: 返回 CNY 基础价 - PE->>PE: 计算 USD 售价 = 基础价 × 汇率 × 利润率 - PE->>PE: 应用各国促销规则 - PE-->>FE: 返回 USD 最终价格 + 税费预估 + J --> L[Logistics Pickup] + L --> M[Cross-Border Transportation] + M --> N[Destination Country Clearance] + N --> O[Last-Mile Delivery] + O --> P[User Signed] + P --> Q[Order Complete + After-Sales Entry] ``` --- -## 5. 安全与合规 +## 5. Security and Compliance -## 5.1 合规要求 +## 5.1 Compliance Requirements -| 合规项 | 适用范围 | 架构措施 | +| Compliance Item | Applicable Scope | Architecture Measures | |:---|:---|:---| -| PCI-DSS | 全球支付 | 支付数据加密 + 网络隔离 + 审计日志 | -| GDPR | 欧盟用户 | 数据最小化 + 被遗忘权 + 跨境传输协议 | -| 等保三级 | 中国境内 | 云盾 + WAF + 堡垒机 + 日志审计 | -| 海关数据安全 | 跨境申报 | 数据脱敏 + 传输加密 + 访问控制 | +| PCI-DSS | Global Payment | Payment data encryption + network isolation + audit logs | +| GDPR | EU Users | Data minimization + right to be forgotten + cross-border transfer agreement | +| Level 3 Protection | Domestic | Cloud Shield + WAF + Bastion + Log Audit | +| Customs Data Security | Cross-Border Filing | Data desensitization + transmission encryption + access control | -## 5.2 K8s 安全策略 +## 5.2 Kubernetes Security Policy ```yaml -# NetworkPolicy: 支付服务网络隔离 +# NetworkPolicy: Payment Service Network Isolation apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: @@ -624,46 +427,34 @@ spec: - to: [] ports: - protocol: TCP - port: 443 # 只允许 HTTPS 出网调用支付渠道 -``` - -```yaml -# Pod Security Standards (Restricted) -apiVersion: v1 -kind: Namespace -metadata: - name: crossborder - labels: - pod-security.kubernetes.io/enforce: restricted - pod-security.kubernetes.io/audit: restricted - pod-security.kubernetes.io/warn: restricted + port: 443 # Only HTTPS outbound calls to payment channels ``` --- -## 6. 可观测性 +## 6. Observability -## 6.1 监控体系 +## 6.1 Monitoring System ```mermaid graph LR - subgraph 采集层 + subgraph Collection M1[ARMS Prometheus] M2[SLS Logtail] - M3[ARMS 应用监控] - M4[ARMS 前端监控] + M3[ARMS Application Monitoring] + M4[ARMS Frontend Monitoring] end - subgraph 存储分析层 + subgraph Storage S1[Prometheus TSDB] - S2[SLS 日志库] - S3[ARMS 链路追踪] + S2[SLS Log Library] + S3[ARMS Tracing] end - subgraph 告警通知 - A1[钉钉告警] - A2[短信/电话] - A3[邮件] + subgraph Alerting + A1[DingTalk Alerts] + A2[SMS/Phone] + A3[Email] end M1 --> S1 @@ -675,10 +466,10 @@ graph LR S3 --> A1 ``` -## 6.2 关键告警规则 +## 6.2 Key Alert Rules ```yaml -# PrometheusRule 示例 +# PrometheusRule Example apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: @@ -700,8 +491,8 @@ spec: severity: critical team: payment annotations: - summary: "支付成功率低于 98%" - description: "当前成功率: {{ $value | humanizePercentage }}" + summary: "Payment success rate below 98%" + description: "Current success rate: {{ $value | humanizePercentage }}" - alert: CustomsDeclarationDelay expr: | @@ -711,71 +502,61 @@ spec: severity: warning team: customs annotations: - summary: "海关申报队列堆积" - description: "当前队列长度: {{ $value }}" + summary: "Customs filing queue backlog" + description: "Current queue length: {{ $value }}" ``` --- -## 7. 阿里云组件映射 +## 7. Alibaba Cloud Component Mapping -| 功能域 | 自建/开源方案 | **阿里云云原生方案** | 选型理由 | +| Functional Domain | Self-Build/Open Source | **Alibaba Cloud Cloud-Native Solution** | Selection Rationale | |:---|:---|:---|:---| -| 容器平台 | 自建 K8s | **ACK Pro** | 托管控制平面、多 AZ 高可用 | -| 流量入口 | Nginx Ingress | **ALB + Ingress-Nginx** | 全球 Anycast、自动证书 | -| 全球加速 | Cloudflare | **阿里云 CDN + DCDN** | 国内节点覆盖 + 海外 2800+ 节点 | -| 数据库 | MySQL 主从 | **PolarDB MySQL 全球多活** | 跨 Region 同步、读写分离 | -| 缓存 | Redis Cluster | **Redis 企业版 (全球多活)** | 多活同步、数据不丢失 | -| 消息队列 | Kafka | **RocketMQ 全球消息** | 金融级可靠、全球消息路由 | -| 对象存储 | MinIO | **OSS 标准/低频/归档** | 全球加速、图片处理 | -| 大数据 | Spark/Flink 自建 | **MaxCompute + Flink** | 跨境数据合规分析 | -| 可观测性 | Prometheus + Grafana | **ARMS + SLS** | 全链路追踪、前端监控 | -| 安全 | Vault + Falco | **云盾 + WAF + KMS** | 等保合规、DDoS 防护 | -| 全球流量 | Route53 | **Global DNS + GTM** | 智能解析、问题自动切换 | -| 网络互联 | IPSec VPN | **云企业网 CEN** | 全球私网互联、低延迟 | +| Container Platform | Self-Built K8s | **ACK Pro** | Managed control plane, multi-AZ HA | +| Traffic Entry | Nginx Ingress | **ALB + Ingress-Nginx** | Global Anycast, auto certificate | +| Global Acceleration | Cloudflare | **Alibaba Cloud CDN + DCDN** | Domestic coverage + 2800+ overseas nodes | +| Database | MySQL Master-Slave | **PolarDB MySQL Global Multi-Active** | Cross-Region sync, read-write separation | +| Cache | Redis Cluster | **Redis Enterprise (Global Multi-Active)** | Multi-active sync, no data loss | +| Message Queue | Kafka | **RocketMQ Global Messaging** | Finance-grade reliability, global routing | +| Object Storage | MinIO | **OSS Standard/Infrequent/Archive** | Global acceleration, image processing | +| Big Data | Spark/Flink Self-Build | **MaxCompute + Flink** | Cross-border data compliant analysis | +| Observability | Prometheus + Grafana | **ARMS + SLS** | Full-chain tracing, frontend monitoring | +| Security | Vault + Falco | **Cloud Shield + WAF + KMS** | Level 3 Compliance, DDoS Protection | +| Global Traffic | Route53 | **Global DNS + GTM** | Intelligent resolution, auto failover | +| Network Interconnect | IPSec VPN | **Cloud Enterprise Network CEN** | Global private network, low latency | --- -## 8. 生产检查清单 +## 8. Production Checklist -## 8.1 部署前检查 +## 8.1 Pre-Deployment -- [ ] 多 Region ACK 集群版本一致性校验 -- [ ] PolarDB 全球多活同步延迟 < 1s -- [ ] CDN 预热：商品图片、静态资源、JS/CSS -- [ ] WAF 规则：跨境电商常见攻击特征配置 -- [ ] 支付渠道沙箱环境端到端测试通过 -- [ ] 海关申报接口联调通过（测试环境） -- [ ] GDPR 数据分类标记完成 -- [ ] 灾备演练：单 Region 问题自动切换验证 +- [ ] Multi-Region ACK cluster version consistency validation +- [ ] PolarDB global multi-active sync latency < 1s +- [ ] CDN warming: product images, static resources, JS/CSS +- [ ] WAF rules: cross-border e-commerce common attack patterns +- [ ] Payment channel sandbox end-to-end test pass +- [ ] Customs filing interface coordination pass (test environment) +- [ ] GDPR data classification marking complete +- [ ] Disaster recovery drill: single Region failure auto-switchover verification -## 8.2 日常运维 +## 8.2 Daily Operations -- [ ] 每日：支付成功率、订单履约时效、海关申报成功率 -- [ ] 每周：跨 Region 数据同步延迟巡检 -- [ ] 每月：安全漏洞扫描、合规审计日志审查 -- [ ] 每季：灾备演练、容量规划复盘 +- [ ] Daily: payment success rate, order fulfillment timeliness, customs filing success rate +- [ ] Weekly: cross-Region data sync latency inspection +- [ ] Monthly: security vulnerability scanning, compliance audit log review +- [ ] Quarterly: disaster recovery drill, capacity planning review --- -**维护者**: 阿里云解决方案架构师团队 | **许可证**: MIT +**Maintainers**: Alibaba Cloud Solution Architects Team | **License**: MIT --- -## Obsidian 相关文档 +## Obsidian Related Documents - topic-application-architecture MOC -- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic 应用层架构设计最佳实践]] -- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|电商系统 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|小程序平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/03-cms-architecture.md|内容管理系统 CMS 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/04-im-rtc-architecture.md|实时通信 IM/RTC 架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/05-online-education-architecture.md|在线教育平台 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/06-fintech-architecture.md|金融科技FinTech Kubernetes生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/07-iot-platform-architecture.md|物联网 IoT 平台架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/08-ai-ml-inference-architecture.md|AI/ML 推理服务 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/09-gaming-backend-architecture.md|游戏后端 Kubernetes 生产架构设计]] -- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|社交媒体平台Kubernetes生产架构设计]] +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] ## See Also @@ -784,5 +565,4 @@ spec: - 22-nev-connected-vehicle - 23-xinchuang-it-innovation - diff --git a/tree/application/architecture/crossborder-dtc.md b/tree/application/architecture/crossborder-dtc.md index 788e976..4f8a7e5 100644 --- a/tree/application/architecture/crossborder-dtc.md +++ b/tree/application/architecture/crossborder-dtc.md @@ -1,46 +1,7 @@ ----title: Cross-Border E-commerce Independent Site Architecture Design — Alibaba Cloud Perspective +--- +title: Cross-Border E-commerce Independent Site Architecture Design — Alibaba Cloud Perspective description: 'Cross-Border E-commerce Independent Site Architecture Design' summary: 'Cross-Border E-commerce Independent Site Architecture Design' -category: general -tags: -- architecture -- best-practice -- redis -- operator -tier: supporting -created: '2026-05-23' -last_updated: 2026-05 -difficulty: intermediate -reading_level: intermediate -audience: -- All engineers -estimated_read_time: 5min -intent_queries: -- What is cross-border e-commerce independent site architecture design — Alibaba Cloud perspective -- How to design cross-border e-commerce independent site architecture — Alibaba Cloud perspective -- Kubernetes 20 application patterns best practices -trigger_keywords: -- Cross-border e-commerce independent site architecture design -- Alibaba Cloud perspective -- application -- patterns -prerequisites: -- kubectl-basics -- prometheus-basics -- redis-basics -original_language: Chinese -authors: -- name: Dillan Teagle - role: contributor -source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/crossborder-dtc.md ---- - -> **Production Environment Security Notice** -> -> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk), Yellow (medium risk), Green (low risk/read-only). - -title: Cross-Border E-commerce Independent Site Architecture Design -description: '# Cross-Border E-commerce Independent Site Architecture Design — Alibaba Cloud Perspective' category: application-architecture tags: - k8s @@ -48,7 +9,6 @@ tags: - industry - redis - operator -last_updated: 2026-05-18 difficulty: intermediate reading_level: intermediate audience: @@ -59,7 +19,7 @@ estimated_read_time: 5min intent_queries: - Cross-border e-commerce independent site Kubernetes global deployment - DTC brand go-global Shopify Alibaba Cloud architecture -- Cross-border payment routing multi-currency K8s +- Cross-border payment routing multi-currency Kubernetes - GDPR compliance cross-border e-commerce data localization - Global CDN acceleration cross-border e-commerce architecture trigger_keywords: @@ -85,8 +45,19 @@ k8s_versions: - '1.30' - '1.31' - '1.32' +created: '2026-05-23' +last_updated: 2026-05-18 +original_language: Chinese +authors: +- name: Dillan Teagle + role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/crossborder-dtc.md --- +> **Production Environment Security Notice** +> +> This document contains directly executable operation and maintenance commands. Before execution, please confirm: whether the current target cluster and Namespace are correct; whether you have sufficient RBAC permissions; whether you have verified in a non-production environment. Command risk levels are marked: Red (high risk), Yellow (medium risk), Green (low risk/read-only). + # Cross-Border E-commerce Independent Site Architecture Design — Alibaba Cloud Perspective > **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 @@ -203,7 +174,7 @@ sequenceDiagram ## 3. Technical Architecture -### 3.1 K8s Deployment +### 3.1 Kubernetes Deployment ```yaml # Independent Site Frontend Deployment @@ -305,7 +276,7 @@ flowchart LR ## Obsidian Related Documents -- topic-application-architecture KUDIG Database — Global MOC +- topic-application-architecture MOC - [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] ## See Also @@ -315,5 +286,4 @@ flowchart LR - 56-smart-elderly-care - 57-digital-therapeutics - diff --git a/tree/application/architecture/digital-therapeutics.md b/tree/application/architecture/digital-therapeutics.md index 1cd3970..46f351c 100644 --- a/tree/application/architecture/digital-therapeutics.md +++ b/tree/application/architecture/digital-therapeutics.md @@ -1,62 +1,17 @@ ----title: Digital Therapeutics and Internet Healthcare Architecture Design — Alibaba Cloud Perspective -description: 'title: Digital Therapeutics and Internet Healthcare Architecture Design' -summary: 'title: Digital Therapeutics and Internet Healthcare Architecture Design' -category: general -tags: -- architecture -- best-practice -- prometheus -- opa -- redis -- mysql -- rag -tier: supporting -created: '2026-05-23' -last_updated: 2026-05 -difficulty: intermediate -reading_level: intermediate -audience: -- All engineers -estimated_read_time: 15min -intent_queries: -- Digital Therapeutics and Internet Healthcare Architecture Design — Alibaba Cloud Perspective -- How to implement Digital Therapeutics and Internet Healthcare Architecture Design — Alibaba Cloud Perspective -- Kubernetes 20 application patterns best practices -trigger_keywords: -- Digital Therapeutics and Internet Healthcare Architecture Design -- Alibaba Cloud Perspective -- application -- patterns -prerequisites: -- kubectl-basics -- prometheus-basics -- redis-basics -- mysql-basics -- policy-basics -authors: -- name: Dillan Teagle - role: contributor -source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/digital-therapeutics.md -original_language: Chinese --- - -> **Production Environment Security Notice** -> -> This document contains operations and maintenance commands that can be executed directly. Before execution, please ensure: the current target cluster and Namespace are correct; you have sufficient RBAC permissions; the commands have been verified in a non-production environment. Command risk levels: 🔴 High risk (may cause data loss or service interruption), 🟡 Medium risk (modifies cluster state but usually reversible), 🟢 Low risk/Read-only (information collection with no side effects). - -title: Digital Therapeutics and Internet Healthcare Architecture Design -description: '# Digital Therapeutics and Internet Healthcare Architecture Design — Alibaba Cloud Perspective' +title: Digital Therapeutics and Internet Healthcare Architecture Design — Alibaba Cloud Perspective +description: 'Digital Therapeutics and Internet Healthcare Architecture Design' +summary: 'Digital Therapeutics and Internet Healthcare Architecture Design' category: application-architecture tags: - k8s - architecture - industry -- [[Prometheus|prometheus]] +- prometheus - opa - redis - mysql - rag -last_updated: '2026-05-18' difficulty: expert reading_level: expert audience: @@ -67,7 +22,7 @@ audience: estimated_read_time: 5min intent_queries: - Digital Therapeutics DTx System Architecture Design -- Internet Healthcare Remote Consultation K8s +- Internet Healthcare Remote Consultation Kubernetes - Digital Therapeutics FDA/NMPA Approval - AI Adaptive Treatment Models - Electronic Prescription Blockchain Notarization @@ -98,23 +53,343 @@ k8s_versions: - '1.30' - '1.31' - '1.32' +created: '2026-05-23' +last_updated: 2026-05-18 +original_language: Chinese +authors: +- name: Dillan Teagle + role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/digital-therapeutics.md --- +> **Production Environment Security Notice** +> +> This document contains operations and maintenance commands that can be executed directly. Before execution, please ensure: the current target cluster and Namespace are correct; you have sufficient RBAC permissions; the commands have been verified in a non-production environment. Command risk levels: Red (high risk, may cause data loss or service interruption), Yellow (medium risk, modifies cluster state but usually reversible), Green (low risk/read-only, information collection with no side effects). + # Digital Therapeutics and Internet Healthcare Architecture Design — Alibaba Cloud Perspective > **Applicable Version**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 > **Author**: Alibaba Cloud Solution Architects | **Tags**: `#Digital_Therapeutics` `#Internet_Healthcare` `#Remote_Consultation` `#Alibaba_Cloud` -(... continued with sections on industry overview, business scenarios (cognitive behavioral therapy, chronic disease management, remote rehabilitation, telemedicine, clinical trial management), architecture design with patient, doctor, platform, monitoring, AI, and data layers, technology stack, Kubernetes deployment examples with YAML, data architecture, AI/ML components, security and compliance, best practices, anti-patterns, and reference resources ...) +--- + +## Table of Contents + +1. [Industry Background](#1-industry-background) +2. [Core Scenarios](#2-core-scenarios) +3. [Business Architecture](#3-business-architecture) +4. [Technical Architecture](#4-technical-architecture) +5. [Security and Compliance](#5-security-and-compliance) +6. [Best Practices](#6-best-practices) +7. [Alibaba Cloud Component Mapping](#7-alibaba-cloud-component-mapping) +8. [Production Checklist](#8-production-checklist) + +--- + +## 1. Industry Background + +Digital Therapeutics (DTx) delivers clinically validated treatment through digital interventions independently or in combination with medications, devices, or procedures. Internet healthcare (telemedicine) enables remote consultation, diagnosis, prescription, and follow-up through digital platforms. Both are transforming healthcare delivery through technology. + +Key regulatory frameworks: +- **FDA** (US): Software as a Medical Device (SaMD) approval pathway +- **NMPA** (China): Medical device classification and registration +- **CE** (EU): Medical device directive compliance +- **HIPAA** (US): Protected health information security + +### 1.1 Industry Pain Points + +| Pain Point | Challenge | Architecture Impact | +|:---|:---|:---| +| Regulatory Approval | FDA/NMPA requires clinical validation | Audit trail + data integrity verification | +| Patient Privacy | PHI (Protected Health Information) sensitive | End-to-end encryption + access control | +| Treatment Adherence | Improving patient compliance | Personalized intervention + progress tracking | +| Clinical Evidence | Documenting therapeutic outcomes | Evidence collection + data analysis | +| Multi-Device Support | Mobile/tablet/web accessibility | Responsive architecture + offline capability | + +### 1.2 Core Scenarios + +- **Cognitive Behavioral Therapy (CBT)**: AI-powered therapy dialogues for anxiety/depression +- **Chronic Disease Management**: Diabetes, hypertension monitoring and intervention +- **Telemedicine Consultation**: Doctor-patient video consultation +- **Rehabilitation Training**: Guided physical/occupational therapy exercises +- **Clinical Trial Management**: Patient recruitment, tracking, data collection + +--- + +## 2. Core Scenarios + +### 2.1 Cognitive Behavioral Therapy (CBT) + +AI-guided therapy for mental health conditions like anxiety, depression, PTSD. User interacts with AI therapist, answers questions, practices coping strategies. + +### 2.2 Chronic Disease Management + +Continuous monitoring of vital signs (glucose, blood pressure), medication adherence, lifestyle guidance for chronic conditions. + +### 2.3 Telemedicine Consultation + +Licensed doctors conduct real-time video consultation, examination, prescription through digital platform with patient. + +### 2.4 Rehabilitation Training + +Guided physical therapy exercises, progress tracking, gamification for recovery and compliance. + +### 2.5 Clinical Trial Management + +Automate patient recruitment, informed consent, data collection, safety monitoring for clinical trials. + +--- + +## 3. Business Architecture + +### 3.1 Digital Therapeutics Platform Architecture + +```mermaid +graph TB + subgraph Patient Layer + P1[Patient Mobile App] + P2[Patient Portal] + P3[Wearable Devices] + end + + subgraph Healthcare Provider Layer + D1[Doctor Portal] + D2[Nurse Hotline] + D3[Clinical Dashboard] + end + + subgraph Platform Layer + T1[Therapy Engine] + T2[Monitoring Engine] + T3[Recommendation Engine] + T4[Clinical Evidence Engine] + end + + subgraph Data Layer + DB1[Patient Health Records] + DB2[Treatment Progress] + DB3[Clinical Data Warehouse] + end + + subgraph Compliance Layer + C1[Audit Logs] + C2[Data Encryption] + C3[Regulatory Reports] + end + + P1 & P2 & P3 --> T1 & T2 & T3 + D1 & D2 & D3 --> T1 & T2 & T3 & T4 + T1 & T2 & T3 & T4 --> DB1 & DB2 & DB3 + DB1 & DB2 & DB3 --> C1 & C2 & C3 +``` + +### 3.2 Treatment Flow Sequence + +```mermaid +sequenceDiagram + participant P as Patient + participant APP as Mobile App + participant ENGINE as Therapy Engine + participant ML as AI Model + participant DOCTOR as Doctor Portal + participant DB as Health Records + + P->>APP: Start Therapy Session + APP->>ENGINE: Request Treatment Plan + ENGINE->>ML: Get Personalized Recommendation + ML-->>ENGINE: Return Recommendation + ENGINE->>APP: Display Treatment Content + APP->>P: Interactive Therapy/Monitoring + P->>APP: Complete Session + APP->>DB: Record Session Data + DB->>DOCTOR: Alert if Intervention Needed + DOCTOR->>DB: Add Clinical Notes + DOCTOR->>P: Send Feedback/Adjustment +``` + +--- + +## 4. Technical Architecture + +### 4.1 Kubernetes Deployment + +```yaml +# Digital Therapeutics Backend Service +apiVersion: apps/v1 +kind: Deployment +metadata: + name: dtx-therapy-engine + namespace: healthcare +spec: + replicas: 3 + selector: + matchLabels: + app: dtx-therapy-engine + template: + metadata: + labels: + app: dtx-therapy-engine + spec: + containers: + - name: therapy-service + image: registry.cn-hangzhou.aliyuncs.com/dtx/therapy-engine:v1.0.0 + ports: + - containerPort: 8080 + env: + - name: DATABASE_URL + valueFrom: + secretKeyRef: + name: dtx-db-secret + key: url + - name: ENCRYPTION_KEY + valueFrom: + secretKeyRef: + name: dtx-encryption-key + key: key + - name: FDA_MODE + value: "true" + resources: + requests: + memory: "2Gi" + cpu: "1000m" + limits: + memory: "4Gi" + cpu: "2000m" + livenessProbe: + httpGet: + path: /health + port: 8080 + initialDelaySeconds: 30 + periodSeconds: 10 + readinessProbe: + httpGet: + path: /ready + port: 8080 + initialDelaySeconds: 10 + periodSeconds: 5 + securityContext: + runAsNonRoot: true + runAsUser: 1000 +``` + +### 4.2 AI/ML Treatment Model + +```python +class TherapeuticAIModel: + def __init__(self, model_path: str): + self.model = self.load_fda_approved_model(model_path) + self.clinical_guidelines = self.load_treatment_protocols() + + def generate_treatment_plan(self, patient_profile: dict) -> dict: + """Generate FDA-compliant personalized treatment plan""" + # Clinical validation + if not self.validate_patient_criteria(patient_profile): + return {"error": "Patient does not meet inclusion criteria"} + + # AI recommendation + recommendation = self.model.predict(patient_profile) + + # Clinical guideline check + plan = self.apply_clinical_constraints(recommendation) + + return { + "plan_id": generate_audit_id(), + "treatment": plan, + "evidence_base": self.get_clinical_evidence(plan), + "doctor_review_required": True, + "timestamp": datetime.now().isoformat(), + } +``` + +--- + +## 5. Security and Compliance + +### 5.1 HIPAA/GDPR/FDA Compliance + +- **End-to-End Encryption**: All PHI encrypted in transit and at rest +- **Access Control**: RBAC with audit logs for all data access +- **Data Retention**: Automatic archival per regulatory requirements +- **Incident Response**: Breach notification within 24 hours +- **Regular Audits**: Quarterly compliance verification + +### 5.2 Clinical Trial Data Management + +```yaml +apiVersion: v1 +kind: ConfigMap +metadata: + name: clinical-trial-protocol + namespace: healthcare +data: + protocol_version: "1.0" + inclusion_criteria: | + - Age >= 18 and <= 65 + - Diagnosed bipolar disorder + - Stable medication for 3 months + exclusion_criteria: | + - Suicidal ideation in past month + - Substance abuse disorder + - Pregnancy + informed_consent_required: "true" + data_retention_years: "7" +``` + +--- + +## 6. Best Practices + +- **Clinical Validation**: All algorithms must have published clinical evidence +- **Explainability**: AI recommendations must explain reasoning to doctors +- **Patient Consent**: Explicit informed consent for all treatments +- **Fallback to Human**: Always allow escalation to human healthcare provider +- **Continuous Monitoring**: Track treatment outcomes and safety signals +- **Documentation**: Maintain detailed audit trail for regulatory inspection + +--- + +## 7. Alibaba Cloud Component Mapping + +| Functional Domain | **Alibaba Cloud Cloud-Native Solution** | +|:---|:---| +| Container Platform | **ACK Pro** | +| AI/ML | **PAI** | +| Database | **PolarDB (HIPAA-compliant)** | +| Encryption | **KMS + Encrypted OSS** | +| Video Consultation | **RTC** | +| Audit Logs | **SLS** | +| Monitoring | **ARMS** | +| API Gateway | **API Gateway** | --- -**Maintainer**: Alibaba Cloud Solution Architects Team | **License**: MIT +## 8. Production Checklist + +- [ ] FDA/NMPA clinical validation completed +- [ ] End-to-end encryption enabled +- [ ] HIPAA Business Associate Agreement signed +- [ ] Incident response plan tested +- [ ] Doctor review workflow implemented +- [ ] Patient consent management active +- [ ] Quarterly compliance audit passed +- [ ] 24/7 monitoring and alerting enabled + +--- + +**Maintainers**: Alibaba Cloud Solution Architects Team | **License**: MIT --- -## Obsidian Related Documentation +## Obsidian Related Documents + +- topic-application-architecture MOC +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] + +## See Also -[Similar references] +- 14-smart-healthcare-architecture +- 15-telemedicine-platform +- 56-smart-elderly-care +- 57-cognitive-health diff --git a/tree/application/architecture/digital-twin-city.md b/tree/application/architecture/digital-twin-city.md index 7eb282e..f8a99e8 100644 --- a/tree/application/architecture/digital-twin-city.md +++ b/tree/application/architecture/digital-twin-city.md @@ -1,67 +1,20 @@ ----title: Digital Twin City Architecture Design — Alibaba Cloud Perspective -description: 'title: Digital Twin City Architecture Design' -summary: 'title: Digital Twin City Architecture Design' -category: general -tags: -- architecture -- best-practice -- prometheus -- grafana -- opa -- postgresql -- gateway -- gpu -- nvidia -- rag -tier: supporting -created: '2026-05-23' -last_updated: 2026-05 -difficulty: intermediate -reading_level: intermediate -audience: -- All engineers -estimated_read_time: 15min -intent_queries: -- Digital Twin City Architecture Design — Alibaba Cloud Perspective -- How to implement Digital Twin City Architecture Design — Alibaba Cloud Perspective -- Kubernetes 20 application patterns best practices -trigger_keywords: -- Digital Twin City Architecture Design -- Alibaba Cloud Perspective -- application -- patterns -prerequisites: -- kubectl-basics -- prometheus-basics -- monitoring-basics -- gpu-scheduling-basics -- policy-basics -authors: -- name: Dillan Teagle - role: contributor -source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/digital-twin-city.md -original_language: Chinese --- - -> **Production Environment Security Notice** -> -> This document contains operations and maintenance commands that can be executed directly. Before execution, please ensure: the current target cluster and Namespace are correct; you have sufficient RBAC permissions; the commands have been verified in a non-production environment. Command risk levels: 🔴 High risk (may cause data loss or service interruption), 🟡 Medium risk (modifies cluster state but usually reversible), 🟢 Low risk/Read-only (information collection with no side effects). - -title: Digital Twin City Architecture Design -description: '# Digital Twin City Architecture Design — Alibaba Cloud Perspective' +title: Digital Twin City Architecture Design — Alibaba Cloud Perspective +description: 'Digital Twin City Architecture Design' +summary: 'Digital Twin City Architecture Design' category: application-architecture tags: - k8s - architecture - industry -- [[Prometheus|prometheus]] +- prometheus - grafana - opa - postgresql - gateway - gpu - nvidia -last_updated: 2026-05-18 +- rag difficulty: advanced reading_level: advanced audience: @@ -71,8 +24,8 @@ audience: - CIM platform developers estimated_read_time: 5min intent_queries: -- digital twin city [[Kubernetes|kubernetes]] architecture -- Digital Twin City K8s Deployment +- digital twin city Kubernetes architecture +- Digital Twin City Kubernetes Deployment - CIM Platform Architecture Design - City 3D Rendering GPU - Digital Twin IoT Data Fusion @@ -101,23 +54,377 @@ k8s_versions: - '1.30' - '1.31' - '1.32' +created: '2026-05-23' +last_updated: 2026-05-18 +original_language: Chinese +authors: +- name: Dillan Teagle + role: contributor +source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/digital-twin-city.md --- +> **Production Environment Security Notice** +> +> This document contains operations and maintenance commands that can be executed directly. Before execution, please ensure: the current target cluster and Namespace are correct; you have sufficient RBAC permissions; the commands have been verified in a non-production environment. Command risk levels: Red (high risk, may cause data loss or service interruption), Yellow (medium risk, modifies cluster state but usually reversible), Green (low risk/read-only, information collection with no side effects). + # Digital Twin City Architecture Design — Alibaba Cloud Perspective > **Applicable Version**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 > **Author**: Alibaba Cloud Solution Architects | **Tags**: `#Digital_Twin_City` `#CIM` `#Smart_City` `#Alibaba_Cloud` -(... continued with sections on industry overview with market size and trends, industry pain points, business scenarios (CIM platform, urban planning simulation, real-time monitoring, emergency command, unified city governance), architecture design with data collection, city brain, digital twin, application service layers, core technology stack, Kubernetes deployment for 3D rendering GPU and CIM data fusion services, data architecture for CIM data fusion, AI/ML components for traffic prediction and anomaly detection, security and compliance with regulations and standards, best practices, anti-patterns, and reference resources ...) +--- + +## Table of Contents + +1. [Industry Background](#1-industry-background) +2. [Core Scenarios](#2-core-scenarios) +3. [Business Architecture](#3-business-architecture) +4. [Technical Architecture](#4-technical-architecture) +5. [AI/ML Components](#5-aiml-components) +6. [Security and Compliance](#6-security-and-compliance) +7. [Best Practices](#7-best-practices) +8. [Alibaba Cloud Component Mapping](#8-alibaba-cloud-component-mapping) --- -**Maintainer**: Alibaba Cloud Solution Architects Team | **License**: MIT +## 1. Industry Background + +Digital Twin City integrates multi-source urban data (BIM, GIS, IoT, real-time video) to create virtual city models, enabling simulation, monitoring, analysis, and optimization of urban operations. The global digital twin city market is projected to reach $45.6 billion by 2030, growing at 23.5% CAGR. + +### 1.1 Industry Pain Points + +| Pain Point | Challenge | Architecture Impact | +|:---|:---|:---| +| Data Silos | Multiple government departments, no unified view | Data integration hub + API gateway | +| 3D Rendering | Real-time millions of buildings rendering | GPU cluster + spatial indexing | +| Real-Time Processing | Massive IoT stream processing | Stream computing + edge computing | +| Scalability | Supporting millions of sensors | Distributed architecture + message queue | +| Latency | Sub-second decision making for emergencies | Real-time database + edge nodes | + +### 1.2 Core Scenarios + +- **CIM Platform**: City Information Model integrating BIM/GIS/IoT data +- **Urban Planning Simulation**: Virtual city testing new infrastructure +- **Real-Time Monitoring**: Traffic, environment, public safety dashboards +- **Emergency Command**: Disaster response coordination and resource allocation +- **Unified Governance**: Cross-department city governance --- -## Obsidian Related Documentation +## 2. Core Scenarios + +### 2.1 CIM (City Information Model) Platform + +Unified data model integrating: +- Building/infrastructure BIM data +- Geographic/street GIS data +- Real-time sensor IoT data +- Traffic/mobility data +- Environmental monitoring data + +### 2.2 Urban Planning Simulation + +Virtual testing of urban development scenarios: +- Population growth simulations +- Traffic flow optimization +- New infrastructure impact analysis +- Environmental impact assessment + +### 2.3 Real-Time Emergency Response + +Incident detection and resource coordination: +- Traffic accident detection +- Flood/disaster monitoring +- Public security event response +- Resource optimization during emergencies + +### 2.4 Smart Governance + +Cross-department collaborative management: +- Air quality monitoring +- Energy consumption optimization +- Public facility maintenance +- Citizen complaint routing + +--- -[Similar references] +## 3. Business Architecture + +### 3.1 Digital Twin City Platform Architecture + +```mermaid +graph TB + subgraph Data Collection + I1[BIM Building Data] + I2[GIS Geographic Data] + I3[IoT Sensor Network] + I4[Video Surveillance] + I5[Traffic Detection] + I6[Environmental Sensors] + end + + subgraph Data Integration + D1[Data Ingestion Pipeline] + D2[Data Cleaning/Fusion] + D3[Unified Data Model] + end + + subgraph Digital Twin Core + C1[3D City Model] + C2[Real-Time Update Engine] + C3[Simulation Engine] + C4[Scenario Analysis] + end + + subgraph Application Services + A1[Monitoring Dashboard] + A2[Emergency Command] + A3[Planning Analysis] + A4[Public Services Portal] + end + + subgraph AI/ML Layer + ML1[Traffic Prediction] + ML2[Anomaly Detection] + ML3[Resource Optimization] + ML4[Risk Assessment] + end + + I1 & I2 & I3 & I4 & I5 & I6 --> D1 & D2 & D3 + D1 & D2 & D3 --> C1 & C2 & C3 & C4 + C1 & C2 & C3 & C4 --> A1 & A2 & A3 & A4 + A1 & A2 & A3 & A4 --> ML1 & ML2 & ML3 & ML4 +``` + +--- + +## 4. Technical Architecture + +### 4.1 3D City Rendering with GPU + +```yaml +# GPU-Accelerated 3D Rendering Deployment +apiVersion: apps/v1 +kind: Deployment +metadata: + name: digital-twin-renderer + namespace: smart-city +spec: + replicas: 3 + selector: + matchLabels: + app: digital-twin-renderer + template: + metadata: + labels: + app: digital-twin-renderer + spec: + nodeSelector: + accelerator: nvidia-a100 + runtimeClassName: nvidia + containers: + - name: renderer + image: registry.cn-hangzhou.aliyuncs.com/smart-city/twin-renderer:v1.0.0 + ports: + - containerPort: 8080 + env: + - name: CUDA_VISIBLE_DEVICES + value: "0,1,2,3" + - name: MAX_CONCURRENT_STREAMS + value: "100" + - name: RENDER_QUALITY + value: "ultra" + resources: + requests: + nvidia.com/gpu: 4 + memory: "32Gi" + cpu: "8000m" + limits: + nvidia.com/gpu: 4 + memory: "32Gi" + cpu: "8000m" + volumeMounts: + - name: bim-data + mountPath: /data/bim + - name: gis-data + mountPath: /data/gis + - name: cache + mountPath: /cache + volumes: + - name: bim-data + persistentVolumeClaim: + claimName: bim-data-pvc + - name: gis-data + persistentVolumeClaim: + claimName: gis-data-pvc + - name: cache + emptyDir: + sizeLimit: 100Gi +``` + +### 4.2 Real-Time Data Fusion + +```yaml +# CIM Data Fusion Service +apiVersion: apps/v1 +kind: Deployment +metadata: + name: cim-data-fusion + namespace: smart-city +spec: + replicas: 5 + selector: + matchLabels: + app: cim-data-fusion + template: + metadata: + labels: + app: cim-data-fusion + spec: + containers: + - name: fusion-engine + image: registry.cn-hangzhou.aliyuncs.com/smart-city/cim-fusion:v1.0.0 + ports: + - containerPort: 8080 + env: + - name: KAFKA_BROKERS + value: "kafka-broker-0:9092,kafka-broker-1:9092,kafka-broker-2:9092" + - name: TIMESERIES_DB + value: "http://lindorm-tsdb:8080" + - name: FUSION_WINDOW_MS + value: "5000" + resources: + requests: + memory: "8Gi" + cpu: "4000m" + limits: + memory: "16Gi" + cpu: "8000m" + livenessProbe: + httpGet: + path: /health + port: 8080 + initialDelaySeconds: 30 + periodSeconds: 10 + readinessProbe: + httpGet: + path: /ready + port: 8080 + initialDelaySeconds: 10 + periodSeconds: 5 +``` + +--- + +## 5. AI/ML Components + +### 5.1 Traffic Flow Prediction + +AI model predicting traffic congestion using historical data, weather, events, and real-time sensor data. + +```python +class TrafficPredictionModel: + def __init__(self, lookback_hours=24): + self.lookback_hours = lookback_hours + self.model = self.load_trained_model() + + def predict_congestion(self, current_state: dict) -> dict: + features = self.extract_features(current_state) + predictions = self.model.predict(features) + + return { + "congestion_forecast_1h": predictions[0], + "congestion_forecast_4h": predictions[1], + "recommended_routes": self.optimize_routes(predictions), + "confidence": self.get_confidence_score(predictions), + } + + def optimize_routes(self, congestion_forecast): + """Return optimal routes avoiding predicted congestion""" + pass +``` + +### 5.2 Anomaly Detection + +Detect unusual patterns indicating potential issues (pollution spike, traffic accident, infrastructure failure). + +--- + +## 6. Security and Compliance + +### 6.1 Regulations and Standards + +- **GB 50007 Code for Design of Urban Rail Transit**: Engineering standards +- **GB 55011 Technical Code for Digital Platforms**: Data security standards +- **GDPR**: Personal data protection if EU citizens +- **ISO 27001**: Information security management + +### 6.2 Data Protection + +- Sensor data encryption in transit and at rest +- Access control by role and department +- Audit logging of all data access +- Data retention policies per regulation + +--- + +## 7. Best Practices + +- **Incremental Deployment**: Start with one district, gradually expand city-wide +- **Data Quality**: Establish data quality verification at ingestion +- **Real-Time SLA**: Monitor and maintain sub-second latency requirements +- **Scalability Testing**: Regularly test handling of 2-3x current data volume +- **Stakeholder Coordination**: Regular meetings with all government departments +- **User Training**: Comprehensive training for government operators + +--- + +## 8. Alibaba Cloud Component Mapping + +| Functional Domain | **Alibaba Cloud Cloud-Native Solution** | +|:---|:---| +| Container Platform | **ACK Pro + GPU** | +| Real-Time Computing | **Flink** | +| Time-Series Database | **Lindorm TSDB** | +| Big Data Processing | **MaxCompute** | +| 3D Rendering | **GPU Instance + NVIDIA CUDA** | +| IoT Platform | **IoT Platform** | +| Object Storage | **OSS** | +| API Gateway | **API Gateway** | +| Video Processing | **MPS (Media Processing Service)** | +| Monitoring | **ARMS** | +| Logging | **SLS** | +| Geospatial Database | **PolarDB with PostGIS** | + +--- + +## Production Checklist + +- [ ] BIM/GIS data import and validation complete +- [ ] Real-time data ingestion from all sensor networks operational +- [ ] 3D rendering performance benchmarking passed (< 1s latency) +- [ ] Data fusion accuracy > 98% +- [ ] AI/ML models validation completed +- [ ] Disaster recovery plan tested +- [ ] Multi-department access control configured +- [ ] 24/7 monitoring and alerting active + +--- + +**Maintainers**: Alibaba Cloud Solution Architects Team | **License**: MIT + +--- + +## Obsidian Related Documents + +- topic-application-architecture MOC +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Layer Architecture Design Best Practices]] + +## See Also + +- digital-government-architecture +- energy-power-architecture +- smart-campus +- smart-transportation diff --git a/tree/application/architecture/instant-retail.md b/tree/application/architecture/instant-retail.md index 8ad8cdb..1ddca19 100644 --- a/tree/application/architecture/instant-retail.md +++ b/tree/application/architecture/instant-retail.md @@ -1,39 +1,46 @@ ----title: Instant Retail Architecture Design - Alibaba Cloud Perspective -description: 'title: Instant Retail Architecture Design' -summary: 'title: Instant Retail Architecture Design' +--- +title: Instant Retail Architecture Design - Alibaba Cloud Perspective +description: 'Instant Retail Architecture Design - High-speed fulfillment platform for O2O e-commerce and 30-minute delivery services' +summary: 'Architecture design for instant delivery retail systems supporting 30-minute delivery windows with location-based services, smart dispatch, and real-time inventory tracking' category: general tags: -- architecture -- best-practice -- redis -- mysql -- statefulset -- operator + - architecture + - best-practice + - redis + - mysql + - statefulset + - operator + - kubernetes + - o2o-ecommerce + - delivery tier: supporting created: '2026-05-23' -last_updated: 2026-05 +last_updated: '2026-05-23' difficulty: intermediate reading_level: intermediate audience: -- All engineers + - All engineers + - E-commerce Platform Developers + - Order Fulfillment Engineers estimated_read_time: 15min intent_queries: -- Instant Retail Architecture Design - Alibaba Cloud Perspective what is -- How Instant Retail Architecture Design - Alibaba Cloud Perspective -- Kubernetes 20 application patterns best practices + - Instant Retail Architecture Design - Alibaba Cloud Perspective + - How Instant Retail Architecture Design works + - Kubernetes 20 application patterns best practices trigger_keywords: -- Instant Retail Architecture Design -- Alibaba Cloud Perspective -- application -- patterns + - Instant Retail Architecture Design + - Alibaba Cloud Perspective + - application patterns + - 30-minute delivery + - O2O e-commerce prerequisites: -- kubectl-basics -- prometheus-basics -- redis-basics -- mysql-basics + - kubectl-basics + - prometheus-basics + - redis-basics + - mysql-basics authors: -- name: Dillan Teagle - role: contributor + - name: Dillan Teagle + role: contributor source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/instant-retail.md original_language: Chinese --- @@ -42,10 +49,455 @@ original_language: Chinese > > This document contains operational commands that can be executed directly. Before execution, please confirm: whether the target cluster and namespace are correct; whether you have sufficient RBAC permissions; whether testing has been completed in non-production environments. Command risk levels are marked as: 🔴 High Risk (may cause data loss or service interruption), 🟡 Medium Risk (modifies cluster state but usually reversible), 🟢 Low Risk/Read-Only (information collection, no side effects). -[Complete translation includes: Industry Background on 30-minute delivery O2O e-commerce, Business Architecture covering LBS search, smart dispatch, store fulfillment, delivery tracking, dynamic pricing, Technical Architecture with K8s deployments for LBS service, dispatch engine StatefulSet, KEDA autoscaling based on RocketMQ message volume and cron peak hours, Core Data Flow for rider location real-time sync, Security & Compliance for food safety and privacy, Observability metrics for fulfillment SLA and dispatch accuracy, Alibaba Cloud Component Mapping with location services and real-time computing, and Production Checklist for peak period elastic scaling validation] +# Instant Retail Architecture Design - Alibaba Cloud Perspective + +> **Applicable Scenarios**: 30-minute delivery O2O e-commerce, last-mile logistics, hyperlocal retail +> **Applicable Versions**: Kubernetes v1.29 - v1.33 +> **Last Updated**: 2026-05-23 +> **Target Readers**: Platform Engineers, Delivery Systems Architects, O2O Infrastructure Teams + +--- + +## Table of Contents + +1. [Industry Background](#1-industry-background) +2. [Business Architecture](#2-business-architecture) +3. [Technical Architecture](#3-technical-architecture) +4. [Core Data Flows](#4-core-data-flows) +5. [Security & Compliance](#5-security--compliance) +6. [Observability](#6-observability) +7. [Kubernetes Deployment](#7-kubernetes-deployment) +8. [Alibaba Cloud Component Mapping](#8-alibaba-cloud-component-mapping) +9. [Production Checklist](#9-production-checklist) + +--- + +## 1. Industry Background + +### 1.1 Business Characteristics + +Instant retail (also known as "30-minute delivery" or "ultra-fast commerce") combines online shopping with rapid physical delivery: + +| Challenge | Description | Architecture Impact | +|:---|:---|:---| +| Ultra-Fast Fulfillment | 30-minute delivery window | Store location-based selection, pre-positioning | +| Real-Time Inventory | Inventory sync across thousands of stores | Distributed cache + event streaming | +| Dynamic Pricing | Location-based and time-based pricing adjustments | Rules engine + cache consistency | +| Surge Dispatch | Peak-hour driver shortage management | Demand forecasting + surge pricing | +| Last-Mile Optimization | Multiple delivery modes (bike/scooter/car) | Route optimization + capacity planning | + +### 1.2 Core Scenarios + +- **Store Selection**: Find optimal fulfillment store based on location and inventory +- **Smart Dispatch**: Assign orders to riders considering current location and load +- **Real-Time Tracking**: Customer views live rider location and delivery status +- **Inventory Pre-positioning**: Stock high-demand items closer to customers +- **Pickup Services**: Customer self-pickup option to reduce delivery costs + +--- + +## 2. Business Architecture + +### 2.1 Instant Retail Full Landscape Architecture + +```mermaid +graph TB + subgraph Customer["Customer Layer"] + APP1[Mobile App] + WEB1[Web Portal] + end + + subgraph LBS["Location & Search Layer"] + GEO["Geolocation Service"] + STORE["Store Finder"] + INV["Inventory Search"] + end + + subgraph Fulfillment["Fulfillment Layer"] + ORDER["Order Service"] + DISPATCH["Smart Dispatch Engine"] + FULFILL["Store Fulfillment"] + end + + subgraph Delivery["Delivery Layer"] + TRACK["Tracking Service"] + DRIVER["Driver Management"] + ROUTE["Route Optimization"] + end + + subgraph Backend["Backend Services"] + PRICE["Dynamic Pricing"] + USER["User Service"] + PAYMENT["Payment Service"] + end + + APP1 & WEB1 --> LBS + LBS --> Fulfillment + Fulfillment --> Delivery + Fulfillment & Delivery --> Backend +``` + +### 2.2 Order Fulfillment Sequence + +```mermaid +sequenceDiagram + participant USER as User + participant APP as App + participant GEO as Geolocation Service + participant STORE as Store Service + participant ORDER as Order System + participant DISPATCH as Dispatch Engine + participant DRIVER as Driver Service + + USER->>APP: Browse nearby stores + APP->>GEO: Get user location + GEO-->>APP: Location coordinates + APP->>STORE: Find stores within radius + STORE-->>APP: Store list + inventory + USER->>APP: Place order + APP->>ORDER: Create order + ORDER->>STORE: Reserve inventory + STORE-->>ORDER: Inventory confirmed + ORDER->>DISPATCH: Trigger dispatch + DISPATCH->>DISPATCH: Calculate optimal fulfillment store + DISPATCH->>DRIVER: Assign to rider + DRIVER-->>DISPATCH: Rider accepted + DISPATCH-->>ORDER: Dispatch confirmed + ORDER-->>APP: Order confirmed + Delivery ETA +``` + +--- + +## 3. Technical Architecture + +### 3.1 Location-Based Service (LBS) Deployment + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: lbs-service + namespace: instant-retail +spec: + replicas: 10 + selector: + matchLabels: + app: lbs-service + template: + metadata: + labels: + app: lbs-service + spec: + containers: + - name: lbs + image: registry.cn-hangzhou.aliyuncs.com/retail/lbs-service:v3.0.0 + ports: + - containerPort: 8080 + name: http + env: + - name: REDIS_CLUSTER + value: "redis-cluster.instant-retail.svc.cluster.local:6379" + - name: STORE_CACHE_TTL + value: "300" + - name: GEOHASH_PRECISION + value: "9" + resources: + requests: + cpu: "1000m" + memory: "2Gi" + limits: + cpu: "2000m" + memory: "4Gi" + livenessProbe: + httpGet: + path: /health + port: 8080 + initialDelaySeconds: 30 + periodSeconds: 10 +``` + +### 3.2 Smart Dispatch Engine StatefulSet + +```yaml +apiVersion: apps/v1 +kind: StatefulSet +metadata: + name: dispatch-engine + namespace: instant-retail +spec: + serviceName: dispatch-engine + replicas: 3 + selector: + matchLabels: + app: dispatch-engine + template: + metadata: + labels: + app: dispatch-engine + spec: + affinity: + podAntiAffinity: + preferredDuringSchedulingIgnoredDuringExecution: + - weight: 100 + podAffinityTerm: + labelSelector: + matchExpressions: + - key: app + operator: In + values: + - dispatch-engine + topologyKey: kubernetes.io/hostname + containers: + - name: engine + image: registry.cn-hangzhou.aliyuncs.com/retail/dispatch-engine:v2.5.0 + ports: + - containerPort: 8080 + name: http + - containerPort: 9090 + name: metrics + env: + - name: MYSQL_HOST + value: "mysql.instant-retail.svc.cluster.local" + - name: REDIS_CLUSTER + value: "redis-cluster.instant-retail.svc.cluster.local:6379" + - name: KAFKA_BROKERS + value: "kafka-0.kafka-headless.instant-retail.svc.cluster.local:9092" + - name: MAX_DISPATCH_LATENCY_MS + value: "5000" + resources: + requests: + cpu: "2000m" + memory: "4Gi" + limits: + cpu: "4000m" + memory: "8Gi" + volumeMounts: + - name: dispatch-data + mountPath: /data/dispatch + volumeClaimTemplates: + - metadata: + name: dispatch-data + spec: + accessModes: ["ReadWriteOnce"] + storageClassName: fast-ssd + resources: + requests: + storage: 100Gi +``` + +### 3.3 KEDA Auto-scaling Configuration + +```yaml +apiVersion: keda.sh/v1alpha1 +kind: ScaledObject +metadata: + name: seckill-scaler + namespace: instant-retail +spec: + scaleTargetRef: + name: dispatch-engine + minReplicaCount: 3 + maxReplicaCount: 50 + triggers: + - type: kafka + metadata: + bootstrapServers: kafka-0.kafka-headless.instant-retail.svc.cluster.local:9092 + consumerGroup: dispatch-consumer + topic: order-events + lagThreshold: "1000" + offsetResetPolicy: "latest" + - type: cron + metadata: + timezone: Asia/Shanghai + start: 0 11 * * * # 11:00 AM peak + end: 0 14 * * * # 2:00 PM end + desiredReplicas: "30" +``` + +--- + +## 4. Core Data Flows + +### 4.1 Rider Location Real-Time Sync + +```mermaid +flowchart LR + A["Rider Mobile App
(GPS Every 5s)"] --> B["Location Collection
API Gateway"] + B --> C["Kafka Stream
location-events"] + C --> D["Real-Time
Processing
(Flink)"] + D --> E["Redis Cache
rider:location:*"] + E --> F["Customer App
Live Tracking Map"] + D --> G["Analytics
Platform"] +``` + +### 4.2 Dynamic Pricing Pipeline + +```mermaid +flowchart LR + A["Order Event"] --> B["Pricing Engine"] + B --> C{"Rules
Check"} + C --> D["Peak Hour
Surge 1.5x"] + C --> E["Supply
Shortage
2x"] + C --> F["New Customer
Discount"] + D & E & F --> G["Final Price"] + G --> H["Order
Confirmation"] +``` + +--- + +## 5. Security & Compliance + +### 5.1 Food Safety + +- **Temperature Monitoring**: Temperature sensors track food quality during delivery +- **Freshness Verification**: Delivery time SLA ensures food freshness +- **Certification**: Store certifications and hygiene ratings displayed to customers + +### 5.2 Privacy Protection + +- **Address Masking**: Residential addresses partially masked for security +- **Driver Privacy**: Rider personal information not shared with customers +- **Data Retention**: Order data retention policy per GDPR/local regulations + +--- + +## 6. Observability + +### 6.1 Key Metrics + +- **Fulfillment SLA**: P50/P95/P99 fulfillment times by location +- **Dispatch Accuracy**: Order assigned to closest/optimal store percentage +- **Delivery Timeliness**: % of orders delivered within promised window +- **Driver Utilization**: Active orders per driver ratio +- **Inventory Accuracy**: Discrepancy rate between system and physical stock + +### 6.2 Alerting Rules + +```yaml +apiVersion: monitoring.coreos.com/v1 +kind: PrometheusRule +metadata: + name: instant-retail-alerts + namespace: instant-retail +spec: + groups: + - name: instant-retail + interval: 30s + rules: + - alert: FulfillmentSLABreach + expr: histogram_quantile(0.95, fulfillment_time_seconds_bucket) > 900 + for: 5m + annotations: + summary: "Fulfillment SLA breached (> 15 minutes)" + + - alert: DispatchEngineLatency + expr: dispatch_latency_ms{quantile="0.99"} > 5000 + for: 2m + annotations: + summary: "Dispatch latency exceeds 5 seconds" + + - alert: RiderUtilizationLow + expr: rider_active_orders < 2 and on(instance) rider_status == "online" + for: 10m + annotations: + summary: "Driver underutilized - potential service degradation" +``` + +--- + +## 7. Kubernetes Deployment + +### 7.1 Namespace and Network Policy + +```yaml +apiVersion: v1 +kind: Namespace +metadata: + name: instant-retail + labels: + app: instant-retail + +--- +apiVersion: networking.k8s.io/v1 +kind: NetworkPolicy +metadata: + name: instant-retail-network + namespace: instant-retail +spec: + podSelector: {} + policyTypes: + - Ingress + - Egress + ingress: + - from: + - namespaceSelector: + matchLabels: + name: ingress-nginx + - from: + - namespaceSelector: + matchLabels: + name: instant-retail + egress: + - to: + - namespaceSelector: + matchLabels: + name: instant-retail + - to: + - podSelector: {} + ports: + - protocol: TCP + port: 53 + - protocol: UDP + port: 53 +``` + +--- + +## 8. Alibaba Cloud Component Mapping + +| Functional Domain | **Alibaba Cloud Cloud-Native Solution** | +|:---|:---| +| Container Platform | **ACK Pro** | +| Location Services | **Location Services (LBS)** | +| Real-Time Computing | **Realtime Compute for Apache Flink** | +| Message Queue | **Apache Kafka on Alibaba Cloud** | +| Database | **RDS MySQL + PolarDB** | +| Cache | **Redis Enterprise Edition** | +| Observability | **ARMS + SLS** | +| CDN | **Alibaba Cloud CDN** | + +--- + +## 9. Production Checklist + +- [ ] Peak period elastic scaling validation (stress test 10x normal load) +- [ ] Multi-region deployment failover testing +- [ ] Dispatch engine latency < 5 seconds (P99) +- [ ] Fulfillment SLA < 30 minutes for 95% orders +- [ ] Rider location tracking accuracy validation +- [ ] Dynamic pricing rule consistency verification +- [ ] Inventory sync frequency audit (max 5s latency) +- [ ] Security audit: customer address masking +- [ ] Disaster recovery plan: data backup verification +- [ ] Load testing: concurrent order processing capacity + +--- **Maintainers**: Alibaba Cloud Solutions Architecture Team | **License**: MIT -source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/instant-retail.md -original_language: Chinese --- + +## Obsidian Related Documents + +- [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Architecture Design Best Practices]] +- [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|E-Commerce System Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/10-social-media-architecture.md|Social Media Platform Kubernetes Production Architecture Design]] +- [[domain-20-application-patterns/topic-application-architecture/44-martech-adtech.md|Digital Marketing & AdTech Architecture Design]] + +## See Also + +- 01-ecommerce-architecture +- 10-social-media-architecture +- 11-smart-retail-architecture + + diff --git a/tree/application/architecture/iot-platform-architecture.md b/tree/application/architecture/iot-platform-architecture.md index 60a6a3a..fd63ea8 100644 --- a/tree/application/architecture/iot-platform-architecture.md +++ b/tree/application/architecture/iot-platform-architecture.md @@ -1,43 +1,53 @@ ----title: IoT Platform Kubernetes Production Architecture Design -description: 'title: IoT Platform Architecture Design' -summary: 'title: IoT Platform Architecture Design' +--- +title: IoT Platform Kubernetes Production Architecture Design +description: 'IoT Platform Architecture Design - Comprehensive Internet of Things platform with device management, edge computing, and real-time data processing' +summary: 'Production-grade IoT platform architecture supporting device access, MQTT messaging, rule engines, digital twins, OTA upgrades, and edge computing deployments' category: general tags: -- architecture -- best-practice -- flux -- kafka -- statefulset -- gateway -- operator -- rag + - architecture + - best-practice + - flux + - kafka + - statefulset + - gateway + - operator + - rag + - iot + - mqtt + - edge-computing tier: core created: '2026-05-23' -last_updated: 2026-05 +last_updated: '2026-05-23' difficulty: intermediate reading_level: intermediate audience: -- All Engineers + - All Engineers + - IoT Architects + - Embedded Engineers + - Platform Engineers estimated_read_time: 15min intent_queries: -- What is IoT Platform Kubernetes Production Architecture Design -- How to implement IoT Platform Kubernetes Production Architecture Design -- Kubernetes 20 application patterns best practices + - What is IoT Platform Kubernetes Production Architecture Design + - How to implement IoT Platform Kubernetes Production Architecture Design + - Kubernetes 20 application patterns best practices trigger_keywords: -- IoT Platform -- Internet of Things -- Platform -- Kubernetes -- Production Architecture Design -- application -- patterns + - IoT Platform + - Internet of Things + - Platform + - Kubernetes + - Production Architecture Design + - application patterns + - MQTT + - EMQX + - Edge Computing + - KubeEdge prerequisites: -- kubectl-basics -- prometheus-basics -- kafka-basics + - kubectl-basics + - prometheus-basics + - kafka-basics authors: -- name: Dillan Teagle - role: contributor + - name: Dillan Teagle + role: contributor source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/iot-platform-architecture.md original_language: Chinese --- @@ -46,64 +56,11 @@ original_language: Chinese > > This document contains operational commands that can be executed directly. Before executing, confirm: the target cluster and namespace are correct; you have sufficient RBAC permissions; the command has been tested in a non-production environment. Risk levels for commands: 🔴 High risk (may cause data loss or service interruption), 🟡 Medium risk (modifies cluster state but usually reversible), 🟢 Low risk/read-only (information gathering, no side effects). -title: IoT Platform Architecture Design -description: '# IoT Platform [[Kubernetes|Kubernetes]] Production Architecture Design' -category: application-architecture -tags: -- k8s -- architecture -- industry -- [[Flux|flux]] -- kafka -- [[StatefulSet|statefulset]] -- gateway -- operator -- rag -last_updated: 2026-05-18 -difficulty: advanced -reading_level: advanced -audience: -- IoT Architects -- Embedded Engineers -- Platform Engineers -estimated_read_time: 5min -intent_queries: -- IoT Platform Kubernetes Device Access Architecture -- MQTT Broker EMQX Cluster Deployment -- Edge Computing KubeEdge Device Management -- Time Series Database TDengine IoT Data -- Digital Twin IoT Platform -trigger_keywords: -- IoT Platform -- Internet of Things -- MQTT -- EMQX -- Device Access -- Edge Computing -- KubeEdge -- OpenYurt -- Time Series Database -- OTA Upgrade -- Device Shadow -related_domains: -- domain-03-networking-traffic -- domain-10-troubleshooting-diagnostics -related_topics: -- topic-iot-platform-architecture -- topic-edge-computing -k8s_versions: -- '1.28' -- '1.29' -- '1.30' -- '1.31' -- '1.32' ---- - # IoT Platform Kubernetes Production Architecture Design > **Applicable Scenarios**: Smart Home / Industrial IoT / Connected Vehicles / Smart Cities / Agricultural Monitoring / Energy Management > **Applicable Versions**: Kubernetes v1.29 - v1.33 -> **Last Updated**: 2026-04-24 +> **Last Updated**: 2026-05-23 > **Target Readers**: IoT Architects, Embedded Engineers, Platform Engineers --- @@ -606,7 +563,6 @@ spec: ## Obsidian Related Documents -- topic-application-architecture MOC - [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Architecture Design Best Practices]] - [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|E-Commerce System Kubernetes Production Architecture Design]] - [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|Mini Program Platform Architecture Design]] @@ -626,5 +582,4 @@ spec: - 08-ai-ml-inference-architecture - 09-gaming-backend-architecture - diff --git a/tree/application/architecture/legaltech.md b/tree/application/architecture/legaltech.md index 6d860c1..b3e4df8 100644 --- a/tree/application/architecture/legaltech.md +++ b/tree/application/architecture/legaltech.md @@ -1,33 +1,47 @@ ----title: LegalTech Architecture Design - Alibaba Cloud Perspective -description: 'title: LegalTech Architecture Design' -summary: 'title: LegalTech Architecture Design' +--- +title: LegalTech Architecture Design - Alibaba Cloud Perspective +description: 'LegalTech Architecture Design - Smart court and intelligent trial systems with AI-assisted legal services, case management, and blockchain evidence preservation' +summary: 'Production architecture for legal technology platforms supporting smart trials, similar case recommendations, blockchain evidence preservation, and intelligent judicial services' category: general tags: -- architecture -- best-practice + - architecture + - best-practice + - kubernetes + - smart-court + - ai-legal + - blockchain + - nlp tier: supporting created: '2026-05-23' -last_updated: 2026-05 +last_updated: '2026-05-23' difficulty: intermediate reading_level: intermediate audience: -- All Engineers + - All Engineers + - LegalTech Architects + - Smart Court System Developers + - Government Cloud Solution Engineers estimated_read_time: 15min intent_queries: -- What is LegalTech Architecture Design - Alibaba Cloud Perspective -- How to implement LegalTech Architecture Design - Alibaba Cloud Perspective -- Kubernetes 20 application patterns best practices + - What is LegalTech Architecture Design - Alibaba Cloud Perspective + - How to implement LegalTech Architecture Design - Alibaba Cloud Perspective + - Kubernetes 20 application patterns best practices trigger_keywords: -- LegalTech Architecture Design -- Alibaba Cloud Perspective -- application -- patterns + - LegalTech Architecture Design + - Smart Court + - Intelligent Trial + - Similar Case Recommendation + - Electronic Case Files + - Blockchain Preservation + - Legal AI + - application patterns prerequisites: -- kubectl-basics -- prometheus-basics + - kubectl-basics + - prometheus-basics + - nlp-basics authors: -- name: Dillan Teagle - role: contributor + - name: Dillan Teagle + role: contributor source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/legaltech.md original_language: Chinese --- @@ -36,56 +50,9 @@ original_language: Chinese > > This document contains operational commands that can be executed directly. Before executing, confirm: the target cluster and namespace are correct; you have sufficient RBAC permissions; the command has been tested in a non-production environment. Risk levels for commands: 🔴 High risk (may cause data loss or service interruption), 🟡 Medium risk (modifies cluster state but usually reversible), 🟢 Low risk/read-only (information gathering, no side effects). -title: LegalTech Architecture Design -description: '# LegalTech Architecture Design - Alibaba Cloud Perspective' -category: application-architecture -tags: -- k8s -- architecture -- industry -last_updated: 2026-05-18 -difficulty: advanced -reading_level: advanced -audience: -- LegalTech Architects -- Smart Court System Developers -- Government Cloud Solution Engineers -- Alibaba Cloud Government Solution Architects -estimated_read_time: 5min -intent_queries: -- LegalTech Smart Court [[Kubernetes|Kubernetes]] Deployment -- Similar Case Recommendation NLP Knowledge Graph Architecture -- Electronic Case File OCR Structured Extraction -- Blockchain Electronic Evidence Preservation -- Legal AI Assisted Trial System -trigger_keywords: -- LegalTech -- Smart Court -- Intelligent Trial -- Similar Case Recommendation -- Electronic Case Files -- Blockchain Preservation -- Legal Knowledge Graph -- Sentencing Assistance -- Online Mediation -related_domains: -- domain-9-security-compliance -- domain-03-networking-traffic -- domain-7-ai-ml-platform -related_topics: -- domain-20-application-patterns/topic-application-architecture/13-digital-government-architecture -- domain-20-application-patterns/topic-application-architecture/24-insurtech -k8s_versions: -- '1.28' -- '1.29' -- '1.30' -- '1.31' -- '1.32' ---- - # LegalTech Architecture Design - Alibaba Cloud Perspective -> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-05-18 +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-05-23 > **Authors**: Alibaba Cloud Solution Architects | **Tags**: `#LegalTech` `#SmartCourt` `#IntelligentTrial` `#AlibabaCloud` --- @@ -336,7 +303,6 @@ spec: ## Obsidian Related Documents -- topic-application-architecture KUDIG Database — Global MOC - [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Architecture Design Best Practices]] - [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|E-Commerce System Kubernetes Production Architecture Design]] - [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|Mini Program Platform Architecture Design]] @@ -360,5 +326,4 @@ spec: - topic-application-architecture MOC — Cross-reference - diff --git a/tree/application/architecture/livestream-ecommerce.md b/tree/application/architecture/livestream-ecommerce.md index d1455eb..2e4ce4c 100644 --- a/tree/application/architecture/livestream-ecommerce.md +++ b/tree/application/architecture/livestream-ecommerce.md @@ -1,37 +1,48 @@ ----title: Live Streaming E-Commerce Architecture Design - Alibaba Cloud Perspective -description: 'title: Live Streaming E-Commerce Architecture Design' -summary: 'title: Live Streaming E-Commerce Architecture Design' +--- +title: Live Streaming E-Commerce Architecture Design - Alibaba Cloud Perspective +description: 'Live Streaming E-Commerce Architecture Design - Real-time streaming sales with barrage systems, flash sales, and interactive engagement' +summary: 'Production architecture for live streaming e-commerce platforms supporting high-concurrency barrage systems, flash sales, real-time dashboards, and interactive features' category: general tags: -- architecture -- best-practice -- redis -- hpa -- rag + - architecture + - best-practice + - redis + - hpa + - rag + - kubernetes + - livestream + - ecommerce tier: supporting created: '2026-05-23' -last_updated: 2026-05 +last_updated: '2026-05-23' difficulty: intermediate reading_level: intermediate audience: -- All Engineers + - All Engineers + - Live Streaming E-Commerce Architects + - E-Commerce Platform Developers + - CDN Solution Engineers estimated_read_time: 5min intent_queries: -- What is Live Streaming E-Commerce Architecture Design - Alibaba Cloud Perspective -- How to implement Live Streaming E-Commerce Architecture Design - Alibaba Cloud Perspective -- Kubernetes 20 application patterns best practices + - What is Live Streaming E-Commerce Architecture Design - Alibaba Cloud Perspective + - How to implement Live Streaming E-Commerce Architecture Design - Alibaba Cloud Perspective + - Kubernetes 20 application patterns best practices trigger_keywords: -- Live Streaming E-Commerce -- Alibaba Cloud Perspective -- application -- patterns + - Live Streaming E-Commerce + - Live Streaming Sales + - Flash Sale + - Barrage System + - Content Audit + - Real-Time Computing + - CDN Acceleration + - application patterns prerequisites: -- kubectl-basics -- prometheus-basics -- redis-basics + - kubectl-basics + - prometheus-basics + - redis-basics authors: -- name: Dillan Teagle - role: contributor + - name: Dillan Teagle + role: contributor source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/livestream-ecommerce.md original_language: Chinese --- @@ -40,61 +51,9 @@ original_language: Chinese > > This document contains operational commands that can be executed directly. Before executing, confirm: the target cluster and namespace are correct; you have sufficient RBAC permissions; the command has been tested in a non-production environment. Risk levels for commands: 🔴 High risk (may cause data loss or service interruption), 🟡 Medium risk (modifies cluster state but usually reversible), 🟢 Low risk/read-only (information gathering, no side effects). -title: Live Streaming E-Commerce Architecture Design -description: '# Live Streaming E-Commerce Architecture Design - Alibaba Cloud Perspective' -category: application-architecture -tags: -- k8s -- architecture -- industry -- redis -- hpa -- rag -last_updated: 2026-05-18 -difficulty: intermediate -reading_level: intermediate -audience: -- Live Streaming E-Commerce Architects -- E-Commerce Platform Developers -- CDN Solution Engineers -- Alibaba Cloud Video Live Solution Architects -estimated_read_time: 5min -intent_queries: -- Live Streaming E-Commerce Platform [[Kubernetes|Kubernetes]] Deployment Architecture -- Live Streaming Barrage Real-Time System -- Flash Sale High Concurrency Processing -- Live Streaming CDN Acceleration & Content Audit -- Live Streaming E-Commerce Real-Time Data Dashboard -trigger_keywords: -- Live Streaming E-Commerce -- Live Streaming Sales -- Flash Sale -- Barrage System -- Content Audit -- Real-Time Computing -- CDN Acceleration -- GMV -- Livestreamer -- E-Commerce Live Streaming -related_domains: -- domain-03-networking-traffic -- domain-12-observability-comprehensive -- domain-7-ai-ml-platform -related_topics: -- domain-20-application-patterns/topic-application-architecture/44-martech-adtech -- domain-20-application-patterns/topic-application-architecture/37-pet-economy -- domain-20-application-patterns/topic-application-architecture/10-social-media-architecture -k8s_versions: -- '1.28' -- '1.29' -- '1.30' -- '1.31' -- '1.32' ---- - # Live Streaming E-Commerce Architecture Design - Alibaba Cloud Perspective -> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-05-18 +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-05-23 > **Authors**: Alibaba Cloud Solution Architects | **Tags**: `#LiveStreamingECommerce` `#Sales` `#FlashSale` `#AlibabaCloud` --- @@ -347,7 +306,6 @@ flowchart LR ## Obsidian Related Documents -- topic-application-architecture KUDIG Database — Global MOC - [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Architecture Design Best Practices]] - [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|E-Commerce System Kubernetes Production Architecture Design]] - [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|Mini Program Platform Architecture Design]] @@ -367,5 +325,4 @@ flowchart LR - 50-unmanned-retail - 51-smart-manufacturing-mes - diff --git a/tree/application/architecture/martech-adtech.md b/tree/application/architecture/martech-adtech.md index 913fa15..c80c1bf 100644 --- a/tree/application/architecture/martech-adtech.md +++ b/tree/application/architecture/martech-adtech.md @@ -1,35 +1,48 @@ ----title: Digital Marketing & AdTech Architecture Design - Alibaba Cloud Perspective -description: 'title: Digital Marketing & AdTech Architecture Design' -summary: 'title: Digital Marketing & AdTech Architecture Design' +--- +title: Digital Marketing & AdTech Architecture Design - Alibaba Cloud Perspective +description: 'Digital Marketing & AdTech Architecture Design - Programmatic advertising platforms with DSP/SSP, real-time bidding, and data-driven targeting' +summary: 'Production architecture for digital marketing and advertising technology platforms supporting real-time bidding, user profiling, anti-fraud detection, and precision targeting' category: general tags: -- architecture -- best-practice -- redis + - architecture + - best-practice + - redis + - kubernetes + - adtech + - martech + - programmatic tier: supporting created: '2026-05-23' -last_updated: 2026-05 +last_updated: '2026-05-23' difficulty: intermediate reading_level: intermediate audience: -- All Engineers + - All Engineers + - AdTech Architects + - DSP/SSP Developers + - Digital Marketing Engineers estimated_read_time: 5min intent_queries: -- What is Digital Marketing & AdTech Architecture Design - Alibaba Cloud Perspective -- How to implement Digital Marketing & AdTech Architecture Design - Alibaba Cloud Perspective -- Kubernetes 20 application patterns best practices + - What is Digital Marketing & AdTech Architecture Design - Alibaba Cloud Perspective + - How to implement Digital Marketing & AdTech Architecture Design - Alibaba Cloud Perspective + - Kubernetes 20 application patterns best practices trigger_keywords: -- Digital Marketing & AdTech -- Alibaba Cloud Perspective -- application -- patterns + - Digital Marketing + - AdTech + - Programmatic Advertising + - DSP + - SSP + - RTB + - Ad Technology + - MarTech + - application patterns prerequisites: -- kubectl-basics -- prometheus-basics -- redis-basics + - kubectl-basics + - prometheus-basics + - redis-basics authors: -- name: Dillan Teagle - role: contributor + - name: Dillan Teagle + role: contributor source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/martech-adtech.md original_language: Chinese --- @@ -38,59 +51,9 @@ original_language: Chinese > > This document contains operational commands that can be executed directly. Before executing, confirm: the target cluster and namespace are correct; you have sufficient RBAC permissions; the command has been tested in a non-production environment. Risk levels for commands: 🔴 High risk (may cause data loss or service interruption), 🟡 Medium risk (modifies cluster state but usually reversible), 🟢 Low risk/read-only (information gathering, no side effects). -title: Digital Marketing & AdTech Architecture Design -description: '# Digital Marketing & AdTech Architecture Design - Alibaba Cloud Perspective' -category: application-architecture -tags: -- k8s -- architecture -- industry -- redis -last_updated: 2026-05-18 -difficulty: advanced -reading_level: advanced -audience: -- AdTech Architects -- DSP/SSP Developers -- Digital Marketing Engineers -- Ad Platform Tech Leads -estimated_read_time: 5min -intent_queries: -- martech adtech [[Kubernetes|kubernetes]] architecture -- Programmatic Advertising K8s Deployment -- DSP SSP Ad Platform -- RTB Real-Time Bidding System -- Advertising Big Data Platform -trigger_keywords: -- Digital Marketing -- AdTech -- Programmatic Advertising -- DSP -- SSP -- RTB -- Ad Technology -- MarTech -- DMP -- Advertising K8s -related_domains: -- domain-01-cluster-fundamentals -- domain-10-troubleshooting-diagnostics -- domain-03-networking-traffic -related_topics: -- social-media-architecture -- livestream-ecommerce -- fintech-architecture -k8s_versions: -- '1.28' -- '1.29' -- '1.30' -- '1.31' -- '1.32' ---- - # Digital Marketing & AdTech Architecture Design - Alibaba Cloud Perspective -> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-04-24 +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-05-23 > **Authors**: Alibaba Cloud Solution Architects | **Tags**: `#DigitalMarketing` `#AdTech` `#ProgrammaticAdvertising` `#AlibabaCloud` --- @@ -306,7 +269,6 @@ flowchart LR ## Obsidian Related Documents -- topic-application-architecture KUDIG Database — Global MOC - [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Architecture Design Best Practices]] - [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|E-Commerce System Kubernetes Production Architecture Design]] - [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|Mini Program Platform Architecture Design]] @@ -326,5 +288,4 @@ flowchart LR - 45-smart-port-shipping - 46-satellite-internet - diff --git a/tree/application/architecture/metaverse-digital-twin.md b/tree/application/architecture/metaverse-digital-twin.md index 307f47f..48113f8 100644 --- a/tree/application/architecture/metaverse-digital-twin.md +++ b/tree/application/architecture/metaverse-digital-twin.md @@ -1,36 +1,48 @@ ----title: Metaverse Digital Twin Architecture Design - Alibaba Cloud Perspective -description: 'title: Metaverse Digital Twin Architecture Design' -summary: 'title: Metaverse Digital Twin Architecture Design' +--- +title: Metaverse Digital Twin Architecture Design - Alibaba Cloud Perspective +description: 'Metaverse Digital Twin Architecture Design - 3D rendering, cloud computing, and real-time synchronization for virtual worlds and digital twins' +summary: 'Production architecture for metaverse and digital twin platforms supporting GPU-accelerated 3D rendering, real-time data sync, concurrent user interactions, and immersive experiences' category: general tags: -- architecture -- best-practice -- gpu -- nvidia + - architecture + - best-practice + - gpu + - nvidia + - kubernetes + - 3d-rendering + - digital-twin tier: supporting created: '2026-05-23' -last_updated: 2026-05 +last_updated: '2026-05-23' difficulty: intermediate reading_level: intermediate audience: -- All Engineers + - All Engineers + - Metaverse Platform Architects + - 3D Rendering Engineers + - VR/AR Development Engineers estimated_read_time: 5min intent_queries: -- What is Metaverse Digital Twin Architecture Design - Alibaba Cloud Perspective -- How to implement Metaverse Digital Twin Architecture Design - Alibaba Cloud Perspective -- Kubernetes 20 application patterns best practices + - What is Metaverse Digital Twin Architecture Design - Alibaba Cloud Perspective + - How to implement Metaverse Digital Twin Architecture Design - Alibaba Cloud Perspective + - Kubernetes 20 application patterns best practices trigger_keywords: -- Metaverse Digital Twin -- Alibaba Cloud Perspective -- application -- patterns + - Metaverse + - Digital Twin + - 3D Rendering + - Cloud Rendering + - VR Virtual Reality + - AR Augmented Reality + - Real-Time Sync + - GPU Cluster + - application patterns prerequisites: -- kubectl-basics -- prometheus-basics -- gpu-scheduling-basics + - kubectl-basics + - prometheus-basics + - gpu-scheduling-basics authors: -- name: Dillan Teagle - role: contributor + - name: Dillan Teagle + role: contributor source_path: /Users/teaglebuilt/github/teaglebuilt/knowledge/tree/application/architecture/metaverse-digital-twin.md original_language: Chinese --- @@ -39,57 +51,9 @@ original_language: Chinese > > This document contains operational commands that can be executed directly. Before executing, confirm: the target cluster and namespace are correct; you have sufficient RBAC permissions; the command has been tested in a non-production environment. Risk levels for commands: 🔴 High risk (may cause data loss or service interruption), 🟡 Medium risk (modifies cluster state but usually reversible), 🟢 Low risk/read-only (information gathering, no side effects). -title: Metaverse Digital Twin Architecture Design -description: '# Metaverse Digital Twin Architecture Design - Alibaba Cloud Perspective' -category: application-architecture -tags: -- k8s -- architecture -- industry -- gpu -- nvidia -last_updated: 2026-05-18 -difficulty: advanced -reading_level: advanced -audience: -- Metaverse Platform Architects -- 3D Rendering Engineers -- VR/AR Development Engineers -estimated_read_time: 5min -intent_queries: -- Metaverse 3D Cloud Rendering GPU Cluster -- Digital Twin City Visualization Platform -- IoT Real-Time Data 3D Sync Rendering -- VR AR Immersive Experience Architecture -- Alibaba Cloud GPU Cloud Rendering Service -trigger_keywords: -- Metaverse -- Digital Twin -- 3D Rendering -- Cloud Rendering -- VR Virtual Reality -- AR Augmented Reality -- Real-Time Sync -- GPU Cluster -- Digital Human -- BIM -related_domains: -- domain-03-networking-traffic -- domain-10-troubleshooting-diagnostics -related_topics: -- topic-metaverse-digital-twin -- topic-streaming-architecture -k8s_versions: -- '1.28' -- '1.29' -- '1.30' -- '1.31' -- '1.32' ---- - # Metaverse Digital Twin Architecture Design - Alibaba Cloud Perspective -> **Applicable Versions**: [[Kubernetes|Kubernetes]] v1.29 - v1.33 | **Last Updated**: 2026-04-24 +> **Applicable Versions**: Kubernetes v1.29 - v1.33 | **Last Updated**: 2026-05-23 > **Authors**: Alibaba Cloud Solution Architects | **Tags**: `#Metaverse` `#DigitalTwin` `#3DRendering` `#AlibabaCloud` --- @@ -313,7 +277,6 @@ flowchart LR ## Obsidian Related Documents -- topic-application-architecture KUDIG Database — Global MOC - [[domain-20-application-patterns/topic-application-architecture/README.md|Topic Application Architecture Design Best Practices]] - [[domain-20-application-patterns/topic-application-architecture/01-ecommerce-architecture.md|E-Commerce System Kubernetes Production Architecture Design]] - [[domain-20-application-patterns/topic-application-architecture/02-mini-program-architecture.md|Mini Program Platform Architecture Design]] @@ -333,5 +296,4 @@ flowchart LR - 36-carbon-esg-management - 37-pet-economy - diff --git a/tree/branches.yaml b/tree/branches.yaml index 74551c2..9de173a 100644 --- a/tree/branches.yaml +++ b/tree/branches.yaml @@ -1,5 +1,7 @@ --- ai: + books: + the-context-engineering-handbook-drew-breunig: "books/epub/context_engineering_handboook" application: business: data: @@ -8,13 +10,32 @@ networking: branches: censorship: encrypted: true + books: + controlled-experimentation-of-digital-forensics-towards-formalization: "books/epub/controlled_experimentation_of_digital_forensics" + pdf: + decoy-ccs12: research/censorship/decoy-ccs12.pdf + foci-2024-0003: research/censorship/foci-2024-0003.pdf + internet-censorship: research/censorship/internet-censorship.pdf branches: hardware: encrypted: false + kubernetes: + branches: + ai: + networking: + ebpf: + books: + learning_ebpf: 'books/pdf/learning_ebpf.pdf' + linux_observability_with_bpf: 'books/epub/Linux Observability with BPF' + webassembly: platform-engineering: product: security: specialized_tooling: + branches: + webassembly: + pdf: + server-side-webassembly-building-portable-and-secure-multi-language-ap: "books/pdf/server_side_webassembly" sre: hardware: linux: diff --git a/tree/networking/censorship/books/controlled-experimentation-of-digital-forensics-towards-formalization.md b/tree/networking/censorship/books/controlled-experimentation-of-digital-forensics-towards-formalization.md index c3d8508..1ed5b2c 100644 --- a/tree/networking/censorship/books/controlled-experimentation-of-digital-forensics-towards-formalization.md +++ b/tree/networking/censorship/books/controlled-experimentation-of-digital-forensics-towards-formalization.md @@ -1,11 +1,11 @@ { - "data": 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,iv:teOJSQ1M/hfsXeg1hPZMbNZ+rTazVOVgqOKvVjDEGPk=,tag:8ojvA5sxpCEbH006B+UEAw==,type:str]", 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a/tree/networking/censorship/pdf/deploying-xray-core-in-a-home-lab-udm-pro-proxmox-kubernetes.md +++ b/tree/networking/censorship/pdf/deploying-xray-core-in-a-home-lab-udm-pro-proxmox-kubernetes.md @@ -1,11 +1,11 @@ { - "data": 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,iv:37PiB53fZLc/9CkMJlc4uqABL7FwDXMyM3XgLr9AzT0=,tag:Gy8DEcOfrWdP3kFk6wNH2w==,type:str]", 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Sv+xrlSfXGkIBKvzfkZH5zINcN99yG2fXHH6EOqeFuWssgrXzqnseizfkGICOUmM2mloZ5nOseaete8HEXpI9siDa4qDN04aCcYDEAvYVLXBh5qj9MVlDowNEp5AlbM+N1BeqfEMzWvYyah9ITZVtJREr5v1gjYpOGpTy0LoY4tPnvmRZbUPFtPuWpwYBvSa9sbFCNdtHrOQ1JILElYbIC+N3sGN7FmW5pILEjqKV/OJIOEv9rS19Mho7dFRu2proDr02zG3ddsS2NFRc4a/6b+gnR4eAQ5kBXE74Iu17MraP3pvTeY2KJSRaOlYhhY6hzm+vLw2zs275mlJVpRKB3OJJB4gT7v87IVIHh/ixd6ksFB8bQduDnjGQvQCdDFLPeyfCebf7TT5ldXykipXbAFgxRiNAc9xNvRmZH6FDjHa3g7F4Yie2DP/l+RL6h7IX9Cn1NzW9Fsi2RC6nF6b71Bat2pHihP33bq+b9nA1T0mMz+klfEx7juFNkyaFwToK0FVQzZdKGRj7giYIfud5nYs83MCFstaIA+852oihafw2KakhJT/M8pVIz2TxpGQqw34QqLuaV/OBkiUv2gHVTUDbCmAQ8HE06IsNsfsVhP4TBgFlptl6XYElw1TMPyRciewZWAzAutLlKxsfkWn+M8kATgUb1UmkEUNuOzQfT1I7TMGdcIojQyo1UeQlC4FNMb3WpXA7d9yDZF3ZuUW4pO9dXiIjo9Cglazjd0wNiQnkEjJJiFCPHCqjXDHzcUmJ7arnQ+IrPPspJwXe/sl5zPXOwUdHTS6hFPuILhSKUpLcT/moTu0knTfLMgRCTFy5KaX/nAzF/LraGhUEP8Jg6uUYhc+DCYo+f27LDcqqgJBPdD2T+6tpKwrQRWLk5nKkmUCFfkHhJpvNXy5vEUY3tW2hHab8rQeZ+1ue3/dv/q5sG/M+vue3jo9UR+tmD8XgqzYj84ZD/gDXUZjt9i7neigTo397UU4wQo7ZEf3na7nuKW/WcgSezKZEH9XvcUumf8TApmWDNak1fveLdxtaoHj/wUpuS961zcPKH8LReveWMiePUN3TGggzoXBzA83gE9yms1CEhaZGg9KLPowEarmlIbmKsLhAZ2EtjZoHMUM+La8ZJSto30y6ULw+V5YVvPrKNK9OeOleLTO3pwvJfs7jVJ7zyJS8AAndhoeuvBxZLJHAZHG34/kwjESYmP11UzY0pk1YYrAwqnZysXCSzLb+YAmuUW14VHJFqrOtWZp9svRtADIcAUf6WnPV/QeWm+D5SlmjkQ54xuy+6fsw1Ytk+gS7gVV4nP3upyVuenNGz/WTJ9qDPhoKnguCnamjsDILfRY9TG/4W1haRjU34DyL7nChFiUFA8+M/JyAOpDkbUXsX8G/1WALE+MYOyYlEnGumJJMp1Yw06E5tzc7+GttElpLBunfYm7PzCIopDddaVzTK43wtJX2iZvnnQXFo/KYcWm7UH7wyNNNT83ryqzRgRsxPE3RPrql4oJ9cil06eU0Rw91CnxxFLJUaZp4rzzxzpAY2WmfCOO8Fmj4UN3VveLnyJa5Ps7ivSkZRURh1T2JiF6pnGLPMIDJhuzvQpYn7rb+vVbEn94f3OK2hXXEUXe8C0rKniKePy6bDDOgAjDjKfTwKUmEJNZhu5hYgejnOMzkgzPcvMHizsr7/JFl+b5BD8NNXzZikrXU3FhRUdNOB0sCxA/XDhyF7F55WaaietXh2KVsaI2NwVCUxWDI5XgmojDYVeaNNUO6v/EBrUFFfUqVz9nlffIL7G3+W91POcsdeTCHEDxOt485r/CzHa0XmOuceC65ho9zgJViL/nUQssiYwpgjDEMs2CYaVJnsBTG/NCEIzoXmpfXwCr6Rk+DlIfZiXSJA/GGbqQ3e9CNN62ELFMTB1vhAnZZKv8AcBdOtAtda101DohhmnEXeh4s0cR+yR4aEnnwe1+UYO+lJe2sWaV4Dr99kUV8RqFUBzTPgLMi2/1h+L2iU7BrZu7tq7JzBzyiM7eLAP9+dhsN2cyTjjtU/D6PD98de4eKiC3tesxJCJQQJFwmMqLp/zJob/e93JynC2ERvvBVycBNBv9wzdg2iMJ93/wIcTRiMNTmQv7woFJywahDp8DSUhbE7+Gok/3p28FmSdSSF/W5YPSzHyPgYU2EqiesePf/lYu5LGlAIkmfpgDJ7WMg2KJ4SHGsLDrXKEFLOghNS+ac612WHkmtJJsYRp6yz1W+T2s0I1HIOG6vnCRn+XQ6p/UxpVNrFhE78ViH66YPs2Wys9wQ8rsjjGss772GY4wZaxM7NifG5EZS/KvIubstMT7Rcf8mlx/jSxAJsHDcuK9InA7bc3lk7fDhl01VmOgUGPfUSKh2hvhksa5stOSkm6316BUPNeNpWKRQfYvlmc/POZO+p+7nlY2edUDSJFc9YzNjRLYYvMxOUJi1kF5kmkUtsM3yFFupbLWir8dHesVuxCIYIm2p8XeydeSc8yo6yjsN73v4kAGx0+91cVr2IXA2HGVrc40Ro7ExUC0yws1KiGt7hmqgX8hGeSoZ+wMkeQxhRc7kHpSjlw7nrsK6Igin8MnZrsz1vqx18rUFz2QllUHdj8P5smzgHsUaVmOw9PBFNwZxqeoto4OxMRNZclZWOyW5QvJaE7jVjJmy7+foP2Ecl2Yt++DcFbAGn9JUv2fQ12cAEcVeJ+OSX39LUO38xHENwKu4wgjpH7Wh5wT9U33GnQHzNs+XJmijYQ8a10BYeetbwbg1a8oO+oF0ec2VdBobLfjwgUFIl14e0hJfF9MaUAANYt7D2MbHCm+PBBbtxus3io1AKWLUj/c3GEAbPJdtVfDtc2f6S8XMcfWB0GIgMKlXTkSkU08oUyNi3msJ0zr8BqiUMExluZJXa2ifcVxYcvBfaiN4iPfaeNVuX7ejTscGIUHbyTAixYbA+ODVfcS59MHgnNro7r6sZAG8m73o4SfdgxuU8jwZygvFpokAuZp2S3q+pTCHPMz2KVeCEfNpUbOYaHABbqjVE7V4jfuV/EGFJk3OdN+AZzo9cfNjG/DetPeTi5tCj4TB+FHavMKMPMnAvuNrEM6/ZoYMiHKBrmqm8T++NlJselsuMiKRqj+bGYznmjhvhRciAwAXgBPBDyuR6XKeaeE1INt0VA9VwXCClWP1qlMP8drqX1qUqFlDD8WooqT9EYgjzs1y8m/uteCnxiYJn90hlzdsNzoI1UOjGb0W92NymlCrf3bCpGUnTyyBvRv2KazHGezm4eAWM8hf1O+e0ucHxQfUvQanLiCDdtm/RowtkSS9kYJDsno32xL556pGsXoSV9U2takFjYWFkENbDm31Un0/jdnfLdMBCBeuitoT+5OvPZgD/1a2kOLcwblnW2gmM39B8RP2qpg0qcQaZhy5M9z1A98fqrxyiZfKWjY+8daQv7FKoDmrKL9EouMOF11VjOjcAyzetGnW9w/LsFcEr+FNAxTaANzkd+f8l27Zavm6QnU52bSNlkaa1GF2+3nCKQ//v+PTfNU/UrnQeWBNCKdBPtow3YIq8E1f93lh7mh2K8lBjM7Xl3dPYV1pM7G38ShAqhcarWoZLLE3MYCbW8Cdx9+douNPmrF9KSFFl3dqsdbiP519PtXsdptScUEToIHSBV/pqG/8ud4skAICV/EHOsUqPQis/OxLStBsBZG1zb41tZ7CUEnvR6VMutdmb6fIniGK2/FkyF15GVmdnVxo7BHacceGH5A8XzOAcH3A4e/Le6RHX4m89TPhGkGRRSfU1l9E5TxZilCAr57Pe0PShyqtyqhVO12H0BHDSioVqr9Cct7tVGFx8qiBngqaVdR/LOU0mIPkqxfe6DkImvEGwlM5lA6NlEHWLAFK+g3cckxo8yoNNwYCxz4EIzgs5RyQHBVSOvscw6r2ySFiSvNn3EHu3X6of4MaiB8Uz8Iw+L0oTGA8urgiy5HysXBHsPb8/lTu2dFkgQqx53Fuxa9HtQhZZ1S4OsGWnJieIDL/QIXmIZAU2HHuKpklfcLiIO10A+khL8Jq03SEcyHrtIFnugY5BFt7pLZt2PnMVoeBI6KKdIq2ww/SGEGtbRJWpiYBscfIPkwSHGbehJ2CmQeY5isucB5h6AHMiD+wDnxjMA/+VvfWZ4zCaAtpWoXHUFmh7EBxV/+CjWLBHSNsAPlYjiMOL2Dw8drYv8rFUIWL3y0BRr/AXttamt8pM3J4ygl8Pq50PMISWTuHJeBOAp5jC/0RWf1whOcnWjiVFxW8dW76yfZtppC4j3WXyb9tc/ha8bEQEPWbkUS8DRjp/xeIFibuhriqvdQVCI/qgFuI9NnFeCN+8HZJhlJmJ0DkI+yuMM8afMvsbE/y3FTxptUZkKZ9Lmm4IWaJKDkqwrYU75t6P3I8ImivJ8ZB/q4vrPm4ivWhKewDizsuc8ANCMDl6uULgr5otifRH/nwITxn1pJZS7F+QvByNdzE5h0pedoYg2NlGxjTv2Lu4rM1T2EV/XWkSqJkcfzAwkb4+44r0Sc0XfcierYEBw7y+VIhrM8TspRrsXPFFX6KTTDjuJRJN9cVfQNniCfA/oyp23MMgqn7gTedYCpm9LDanaHahVjLM2VBrquENREc/SJE0Wqwscu+fN9dM9a/x5aN9rQB7PT4xRQuYJYdOa2giQvLQKIyw4wavU1fBQwOF9e+W0mA5mzGJ0ESaHt9uuKAlFvTBR0walMgqQjrS05O3LXfZoWHO17zYB3DGiCLwBFMREpVNUBIkFQ9OlBXDW/Uhy1lkkw7D5AYAHmQnB0XKskSMCcr1yfxMdIz1jRzdB3Vy25GE1ubkgumbEZBc59y9vu0yeWNZqBO6/vSjGZzYBnqt/qjn2JMY8o6YNHdNuLbVRUTZA9A2uzTJQL61ZzXRaD7AGlPMxQU07/lW6fr27bjauwrevEYKKSgsYeMCTaCHDFY+JoSlzedOQquLrm32RAS1n1i4HlrWk+EPoN4prCyV7lwy+piRVpR06uTC7K64o9C2adzLAEaEFDcOC5Nf6LV2bygfmBJDp3xSo4WZ2gZvmLvrlpwd7ECNvGwgHE53SegIYNkOU17RoJ4nMde2A37KC3JNPPFxh5qXrgLPbI1X+d2LKyWJyxWWL/m9oETZd9HlwbFgv8eLYy8N5N6yVPfY470VdXLpKpAnCAhgaInCzFaVKdlWHXXBCThdDF9o1t98hsOwvtQ/tNV978ggV7DsfVAcYiTKCK3okT7k6Nk2D6fQYT1WAhlN4YhAN6dSsczLEjlCo5qHa8jbsTq2DVHYGN5GmvWz1uLkGUyYcURdDyqVo16lynEeS+c1ggceizF3qofAuM09bj3RNUI2pecnGP89Ei0+bRymw2ARXTPF6BEYLqvb96oVfxdeZ4Njz71cIbgbYouqfPTXCDgX8WJ3hVZ9ZcjhGxibOs6AkKNeuflLfxCLVDN8mTXB9fQw7dS1wX968mGiwcxx4TDM6O+DHPYicGK3kahC2gUE1wgaNKMHuuXwWfGHz/m3kpJH/jBoPe7Wd20VY29lm9HO8LkW090aFBjkd3AHeJD6pTXLc+juM5/RSn/RZD3f/evpiWTtMvF4nnqNC+yUw4+fy+raBSHi9gHyJVFk9byAodDviVTl11WRME9YPM9UkklTL20ZB/STrtWwxH7Cm6xHwSWz/+6BENgRmC4qnGSx1fMSQAsDCT3KCbMVgbZCYrDPvLqsyFUs/4uqtUAacg330HjdPslUrfAPlD9VQnSwe128MQfs+yupm1001+5ivKKpErCH8g/nfVl4XcQwG2/FdfviXmXSdRJcVK0GBqI8HFBjok+JA0tHnzmVFPgCK1dxEcf6Pf6Ivfq2K3xOjN4CIisO0tCh4lJSXeEFqRzV1uYfxVP5u8Hejz1t8sBtCYf0/0CNAtlJLgmVf8gp85ugfkevHuz/tJlExX2SU0p9LNab8KzdYom6/iANG4vryhA0/Hat1inG2U0CAnHlgVwCBDh2ikxBWni9Jdejv8GywWDNmInPmMaa40nyZ63EcPzb+vttKNZOPQa6wuOMv9pthjCOux7t3S9zMK5a0mzE5tOgOomnIEvRQW5BmV1GRTHllwTcYZwsSUGRfuqMl1CEgSm7pdf5Lod3U14bMSIe+ZSkgN91KPCDx4COn9uP9MzE4h58W/YL5VSOQPUkiuJ96zMV/IJyJy3oGh9nF34lALHnol8sR9hGXV6U/FIlaqQEi1G0TQ2IMatt2zcSYQUXVqXHC8V3EwYwoJDc2OFyqCFcy1nuIyNlgDaqyULQQ1BERSvsg1Xo+4wy+11PRek3yHRqOJK5JcEvpZVQfex0SfS0rFFxruqKYvuxjFQ2wa5iucpZ2SYvtVVmL80RebxWCr9xAOdJ5bgrRmpV0xT6BGIXO6Jl649uk82iOb+7GmigrPE5+JmZcNGjRLZAVAJG5n6SqyTGewNuT+zL0yN2yQ1LVHVmKdIfJ5g2KHTkgH/5heEHsX/JA5Q9HI3AGBUETBAre3qImYJ31KEKDKZir+dkGk+vtIUiIvZOEmtS/+/cNHSy/HOLyrbKBFntKaBA3jQafJaGf8tJe4Ob9Utfxv5DORKNmOgbWcRSdb07AoXg1lKq/D9Kk6irh+F8MDuwiErcpf5Uz2wSBZ6EKG1uklI1yswQQz1AH3aKGg6YXOuDUwmy8NjO4I6ucbTwH9Q0S+7Y6Gbvl414eifR8z5NRrxPVpaKuFci3qw8fOHyxWBHk1gHlYDfIjm4+3s7ueF3EuRfKi7dG8W4GnRc0vWlHXh99Y9H/mrGP7BOf/+unRjHLTj3qPkid2tDbDps5q/hlBhCp2+t3yTBqNfIEQaxTI4MdDQ+KqHGod7051pQekmqs+pMLCFSEGca5eIEv3RkMU1SOw5ilv+SdoaW2rXUIKpBwU3bnyAvqv9X+sL0dHY5F4FP+lrtk5kLWN8jtAgwg6njNq8j8EIMRUE1hfnT5aZUsB8NmI1N57Xjy2C54RvZDHImn/LONH3wxQoKd5s0xWjbn7IIGPtDiwYUUt08+FryqvKVZNCgPFIrF1sp/G2c7L5AkpRv0Xao/srJYrDEg3zKsqxEEr5wSDuHfjLA4w/SwQneREBzM3iKXAY5GO72jmaitO5Od5nsTFevXEJj7GRkBVbK3riaPQ2LX2gDJHuTM7/+rrLYBDBCTIgsk0diNG9xbEzmajBvABwqeiW1xgSOBKgl6/h8lM3IBeE1hYYn1nfKSfQlx+y6Qdo2edpVE8fvncK0YbKkQxkXvCglffbOMpbWNIMRKltJp4R/VjMbAeYMj5GfqFqLVbUgbjQsON4JOVAewQQ3jHDamYxgk8yn1H2gdpKyUeI8ynpN3YL4Ud7T14X+UnRVeTT4Y0gM/FMqefheZlgnrg0p1slQ7lkya+SeQqkl6GOlAG70hCqze0S5Xul6vyV6V+jrK8MJ9wTEOD1THo6+hvN/R1hY2vNAuWcFvUcXWqje8NtXGe2JYs7zYVvS5VbS/59unP1Bu7sFRzxu6hKLk/jw5jZOzljmaUD5YaEjMP20yoN/2UF/gb3YJgIVwXZ/4h/TQrfLxmoIE3UkQ6nX+AxxHOqkjT8BSn2sMUvSq83FQxJ7y5OA9zfTnhMrqLUtv4mUh3f1mvS7FJNCjE1g8tP34Duha/hI/kL2AriHVTyzq9voZqNRHFF9awb1vK4UpWf51IfDiAw6gf6GVQJR+Vo8ewcdTBmJgeJjer6S1LYqS3i2Y2DosWe1NaLGRRv2iNMbY2suoqZ801Kb1H51zZQoc1fRdWu/3mVeya9+y8WAI8TnOzxolgms9JMNvrFTjIcEr3gbvEFjkRSK4vGDmJJuckRDAMK3AsmZT/NI/YsrwiFpJyhj7rEvzyXHxAJ1AwsstDqcHYbgAVaJjA/CoUIIcqg4JWhnzA9Ac+wkDBpTMq5oj3TJI4VgjB6fhe3/arBX8ihLcMGU9C4BirX1uBpmv61EMiIjFjMlVu/WPpy3qS2e0idH8hgmk9SWIwp42xinkTvSRvsom+k62COd9MDytVhHJaqJ/AUyVYuCRZe3fukwkBLV+rme5AOPLHKY0cRZRSY2FQtl2c32NmomHha270OgaRwdpu1/3m5/dqMjeUB+8P8HtGeuHYn22nyumtOXs07APhAKFoOFq95Gp33/H24rFK3K4YOsQe2feIFMufPBdCv0+AGEnjMx9H3kvjKxoYYU7xpmbPQDv437jWXazrEGYAtqxsIRnD0TO0s/PQW1XzuHV8gK+P20fLnhpN16O9Q1PWjxMV07fMj1XEw3ok4+Oniq78KxarGv7/bZmaNJWSqiwyHxDh2+Ot1XAAtUO8bz/Cd6m9uClnkHr4BZ0nTJ4yf8znolKmK8C9HEftQVcqCV6CC3s2RaEzNXDJIQZt2Zn+A4PzNYWRzz5HjJSz/C67FEKNVEKX/KS3FNljzUiFAxgW8ElokYzS0iR8rSlg25AmUWjZboa8zvWBbTtxi6PEGS+5TurmrZdSjsdn7HKtjQOACbInHUeGKFUzYMFf7vpeCRW3ZK0OeyfF8Lb/DqwcwI3y5sB8qbtPDg4N8u9j6W62m4+eMbEtbSpIItY9qgtmvfMbPRhnLZxELbZXudQbKgliI1QsfbFlOm26c2f0eksVIMkOiXYPZz2awtnxhuqikU4gf7rnv9xWDnl0C3edHfsvHZJ6aVl8Z3t36l3vInbXIxnlqpvZD/49iEfKp4YeoG1ZKULO7iX8LP+fdtBpRVpLbIZUCMKimHmnXkcjc6DPwxRXyB47SSy3zUFbgWYRlv3rAVua+52ntSasmxu4OH2A+K3OvSxaZgGfXr1BGGvVbGeoxYqJq+b0XOcbSY0qysaql9dnoUPofRL6B5PPGqUBxE1zFlnZtstdUbqTBReJ58eXxgJSQkSr3pKWTVjHVOxzapfGPDMMxGQg9k8B6PwTYbbqMYkl9/+ttT9D5Ycgpxd9dXG872XruuBPg3WHGEojk95dAFks8y2w7SG+ew7jCXHPr46idQ5EInnc5NOdWfptcHtgtqM/oY54s+DSATagRNaz1ST7f5t2GmO9xiUWehS5TisDnnNiCIl/BCVHcv0VV9rnnF+dBxEbPqHlkV2XAHY+/vXJ3f//pCP+rg1VSnuD/qXvl3XTdn/bmWMUP3KnAbaRBO8cD7u7vmQzOtZQrhWjBM2rKRybJP8ehgkuDoT92jzLpDF1zfr3iDDZbhyAlrq0JchaRoK2bNTfmU0EqXF0BwW3iOFSVZRC3rA37phvGqgKPlFnDeCKVPNAdNZKx0LZfaepHOZUfRRAcRhpKMyzM5bJAqaV6ZLXVYVp1F78BAzYKzgA1M/GykKWQXFMGu3Ovv2puiFDiq5ViWtCXDBCQ+POZ86Vex+MHkBmhzawBr8AEVRDnA/7GXcl3Y+v46gHC0kESxEGNKZdNtK1sietfSG6kf+N5oz52FR5xZv57a+ca2gD50SqvIE7EMug8NdXvBog60dROWDuw165MYGHsfQIzeV/MS9bQoKKEw3I5sCO+UpYgSgjfAnjbduA56k2ZPHu9T7zqiEEN2oOjThBV50uOlJtihYn/xmerFZy7DW7/aYsnREZPKMEXiq/ZkD+V9qetc/jRYRvrnbKOFMlp3ufhvfZrzYhzGcXwZ3ip6l0mSrp1uz7dG/bkUw3ORETfuYIJOdDYmmk3aHaEdyq+Fn2Sgxy0mF0R3MQIMgTV/740CI4jAXSjYCqjbg85d9hE/4kQ3aP9eTSjmhHqKhOWPjbpsk5wY3O483V1B26L+VAp7F6WlM3RujYuSXGQPwW8117frf2xMa82U6/9MY4jaVupK20+nvNBLGrLVRhrVYB9aXgsUQ7elyfxskz/M5TgJDWBuFQsEVXyid1GGO21yEMXPAIzVZS2vjyhXTsz8EMbJTUv3SNxaYJD+8yAnFrARLj2AU3dXPhCchoPw3m0b5+njK2Y92vWvGW4v5G6D0RiKfYRRvmsykPfoBf8dGtf0XkZbDSkWSPrtoSNVwtfQ8VDolO8ohom29pe9BKj9guf+4hTlUDXEmyp0J9mYW6E98X8aQPejImrNUX8UHGTUD1Ih1PLsFsBdix6Beceb5T557Q8h+S5J3hec9jw/lULYo2w413WoT9I27exQ50q8o2SbGrDkTDuh+TpyaYueXIL/grz+0+3GiSGbB7GTMQWC5eHrtwSh5/0pzu4IBujzCcj4amoq1lsvBtgkJOid5KxMdsEfNcHEc22hfpuHEC7lwZhufL7mmHADjTLhlLe5tbNA04WyZYwtcN1vnp/cB/l39b06BnaIaKeYjchtPm7Z0d8OQ+eRANeVuHS55FHY0iv3eHkgdSabgByRQsXq+7GU8FJiVIXTwAXxT5WAvDY/xvVvorvmjnpaB02r28LaSw/E6r3FA7snptA5SdkqcTvNmvl65ClWjz269TUNuMOdDjUnL/7HOFZYl4tFyE4YtuzGLg0YGjqQA0oaJPObfeLmBcJdlUJu7YXeX6V01aNhRuWPJ9MVp8D4gyZMa2VYGeWLPpWoyRtqqLe4XnjCsXZSeoKNOAs7jl7cU6xw3s4cZrbD7ZlXCCiWHD+yp5vuljGlhgw+HE1B7q4z8OJ0g6wsE2vULbek1EiT+hiVL6h0ph2HobsnQBi0wMoSTwMZlipq7d694sbGPtwXZSiSjRWFdxpcRmRSPfkJCLVxUj4UCuDdg9p7pFNJviluQcDZuWhbK1TKiKgtExE7xJdBvC9jhlvO0p2o8r8R1tkHO9VsaNXiDZ/uMXW4bz7oUfVe6uQwQk4YdjYBHZ+vxqGxsucGTKCctsvCp0fXkfX/wMJjT7/nfposNCpBC/5GrDlWOOdUgwXIW0JZ++GRI0qf34yBUcNe7wPhnGIW3ejTmeaGjPFgNroG4kkgwrh8accT5xrB5pNR/h3LkOMTfwvSahv5gg5yMYhXqrT7kYCY46yLgsgU84gEKA8TMxlLjh1XyvqvqZILf7BZWQnS1WoCs8RJey1GaSt0P6wtzEjsr5ML4dTBSS/PfshqORL/4PenUYrJpj5cg5kMiq4K9cHZGlXu00OnC0mzZUIciF9jUxymIvSdH+5w8lanD/bLvOAz0QrTaWAo+knvkWmt0fEo1Q9OYOTx+eVVREE5m54aYGGbeJvyefGV669S4LyRgZhEiKHSxu/u0iuf2t5/giy4x+uGGiWyPWx14IO6JSOvHCkvNsDzK5ypa/gf2vWIifFhS0Dj6w/u7kJsgHzqBtjBZ5bpVmVnReGv1AvoEeVNLKXSGUjqCSoF3sUT2m0yWpTc8UQM4Jlyrv1dAb5g7sLDHdmhE9LogW1PrGItYY+EX5H0Ns3SOX2qyCCpvHVms1DpiM6IrvWfQeqLopF0KEGh+C0f7YaGyKIGml7r8wdwdivADSVX5QHMOMZPaXa22IlzImZXcWPqEglyDPA4U3TPA31yQMWhDr4FJGmkYlAmqWpsgS0Dem1Ej7qg4T+5vpkZXd3M/eqTpQLMdmxePkQKg0qtn6D7dqsAh3A7jgG87c8Gb4NcRDNJziOIFhPPh1igxFaC3kqNUgyGk7AsPYypLO96MASRrKGNB5au9LM6yowXDb8m3gciadw1BO3xm8Snpxq+mzzsjefcyUXrvyRZVwssFLAUKeEOGmeu/BlJhkupoNm/98YwwLkGr8SRddGE9sqlmLt7fvFdHImNcpC6BySGaLr/AlHRk3WgFaY06+0YgMx8FpxqgIivijTmXGuCi4PHA+oN0neChR14aVih2our/0SM6in15IGJUygPefJDWe1yg+PryQ7vdnpB9J+e2RbhE/NJH5WP3BsprnaFGZDkKBH9Na6k5BhqNzSaoSfz71HAeYWsG7MgmA7zwCHgY15EBUZFcahJakCvZ4D45YodtDB/XAM96eetXeFcODDGeguOQuRtIJC0WBU0z2+9imr+9B5OpefRaejhN6TngRYhuxLCOIW3/2pq5k3v2UlJgARZX/YbQTNMxaadTgwNu6EXTZccwbphMmuyeTZefkR+QuCwtkPyzXbFzvgEwRT7JlQ7REcDL3WcfeK3kGtOuoH9NrTb9H6lJEzfa6CyckBlad6TOJLSWE2vkcwQWf/UUqDMxavkQApAb2Lz/8VJ4QHT0+uA1TjRbXNKOlfGiyny+/AI47FubZIfIvb26/Spi2Gw5pP9mEfEKMa51S8iHU/aKxxbmaXVWUfZ7zS2Qrqd0sLXMWda32iI7kZTRUnzYBwxx+SjOr7Ypx3hc35Xyk83FV+ZBC0fDEAafl8BkNJpiU4yFlKBr7yB9TvLNrs8D7nrkbY8BHrd2UVkJS7ntzhZKTv19MSf4FnVnntwUUJpNXjeTS456uI/WKICSBTtfUnSP7N2GKn3Ky6K/l8ygHyu45Yy9Vjh3u6ZKfrXVBM4A7gV6PEZy7mO6NrS38bBLutzsznP/ybPvjgCP6jCUUJxW8gJR+ijA5syrbzQ1zPE0z6c39zAHi9fCiLJyXmLb6WYuj5LXFWDkkNzW8voO/55pe/TQzrMDNKGDdxWsv/7TEG9NbLSOG2UbFDkRan++AZZOVLVlpbxWOf22TXyK7kPGwuuMxBFS6ZS/zq5LwxHoZnVAUxo5xCZ7FowqjtrzDmG6JmPxhbw1eWzjixZ4VhWu4S1Svn48QDKNGTlubdd4hFoYlc86IehQlc4BEEmRl5UQBPhPXNXzcvG+j0tZ0tlx0d8Tc53wEiwEgcLLPjt2+KBLtDvo8QGQ4DIDPxKNjQdL1i/rmssdayatZKcTn+F1yE+BeoKDy4qdMyzZyQNnwVo7tdnnpfueBDLViDjySOEXZpcwjTgRqRtMUzr4tNtGkW5ZYZf1oDHLfCtlOaHmABNDwb7rGVKCqvozQzvhiKuHnIWVDAOQ4C/RE5y8gHdaQr4TJ1JhNitduSZ6xCEOT9jcIDbbZVd1sOFyHIng+LGKEk7Dl7DPcupQTT7EwiAQ0T2Ioh4Xepa1HzYiHvRkKnIYABLi/i6g6fVemMR5jad3kAcMqxzEbnu72P4nlBtoppedNL72Ef1cpKK2qFAGsj8hni2ScixAnwmEuXSAOVPIKMyf/IyIGSVCbEeiyGs9PhpTFzklAC7v2DfjxIo7fhrCJ8I6wNSHuDu92kN7Jh5oPRTyHtsliKuqIgAuYbFdnrNAIwXlafjB5g9GGnV2qk2snvVpjJIrBphncHBvzmnVcXQIDWnPjoSBL/7Xd7U+UJN4xSQO+pRo1sCt49U8T3NnSdCo1QcucSkr+SHmTAczyAP7VXTU3puidT3XJRgHUpmhJoJJFXBIvBmPmLUpMIsUEmg8pHqSbDbbO5qQZqBmnnIkC7wNjZogbXk4CjT+qXyCqOe80E27bCeRsY1t4bNCyWIcxtSiRcvcj4wu9jM2Hcx+60wUwK+94P8zjLp/OTBqJJq/0rhUVyrM+I++YyljSbw6vs+Q/bcHIotfr2EEim4piffA3qLLH37emv+Ghm/sEv2BlCQyQnFUKIgK9NSp0ahHMc+v8OILoU6//VfQON2R4f1Ff78DtFF2svAwzYNhgCZ5/Uh1elJi0q9NUxMSK13MurHXE7audNKyiUUud5tRb87fj/6j9GZk4/jf0jNZKDBNOMuuNNMXW7DdrQoUehmr8GwX5/ZXOlt9jfzVsfJwDNJDJdy0luuRhXYU0afQ7qgNAmPB7x6t5wCOZx8HqZVlaFKyAhvy6Gxj37Guhs0cHOrKue/jOKVtdXofpImsEZmZOs7tpNSFltifHHEpuKeDroKeXOQKPNf//UxC2p2zJPsMV9vek07gCJQGbAKHeu/QORYLlXB6fTJ3ithIdnngCrO/GDIUZsTYXO3mGdaEnXgOapmX+fDEhb5z+BvXW9DHKkFcBelA50uzq3mDheoKihXBpKz+jGp6iTCjPMSwxHoai04Uh+2QQYbgg9CAdi6OkJlUgWK9Pb3ft8i6UfDyzBf2PgfopgaIaeSM1lGipXM8O0PdbxKtlp9eJgXf3AFSBafIVkLZGXoLuSis4Is1HLsoLZLLMF4dqTlS7zIxk0nTRpmhQJH3wLzpspDfBLf/tcMWyhpMjKpNpTC6WZ4vNGEPvYkswKtHQ44sD85cpW5+IJ0JGsaQyAl9UP3Y0l1KjJ2MyPoM9kycoIaVjJJvYpJjJ7c7eHQsYhICKqvVrXeWqhMHOvB2psiyha/vMSjqW/qVl850KlgAXF98dvn7yS5qFs17w44bO2FdHd5v09MDt+qAEmo84E8ZrgYvZxZ0ySgKy2kc33HIW83q8oZ4Zfk4hEbYbmERrwPNrogh0tVtHCnE2P86bNdiXNbP/TKo+mtVOjIjE+BqGcYGDpDYg0X8I9zr7vu33rteKvF1v7wqyl0t6h6iiO4U2B4NBIPSdGHhVFXchJL1tXjrKAZzf20BoNSK487hfxNGKJQByrJCFjMz6IBi+y4tNe12cT/Dr397g9gr1vNTorsQJ1nN2leXbbkd0ZDlEwvFeMAYcVAxbqxd9rtPuwAIQWA1/7dnIlL83HCxNx918w2vtvQWNhyMZJAXKzcjLAh4l8CKkb51WkthU2nnfdvq3YPBpIu2NmGGS9t71g+VxjFoQBFeYRbtJkqNPlccg96Wm1joukneLiCUS34zqSNzcYFltYB15Ll3w8EZqhC5VbUsgjOB9OZ+MyYEvnFRpEVe8WnxGoRY7B49lBXukI5oYyv5utfxPxbYgFsDcvyfCyjz5LSyKLa0juP5hJMvd4O4pIBa3pbjsNQ4ZeMWcuKxna1nnYF68mNajWhWdzqzt/tLp5ZMXRGR1u7TkrP2CwBgfY1//3XOQKO9bFchHxXHFoMnEWbIb40iqb8zgD2e31ZzQGnkV9/K0NjM686G72OKaKgn2K9RiEkCweWdlON3mQwPBf2OCM2kEEgTiU43MTsi5n73IDMkTQVcmFMcWikTzye+dkUXI7pCYnI06dRVmMMsJSZ9dHUzSlDWSupW4nGLrHR2niO1iCwRYkDUSCZk62ydRXb4ymUcaEkP302W98gV36eU/hvrw+MSBkuy3kswZxFc8nIOj8Di+QoaLOd4ulV7IjVBJJ9exiWhEeZR26/xUwG79DzzXIkPc5kmBEiJUjHqi75giQpTzK29rEHWP11LPIG/sbz5uzRlxkamFPgtyqnCJdXQo8rIv4xBXxBEcF9Nl5+3AO7fvLi7dvVpMo8RyaDeI1w55RwkynHJUIdZotRPeEYd7Tt/b305wuivgwrO1DfxKoUe5xFbOxvjSSl1l8gTGIJU0XPBk4dqQ++nLin1ScKFWVB1MwR2wMu7lsqiPI8IG7K3rhMx9b6V+1x0Un1ZHUnGg2Z8VOUfqwxCwwa7dHNJ6+xnW+sjPMB2PibkdACqHHJBESRCuAwBHzDgdPxUgngBURaVs+uW+Dm4H9ebDt2YsojotHMwcwrQgU+OXtKoObmzVND9H1EByITTD8KyCLnskDUu9ymk2shEPOeWAPkAIMGU3vr2iRW9SNX+28V1WtKoCP2a7GNqXnrAZnbK1ku/cxUG5t3g8crR6O0CFh/WdxSaEximQfLm89dj3BJQG6nLB4LIbqpEg0pigcO1xJGxxWwzlLfVzjTZO0hqdMPtSqje/K6wrenMvDqsaqIZYwD02HqXshMK43DMEVJUQhLZLkNzoZiOHVT4qPBImO/5X7ZBv+c3E6RPqCIewV+emjBcv6ds4WW0hAMHKZli9CTuADb/D1vMNp0/U+CkwHuxQqQeAYQkfiGKMMR/+I9NS7htEQ4oeUpy4XflHCNscLCZgBB8c8mesnU8G1OTxGoz4Sss7I7NaLRAFB9b2MdKd3AVifAx/JPpGgsRK0/A+KYHLthMzN3Q0t8TTenvxhNyDFTFwssb6fJOeKWpVzYdUn8EsSKJGW9D4Ax6jTGaMwD2gq9UUW+FIWmDl71HDeOcKXRxGZ4ZbsJThGOjSJsb38gSIbdETpjWLx5lRhPMPUyNg+9wci89gTqiGXya4xmp4Y4vZlho60Z18CzrmowDi5mW2M/UJU9ozIshVlS12EJYf6sZcjuiIvTOewo4rsTS9rhxT8Wp5YZJjcPfUxW3nDcNlEi/fzbLbM1ykWhYbBCbLBDA5YRdLQTOn3tC9usDXTZLRZi4S6Vyh7bblj0JwuQ7YN+p/t66hRxW2KkrJ/iR8Y1BF7NZzJhUBuKTSDEf3M5bvXgp414l3UYcuigRdYOnWjXEDOsKntZu1Qp8f7ZfM+HzNL7omtc1+gRfIdntLug3OJqcjYQ+os17DAUGvl7EgEVCIYg8pScmvBVmFkc/JFWW8OOmUd/u9BO66FrM1VsWdtHto9kOaFxlSNAW3Ic+pJqeO+8SBQ3UWXx/CI5mO4DQG7AEYtCGMDZ3UMM4x8PU0SpwluRtEjivLmq2sFz5RaRK6ooy4d6xT5/LZYOqugZZS0mQqcvlgPPlgn4CEVPHo+zp5qXk1zGn803b2+v+sDGT0+95h/P8dxHplBTsUvtf9PPjRw2FuvE+QMSArOIaz1BrLvA3Cvtz6cgQhcSKLMm1l3aG6UuCx5Qk2ejRrW8dhkGJIirknP4+Hl69yok5/28H+TeZUfYJXILN5vewL8fMt4/pXvbZi2YFzCFBLN3rUzP+k+JaPxaN3Mjd1SO/ZR87y8sa/kxIw332sk25MKhaVRpJ7lRLEookQcMNX81sk7kqSsunZKQKgeZAd368XJPmepL0vV9KeVMYH19TQuperNtdO/1ESPpvW4MVrX34p9meQpDN9UJuzDwPjEpX3hNs2xWJ4VbEZpP0cnEQe6qEmzWX+swwZCpEN77t+MgwVpLK+Fvk7E6SzQAWxGmRiJuJVjxXVkePhDwD46sZWp0WWd3yr27U1t1MdYgzic0B/NYw4DptP5asHxI9Megz1eHW7Bfg3mXbeA5vHGfrALVOoNaCpgKphW53fTXInJGaDxsG8qtRiMIrnz8DGl0KdgbL0uDPp/pTZV1eyssAi5KRaBapaQelti5xS/Cb8Oz6rfnHivQ6GOKoVUeF9HgDBg9azi0UnHNZ+lzrM39uxRpest4YQ54Z49oQoc1mAGrZkE/0miYSpUxIT7muASlULVL9ZyElqXxXInTkO4KffIuoLDLSwT+TmKIn/GfU6lO99XQM4vSw0dHqlvOgZVlddykrnBZRcqUl/8UCN4x+tAbvVTTPn7ZZDU03WUMsL+B+Gu8YxmkhLbpqc72/qD9Y/hqTbM2QQ4zYVPE20hYsHpGeQuD7Qc80buAJPKIYpzrjIA1QRC8Cy0DC550ig7BOsP8zulLfbmElxLPQ/EwPNE1u/UmgEi9cWc84pNbmkV9tffQZW0Ij5kgNwbvOaqz6gVsOjz7UMGxN7dWy8/Exfe7ARAxW8/AF/DHD4ixZ7YrBEgmjpMu/RSP4gaqutCXeZf4qzX73lqVkWbpKnA78zoWsa2XWcadFMYZTtTtq6jnnUzxRj60D4fD2TlOURWHKof951aX4EKEGQJwLVQcHwquNHI7cTTowaTjJxteYDTWIkQcXd1Y5hXp/c/Q8LM9uP0PON6uaVONjZdksBBddEWgPWMPUvGpr4MdPd0MkAMGkcURQW9wNkowBj2zNwl470dknA6zU7qGWFci2J8pVIL+1lF5WJl9K9On8Or/QZqYV5/1/W7UDvqnvw+bfFMNdeL6VYI/tPa0NnSdv3jN2HAbIJCZMIPPr7+mwmoUUM12DMDjWqSmImyzHD162LfXvfg5rpxSQ75oOXk9Vxt21GbXb2cJFCOmqdfXdeaFxILUNJrehalEHHghdoGMWjvSWW1/C8DyfBqetoRd+4gLcXHAkfX8iSWgqfWiFjShPTwYYexSq1Pf5KTLVOalgI0VCOpuhNmlKA/2UEe3uqjmqfkBgpRzWHxmhIVOqH4GcMY/su2sNHbzWB5h3W1Y1ezd/vPU+ftazhMbZL8JT+48GSbP6x0OXCQFtjvYWGvPLqonLgggbfUZ2T9tWlOIj0pyjchQVmV62pYm4uMBCsr3ak3ZkyM6WcGERuL86fWccI5EadgdmU0/XkGN9ZX0sF5Ekwgn150qoAXFFXo4YN26i8jvo4u5wyQMTzO9+UqNFgpvt0UmR2IODemtOnyInOI/bGy6RXPa5/JtD9NxG6anPe1FwBf0zWbgTuPHA1qvsIABe5Lg23W+5TVcyqGUvA0ow38rWE152BoOhPa7Y4ECfTrzlaJbI7aC/jwJJswOvocKC1TVsj5cKWPBqK2pC7TEVdwNqfK4YT1KwlkoZJMUZfc9Ce8MSnJNh2FB/UDK/KPa6iAy18h5kICsddxb8V7w+tLc+/1vAI4M4MwswOYTdy0MbQkGs1CpJaW1wBZGPtBEts9D08FAxu2qrIEBHBIZTKdCoIu4Qut9jMUE3zTV/AKk08VpJ7O/4UFMs3fmbwig3cXYcY9C7kcRUJsosoER8xxbj3pg4/KKzpzcwThXXrhxUyaJWy1EusaGGAFUzYX/ayj5mBsF6ZPou1kHig6qKtP4eqtTnTa+lUmGiCzLxPtWTdS6/UwV0EeMvboyySeLcS5SgfHxGQlOktRRzAkScvwzRkixvNJuULnH2bKT6LITa5F02mOWJuiCh/bTnSyv6B8/7v8t//OXwi0Y/Nbk//2w/Yx1qO7Ot9u6ORH2/P2SGPGi7wJe4ddCRuV+s44pmJz4FF5f0HAvfZdxRzKjPnX5OhXep2FIOCzppUux0iZbKWz5v5N1YoStKKe2h9GGoY6yv8MAky8fN9gjAKLSjoM8FZ5tprN+BxDjL9pmYpqggsqjkQ+o5/FDf7ZeTC84BZax+wK8tv92aKJ6YKMyBHV0zfmtAfrvhm61EM0WgkC4VTvgTjtYHxeMsbKyeBmmXUHQGjUPYZ0gWdXT07GAuCZGpnwvjDiYCIvA3TXNVw9tb2+fID87L/3W0p4qUOz1AJZsXHQ1AhurykrPtLOJOsLjraSJlKohki8fm+blazRR/aN7o9syUB5utRXgtyU2yDNSCcuwNEJQSxltDlouOTGv6s/kq1yEVpezyrG4LJ/GIimu0U48LEWHyE26nHjHb1kwtYQ90B9rQJGG2kbg8SidE4D02hYZP7mFgvQut5zgIOtAVlPkth9NBbH62pOCwluiePLLAon5lWHX5+0XqzblaSZrBEC27MJQylDAiN1Wxfb4BzFVGtbYVgWadjj2zMBMa9sLcIbKC3TRvAxrjh2YBNXq3VzIbp0EZ36OjAbYxLo9jia35CAk3fr/BTrIZS3TUhPjB/btVq7+v2d+wBeF/uu8ZuobL+ESZ80hhRA2K51Q+l6Oy5emb7QegfZeyRvlMKoQ5ySrus5xZJTAJKnpBimgL3pDTcJRrJJA5SOEzSXxew5eR8anHjCbEcFeNbxqwFu7HV0dwFbs9UW6H4c+cjnp8Lt3QPjImEmzKkBP0zPqVtq4CRvAKgPIMTuRdG/4v+CyT4S9ju3nN7Bj0A69YKgnATv+UJlBB+RELiEjFuv7A5+O0gtL0zwcnEOkGZ3eRSnMJKAOdJy0VTyCYU+PeEJlKWob2mO8JqiCjwXrzZQkq/s9YWgWaxKWJKPVPXYtkWBimetZkTTnp5zyzFru2NHRWYQwBhd/Wu79SFNcJm51s7Fy/N0OzfK3ohpjt3K0JM+pOV6Y3b7Ta0MGNIfFmvODIpZGNE3gHYDifI6bdhnLXmAbWg/mQmlrMd7dsVqPQEJF7f3wY9O7sXQ8yeTclYQPScCHbCi9RmIYmn6Wd77Tlvjwlt43j1Ui+llZgqbviHBj3NVMLCeBa5O0VRxBqdiJdaDjvoeymIyYxMUVIJwq5VGpCTMaVCFUvwEnj4OTDS9xpomkelpQDo9CGQC68EmsW75/5XflEQTT/M9y6J1uYkblvZSAqP+r/GzRgfM8aMrESY9fdg1Z3xpL9N7GRkSs7MpXEyKYPKMBwbLcD6BRA3t9x9n6lJpHSm2zvP+fbfXZ4BUp8E9d5I7hcDQbl8c9fOKAEmO++EySMjzTZP8fy+vQHBugEgCjkiqUXOsaLNMjdj4sHk9E16GgmuEmK/e5dASGQE4wDFyDB9sGwyQkQsdpmo8OloRuAoNYM5rgi/trlQJqL7iI8gLDCmZLEiSQiJNtzgfJI7uNHbEVHh8CF3EJE+4wBMJWyseZjX25rgbeBLYQFJqXHxro+iJf3c49QUAFJFhb02XSuFLegoPHmy/MteO0CO1dVHtCBqcktSMUv4E7VNnUzWfvrKTEsSS1ZryVRyRoJYp/TTjC/QY0jUD1F+KAxFEcil7CCFF/3/NXdym7lHBHTLq3LkL5iw/4n12etmf1ld77i8lP2Gn6rsy0WwE0xyB8eNFB2Ls7nT0717M4ojCh4TBIJTcdW4DlXPpJ2tyCMmXQ94EHAnFj47l8EfbHm4rgwtdHvUbtrywHgqDWQ+jRxrh9+tTVBuDiTculVIZuoHQvmq7BRcC84Om/L+mbZrm6n3BURVrHPs7A1L7vQ4/8YRPdQNlNzx0AXzyNYVdLM3tLngzeac3E8JojvcbNsZXQYWY4Cb+Jy6ig0Xl3LHsYZPeK9WZpRwLG7VuXimFYg7KXDkM31AnxOjI7nvAlWbTSZkGh1+vLP4c+kGkFQk+CWntxqv8VTJ6nVhhAmyO6dRvFHL5Yda2u+/5wNsYUhOdSTE30HPxObpvn0ZzwTJ+oUyjD6UpZo1ABPJLaOfnJsgjQS3+WbRvY5DQRxOlV1B74Qrpa7+3a5LFOb022eJdM0dY+kPJJX+q2yyVdJIYT29K4vmtMX078X65pv+WyDL2vUiZSKT1uppIZF6/jZ7h6zDTAhn+W5x7GnL5g3jOYz+dnMe9pZZSq/bcuy09TNNL46QKnwZ7QC9sZTTEWvfBxxrOcPD0KAbSpBhBaNRrfrn4a+puFFTHCsjhUBMzGZ7kcdmHV01JJ3eqdM/bvYDh9c3UvmvKo/uRU50YDgN01RRrl9NHVHfzak5t7lV3KJAm7+3/LuZv/t+WFXwYNwXmsueVNa01hzq7RxkEWaT8BwxHy23+SqRWNXy8jUxoM6GrtNv/M+XfJMKje7R1nUOBUOZkZHQV0vgxdCJbqp6m7CmIbp52d8Ckz8P9Cxg6dCtJTN74ZNyQSb0Sj7RCEqYAo9a+GYfI+fz8c94UczKhoCDyVJU58hkcZCyjMRd4LmGvFa7JSmO0fS7auNTGxgBbGXZmRYMnnYIzlwg0/xAV4N1rZUlqBF62OZ5OnvSz9nbX2pOfWZM8TwnahzLFuHJKZCq6t4vTycIYk+y9+ge4cdk4ItH+hN2dYvU8ZaoG9UUlp7s2HVm8CtgNQkqnffzdhxxVjgUvYjWhNSm0vwBqD1Q8QECR/RLOioWlordgIlr/LVGv8NwGpabfYq2XnggvU+MWsNqrHM3O94h01hsZt60UCCTVo554utm/dT3idNyNPyUGNFlwLJr2UYDnNYUA5YBygBz/e2T9DdTUZ0alwloaUPUUylXwdVFwX57ifAEYN3Y9229gd37u4WLSCKCkfimAQB3MLJ+amOrC9m64CgFownEKyA4mEvLhUNzcctMNj0VTPusA3Y4q4ak1f9vJrz2MRirfTwnPK17PfkzUBmkOT8wqLuw8tAQDnGJPx8nyddSsn8l+qrUeLyqS8Akn5kku3DhOrc8VTCE7NudGz+2bb0cM1GGGbjd3OjVkBkTA4QGb/RtH2GnEYey2+RdRhVRAziRov7o8uqguzY0Vi/A9XfSOJYmIxDcZLNzRq/nbztW35WGFueN54uCHcZiE4OzmKDhkNt/dOyj7efvX+gVa7DFhHlsYCeGsosRNd9ubpkRB3f+Bv9/gjqul/HAmV7/UNrR+XhSFtw9vOD2U+SyPnnSozQyhcr53/o7KQKly8vwSJjrGsP37JP7MVj9+EHdCv/+mCfgtqaNb0WIjqMG0DwRSsQLMnNAc8pxdn7sLj+ZJ8BtnMJDqndRsdcKbhM00x8HHgzUB1oHITDzUMvmUDQPXJVLqgB7Pnt3WxQ+/i5wL3xBYTqaIPQYfJR2tGl9dqsAMTYYYc47kiKquVR/xaQbDyBZxXaZe3UNuMRwgDAWbZqUjGF6/ztmMPYMAIKfrw+IE23urwJclLSeex5muncH4pLkQG43u0rf63KpahWBvGuTKQ3DWkjtSef3iIPJOx5iR6+5qCMOeF4AbQSX5ThyGU6WcS+nfdxIFAzkg9OywqGbtclNN3wwQsi11sTTbrBmJxo8ET+jfdP17J4X3TrzJDgPQWIo4Xroz9KeJD6bbBKrtAp5ZoQoSdbRtrvZZCqBL85qO2f98INEG/lAr4uxffOM5E6LOI8zw318rFhfDLqIcDDowJIH4RWxc8ymVeUp/D2KkWmE+i82VGP0j2fgUAKgGEzr3LVGhYepAQaumaGdeKb3j3jqnfA2phixlzNNasd7nYu35W5TyZBuMkA6zsNXLtel/vRS+UWShh88Gfg9VcEsbECxG/tRNF9UX6P6NjIAUhwhsPzpdN5REo6c+4PAz1uFMixjxljQSXh8FYZDHGOPUfYGk6/vsgwz3kEa5x2FbHRexphp3/pa0gpMHHEAdFVSTMaonQFPmQrnHXQU/kSSYzqdYSS4q/Mjjee841CDsmQeRSJryQHUsmvwCbHG5i29F2L5fGeOruxbFlLxOylpuzB4HUJmEslEG/ehrusJWHeullVUmC6pNpYZlAkcZPSDozugtQzEgUUu0XpZSCHV2SxcJx15AYwVk9cOW9FBZ0BfdsEbMykdkB8kcUAJyrU9Yg5uvvqNHUi1B8VTOMCEwDvm2YOy1SNy9B6xDzifjhgRmHI+DYeCTjayr5VQRQm0XDo9IjlAZesimj5x925bRSmTdS4K+TeF/ISBQ4Vz/XFz7H4y1apGJElOhlPNH3sATIKbEgOn1R/6895gw2IYDLfj/Ur4aRfVQlEhSQyrEJDenTkppRW7rZLBNs+jH+8i+658LH76iKZrjdHg7MT+OaL4A69DiVqbKuW3ZMc6y9vWy4jK1g6CUSnDA0r886N+2X1IqpGzyDjyTNxMeAqiYNCmhAj1dKAjDIbGX6GkDlllGyCSKS7QSxmitNkiC8jDis3mS97DDVrv9XJT9ILAHDm6vQQzXRMI97fftFgQrt2e2QjRPdeaq0zr+Uuak/2i+lAJ4REO6vRiNMtWmbJdqfv6yFqHD8UWbbDh3quSWcVR6pHXWSvFA+rwZac4dC/GxYdv8WS3zXwONK8aBi1JuQayi7AyiuUinK731qzVMgWKx9aVVU/UFfsh3JbxNNNUcIPzU2DMg/Oov27X8lrtmCqzus+nAZAI1rLbelRCB5AloNYTfhYk6RQUfj2mIhEmR/nG1ACj1DdWpKII/GnaZ2/9U+sVRpd6YezdBKn9ULFbkP4fcPbEEvvbo/9mXXJWyY3AZNajspxDKedsjd/eW07muiTooPpDcZDGpi4ZA8rqj/1r3/FwrEBMzsm0IuJJbVJ5j3EILbqcaFSnKeaDUG6ua0yQG2II/+peYpAXSpSn0+w/RdVFHEavoBjDscUXzRjUsVNaQ0etMLS6J1a5L1HN/F+yoeU6lgXwdG3kzVdB9nKAO6XjTk71DNKMuFssVhvnA0L1140RCwmtOiEqxFs0+8JJZWr8xf6FeueF9pe2315KKuxZYpWleKHz44phKDVwuy/bajuLDwjxiGJDXdN3iBmV0PQlMv52yOd8Vd5to2cvwTajlJfssFCHM5tagM5a3bnC7RDjUl/fIMeyI4a9JH+k8QaGOt7mxcUsvztr3FHRBFSwXz8QuEjL8fZWLLbzmkqcCQsp/v4J5qQfdnpWGys1FAVDZ3momubQQMyX7bFwi+2juyEemMROCXH+wrust9kVlBfbzdz2YQ/rl433DnCqQA9DmYsx9v8gEcwCiNS+Bt5SyaKVVHsWANVmO8+X7MwsPNcEp64K7OG8JjuM/L7IdZgofrZyDZJdfPqnkrH7KD/+mUJZBD/t1uHdWNyVrHktcWm4wlS2mh3JWxwwcBVc4mo6VkrSBdgE0UhLhXVnRySrTB9+I9lsWYXo771ADl6Tes/jdoPSPs1An3dw5pGayrx5ilkiGe8wEWuN9zEO8UhJOyblL4/rqqDDXWBZ+JIWmnmqexlezHH6YfQC88OdpjOmXWORHszz0HRdZPDWX3uViOkMaWptSZVTGnkjOrGZXPS3J3YEoMOA1yN5EGWIRo/FepVavjTRVt5vrgIfcub7OqB7n5ZyRlU7nVQ/Gu3paocLrOn7hr/RGvmcE3lYS+N/lVPWcEjyjLr2Ka6AYsKU8BcfoD0JrOY8X5MTKCfYcWGMuJka2wblhFtHrvOGsbdGodwzdvICaT+0ARCv1yn04s8/q+5KYvGUqwTWuJ3oK2u86KwpxvEZb4nr4Anq9K6lK2hWsaljPU1rKd6XCXLxIZeMD82da5S0neZcqzbF8+6F9akqECpmH0g+pGw1XJeF58DCSXGLMg5POddCeKwBkJy/NkR7wH0TCVyXllwjFjYGAPnDSYdBqdjDwCqyPtsYS9suT34cV4ZtknD0nyfHHHAtd4J2yoWwInHvS5eGBWukxB0knQZnznJCw1AvrKNPdcTQfPQ+sF+1X7/HWAs5S5Vmdm7PupkdMr/fvfzqJxloto/ytyVmxopRyCBsHgE14cmQ2UqgRb4fdil6soh1ldhVxJq1MyYgVx8PMYnPWi1P/izZrMW9u85bxTuPEL52bchpNWbDufG1PtrQDvyNee+bghvN051wYyq5Dtz31RNP3diKSdeJUqQWM7SPAyIXIUDP4FnDfBndXk7XnNHnL76LlNHYlooMDotJVIXXFGsm/KpfvwD6DfwWlI15snjdyZmOclFLjSJejy7wFQkAwLq+Twq+PXA5BrllVWdBw/qToQJnDxj7mi5QYpmwQCMO6zPIBghQbnDf13MF/cJTYTRLu4hlVH2yLxnh6+39stfKQ7akjCOGRedLG5zeCkTg6unsV+lVk/G47F7UW9mYVuTDsTFvrjcEVbqd90cd0Zw9GFOUIsePcfyCgM+xSZz3Xli06B+7fTya0jVwccN/OwibaCJPEijwCfeJ0lMy6AS9evB6C5JgwDL/L1YHJc5efsyzBDF7pSZ0i/NFTyHTat6JzeM8ryRoVPev6kLQdESOjQrBUkmcv1+H93NeLd37MK6fGq//1N9elbOWRLWRwJ1DfaVCtXdBsKyLusQEkG1/rd1yIArdHuSdXQM6/2Em2aQjzPBqd25vRSXpEcxaTKeTyx5VdJzzc3wNcoz9LRzPsR+S9rqfsThNEbbvfYdLgVaEtXwyL3dbrqKDcpMdy+xi1yPo+0KJEiBMryLZMj6+f42lVEVH0eh19vf/XvoFH1PLe7WYbgZ5Yw65TAqVMyhfEWeM+k4gjgpy6kOUgvk623QiNmGj2cgLd7QvNUClJKy01GmetywPPXCqJr2fjat0KtZyBph4VOQPVlJRGvjd0Qdxh5OQgo0+L4cfwn508HxzZ9r1FfsLz9h7CDYQCLRe6Cf0ApLsoygFEw8/IfADLUoVBcA06aj57zAxKhLd0FB0vV5Mqqmj2B3+jfsjnikHUo3S30KWDwGueiZ1DLD15ZkryvOVjc+BiwHICDKW/BFkPJ+azst5dwd+2vcNuffaVqWKHWLE7oKU6QTaoN8TxznP9YA7SG6Sokhso1+L49ukO+s3f3CbPXpJ+QtgzhsKkPEuEj2o53Os/PLycy8HlhqXbXEFeEqKEeEc277QUySM++mz5S/8bA+CcyHjaojDNC8cJDLNCAJ0nnWSCgecdrOIvLfwmJBelptkvZYRdXd7e9x4e+C/g5Ah9MP/4z1gNm3HRcbY35ko6fH+ZcAmFyP3IAZeUx2jeuOq/15YayAvQwhsG6aBUBuLfSC+BQ+zCPBpzx2NxCv6oOXqTmy9nh8Z+zIzN3Z8r78hwpqRFh6eemiuDDV++aGMys9b3WGzo8QdogqHz6sKxPgCq/pqWuAe9ymPpKqheEMqkJzYuK57RgpEWdmh7PuDr7zTemYWYv4gv1wd4ErKSebGzbNxWGigh7IShdqH/8idHv7TDd/cnIDzBCtq762SagJRpuhc/OjiGHvLSQ0bAVcXyCoG9ElML1ouSgtQjeR/7ZeyH3c/AOVqOq8X8ca0X3wFrLWyofky/94xRrxyQ+TYF6NCqg2wE1jNOfFrM1czQpOH4c15eK/ru2ohrgBirZgPIwJZbb4AM6JVUdrKLyzaA4mE1RxWo0b1Uft0KP1uQf0OBvqtaBMkEPapZ7VCdTZGYEHYK2u6ZWhqpq4zv3WWIspzpOOdlZVrFnK2vVRW6G6FBl34TKjmEYC3gSPXCAoRkOavQh5d2VDI9BUaVg1giET54YkV0SYfOmHsNrpEKep/ooNgdX0D1Xg7RALRFU4IymK1uP4cSGIbEYXh2jMLF8yegrEyEW+HwnrKeZZZVB6TYEDSJCbiZxnQw4CwQDsA0X34i/8hNHqADD6J/BdwcPtlnLnfx3JDO4xkSI/HlEPChzYMybgVybxYzhqlmc5/WLr+RJKwH8eRL9BFNgYi3XUKaWPApQ/wz2NqBkaukSXa/5sk/TeUpi51zvHi6rYKRvZ+Zo3g4/lg+tHrlkYk+L8jIcoEcIkMQJPwuIWU9Dgw3vnoHW1PU+wS6EBuj38qvPmd73iK8f00mhEq0H6kQ1p2f9IlBxzCK7DiI8cJU+lLHdPZEnGg5iPFTUFdNNBgaJ/ego4v9Qu1lIaHe5mKNnjyVzJivLOqAO4wvr9rKmrxYsWmEF6hN1hVSqep1fjzvnO9zFbR8/P1Tp1UeXh64+dHNu8LMeJ5LRnHH9q0irEyO1fkXU3jdLMMz9T8/A5SmPONxPFiEcc09+z4YNWMJ99BnjgHnS7DbfI9jpxwEJeYASMBUoMglqBHxakMlLWz19NvR94zpgj7eMnP/1OuBhrUH/XdXYzdfr/JbIKGOof7aSzx9/yQRobgK8tDch45UqObnJthgo3TLnsEepHj1L4tdUfZkhqiLUZeqlJrxHihI/uBscDM+mzBDQsA64gpkPHEKL8Okc16FGdOXh/PtP5JAaDwzKSW0ZP8cEXTV5+NzNCTR6iM6+YzZf0J6fcMpjZGPzGafA1Ee213/0kjZBKI9Lmna+FxkY+2Z7LeBycXrRRJ7MiVPGjp22ZNnnx7/zOiyECUFU0y0qTkwyiNa6PgFxMUSEJPuckY1gKLZR4jxxDe90dX7GSaeVWzRIWQJeR8lV187TU9Q8qJThusdkNyq7HeGZ1O0eW7VDy1gFmI/umYl3LxnfT9ac5MZ8fR/zndzVBscLvEW6MjTboSwdw8al7+KBkqi36MfeFAb+YZkysOSK8u6qq/LDct4Xq+vXpyGEtIHNxuhs3ENjRXS4iVsu7CziE5u0utgeCakjRnSl//WBSnPcHkFoPy+ffHPJcP9pggn4tGv38jxMsnKCYclumlJjyElS9rn0tBuHIVK0ra9mvumoejaa7UwwuCn+cvWYeRQeQrUsxdiSYg3dnu3NFjNpUQeBbTTkt45PiduUEHfIJoZd0nrTrln2TOunHXBcdQnI1E4zKC1ym7r0VvMvztgxypTVKRQEZOGnpk4Gu485Tm3mUNFouNlnzZzTprXXLr3rvbJulMjnidU7QI+Xg3tncUVoJvkwZy3/Ya9qYmejM2pN/ZY+LNLJYdM0G7TWegkoFQAGZfcWgQzGo/BG0iuGk6HdN+ngMI7tWfnH5VZrrEs+Fdf4ebguZYwcJNVh8/CzV7btn/JMsTI5Gh67rOB8HVE5T5Y1ePWOZhTJ8Vh1OQTHasrVnm3odM9xnchEFB0zFmYnBjmGZpmhxSR70TgrEycw/T/WNg78wn7YyINziWeVDA/wh6gOOpJeJdoVorXbLGep+PGG0lMa/L3diz9SrjM3Tqf8rlHCbKxF78+1LFsIk67mntt6Pb2t503wzAylVDimfxXWrrb6YBwhKRf+4B1uCyJl3UHOgfTnAhKQGPyrzGcSHEChOHSF8dtXB/8/r7N4KRZOCObH4oAAelo4ItjESKWiMx2TyB7l+aTgZmFU6h9S86uEZ4KyErdE/pETaUP575eJvaZ0jTYNeM+JaaPoTvNfeG3HqrsMM9Hzx/6SiEa3WtKQ2bS54y65ENxZcCZROCujF8yNuxOlzQx/IdmCOcP03x7+nWKB0gfspIo0h/x31o6gFL21/ehd5IhH9vPSm5DobWB99ijwa4wU0z4EEewz87XzTWpV/XRjomKGYIleb+6waNoYGOmHIgVb3hIaaTabJNjnAlxwW77UzvAl8MfjYFyFTuQ5RUD72e8tvNWBEu3wAle/Yg9qz19XwlqkmPJvffzdXPRz5Xnj/OuvGHN2cHIV/reBRDoUF1LMnAvsLknjgKFqZPm8XDAvbzE/hP853RpJBFOBYBFAfTr4VastleWYM5ddajzbcxHdmRjy3md5h4R05n+BLqqLsje3Z9iFDytKuVkGZxNd7fA0ntxRrWDBe09U/zaUrU6eN/1v/dL/LqV+pkTYgHojyfCbCXknezPyl4ROySqvD8yVxYK9ZAUgIXNxkTF5fPSkzqf+00LjKFaWG9h+1PB5ExS/590bK+FsB5bAZ66PSAPj0UnLWRlxT1qu1LWl3npDDqRxqkAoe3DSyP7O4JYA4nhlGYbXp0bsqOYN9D0hsZLOMazsnri9C77WFESuBzEKD8BvGz+PR2nKUSt0K0tcNx+RiMWDXJCUWTjgX+nNFFReVVMZyXvn8Tq4BsWXzYF+bV5Apj/z2xECOm1UJuSnH/VerenRmH2yYdDo49l5p0UsfzXlysinIeuzpwcqEMrOORQhh9asznXPgyJuPT+IFzkqKWy/piDdN6h7vtx/IKBHOk/8w2Vw1v01CH1/dsvqG0o+sqNy3PBQYOZRbcG/Cq5VRRXGLeTECsYSaDhtWfyXgB3J4QnGdcoJvt6r8EETSBMSVD9/5y6VYJVjybTkpZJJUo4BtDp/susro8M3h43kVSJkBrnWpinYwD4CkniloUUu++5OWQnsOpJW00LfcFvHFAn57TDAlc/ZPreAd6bw6CeYUgjpXDmSjaWQBvTq/RB/ANt0AIhIulZzHbh01Bc/8+lWtQLGJ14hrQSOE5aF7l08mOkuDcPk47c8lMzY9sm9UOiXAXNDERfFtJOKvnCgBgfiJsd29jJIY29BhF5FiWwpW3xdKYQzo0pWkz+6/luSKwd2VGcUMiJVVRVd4FmGWhNU2mlxYdYfl/hh/kk8AH6ZrwvbghG/5trB3LwSI8vyFhlckY87H94zuY/apnxpIZELcvv8hCC2OgLcJEY9RMd7hQNoQ3mjB0U40sAAkWoLCDHJ6EV07beAvLwZq+zJArakQsMYceuthH42EhbfipSDJW1xc+kITbCtiMRBloD2rUDG9yXX8TBBAO8sfRJQF6SxSdJu07AsGr7PTbwRGlzkvnpLvKIly+ckbaq3iDgC1Db6kZ55g6m1xf00xQgbt1Oz40XNmpO3jeP7Tj9g76ObHgOtauKC1IEIMBVkmu8NEBJncIujn4Kg5qwfXaMo+f7neau7HnuINXiNX/BJJySVH2uPh5+qluuYTvOvX0focmKKfcqRx2BJsVejWi11cjbBQ3XoqgrSVJQPbceyO8yCTyOgdXiw6Z2sATkqPJjKzzKSXvCzK4KUOC861EcEe2N3HEHUHmG3ubKGxWWk+ear6H4Qp/rGkjizX3qsinRO2A9rjXm8Ipt5PJvZmjdizqb7ppYza7m+BV4IxV6lQarPqIRqY3zmIhXe1hTCHiLiKCtX8bK1KTFHQ3euO9y4wzuWWZ2mAcDaGrAf7azVG/ULcdE/5GZHrmN5UIAAniwsDnoGch0hym93iyfNr+rxeANmH9XrgcM5ECLtmDyqKIQzMWuyOzq3zcwwB+bBCQI5+7yT2SsNgg8UdsuxasNZKSYk7TotTXrcfAFIdtASO+UdFwhcurYOCfNmhurlwOvryXMMP46BMUAbUJISKNizoVBI1A5gA+8rISuUfjIfgBd11+7PEn8tg+ZGxuZpssqPudwyLI/Xk7cPmAl/57+7fWpI5m36+F70qXfG878UccDAsdv/6c3/OIstxQMaLdnZRJDNbrf2c45d0JhMzmIyYXggrg9XnDmoxrkwHMBIJ33LPKBb+P/qIpM1D0scNw2ca/srlal0+FA1bIeOWwZeJSQ1yhHgSrkbncFQA0F9J56I+rBK5md4OIYGi9BZSsmsSNibx3aNFD/TEL6aKKa9jxtpPi4i2JAncE8p3KzECRA7CWpF6G//PbRfvM1lLOh30dNa0L5kzjagVWQLmdv7gyIeSmvw0AigOGJ15yvF6xu/V0c8IahHTz6uUTn+K5KJuCw/7R2FrZs7JwFoyXpStkqHls/+iWUuDFCW6kQJtLY2viQAfgXQsFO18dLFSZ6tJ2ebnxh3Zw+68KYhhv5jT53zTRkfVM3WmdCs412h6MZOwJf2AB70ytywKqY7icJ17jbZGtw6a2rMJErvwrgZK/va1h1JdHnvL58bUPnNpuJqTlueImlVjaJFXUrRSA1wjjdwBJYZ23fuDiz997eevTzXKFuXb1kH83ysYl+pOeAda2UXR6cmqgjE/ml6g+tBNb6u0ALjw1xO5BeZ4WFOLT5kfCibd/dUVsv1lCPNQwsmwXa2f5bIInZ26ANesA/9zlq3rRXn4wuGyrHTraZjkCnDzTA9D0kMHwKdZJVQ+4a/uFxHSAOaPqOsLr0ct6RlkfEyo2g7HjLE9cwEDtAaHxmVZpLxfVM5kLZ+QuM1Dy49l8tSxCtt87DGxEVsq626pLZwv+aUQA7s6F4dD8ypBwi6ycV7TezXiYi0VsPfoVQpZ+pAy8QUGSk0NkQ1m30azATmGc6vPsSMzFYZJnuH0CudKnTKAbQ0ZvpUU9l9CBF2zySHSY/Ejw5cbES/7mUpOTedXuFcsfD39xuB1PnQ0+vPSqd3/VOLwpJw2BlsiHZWwlylC5CwG46VcWoNhJB/o4+BDdgaR3EGkrTguv9zKV9mbEMKTXe5icbWvjHqYoWdWCEoq90XxjATgfNzVcNNd43P0E06pf0SjG5j2N65pJ1FSycoF8kGen3FiNxW5X4grtm+XV0tTfpwkYyx49Uqs8ZYb9Sxq3rikwySy74hXjruI2LYKTvhfCPvT8Xwb16IOOd3ohExzaqbmCUBIm8nWGmG4qit2bo4y9fEVIZRw3ZaLhf46O4OFDUERg1l9jKbJbm5xb1lUdJlZNLlhV732m2kxZzpvJtKTWqRDfh2ctARWQUcpy4Zjvw+Lq5AcFluiUFKtYeTKiAq7tb6uXVTRlvr6d71Ap7RArXIpDhzrcU7hSUExi/pLjc186fx+wII2eTaNQsWO6k5msPmYvff2F07jr9uU41TQtaQO7KYUGy+W8zEgAmdixCNoKvl1ragXxPArsQaJRcQBINfJQcyy9oO4RllTqQdBk1q76kxPTStRjyuHpwtLKwczb3PVWGyNENFz8lMzZ7IQzpKEZXzP4T51j8IKQqwRoZxG/TNce8jFwkI4G0ayp4OmlwxTP/LKKE2h07UFheHN30M/20IyYL8KMMulHXEv84D2Cqm/ZqEU4G4xHTOSSnREPRn+KOtwO/zLTIg3Hsk7YfCWK8IM1FpIAWGLTGRfJaACkgWCgio2OCWro8pSm6QPcEC6r3N/+Fr+q13V/K/4TDW7eTfvPco8MjVi6efT7sAFqKdWzDjYZ9UL6xwWUanSPTJZd10h1hG1db818ydYQmRrWOG1IRcvykA5d8MZNggrT8bj+hlRPJeZ5ufVkuK8vaO8waJeh3OC9W1Dy6xgKeUaujwMmoBamQ+2rF9TCIio4r2WJ31g0+06Z3nNO9zsXr2kCez1Ot4jPOhGztlOq1m5dGnCMMBVXYoG4lDUBfW1Fw3mhZgzgqbHkyadmntMfvQXckx3aZjAdxhUtZ4Ik65P4Frs4pWQc24Y5uAz5DUXs6XrMB3eEu2PLmdB9omOpHUOqS8N/hLlk2U4zjABoc4k1cGZDnG74LMspXePoy66VEF5JQ5NlwgtW7QrDrpz/pSLf6hfwEiqAYH3AkJTChsOizCYopWAhjw6zDiNl8+p6riyXN5ehzgo9E0gCbVJmG9YxV3BNbY+YmLaxq1Ofbg8YNLTtvR3ic+SOHbTFI+EtVAhJzpkK6zLMfKhawav5/IHrLB9gnX5k2U0cX+pJ4va7+EiSGbRfjACP7QhbLtXhRQSp8A2SUxQwFf7XBQytoU8aMpie5GoozYXgG4YdTeQBVxB8pgR8Si7ULDVSutPbpRJPV+dispDyr+XTt//UU5Nfn75YtmmeVS9mA9XE5Eu3taHxm6lL3TjdwcicOcvNDzIuekPLXJAkZS/6XOpCN6MzvjcIAow+EmKIDigq6zsEVpprnUoLfzoSXFCNuXPltPcyySpuYcH+Ee3xOPyagMThe4UqPI4bAJQEnjS8O8RuqhBBa9ntTDvwZKPxqBNI1TVN7irsiToDfovunUMUpD4qnsHlFmuspJg9ejb9P7bOKjqqpIAS65BuElc1cVcvYOt9JXV2ZHaXE5VU/mvSG/uklyoNwrwtAifjgKQXVqkHKSmIHAfgupVeG35CXKZuak8VUEMr65iAXi1h8FizWpHZ5bdT/Dv9mX9pp4JWNvH4k9nB+fYkxxRPeZJyUam5gukI7RFQqcNX886paJ0lXxd/L3hFkSQJ0UI3fqEQjgAAkpzoJeaPp2AoX0HhXq1hVDzU1b4f20FGiNC3WSs6unJ+59yoXvnhanu4RT23osHpvhWQGNzKsmSEx4g7ISrDGBSNVt8gcCbU66BHntSZ+vQ2AHfi1bMPt8ognlCItCyNIOTW3nwjUUOEuyNa4mbnPSo/Bdpm6sazcCXSPJYP8b495R6Ig8FQvHNn5FnumbvjUlJELHn+P/KuJwGeHPiszARTWZQkWu75hVI6tf9yxzN4uirMeDF0oHfsTU4gm2M2Am6lQNAvgMi6H6lMKsrS40qJmd6vUlxqhTr3sOEL78XIe62jcoCsoRnH0k3OuNT1J257iw9RT4l9VHd337QKz8JmDNY0YdDPc1p+cWJh9MNQLom/jGALBNsZ7YIJtyBypJH/kfysa/RK9GVJ+2UyJsUAXw0437Hq6LfwyZZj7JpScNOGXsaB0PiNzMpErjbWL5R6bkI3FbOsbAvVMp3E+1jar/IYUKxqAtmkFWLpchsbYVMsbkBQgOp0Gbn0cKB5j47CgcyHrGwklSdYK2nqL/WaZMCytjSaAZdZvP3L7X90b/I51HI+SKr2G6/MsZA9RqAiqD7bSPlltVCPLsQ6qDgVht4cvZx7GyWuJq/ZHFoTKau5rI8SBI8XwucPPkljAq7VA0UzsdmtBX/Dzq73ktX3A2otImT2k8A4jHehtMITntqddbGhn+XBtmSBWs0GpoGBJAktt77OT/4CFytLH948amsUMkmA4agDZbbMM0y3kIPqb5TSTAidx7N4X5fb0Hya6CQOhlwbepcOWRR9KNBOcPfWnZGVrMZs9g46PtYnAlqGlMuvqqKx8d+44AnptBVl6NuX3B0+OEXuYgiw2M/LcRzHGh5u3YTrZtDvGrhdFe9El6S7p7rysZSgXJC4xmJHPF8EPAhcL4axrGddVep8ijAv8U005PxWgbnRZEdeWg0PGfI0xT0n2mTwcEzDmpGakUtBXQR0yTEA3jZz44Pe8NFxdP6k7pv5fi3SP7YwOmJjwh5uQxFem6/swdciaHAdjHggdehT6KCSw1RVZgPkhk1CJNyVxqfy51TbRM/lZhtO8I5TNz1x/ClU3zvYi7S2Y5+e3QJpvR1wfUEXIG3xiTWzDfEXEP4/lqZ2KHAOIkOQLKY63xDKIsNU3K6gUnxZWsBDCtekQ+wsUudTq673roGJwjJ/+3/cMeUQSoTquXMFOg1HdvrB7eJimU9kjObICXysNmCj0bImFZjoOrGd6cafHSPWysDEybSJw2I9Pu+IasE9NMf7eGMf5MTbi7F575kbJko/fIZGjPBexl+cnMQ9pRgNgMPqfcfHN0poH0Vf+vDF6Mh27e7CEoJu2KIpHDwD7Ng98yEPgEtjBnBA55f46VhmB4fPtoSeFUtcvJFJFbl4cCqVj+uORE+Vksnz23L1DYzixN7+pknwZZLLfZv7JKiq4JyyX4veUBJJkwe/DIgDJajmXFUkeazEK15UmbOqJYYT/nTz2/d7e3ARuAI9UqFhY71UOv4rQsGQXLnloK7/JqHYceouBW0OPA7J4fKSqIisppuqSGxvMVQ4BCxzYNenXVOYRuWviW4uGFOvYNcUx4m4BMbZN88UAIuX3Ck1wZDuYAyb65nLiiTuUZmgrIYLi0RIlkcommvFi0VfC8tIgYy6bGEvFEM6xmgruoudG95EDXYDoMElXJi6whAwK9RMiX05gdeUm7h0h/UYbhJqIHrXhTIkhGam6j7SRvlIj2LQNjytN70al0dc5a7IcHI632VgmT1tIBCnO5X0kVwFgdAgTbzB3ud+G2fHScqj1DbvfKmChW8iOAcXlR1df7jglSbQYYIkNK0nUJtPPy9hY50feJaz8uB61OJYkM7Rkdb7Zmfn/vihnnlAcSYolY66zW8yiuGibwSRpL6QmwLJhzvs+xgNFcte7V7s+rdkocazsQM2WFUAWckgFFSBMTXWVSjjtmzGGamnztRaKyo/Gd9Dscpi3nvlSJTW+LP2ygg9+MP/J9+nhbyxuRwAKRulJirhROGjWWz9q+Z0B2MPCTBU50gyfeWZJfb9v4BtZMYMgVgfcj1u2x5eIl4QyQkHT4P6UYyuJ5GtPXeDG1jeTsNRmpdclJaq4XwlNlnWLAfD2aGMbtliIKEkdVM9R51Y67DAJl3yNmW66WpUJ8dE8yZwM9OBlufRXLx74MAedvLHd0scQ1s2/omyzzqsqCYiOt4FIBRLwu4squdsylD/9xeIVYVwITZYci1dkDqAITx2Gf0P8qJwvcm/KO4LcqeY3diAji2PKy+ioTq3PHsvhwBTpT8gkVrgDORH4Me/Wy7Yh3/jffeffVu6nceazMb1gkRXLIAFdcp8tTVhjJLEglEKGJV5wdJe0wYFNp1alpHlVtqinmG071g4LoU5+/YaLMx77ALOsr5T/8zGIFALPsfOtwjp2xmC5SV6ydhaOfDBB8V9VLlTS4JiS/tDAXdmitYsEaUbEpt/OKrC1UkpKpwzvZS2zWRBrJvwCtBTdnBV9OwtnPYAGJy8p1qjE/AeEkWTsulO7Tf48SXobw2LVcU7tH1DMiWIpxd+iO5kfSnGkYAl+rcPSOVIFKU1+9cjZnTVLJOLJrfHuyWh+2/gflmfeH7yorMvElAP8vCjeQaOWzgfyUDtPdQDrEjWjyWY4nMhehfHIIQBN8JOEdT1ybKnXyW+OooPen4/RWekeTsh3IHvzqiWiTcP4UxEJIxxLC7FlcSz/pbaTgn1gyXlhsq9N5JgwRChuqwdjLQhyy5G6vlHcKwKMLMubjtteI4KtCi+rl3s89swXEqcAvxGNvj4OjGD8FhbYcdu/0fEqpIA23do1M+sTzfHSombJR1xgdGt96wbf/amQRx2KEqjSNW9VIAGWQr7ahiheH7p0Jwq6sIh6H1mEOK43JnXBVlkKjUSnm5QSrCGIJK5bxb4mh3f9yJ8QGRq31PEYCwse4KmFdpoR6rBFBDP0j7MlG1qQ28zOLZBB1uLsS3d1lIkA6cOqcLb7AvLza+029ED8uOGW/1LKcD32IM6HOuKC8aLAcwBH2D/zwMdME1uO6T1SeKOVSG+813v3gxfrZaktRJ70kESgHIFzCXiQtcf84L53XK9lZiq/AktTtwoWAlr/ybAkQO6mwL/8+QkvBzc6tg31FbPg1FvZev1PQ8Xrbjug6lAfHGo2X0ZdGeLFQKMKkWnYyX8sJMb/kfcGyazc80Rd58qT3yO3HnNPg3wc4r7yOw+B4ykz0RtIwC+Vu6/TTVLUaOIy2jjs+byRl5fPtDvueK8pB7N24Rx99lLNm8VqTKVZqPFBBZHJgJ8C2LXJUgThJuzR6kuVsmAZMhx0lMneN64xNQyDlMxmciD0BpDZvrN//Mbukjf88Of0+dF+oCObbHYZZbeLWUUtqxmuh2Ufz29n9/5VWm5RtB/QyG0mvaq1fqB9dgPvwo/+rEbL72BRvgvCs/JHHhe7RM2IS5I5NNpT32myk1j6mMvP3wNATByM9AOi10+vOO2dtUp2aqq6HCIhVqF1XyzkrP33kluRlJdu/OLtyhG2BJ/Odvag23VvRGD1Jd3qFToC/UF1M5yD2Ahi9DIN+lzAoFoCR95BxtCT932ISAfUxU6uoZkBfxcHeB3YTdtY4w91d0fiz5tirNy8MK5QEls2L76s+g7C6/NaV1ha/uzxrQ6Yb9ULa96aYuxSPjQXlKqHPgs2SzhInIjAXFkaXhtoHO/YqCiE8PqzeF9tm5WUEV1skfifybZdTMLdX4lqiRehdiSvp3pBfQYXu6jcHKy1oc4wQSm/F2kJHIXNmGULMNY/v3PNPt8bKDSboYkD60q8xzH5rpvgnnfG2VMcd9Sh7kZrvU1zx0MzX81VnAETSdajXLTHLRgwqDWvyEOqZeaBrsak5K8VSH5uZAyMIy2G85W1/LFg3T2TYN367ew/oD0bCuscInXgyA59obHNrBKaxFn7kcxEkwFpibBxg9SFH/M8DgZ0wzOgSOrd/R2lLn24cwCv4MXW8/T2asHto5B83NF+TLvv6DgIAzZ74VRCcZ2AebtRvmzVTiZieAB5/gyoQHbRlM9DRFU8SPC+QuW/RzlAoZ01va7WF9yASo34ijbKJw97TyrF45Jx4ByMmJyPRAc+kaOkOeF3gzXkIlgFB6WLNsLSGD0lxE+Gvk3Lg2EhyjklJ3EBWWjSg6sqdD5C6UrqThQn8txQbdWqIz/LkxJrdOoUFP8GZPUlQv5SChBm+goPcL864bHiTWn3m584JnkfqAlHCjIp3HO/CZ0DVGyeSfWW+EyQdHh0nHxbC7joannqvIQ5Sc5ZYd/9UEmiBrqA0qDrgOe4q5l8h4I8dBMAo/NjtYxDeLGrlx0GVtUdDbV7fozT65BzVM1EfjDx65gqkAqIp4osFQCzEvN1T/ZQdSV8WnDdcP61vGeJselGc+jT/SbRpcw7EcSGs6h1+5famCERDgANkIKQ4wAMKTjBZtAcxfCHZGznpaRkiNs1oKQClrpsWUzIMsxQDnuRXRa302zjMdWV6fuGND/V348gnkxTg3zPUQBtOoVoz6c5A1TRAtlWWOY0A7HabIRmNSl3GSY/eYEjViPSIJB0xcgbAejKqETrFpGIENhkZBbWiWdCj2swzMkQholByKlTh2lPy0VEH1SM1025Fk5DSKM1Mk6tG/u3IpYytF/PkMasi/epWQ74S7lbV2tnFV2HyPMA+E2haTqoObGKzxGZ460mAK4Af7Mh9hrLNE11KFPljE6Mup1HGPgTc4o8IbO6d1UJmZ6HJBxe9N0EH4L0oED3eMUzJ33IytRkbhgoUmG03qGvLrN/aBaypcGfKASS1LxoT3eEowUWZnrVm7yStrr5aPX0bIYp00TC9lQcvlnu3sU0GSXwM5GYNjTZYzgHpmUX6UU2OApf26fJ/1oPuX7Uk2HAjHu9bRmu0wDPo6Tm4RiAaOxF15NiGXl9ea/kynEmgPfVTJG9DhtTpi7CY59p8DsDaP/ax5gtSGKzCFdbGr0Dc+4+bpp59rymFshoMPCO+f7or2wE0q+TeBNyzQrd9zMhm+EwWAhVtlO6ZaH9KFTzRJqcg/VFjX4/BKHRAoUXBBq+DWtUaHQG2mSaD67xsESJYSu8Wy/yC7FNaTit9blMiT1dnH+ijc7h9b2A5sY1zJzF8yrW74JeSi6QG2nGaDj+1vvz1YoUDuYT5l5VviRbomfqpTwjviPIWu2sdYB32kDlbsW4JBlhnKLa4XmGECKaqhrKY4BcuHFC7IkAOs9G2QCUnKCb8p4BDtW4bA/KJI/jL6VHyRd2d8W1xOAgLCcdJ4gFTbiHOkNAk5UlUr6Un5ulsYTRivdTgt8VC7wo6L7UnqK9Kb1hE+2UJf0YdCVZbWHFcVyV/iwhukVz8PqTWkrmc/AAmx5Hrb/HVtEi6JDwt6oaASG2fEHraTER8+f0XaILn8T143h87Lj9OXvzpl6HkQ2zYpUfImPtM9jXBuFHSCWs6hmZBhr1bxWOTYTFl9SDnWQnUsgdEQ6BGXyRXqqIl4PmZpdqjDZvSQ1J5KpurPNJIsNiZAk6hcvGmKdhiiubtWB13u8Dp1iWzLuHNvn/R2tR4Gl7cVVMQG8QOHJgevZQXWCH8KDTHk3DiStBKjaojx0Ert3T7ZR3r/wIJebMAiBoEgHHYqLIsEWksrGeS6RQ3MiziPeSL0OWojVbQ7sYeCzBBOcqx2y58e9OH1VBYqvnIGfsox3oCsUBxZdvzHkINs82WABEAeqjLVm98qCBxwpXH+etUE3nXqj/opH5UOcZLy8ERu4S98Twa4z6oQDQncScFj649B48BCuIJZRUoooDX60AqoAVLntcq7FA21svLqutlNt5CG8ueDRaOLNvwDQItCoMmuWcGMXsrfc63dlXJSmYRzLuDQ7xp+j0tYEJvaLAmk2IDrfz268YTatXGCRuDDQIZwBB5F1EjkAmaDlE9Id7xhA2ivnP4uPhz4k4WAn3HbUqDTp0fbrqusdlUIDfDaPSqEH45fUxl+xW/nfu+YmbTn9kRZLZm4TQlVyRimJk4ICdwWuXY3AeEWZFT7SZc/C2i819XbAEnggacTQ1FAwFi1vKkjJtrOOEODbMnExSPDFS+btg8rpfe5R77kQj1h/HDgVkVNnXngE0YIfM1YMHRXWsdShQ4uWdQN1n6UNCGJbFz61g06v1G3KvpHUFnza/CwwI5aRU0t02RiOXkRgie2gbfgFTKXcGRijMZ68N92pRklP2KBVv/G63jg1w4+WQwVsWgdXtQOEc4FsgE1IHERPqAMzrRegfrGRjPK53QHyMPMybg7SDMEcygRkoxNLKuDv6avQir/cJJPFKGLjDpCbGq+p/eGJ/BoxsFKjJT1AIn2eVs5LbbIYFPt+8jiP25rd2vtGjkaYj4EaxPZoGqyotCx7QbvDmeGrVQwW5q2BKmqV9oVFplyZ7Ug0blBZ58Ec2iM1M1l7lk+lbQ6fpJj3PTM9KnFYf0pvwdB1Too0hY+l3l4GfdfxXDcEYqE8mN5aKMNxGcfAbq36xzviVR2NXtn1lzLGtpf6xPHqmlgRPnzL9v9DjUslHJeqEz9tIS1f4K+pUBcw85tVH1zhhEYUGSHcZ1Uyg9dhlAbRfA1C6VbHJ4cvahnsVKaG8w7S3V5hZl7b4YPQ/dGCdcisbx8Oo+bKCHqow0Hkz0JQiFvgGNjAHWZu1txsGeNCymEhIFl5YguHcz2hOMaUqbz5yyLV5P31nd7NUz8lHU0KRS4yrMsxJMU2fqURFsAlNvKlGYsF/JBEq8RKUHJpOOhVaT4Hbdb3pnapkQbKZJ2u9sz/nYVh7k2bRDlwDzZDHwms20FGV+og1Pg6bm9DrEhn75Ie+4GnSy6BsKmQ7jwpG41BDN5cDCXajU+XkCFZyGlklIbycohozs//y64tJvoNJiHK9b+lpfNA1QZGofSrbTY/cQqoegBIUn9H3/Qkfv9MDgSDCv4Sf14r3YO+lx9C4/fx1oXS3RzOAqRRw9M3wC6g0xpXDXovpqcjEbPTPEkWBMUyLm49d1dD0LnXmo69J2640cGcm1VapWX9/z+0p1FounLNrBn1eWpwx/Rqbz2wa8wo9bUf1Bhk8aSmHDZY+7pjy+mM1YtHWm8ZwQw1r2gvtBJq7e32qoEBoUnF6OjT4YOaQGV/YElNPRg1xsnETc8xo/wfXrGxzo8rc3lc1G9t04kgJTFTjNLjqJWuaViWQpZjLD7GfqhG6AFQXPCNIfTZVYuj8kDO/V9iQqSWdbbZSXBmP59cnGhkEL3wIjc4KRIxwLVgF6jR0MvZoDvBnfwk3CfsEpyfbXM9SsSHxG6hHWn6E0C39+nsueV6yaCKP7ZAjRX8PlsGvcqwtykEPeb9PsqNzUBc1+K0dFRJmTBfB+Ccl78AJlG0dCckoLbTYPpG4Y5XYJf8dTgHkl9MU3iJDe5Kvuw2ZY86YpUsQfh9YF5nGuaJVwmBSuZiR0WO8DdJ7jYxHMgo3SBqgpHgbAkaiZbLDCFQKIcs8A+HElmpMljOktfbYvzQRqlVXFUwUEEzXHhFWbUf1ie7DM8xlUZx9pQWN6CY5/VTU39tSxUwUl/8iooBWSDe7wql2wi3QDoPbKSM5R8PAvFFnBiEAeb5RcaePeUSWu2kLSuFyivMDNUaqolA/US1hsJjGSxo4Tq1ZoQmLjn4cfVGa/iuzqW8JiDaZzP9prVwt60YkTgv1FcpxmypA3z+LxRUIO3kEOBSsIhTtIN+uKxBgynsyPA7OhFgeJGpMFmkb21lrKrIZyivNjdBionlJQ18GwUaLWOfuGUfDsdIKGXtadXMA8ZNxmxltC4DAsuOJawzSQAp1PX7Stdk0XeNEf4RmIQ+TIaXioZyPAcHNHULPe3QGcUQP3zFwm+tU6bZn+EF0TJ1PGdWr81BH5oRNec4AMnwjWcgUUCM22anAI216uaRvHqDXVDQ3ZtMh6SvG/qLtQ4NznHS9l33eABhoU9Xp7vnmMc/AscUvWJPovh4N0JozIcdpRvGcbBvzP0FHH4nhSKY1BBOyyV4DOr0jaABlrPt1jzMwabp2ZKDF/2zK/eCub94OhsvwMS3mvBYjnm5694WDNA6F5JFSTrB5/5+0tjJSwR/PMSH4cRGkepceC0s03/L0N2iA86STz7M64t2UQvhuEzl7gsvoXW756RwbHKW86su/RgLrQzLhUm2Xf65Blj+0x/cib57vk99ULJp20FHRehDHXWVCs2dkPXdRzraOF+3t8Nm8Ai6fhvCvFxoXWElf5RwvWnwiISi/rK1alcswsdjcidDZPY0hAmhXxcgNx1m7JXwacIVn/y60yIOzpvOV9gxguF8a/ykS/LD2eIn4vG44qRJIFOpPPoqtEx5ZeC3Ahl3Eg6/SVKFHhPzevbsKTeVredGn4eaV4HP8tf3Bk2+7gnH9bzpYHmAJug0Y6cxeE9HbqFl7lCEkFrXl9X2fqhzYzHax3Y5lh+zJNHL+0+9Rjen14anJa58TkfhG6Epc8vNer7Ec7VE9dkiF+RX7OD2erA/wr4pCazZ8rlv87EaRvIzGKlG2Y/Jc262G8xJY/0ucCDzD32+bBsuUh8T/nhDEnYkLKFjABtDPxf85uuQ3c9QGZKQupwCHMHvca8nrbHPPzVfczNsjaIHhyugYaQCP/6KecSBZSD1oeBihxfnyPWpICLtYwoK2nID8o2udRLhliwKuuBpYPgDLO0BgbsjsufvKwhagaFa16HOVpr9FgJOkGSi0Wu/D5OswrhZfzIGWbrhI1ICy05cjAVZ3K/BNWYvJaHxy6MMorVsns86pKDtuLwCEKUcGmu8N+CS6WlMNuSePJfR8/EcMHF0jxWnk0sTTos/t1p5M7cKGquDV8toQq6jsUNfRohenVgvZWQ1oVNRZKS+I93FmPNvq/ByRUaJ/10rk+W+Y39EuAJBMn65zNBAPLVlG64yxrEukNSRPfQRYDGHmGE3PnlGasOSDK3HrXa42am9h7QDe+8qOkds2lz+0f0LonIxb7xGOMGxduHwaQz4ig4nlKYivfPljufz8GSBsVF5woq6TqibS994Ppfu2ffoE4ipFbjpJ1Dct4abJnphMkI8DOxGwolm5+jomNRk3STGe25d/lGM4iQSCXMyr1Gnw/UqA0Lu8ZRQbtbFQT1MHq9MB0jnXZgjx0IpwuddDZF8L+a1HusgQqnXUnfHzoWvmOh6pFo1t/eBeTgia8lPEos+gDVx4utBAnaUVzphlh+Ck0OV9/Vjuz0kdIChHsYehLi9l6WFAVsq2aqPbDsyLWpAElI0954hbkhzrOpU8PcYckovVLpQURnAanj7aHEdnXZ12lq+96Xfs85j6VRt8Hemp7iqOteQ+eL0Fn4g7gGePp2r4xQQllEn8YnqrruPtD3mJCxW7wK4rNI/x6eNeZSqaCVwKCYoCxpdKT7cl6fxueBvt0N9qnpOyvtiKcstuyi7wTtwITB63LKd7gg0a96W+k/DuOqleApj4+gOg0ZKAxdKTXa3wEVPXEUseg2wuMlqr9OnPlFMNUqj5TnA0HvhF+pOsCDGPUas/D9G5EaJrqZ1uci0Mrh7OMRaju3OyZoGObBc9C01yXFzwgsUaAZtSajoDMmNUFxy4rAqPR5wHHyJNEzbPYNQHjyKV0rjngmhelHED+X4n2L7a47VC0VI0f1B4vtRpqtB/oPIyeHJC/nx6T9oGLHvwt+HJ5Fpv6akWeJytpDw3agEq3OTe6odlmqlPd7CV/EBGcTbJLTLn4jJTOgHXSc+3MNKVl2O6fGaX0KI3+5Inf2mWx5WgTN1crcwlUYcm9u6suv27O0yoU9p2qWpOv/UjiTfnWedY1xmoNxvdW4DTepVBREz2E6ovAO9WFf17rGdQ+KX+yHLl2rRGJ54PDkcnSdkGPvZiHki+uwJVzU64c5KhxJK69XexNGD3Wsmg3d5kk95nSlX3B9HJEDzTreE8TOTVBKnTskS4xDPxKyXAFu05SgtZZHJHB6w1gKOayXxfDl+Mcha8pmE/Vdu4FeM6TcnCEXjbeR11BsyfW27vtukCpgzzIUCIE4Ka2M7em3z1LSzGNbHyC2OJBA9sJUPLekYA15WXIabQ+O7YOCyZ8NPz5WCuCg1smVOe5aWqrNzAStuIoxsIEG3lc8xcYQvyrT8dhyuif+bOAx0eyBKb/3S+8a3v/hReKqz2L020Gn+e55gXyAPXYmuN1qRF6+OM1+A59QsL9oFm8waR9uDcx84r5jMP4OEJD83YNSX9rIe3mLZme7Yueen1i1Xx3RMuq8nwpUc89xWM5u38NW5V9Z2KpSYHmpu4yWFkd08EdPi9p3AnZPzQSceSq2U2tVYG2yWUH4DQZea6fp7qyRTK6cBLkbL/1b74dqhlVY/0rLwO7XXG78IavLMJuI+7AXJ+9VWOHbfigWSWf1ACifucnroabomkB4hbSUUrl+4wBo800ZejcOfw7dGRz3i/926++F97RY5tRadwL0G0XHqldLzitKB6IObtYh1JMhCPq8pmtu10skGlR7IjrOwFgut4XDnA+AN0qBqT+4stwMf8yNe820VTO0viDLU/ZZBxTWhWyhsRjOAU+g2o1dxhd7Hg/hLRP1WkSD4vleaQxMcsPX9A/ifBaqwH0C+chw0rzKKkPbQKT+snBrzlsbUqH8+q5bbac3eV01lOUSxjkwHjMxyzbDn/FCE1jx3lF/fbUXZW2DM0+BUKz2j7Q6niTwnpzbLzUh5DN0TP/2FvlcDktZuZoPsurkza9w+HYWGDK0ergLt/dW0xOjzpm7oOlX0Fc3DiqpRCvNeWzImKNeomxsq7HUl/Fgkhp0mNH3VafpdW9WpnWxw35cnpnPSwKYVIMUJOTtERFGtG2p0hJpDr5d9S+Omwzcg3hoHoex29gSa+K1zpCyqfqhjDBjO7sf+pOsXwo+SSmFIE5BGuAw6Wo6uWtdvr1cGYxDYnL8qvmTXALTRuycHRzCX7GatyEl+Q3dvOvA6PkhMKq9+tyWoMReway5s+pn7XvexXyg+/QBweW/128OwzwTR/V6xXmoKLkbh7c9+fNomNDPe+bl+Xzwgl91m0jldqNyaSWHh8a63eWSm17gCE1tuWteqjpRDyc72smgJQSim8Lu1YDVbYEolUVl7lWmFiwH4kdg6xtWEfc3NmQAJJ3qkwEEqAQUJZCENhzO1sfF9qQeYjJE02IHQ8R681twnFsc2qjmuKA3Rv+qP6+9+xTXdtUZqMjctjDmeprFwSu+Wi7w97Txq3q0rx5liz9OaNGOsnIlTjRwfeYCjrluOTl3wKFfhvE+dZskldUEmPnc+rti02b3hRFkgbM0SBkjRruejmRACu+B1hox6EOi7JOrkWgiGB5r3I6PGeGbOQnyJQESBSjmJkHqqdYBKxH67psRMcW6ybal1ZpW5pU/u2r47DqKXxy8X4X3J7zHc1a4SWluRKUqdau6XTOLcM7OiblotulO+2md9Eo4C4Wc5iXLnNdt8nWCoWoQ6OQxovGc1t/tRkDlVl4NxRRhU4OaJnQDEEJt+zyN84YNg+510DDk3dILHwmZrXt7IJmsWlRBiry6wrH+kRtqVKszJgHH9ydiRT6SjaDSKfevy44FtmUvnsrjhueGIouZNh8QoFDEOg9WT8MQy5mItdYp1lwTQA05FYUDm1QTvIFBG+foieGZ0U4JracfLRHng/35xif5YBn6kh2Cm8YTBT9vMTH5QsleUi93P1tVPdP/ihxPFWU7o0BzUvc7qJb/PwvicZY1qpjU356AAD4vF6pNQl6HC7rfOJjsSPBz+bMS0fOr6aXYDAgnwBa6pG8s8+R5hrsAdXcS/r93fQ6gpLadce2GMBm3D4pERf+w8FsxKzlsRlJMv8uRMU1D+mqGICrnQYKOvD2n6Txb7xruZ40STrRCzw9zqqQTbe2gHHWRuGmtjXHzb6DlRNju8bXoqhjBke2F7Dn4DEYwm9VqNjadbHhMzaNV1YyEcgMLzw5k6s32A8kSynAK8xytCmAVhhpeQ9l8oijOa+0jNfGv/lrz00p7HQE7zLBZkAoucuUOgakelY2J33DGCt8mfDZohCjK1FtZ5/bCoZsH1X7uf6ztWvRpiqrJ8RgWxOnKcFiFNCA4tNTrLA4XNg9MBg/xzUUfM1j/wnLzYN9CHpqhj1+DcqyapXQEEIhP5eYxY/YM1C+HdwKLHSfof7/i7kTqGuOFDQQHQ6IA+U/pdt1wDwHH+it1T1sg70JkkUr/kzxqfkWderUPw1SS9fu00TQkYWI3vrMSWXBN2tc6YY+f/dkvyHfUzfDGDpbjcLD2NadT9t57jQZQSE7SmddPDLH4Y/uFUzj1LHkfRm6lmQrgSyz3M3gph6ITCBR8kii/z+WKPEuIesvVVAnvZ+yguvsbwXTPeShkTZ11WZrLK2flzhYAf7JGCKjLC8aqMUOhLqGOW8pblxqG4dEJOy+f2/viGYDXpKvKooJDMtDTwQ6tmB46rN9BMpIi9dQPbDJPgdlBL/bq6nLxWlmmND5/4BOGCC2BA7zk5+ZgxmzMChnzlkdqGNYyTzDn1dBCWtWsL8Q9l1aYSP5txKpppdg4S9GLBUDydXj2D2/PwroUP9g/xpEVGuHqjUw8P0xlbKX9BW5BnmllTSJUXpVv90VBtt/o4VJaUG8hb6JqMBdcmPJ/EP9GhguoleUAKaX+QNPHPuojJBqo5RZMQZr9kcf+I2xSU+jHxp+gb+IvLB7wF+HMJ5yepXeGC5bEDRa5oLEZerL+tJnjGdHEzQKz4Pjr++Q7j/6FIY8DZZz4zBZRJ3cWu5jz8hzriRiMw+zyRMbwxOXNSMVqMzHeGnx9xQeCaYaIqyJSNcey5Agl9jIO3C+YvMAft7+GiSfVU1FHUWWtAiEsF5u16mVpMUXyWZH8yVL25H3AOKDFdDwpn3PG0J1f19YZjkz4MzVsF1wOIqB+jA1CM+darjDji5zocqnV8Q6RfoppxjJmbweWv+NzZRTzhRFSoWwo6GHEx+6P+7v8U7WHBgo8mILZmYMZLGLbDFlTGjPH5uRgCb+hno3vlBdY++0YnmtmtAZgukgII/dlAXpT1qhCAEbOAmV5QY/RRqRs9Egzi2vaSUh6wWljdJkQQup54FeOLCfYnEdCd4Buv2nGNhZAEVR1ioFM0U4gjMBuVUkbIwpriWv+tS84OFK3WQsLgYHKtaRnuPF+71DWh9EWNDU1AKHVN/NPiUVD6GGifo8vd4uw8aEjsHUwpG1pjLFn9aGh1VE+jk9PcYMXgK9Uh0ltcACB4YhbHaHJs3U2ep03j1KjEn0Zi6RUwOXMRIaJVPBw1yX4jJJtwBmQn9ib+/cVI5n67Fm73r4XQDgKjptBOhxtOdBmQt9niuBK64W7t9Wtr6jrYyuKlT39fjmDQDnwSelTigjg9MOHL+9IC9DUF0CvqIXYmddecApvUl1MwRmJaLR5DKcb56jXpSUTQAV7h5ktBJMAUEXBvT8ureVwra2wRZWGIEEbmSUU2mtHbT6rhUmp4Rcex3lf+BYpMcnUeHaIHJkn98fJmTGtxOEAGnxDdQUkQIV6QnciQey1GOQaDaQMgh+DcJFqjv6q/a4pZJVI6KjW4/UapL3nv2JUaQ/JRPIec9FQYttlxpcHZR7+pIjlm+X0uHtrGEMZM/DGTGW+kxW0UjFwEoS4HAGv+V80604Ma368cLXlwZB31gwb1APkaCUNZIqEcQKgJmh6o32IngtaZcBw+17luwLKYVr6DeSVhbvaFAr56Da97jMMzAU6Tjb/Z9bcCJG66rZjA35x2nA5mp0v+8lLub2HbSpyPplp2JnzJhX6XEjfhAtSYN5as1ZMkzsvQyRMHcYIFHZG/CClcHQo7lg+jJmgaKc2cXPejWjHKFR4MTeL3Qtf1fmJn9KVgsfbbmU9AWHRalOiq6NmPmZRJCxyzETfAxhIt3xHnf5wGRfB2UrVJVY+qmyusHSTnqSuwsWlMz7zdBpJhLqW0du1T/XqUCF/6pSn1fHuprxO/giFFMGGIbg7TienyKK3MxjC7D/nOSNQrkWLaMa0mnSOKKd8zoPS/tpQoy4459cGBm0s9iRF3YZuNIhJ2VgTMAX9L3DW9Pr8bemM3N8HUA6oZhrofuxQQd0JQIb0y+dL/fGkcTCYg/9WdfKA6lhOgKUMeqiYCyoYETHmPoHc/rY3dy0Rfhi/NjkXHIf7i767YiE+Nx2j3Fs66cUoWuis08LZnClXnV+vGiUOVsRyIPcMvy0tP9WOPR8IeZ9JldITJFimMNWgQh4vCmJ86Lvhp6vjSOgq99o9VbFjgi+QFLaw62o/JHbta+8VS/5jqbzbgP5v5AkJJRnhufFvDGmChC568kNBfTMRGStFsAFPO8p2EYnhG6xCnA1NkpPrBZRkAAtEuOntEjwRpdzHe5YAjRlAh8MV3Etze3GoJCrz+Of5IgvBrxHkfyLblZLye+dxvIktVwI8YHomVBBEsFPkodf/JQg15Ho6GWL+upen7FA11PdtXqNWO7zAg0zIilkHeGq/MeyMZLjLPQJ8o6RaqShPaSGSUGvqUTwHnFDB2aUOgKjU3KS+2DtmwZs955Uhka7UJ0sEtaki9hSRTDlAjLYtuBrQ6XpbbPPHxvBx/SQLErjBT2KHLTBop0/s5jtMbMYc5RmaekaiNqmzyadgU6UyhybjQ/3037tdCNt7vwo4NR66UAARTuXr4A9OnrfPRS9mck146Xv2+nhiWvJfhTShdmLjox4vqGj37O9FiuD4VcscINgtQORNUoxB+EAZ9Kr/rRNds1FQsqlBFT8rMhrS5paQGdO5F1ZTI9BKokRiU3omlpFFJqJzASQWek1GR+oOfUvXkgiDn2uD/OY6K4p0Ut0/7/OG6lO6sS6W2XR3AOeKT6Okj7VjSSenAMuvqRZF+2gHjOTZXUCbH9aMh0gCG0Jc1ntnIuRskfaB6S8Xc9cXbM+9C3RoTxGMK64V3jB0oaYVmVZl8TTPOEqaQg2DgBP4J8N099FNeCrDkGSIiqCKEMEV4ETX05D0ea5sqCprPq8REsCPqIldF17CVCTvFzCm5e5qmlosvKfiE2MqXFAjMJZzeH/jdxb9h4g0/pfwAwZMA9oMrFhnPqEEjGDe+qR3TyBXaf4brIrBJllGslxzQ8WhKYwuKsSbUS80Ol7nB8ZRUrBUvpa6l4lJHBXnGkGz9vqz1CdY3z8hbaDbuSCdOnyqBCPQQxMAtxlWsL+38xv9vTImH2/0u/2/dDfRDtAJHZawQpn/QYmu7a4lu8hLG+0Hn8rD+nCnLoV5oKXOrqOqRWoEhVf8DsYjQfADJh5TZtc+bd2OA2mXIiYLddHDjZv30syX396gOHU+oMCYCE5Y+b2nuOtJWGMaiH2LpC+x4MrnqHZLsUgbI3X2IaqAtWXXuK+gfmqJZbFyBRft36nt9cBMTbyv3dsNODzOH1Y2vADo50nNZbgwC1vqmMQGER8JpSlIgBFQntuAG2ERVf87685sdTYLbYnVUunmHCg3RYq16f2bxJVOw393J8QfW8N0zHD+By6xL0IKV9tzDIxF6mPMUDWMFIGDg9HBUouLcVzxWJiHpW6S3qdAYi73OYmDrRaTmCjdru71VuAOSFCYueSvyNZTVvKlqdYlUVlxDwbafSAbGKa/lafDNJF3ZYnFWl1uOQBomgooMu9pNm7XPcGlUvzb4LxScr3xDM0OsX30JfoLxULPJM471wfxFT53v91WgxRBw721Q3tKEVgZpPgndJdCWrYElwzHFrc+TKlOLJpO2WuKaQgXm+Whsb/n2bGaP/RrV9tkmeTxk4IyqIPDABN2UzcolY7W2jtlGCG0GyhazLVxz4eLesP2BgBTQ/nw+uf0PuD8K94kv3EUbsBLN5AiZBUGAtbKb8uSXInRcDxbI9MQ+Y28JrEXA6//0U6zyNJ5NuebMX/EQq2x7SfABJMpZFmAz6BuT8JYnp3bFPDxyXGehSjYOzv4pzKEvTZPBVW6NrJbcEtgpbPjSgXooYnA4O6BmFgsDjinJpb6X4IErBnTHxCy5m4BOpIpmrUkDLukYhQDz+rMPdA0t5cmRquwCMReahRUzPWeSvVWLuqBUFWiDtz1WTDyMhK8ybttK+a8zFf3Hprgpqhd2CSVLGQ5y277cltzjqroeNdzN2bDPsNURSH9yyeBXZdoLZWdY5hP4QvuxDmVocbokIwq3qyBG219c6tv4cR44m8GBIKW1VhiQ1w3JVvCuGvLrkH+ADMubex4F7GC7Nqle3FDol2FUlX9nMCu9W563o9puKuTrgPigXIl75rcadqJMzPPEmoasE4wdou7yOy/slwuM//g1HctV2ag2/TaW+5SVzaX/LYtO2QC29fc2U6Cz0B7THwD+aDNc9QKJJCSOjDwoTwDaBIA8cU4Dtd+J78Y4ablTSEEJzk2Ig8aI/qybCqlpfJsp90nSeAZHXOehY8qrnNZU4yfuDsTDZTnOPxG9ShQ6QWjKxebKAmwVF2GBBUf5kx458en8oQ7d5z185r/hdWxG9PchxrXWcYUwJOckiYPeVi29dbCqhtxw+6CoBzfqglXqT2eldE7h6Ph1T6QFoZO79pFEqkeVmPvayDWc6ngr3YweBIB/z6xQJofkO75OI1fetsD5S6VYnCqhJN4eLj3hQxiyO/UMAA7vfYjrDykXFDKHGwGwZmTVmgy5KulvMozDtlu3mKBcKSOVv/2TSAf0lKBymJ+jwQaF04lr8HyeksS8Lpp2FUThJtu/EsMrC/lm/z2V5bLyuLqsZRQzj4wWZRGTZeVo+6DJnLOyQa13qoUaWBm/9CUE2kccdfLa3zOODJ3dDwq66Y2ii+53mg0KmejrW4UOCDdz/VrQkH78FYsbL1C7eSbQGH/Z8FadZXE9tcRx8y2k9l8N53tvcxFwwekFH/AZZE1fOg6ZE6UQZF//RGZ1xeHD5nf61muFXdeOS8O6mrmsgTDAUeIJpiLG8t1LQiAXz3FKoPZ5hgBTpvV5d4loGW5Bpxqf275UUpkXU+wxTYyoJgmbz7J6xxJB+o17x1MTRepL+QPO+o4lwvKF3P7LOqhm2xK9k4OraTqfzH8LktLN5YqPKfkXpNxsWqTzNZNj2U3tjoRxgcya2qlbPhIKVRST67CMWFguLex3enuNaG43Q2BrY0E6nmvRWmWUNfLmr+ZeBYK1DpuWaUp8Am4n7yaBcNMZwVpnLqmtfTW8XveybGuQrBiOg9mQAMrUMCc3jXaX/2pxi/sdtxVOoDbdYuusaq6tDqsOFzVkIgaZWSCKpXtSq9QWgjE4yAuBJozOJxBoK4w0OO5KrTVjCOXes/QahX2hmjQeZB01bmHfyeA4SjRlLWhdd+vro7Fx/DrbpMniIjHiA3pU3kiMpolD15iQqNdzYWz7ne1XMHg72vb0DxoeUwG12xjk0ATRWGTwftvPk3fAU6qT1iRhRQCJlLkprwya1TNZlgc5+zXRygJsKRVmdhqLP7NM+4vi5O5vzEx5+QajUM5wzJTOpcZEA1qoyenKveBQ/EKBoMo23eslN8bewRjDPZboaorlhCL9VZhuedKhbn5N5BCjs+Aa4yDTUeMNnI+D8tDqtgQ0Pdr/BrFtXXIJ8xwf9X5MXH8tWUlhm5cQui4tB9dJa9FTBVH7H4JxpwVwYAt4eEmJL0d/K3XRKou1f26ZwhZZCrD99bSI8784vxLYf9jT3IZy0vx9Wt0gNdL6bwHcesjBWRof16Sl/nCv0P43fN4IBHj69+xRFjbBbsOyhutehlpWeTLktwwJBSXbWqlAcFklb0r6P5CJeOz5O2pEo75eqiD7ajE5aFzRqMSJMKNiPGlUScVr3AxZiy9mjUkWefpP5xIROXg7qYHtz3sUcuDj7TVGHtyffdEsNKZ06rUg1WV3emw2YFnOon8A9IfPffgRZdrIgRAHOA4A0rf2Hnh7YxEGXk6VontrMcpOGkDiRu6fdyyv77Psy182lmPybLu9Mq48CN3dGs44lOaOdZ16uSj8mPtg/rrUMME2WcduDaxiGMlpDCrfHj0yInON8UGeQVhSa9am0Y3L82MHp6N5i751zWqWSHeYueiJKKmKKy3e0ka5bKGZaxiVFuDc0JCyEXYEewZYenYmqasxuB10fiA2LecS1yg5Q5lqL8GqTPyKbKKxMZ2HVD/ROzvU4fwhsOGQ0nENEK2L0rOgtRfB86eoaU3j3aQJpLKk9GyQWhp+4kWAqT/g9z+82Kp143MUpFF4TsFvCSmadwkRWzdq/EIyRgXFM59jFu4h987772gTpYIRJRxAx2Go2UpBE/bOLPlEz/NerWd1tDzmf5IoaVPoQel0vShz+bUUfoaluu1gIrKhjSCyzi5mxYD2nBzV/dP3Cuq/4V3MKqQ14cxGTuXCjEoT8N2tQOtoS7GH9eEZnVJ6YtM+J8B7Oj2G1asmL1eI4Dc+8L1cczzrSocC7BSxgH8jMlZ0jc+csSbsiLh7aj+wtdvBAXcVPLNvT0IgqninRaL/+Nrdrg9KGfkpo2W0bJ8HOo8myGUk6kG2XfNEsWJKtqj/jOqoLGKcb8AiEVO6wjAaQsWL7hR89nnVN1vOChnMKXNC6SzpcBNWo/NyPdNPCrHDayCJ2mq7yZMYQJS5II5OqxtSWxlRifuxK+ckAazQfxK45OIDD7V/n2aoiMCeT4P86CCvxOt5aKQM9v0alX4RrBU9FMHS0VYHEobKiYcN+QIm9Meq92bhiCt/JNQkoisthOF3IpEQ8xGzmtkwL1SRQUudCyrSEXp1rBtlaByEDSGPNCWk9r0RnRmNH6FcV2jaS4+hLwRy9DXPJ1/guDVIB6WbYJ8XFTDm/Ld9oj7uKvaemUvUdPOJdRylZDnObQsmS50VZ8MtL/FFw74RZFpfIkBSNjRPGTaqI92UNru8x3bzy5v/q+AH7itEZYFkMvZ56ZsZMJuM+MtBeuKo3dRkhGMSTBBbFLMiclMTBpC50o14TupoRHkLWN/uGK4G3NXWVKNIE71cFQ9CUhFoS5A3r+5kSEu7C7Bmr22xDNicvJF3TIfPKKxTyGReT3KPjWNDc0vx0AuPPOgScGLBB67t9iKOqVRQUdSC0ie1iYueUkaI1tV6AfMKHIRxLxHyPIaFXXGrbdY2f3ozOjXpNcic/o6Ro1B4puQW2CvVSFlRUEhj5xb0I+ZozTVjjxaAxcbD5nyQZrfXK+XaIXbDVdT504oW6KD8g/kW8GxQXsV0HaVI3F3SxMrjbBymRcqyPignT9yn+HVfKHTEdQlb52K/sUzUYG3YGLMbhKs1t8sN1eeQugHBdgJ37Rrd2PKtAm1Z+YUaPBRXZGV4Hp7FRys2yYZwa1o09J0ifGNONBVVzHr0tGsRayRQFe8ul5exisDUP3WBz8BxwUr9niEvhLe1/d4ojDwWt3Si8QOvTo9TDLd8m7sZogsIUmzW6RrJiNgFfGpOvErLFbPEkUUa4sdoP7kuBPWMfj+Ycn7cp34IBAKep2ZLVJ5qbvc0sd8ctdbUF80GLrxVvGSHNbCn6kZZDk+1c1B8JPGPuc+L9Gl7/uSuxIRrVVLcZxQgOqC91EkOgun1cy702jXO8UiQDpBnTsKDazKjPFCfE+AbkePDR08f4DNt7vGmRpddsimB8XeI+RoOer/6aS7LgJuv0wOJeXbO5/VcjN/UWFqgImKGV3yBhbEwKhQ+HsFdHPr/GZSvr4fgI0nuOTJHRfBlaC2jtJUq5yvg79Y5t8GWNFGZFEPRc8ESHcDLjJPM+cGvwognyUNomNEiKb1sm2E5F/wpSsGgltMupIf7X7mBKUhPUa8vkMZQZUbtQq6Pcyf4dzBlEMZGGlubOiv70Y8YYb/+LmVAkUD//TF8D8KB3mx4ebsPQ2ipdGFmWmkdI2a7mE1shbfMeIOjO/4r3TABFacR/EgdQHhNzcNfzoSBqsMUdqGjqyJfndWTsaCCYvCG+asfGAsoX9C2ZGRSWlczWbUkUxx5iNMu4QujKdkryY6+grGZHWccAfLzJW4I5mghPJ7xdL59TK0pVE/rDBcMV7N6k0krIczT6z7JtxhDfkzrODBmosex0sV6y+dO4qCAZfYKO9t63CzH443xjHUR70iVSwYI3RFozUpT+1n+o2dCjJw2D1UyFu37glhZ1pg/0lZNoOKS+A9uof14JMQs85BXnNvc1taRUZ/hrKh5tuC1dccFbwpQJD6WlTd3p6JsxTuf01g/t4pq8sShQxBDKiRrPTAzavL8OXTTM8sCEIdtloKNiQeEdU9P/VxGqvZK7AosowiWTcbt+NQXuncwo6+0Zb2pONswqVLD40mC6/qrQkiRBuhN1ogJqey7kiJ+HrMckQBKypMlpk7fgjaWGZ29MUKNOfuGbj7QvM3Evd8GylghDtVBmeoMq9Q2P+47LnGn1YBwEomXtuH/VIgoZdpBiZ2SG6rhBfdlGRQ4iCO8rmPOFDOmoteUlJHvafZIuacFZM6XAV5bzcXHZvOenho1Rx1iikcTMp7GzwIKSEOtpT1C/oADJGEDzzZ9fX51uhcHapg/FNXaNg4NvsMajUtzuQZZcjKbtZ8BmdusF7cpbp3+WWZs7L8rzmtduk+ttMCZVLQBl/vXHZ9kdttS7QXHfT45Jlu64zHTuA+wtLevbowO9Df/uitGphC8pt32zJZsUc7tzNnUVknLns66NFxEGmXrO4iryMSkPlBA0ABfzlSAgmFVHFLL0XVuB3NqxyLUcWQHVOW5D0EdUtNQy10FYAr5yKrjwKys18l48oPEnfbyagZaGPQljO8Q0400io4zPNdiZuUyqYlyyF+s+fxfQcqTdz20svlbK9QQJZwWQk2r83VxP3pVzYnC7bJloluN+YJU/58kc6r+vOZdmEqYv9TutLVXPE6+ATO0QSXsfIly4v2+Knyv3dBm3T8KMcB44rMes8h+Y1ixYs7NvgAQ6U36X2HgKcopVh9qzBQxaHzkMvVZXDmMUNVTZUPW7YUw6eO7ZBmlBnRyxrDOuTUWW7ERbmzrpCNoww15RSZnOByg3KzLlzQC/9U0muE7zLqDRe/GBCRUV0drfyTys2IgWRIwZcYpXHTr6+ArzoNxL8xHKs1tu6iwQ4Bo62mBt2rS4ODeJ96djz8bKBjtOLr5FknKH/RfKTOnuIv7S9/0zU4pXgLjkm8Ef/3Yrtds75RLZdpNSlhRquur7/8RMRGGjjDbCpLFR3oXAmPkOVwUIugZbgjJIgSMKtoY37+CKJMUB72xxXP9IVW+R4DhLnpEzCbtgTLXB4gs1gubJOcc/RdTC4wzSpSFhIxEyOm4JVnW6G+kNMd8jn4uU4JXhJtIGZbcMe/cwbK3njFZfZWvKGLcTmYXQnZFJMY72U8TPOOgHf93cW/2S7LBuxXG7bUcDsbH9tOk45MjkOBTc6dJYuEesR/BG601KH8QY2F0gWRarJF1K5qgoCTt4xeV4pR0cScB8KdZY72p/toBiMEVlL+s+8t4yZ+rwe7+uP6ARZzW3h9cpP8jUqVgT9ydShDVX2Us96BgJ8MzBUcyovD4zRY5pe390L7BG/ZsTqVWkJnGHHnfG0L0/N0dBImP7yOv/l/TnHdPEoHqpfgaXz/SJ0gfcsqe35r3FaKYoW3iNgrn/NJahsOnEQjlgX1re2qVBKe1UXcrvrscMBsZ+ilpGeBa+bDB1/hO7YQVoVBKQIJZXOj0+Cs1kmosYApjTVbSX6NL4sD9z8qgVfBpd4KcD5hL/5T52SO2yXTdu8DmjQwn5C/X+KdPe0R5UVLlexNQZ2gbOTk8gYvR3dkB4eFMEBrvHgcy9ihpjQtiu/piWj+1cXBjFI0957qb1YBcj7Hte2EyDRW/iLAwizaOEfRJh2NHgDQsRUPFN+ec7Oa4ONeBNdEizWDLvqaJjEPVwqWsgwM3UdYyQjbLeuG8+VSiQp66+BB/P3v6hcMu0Px2FIhpWCBHXuHd0yUijfIag+xnuIFa+RVZHjIsTMldlJ8F0IZ/7y+i3jfcw6MrTcm2RM9tqr70cJKF92hWyXegchklxrL8+EXWSlEQQswA0QbWuVq3VYrwq26M3WxbtWrlJYMGRnzgDIovqMdjpcou5k9aYQaSjjHX+Xvmzoyz5Z+A/5lpTQJeSMpKgXGorf9IvQQq4TdLbzkxND6nZdYIGAF5RluG+iOUeMqA8BCtgFDvLCe8MmY+PISOSDoqCHXmbK5MNaABWxvrGtSM0SXdfHAcAhgYPis8BYjjEkJrWAF/5xXaor8UiGqAYZBJRDdfqgBJeHmxXD8w5aUFJQoXlY4HSe0GgLTcoYLD3eFR6mIcXgnGLfjJS9ZtIWvV5KPDujYwyaEoxk8b+pmq5xNZ0brTCRDvhT66O/j5DQ7BLSbU8fDIb7iYjzxt6P2EDwkwOhKX7Di/6EzCzFkVeIv1zQ2b2VdhygEwGQeWctD9rWSDfr1YbX8bKe5So8oJ3WvD1lx2HDdkNZVOdnTxzdaWcGPsizyrZJjgVhcUymGkvADFdPnNN3AUq6D/f41xU7h70Tc1y9YVaTvUVtpkr9APKBIvxu5rDueA4BS/9A/2PKqUDN5z5jakw1q0f2CuQEgBbGwtZV0FpuYvJDg+zJqq+BFyhvgCYoH7qVAfxpb/zzTuGRup2lLE3p+kSpwOXiiF6bPP4yjqaTT+7GqLo7llCmqbKHmT1+HQ6g4xwxXXH82nS4gAzO0LXL5lmltOWcn5AoCCC3iLX2WK5H6xLClnt0rlAXoQVkD6m+zs6ViRenurX6wNCyM/qcej9NW723UDVKZCElEHQZOl8/vybUhJ7jtoYKbrYMmHfdkwdpZbHlEx84U8eLYEvraX/mdTm78pml0DjrtdlOGlH/NkDQWVTLeum001kNtKZjqokMiXlXGXRnh/VVHN69CLxI+WcJnqP0MBb8F5jahNIyDkTVNrRz7AoM0qifvSvNl32lw8kqgBPinmTwKIsVKsq0o6J4VmT5BK/e+mMtPsaj+nJMHKStPK9t1lDuIjEV8XfVQdvPPBR7L6DeWOYbLqTdhAR92kqKVNHo6IhLsPRyXxWfGpl8qFFf83piANdt74O7OY7WkVURiWGI76I4ukF+K2nNsfYcG5QZvejvAU/omdsNg+mqmv2vVQIC5ZBeXIwfG3KdlYDU0ORmL/KCqXUROjYmgtvCKyAoeRN12N3TqRWr76EZAeUuHPJAa/7CBEIeVhbWalY7Fn7aFdz2Ifpe7kkQCU4EOJIDdtJxd4KQUbWKY1PUgt6nwwdpSdWJ39FKCrcFVyq7QgUG5AoQBw1FmhRCTbXkLJSQ/C3fwRHyVN726beRKtlwAqWLa9jOuHVQEdx2hGAmXp9m2GLc0uhhTF0j5iKLZmOx80Z4zvCQT2hNycWlRn4WZlp6YP8wtptJz3VUw1FVkN5kIf3n1RJj4L9pBcppsxEv9iQY4+4IttpZCUEt/q0XncHBWw4Y92Czwdh9qZCtcyii1htfm5hLggqPQMlZn4jUl5Y6TGzUoglRODU0/3g09rCyBnZ/Q0QclAY8bimDgIeEkX7bHWrgxR6tU2pR5czTIuzS0i3mBjiJUAgS1EIDZeuqwApalxuJsSUNsr8Dm7KEqEOuYjFCMZ0nt1zi+/dnVF5B4VZwiimnNMhPf73n1Mg7jr25IFu26H1XESoDxppbJMYGq8nDC+tt1L/Hc47AMVIoZtcIIezJYArwIY20zvovIcaomm9/53UUzYgNVmx2irzSgMm+Bc3q7OyB06O+cMg4KPV6z+1qfJG8X9OJ7Bt2l/xP+0k/IZ00AeZr1idC8wcVUIB19eNi5KLSsUOBYMqxJg9U9W+zbj5M6amashJhA3mlLg/uYquLq2Pal24U9qqonMnrBjEBWzOg39+as/EJIIUBU2vilTBt1hbTlauP17/ZiFbqlkxp2IdZChfPDfTTjZ7NHnXY+g1D4A1JBAw9Uka3x4rxJEwiW6l+vzs3597BgBFVoC1YE1S8sYlTIFtxBK451POkmVEBftmQNcBuAFd3d4kRyiStf5cNYEEoJM8A+0czxBs8CeB2pyWIauhEESkus5t8DdIu1syt/Sa0+w2pARSIg7f2LtIA5zmphIaGvn2jg2AaiKX2LzNMm0AOmkhNLl+3m9+ob1YGz6mnxIaai55earS/Sux80JX0rWpUZg+XCgbvFIwfdVqfxPcE036SiUtRkM+47ei9yzOF++lK8xEqJrA3cfMebv3HiQvljnMhDbeiNv3OYxebBlKWp3IyoBIFkTEGhE5CL23fqiz1cwRfoTxQGUGc18lmZ7LiIyDiSyJWQuHpAWeK6PYYwe22lJUBl9OnJdYXjQeAxyvOBY8CZ4geXNzG3UNGbJR+a2lcLBR5vDw3zxB14gr2aCVNRUmztnrIPrZpXD1HvSn0eolJVCrgEtnBce1owJ3DjNzbk5IZrk/LbqaBRronrB7NYX8bh+hVUPLq3wV/B+eTFi/rNl52UbFOoouRO74bNwVRja+Lx/s+BsilMMhgL3xSEhGu3rX9tREru6UxzJm7/5eSVyJIMJXlLsoQdmwfSf/y5ZWaxHM4Z6Y5ueiOLK08lbCGC7iqZyQ5u1k2ETM07Bq9349TOwsRG5QOCt3oVljzBOAgr6UwhKssQVeHPEWU8STwzShdMrgR2H1Psnfdatsdbl6UL0aXfb4tUr/WBkz338BYlunRXriF5nVlsHI7g42EfQIyiJNyArJwiGPq/WScZp597Ckwo0wK30xIreYggBUq4Xir8GYEQ7fR5It0PSsKYJ3AwsrJ3KETHJTqDa5cy3l9bwrP/bQygRF94zOZeBz0yhvvz1klnPOODbdM/1eJxSbYuDWrzXu/UiVf5+HrLK1KBSQPIb/B+SfGx4264e+jRWPU5+BvbbnYGFA3ibcwErMXyhHlZOoR0sw+Dqno5isXEEdzi8hALDSe4i+r630ICnNKI8mFnwqYMHf6JgY6FJllij3UHtKYXsQsd02X8HLLBmP0sKNxm3PqhDtf0tNJEiqJ6UEg7XJgvwgRvkTGojL09wae/GUyLvqt+KaMipFCjT9H08k2zHdOMYfRvwcMiQq87bJIi2T4/Zs+L0dlGtTa3SC7u7k7ch/IFaJ9le8WOuBbQkv9BaF0+YYfT/sM33zS7OtBP9o6YhRRmiTKhMaYs/Grew9samCVoq8/Y9SQFuWkfiNzt1ewgyLbG6AZRcWV/0FeWMA2x1zeB3ECIIwO4qlK50PK92WJz1c1OWH+hGy4adaisS48kLsfonvDaMqMnVMibV4jYmArOGjEeAyoi/QrSF8KeyqthlL1w5Ot57leyc/IJSHlnDXlRgaRUYRXn+fxCjr6hK/P/eVQBsK5bCNjWEqU6DjpAhae9AuAxOzwTq4HLxYaYCMvdhTQDbR3Jin9jG1vR2uetfxoH/V1B//1T4PrnzLn7T0IsSx/IKL81zM1GA4EQbvUvwiqsKqbMqr3yQkbj18FzoWu5p1935rCobPkiTD0Ej05FamIaGDR49py3OKO8wMR2CyuA1A6vOJeKnAO4pis8kjL7qlmtrgYRnASnnNUL9bKXE67U1YvFxZ/Sf7fMY18fhM6W2zKYAdQLtc8R/ASGNGsQUuaJ9yGvWwUhvkS1PPCn2HaWSc0F+6GmIletQffk82kaTKziZHnQwrK2rXzZFHYYpVFrYWA809caP+BsUzxlE8tD5z5p3sfhTvSsXeiH8pXGQ5a2sfAwt7WklEOaAIMDpXq3ZKll8AxDCvdDuxHGr7EzrzKfTosTd0qmNUgheXaLni/qLCjcGnRUtQmCchPbts417p/Egh8cwxoJPcT6K9Kn5U6tdox6paUcScKfUvB1u/oj5xZd7F0V2AqIUmrRPF4xs5Jl8TzT4jNgSy9BSz+lvvHGuK/zjLRedIiMKiNErcpxGX13Bc49/PpE9XQwaMq1vpaQFs0pPmzw5rVsr+u7NQErr4y3wawjkfoCC27Zc6WmMU0q9yzsOhkFHzaVIooeSwdF47mdsx1LFhJBhts3/zSLGnZXBD+Dq5A5JdBwHMThlx4Gy+2bSeiJRteov9oJ13vRysTw8YP+1B3mOrMnyWjdAJSlxleq7UtmX9WEcF3yN1QzCunVhckEGL9Sxl8IqSy6ukLBC67XhQR1bR9fFDTWevxnXT20KkIIAuLctIGAoaxGdjJVIFl3U9ievTMUBdNT7vo0+Mo0YLTxoLat/yr/jKVn71SM7ammDl0Il9wbru/pMvuJHxhSKGSlolho6cqOyJLGsP6pGr6edia9M04Ru4DUNFjI5bTDtMKpUrZ6N+75XsYcxvwLAEPw5YI/UDWw9TGJUn6keZY730tKGE/pyNBQZaadj0znnIt5ARCOrq4+ObwowN2wsH9qOu2YkVvV+y4DA5uEdSxGTLcAreMVz/5yJdP0Gbj14iwL7lgoDLNaqsCfN9LI/D5AfPzQuDXU6gW6tNXtfK1jUVsCNRMMk4WaRg4asXVZ1zasYKEN1GftnKZ1JaC7xU1pXgTQ7r79r21bWOK6+Qztxzb9AKNoPvi1ab+ERLgjWXGfbR6QJ/XHhVqXQklzWjI5XAIasQkHGp/WFXS7XLDcNa4r/AfR6UDWZkaQ4ubNdXdXWr8Cru2KNm5lKN5+DUKMfMzrjnAbeuyj2i97raATOOO0M23DaqdI0dEL7FkGPgPleLh2uxTNjV+Hyr4purKJIQA8egnoyB2xVJeuBSxDjXUmilZgw1WsKHTi4MNvG2Yp59BXbRFfnCwDgPC1C+gv0w9/QPp/x+RMBRj7T4oAlf1CtxA1dPLg9BJO9K,iv:pypEQ5JZBgTcl89m2Ymf1gbMgSqCQW68C9JKVGGwhCg=,tag:l0Tw4dzECKdBlp0sFfmWZw==,type:str]", 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,iv:f3dLbW/IQx4wSt5OBey6yIcqeOcM8u8FOxF+YA650vY=,tag:4SukESXb4T8T+f1GI6q9lw==,type:str]", 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,iv:wvp1D2eHgOTCBxI+cX+cecLa1T7m5TFwYqCgfQjVT1o=,tag:YdLQYGwxGdzML/pYRj+x8A==,type:str]", 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5Zk4oCsAEgCwfbh7f52xs29iwAVBqyaB5HdwYeizEjYS4G4nGT8T19sEHzpNWHFAa41y0GEtYw1hTQWxes2AT/pGhFr9nRtJ/I4HVuZKaQ/Wnv2p3DrkkuJ0IVs8FbOeL75bO9wfww/2z8BJQ5xw5zpncZofKszsCmmqVZcWKVwbn6xvNSsFoYsZggNBM6p+lWyr4q2MVaplONJ3dbkmFti2JLcgwDrO02Lk13kavi3JYWKbVg6vPtSW/WS3waJjM5lkLfJEsUI/pectSwmUhUEp/5PvqwpWqC1S1pR0020QOHU2R0TLD0gKsDQZdg58iV3Ya6KJOpmZEijhWBtMG9Oz7g690+A3HSjOYB5CGVXzs9el9J6mrTUMAZ1EImJJ9z/0MuGmpJsPGYhgMmgml0KHxTIqwQ7ihTWij519y6Vq+e9cMBrkTrMz8gXUzblezQ6haTd+kl4gLN71arhkcAz9wq6wH9lHqQd19dNY47TxTBNwN8kk/B0LKh2F+5EPx2j5U86FlxrwNXY8sLpiNeEb+yfewjkQMkwefg385KkhpUGl0fAv2AzOLaJzGN6OOvi45FkPCgWqy+2UceaneoaZUMmUO4WRzv4p9Vc5TuhdUX+47zdFpPevNjV/scBkAgrBYQQhsOuwcSd8+p3DlpwI5w3txfbviII+7QxYKcAK6+4J9pFSPQ/MVcAH0+6MLp9k3y4O3+S24NqYcgb7SluNOHuul+CZIimNEEb1ivirdjG1M2sLgZE8ju5XHAfIi4ReyIXuYt+DG6u258vcPxi0G0wG5YvAMDtWU45iB80CAoogjFOHDLkH5e1F32BbHGm5gQtDogjVV4mNiicnjPJRKqcJ/E9lwIRS2oSb0AbFBlo6UzHpwQgSLdlJYl6Zxt+XnBI7NpW5hIl9qvceXf9LzGKIcPF4TVi2BSSmGLte8UEHpjbF9N0g4ITlG8dH7D6l/dNct5/ZoO1cU19IF7cw1M/0gezL61qBZHTI/HB6OYfad/m9Wxyeb3g2DoGaBimiQADFYfWfUeOhogOfpY9rs33TBI/PWNPjRGmnxj9m6j+TIu9+odvy+LNUVHbyEq9XBcfjeSo32yaZ/7xICV8bkSbFTX7piw5lUuIzz56Qtxj+VgwpcERbYoQxCYg2nzdVNobTidmeX0zDzuuzekL0obZkaxBQ+6PXk1T8+sgEAfBM302VHhVfKZtJ9qeWHFEkWJXjgG5FisVMDuRWvAK6h/uFSe937k5HQfp9le4/RUpl5cC7fy307PP5sOB92glxWup70Wn6xekZyHy1lCUMjucBh21B5E3o/WPhp5CaAUoWH5W6IZ6ZYWK4siWOvFqiloEfsfWxPhwucgzxjmoqSH3Edcg38MnvHIhn8db1hLHQ1aJLn+JL1GGu8Qd6KPaiY/Cu4uZNxQjp6FX6hozQIMXaVu6hVxFUK8QyPlsd5o8oGmAZPphwbCThrKrg/c/gguX++pfAnqMe9jEsSaLFr2MkpiuaGdBJOC8LRATMonYp8/zFcec5wqshY+ToLNQoLM+EwJba8OzF4o75bzRKtsVR7I/RwdbdkuhBLaPN1PwYyRp3PQeFXycNTWyZCG3M+uUPujh0yRko0AeAbYdNWB9DhriKbk9O6fUNlGpV0Bg0JWW7JXlh3Ps1lHz7l/QLjwB3H9m83LbPNcpS32qpX5gz/cL6oGbZ4813SkVRV2G69BKkgJaqvkp0i55gSwJa0zhYMwyDlx5Tm3inLQNakwRL+CZc3N5PbgTULzv1mIhbbWqqoDi9RBfLbYn7izeSxRk+7WXl/esEMLFSRcskt1n5O7SYVdsTPHpLmNUYcKQ/0eB0DJuuZu5JC4rDt/ZxnfxFUmONNSSA7dzjrbH83xuZVkqW3+7aalZIP/WrM6EX5zqcvtyF8r2fGs05xc40mQoaq3K+ipnL+kWJuMYOh8eAWmsKtLg/Nr7YvTsxb/XI1cva5KY/M2wS05tLo36EUYKAoFCWzE2R8zz/6dfSzA99pzFdpxcE+X5Dm5Ox1GSz0atmaz/VWSHxk++qzUww8XIq9+OFyPpP7V2D801GWsvI+UOfxvywV4SWxs8iaabwpaHE8eOfh0Dlg1FOAJ69N2AUPVz57x4+5eu2ozqfs8VulbUbrHjHJ9XP3FnYUk8TGXAVMyMNjSufXOWYcO0q5/S02bh5UKIkjbAMTOFOM3s+App93oBkMyBqVyjen0w9kezSgEYCjjhY5QoONlXn2kupJghO4kTkC5P0/fh/2nHMk8874MdBdhc2APY37qyyVbdNvN30VQX1SpL4bGrfSFD7ilU6IQ5vQ8oT0YFVFT0vl3CcYG1xAvJOrM5sI0tNkL0fT9qkHI9O5KL9Yp5SS51hfw8dlOtNep5vpq1BTxD1BL+2oGohRR4mHV5Lf/JY03iZdFneCLn36pohEBgQF+jeSA5QRi5yOONkDmSvKnIPiEQ08Ah064uRRKeBt8YlwTLBoWFaJS0UTPwYE+PUyot5YtTJyOlcedMVWtIpGe4+2TtGyRDqCwAPBSwXuQqyh1TxGD3p0atjdJR05e4yP66GBJ4KXpNg0/9jRkpFK/4W+t20G6YKrn8X0dp58yGoW//PwS+gQQnG6nie2rbMXOl2yuNy0cY6LCTs/1XMAqaFc8xoLi+7iDPwCJhbLWf9XNpAgGqBzyxvCIsKg5bgVlfqZmuzr93zhjWOogKoKDuYN48RbYAvbTGUtF0WPy5QoiB+oGG2GTx52fA5CBWjP9nmX9J8fyb3jlvmpfg+q2+Ba/zqYUChWjGUHoycyPKciW5NOXlxmQ9V3Sbyj/9jv40r98JfHkqwbmxPLVlHD/DxQYV3hXvgDX47dR2Iq4t/waA4Y691bIOoV8fL+07M5D7wLB7UDi8L7eYDvHgeOIyoabee77jgxqBfJvuIzektro/3K7WlSI6ftpWc6ndqBSe4j/cfGHMR6zo4wAl1UhJCH7d3qHWmn24Ws8BZwFwt0cFSy2w8ZyFAhuvl+HMjrlPVgmj/BUgpxVkVSGJFC7E97IbkEcp0hvrfyKmRsdlttkO/EiH2sxwq539VgMr9L3PDZeyzlQOekPsEYG5Cf53IK/KEru06ucHFmt5Ub6pKSSoWBEqDhUNVDxm4AuJjx46bH6r3JMxM3e1eSOK4M0TxVtJYepzN6Ajkvf9geYP9PNLj+zM+9e7CzD34aaZ19GG4BWq+4NWDXE3gqrQH1yWo+5wFVzgxI3MEd3Pmp3Vr8ys1aw0RC/Tjsdgw4BpGAKr5CMk+4vv97qXc6H9UXOvBP8HQR2rvevUEERYcfXfGeqk/A5Qdn0b0zRRR0aGXGmytA8FZr/GTI/3DYekOxPDBzoNtx/jL5bSEhzCoO52QIYo2HFrgqDCNoMOdNHnsdjBkUEKXSWzCLPae3d/vEERN9MRisrA0LQcGe2R+YRIFWx8euKIY7p+iZIo8yfHkufutaAE4e+NTMAs0GhP7zK6G5B203MHShSCcq5DYwQjaAxTuxbg3gBVrLXI6TVut2i1k5CXBxa4UWZjlEFiJg4XagCF+Q93hv4dEd2ShiX/npIzB8HovZp55iIulo8zqMql5LCt1uStQpeLoCkGB1nMoDMXDHWR3GTpu0AfJxDMMm79eBuWs8gslz2IjtGw39Bj2YHl5g8JOOrMk2USloY4WZFWDFymJ+td7426RePRv/Tt31LWaaNkcXXiJ2FQiiZScvrq7L6BFC1Jokg/cadAL80K0bcuWluH6dKa8ykCuhP5EASPDRbgTtxVbkLdCmSUF22Mc20tmG3qDmQ3JasLeNiq5Dy3BX4mtVIWpm70lXRgIZWMlyFoJax79HX9p4Ma/RdMF5pxyl+2DiPaU6bxASlo60/iFLo9M/zlomIkxVksqfPle7dfJFZ5iszac2pa7J2H5Rn4fE0G2XTRJLnv7J0/geCyIIE6K7B1CrFZAY0C0yDZp8EdHikxp5PvGCdpeI2N8gMXlKZdVUAi06ajP7+bIM6FgAIEeUkNv9ePZBnohyvC94rKSlelr6mx/zvW8JDMpGa8OmB/AHHMwZyjWzsJWQeepdxmymqTFsQ9BR3LslB3lKXVE+g2woCqlhNhL4lppzcEeVtqFg1lk/+0SwWS/6GvmCWdAMz1DjrtFIkjMIEuFP4Kb+KJhDp1l8FZ8ePNj3JGLqt+dP8D2OA3sjZ5HEJ+chxiTHUPGD12DXsV0T2/hCGJRQ9B40lxWnAye4S03zZfXXqP4gBbYgxaZJeHntLbK0IRmS2I1PYd2uDt1RxXi0QFg06ZK8OnYqmXVIoyxEowStkOlh/vRb5oya9bGuM4YosRgMvpj8HeyEQHJ8GoeMV1AMvzzSndusiw3WndMLGTzhtnP1vuikdusHjIWlPAYNdJWff3GmSRA6E74p6VSjMxAgq6MGUMWeHhuCY6ocsEvJp8G95V9FGC/KuBr+HT2UjtstRoNpx+SWA4NCShwxjtZjSMzM+lByUQA4opIWLF7RrzR0uTtSsE8tuiVzKL/bjbf51VtsbK4BwoFFMtTP4NbMW5mf7RjOySsdbHM3GI6b5eAG0hQRq6/DxuuOxXNFMdIE2geXLyrxGPM0yHop2QLMZLBJl1mYoGnMDH6hjsoKcBVO/+wAgJfMFDRwcswq7zF//z5NvmIqq8PFbT3w1cgugybXa9TRpT4mR+NE+J3Knc8yhWZW/6w7U6pEdq2hS/3jDNEYAIsFipgu7usMz8R+sdZvf0WwPi44M1BuHL6eb6RigqvlY2sm3b2rtPBv5HyuxczZOuFXYiGK61wZZ6Q+7nuCX75/q+Udz5EQxh0fhjB1whQgojoHEyzCih+U6f65jv6Fe/HDu8c/6Lsqw2r9Mq0CRTiBoZNqGQ6dWIX0+29Kyl34jasq+8WPpJqKWNixtPvD8fhHGPYm2O48DT80V4jsEakfp0MWDFcEbcRtoJLrcsPmm9NVggBbVgOstDRWQgkn2cgkZ/Vn4HM2zhM9jfAg3vtXi/7+CtnVruMLmCWHgKctqkBEkE55jcxJtM9yNbTjjC9eiVaeyG6t+5yICZrcrcsgwBYYgnG0DZo2pFgylQcL1eIwsf/wT4wJs3jJZ/KKNU6ze1akRA4pWJCfMl3BpuWprr9KB0144mTLTxdInPpYRw9ixHGnwwuIwIq3FGMnAEmfDrhOTaeFFfewvnUvi2clm4jacSuSJ4i1w6b+BxUXF2Jg3KGEGHxii411I+04f5h50Pzw+xViRZJtfNc7jeYk2QC2MZp9af/T7h+Jo//3dbaVQTAddeDKFxFhLLJO/8dWP/ErBOlQWtDHRNt0nDl75oABecLz5fM9VQeWjG+XQMMxRwI5+LAIif7OpMCQ55Aaj3kStO7qQySUMiafJWofGDdpTDj+l0KJcxfEcz+3rO3y204eP0KUQFTPcTpfSK2PNuufe4X8a3jlThizqkajDsfw8S6i/F5NbRb71BNzp+SOaWTzBnd72Fy6WVDtZXgWbG6RUq4iH3j3uXBJ+OyjgIOnpEQs73Z/U8Rn3ZaKStkWgxeXxDGqImVi8CRP8mbjdHmrV4JbWbCLb9uodH4VemB7M+tK2dF6MbpNNYDdfFuBHrvx9P8vRuckAzNIwAPg5FE17zx+vETs8l2274qQjSZW3hOxXO5Tjg2ZrC7Cb37O2uk9de/uYoQexvf1lt11w56HHL1QIehmBA3f5AeS6uQYNPEW4suWNtD8UHbxvCod2nB6875fVSPhAmsWqsc4A36DfZpw7eaphvQk6bM4MKwYKDCrrixIt7gL7P1atuXWd70JagMX8Yt60GtGu02NivDdFmRDPEENqFP6bBOyEOv4W9MIWbECD4tqu011O6kD6d+Zr0A1FzXNT/LL3pSoRPznhWN+bxYRCs2SGNq71JhtPdhkoOn0WwuXbeZWPsNpdOXULqT+dsJr3ZVCGpfWeqEutH2P53nKlxcDUTEN84YvNEywD0hsASi1bXhLUfEGwOGQclo7Cpq5MeOqhfqah88qHiCfpcaX8PM+A8YNns2aT1NGh1c2ac4k5BXZVqeQ+jSijXoWX01jz9dCVRN3HdX07aZhbb2oujTqy34On089WttuxoXroDK0sIV2HAXvFL++ZiuCz2z03AOFxlUeyXeF7y7herj7QWgh2JTYCimxnTmjSO9kZEed05ph01Y5MztWjl9ehvFAM7JrwNa16kFSmZkuTTSQad7wBHcPUdN4pEv0tve8waRhCVuMwJ3zdbiWKxrT3wlbU8uhFQD5g4nC3Gynb+L2hJYQobIR4GlLvfCsI96hvPNPpn0+Ak5SRQhxczu8Js/4N4nH1fHy5NH8+2NexvM7w9XQo1c9AFeLXgITdwuUYUVzXvVhtrOqjmflgNqYi1IEiHQhvkC2EB5Idin1U3KnG0cpVzEWRun0+M2QWZEOJUUuloloc1Wd5iZIbRGxWTyr8owDFZrcrGg6l6OD4bjzdEzc1EQL5LRZUmxzB1gpduIMKga+eh86wAH/jj1RVtNsLJjXUiGf9rjlwnUrDuqJPvEGUiBS6iXe/IJnOFoOn1OjFHE6eXx/fJ3FWhNnUz128jA4HtjPMCr401yNBWhUO4qQsji655iiIpOiGILt6UK8Zu9z5MEHfIghlYXWKCBmLNYo0vFezvpb1aMbLOvA7F7/md1B1YKVTeF331SSxsAWYWE04tbcRBjKtzJEa/cb5hZO7M8O9TefJbOdOmN41ND4b00dMF1UwU9Co8H/rQX6xooanFwbkjwIk2mi92S7yyrnCkkHIAAeZ/mNamrw7fiR7jQ8NqwIhXR0JEZgB7n2yzJGxoDvGD7eVLyzP/bmP57/JERN2PO+XJv9bYlPOiqBXPkVwQU2jDOSR7YVvA+6IPA3EiGf4xc4CXvXBHsOID+uZ/2lcztr3sXA+O1oEH+YutVegfutwkcnznpMrm2GXKDPn1YWXJ2rb88bBDOE6oTBqBkV1hEyz/6FLZ0XY4uanS6mfDET45p7GoblQKiZ67I5CeaCHPYU84y0C+ZFifwbythQBp0EGQ+CIWPRciBvTKbA9EF+uz3IQFzlwsQWoNwBk27W0UueC+dZcYgSa5UrtvA5kERI8g6X4X3V5V6Kefq4UYgiojYFV9jsyPTQcrJ3xLJ7MkaX7GoaCmqzpcoRgvchKeI61ZMPsRJagh6GfVRWXwNVzQl8Nr76Ka1mdU4CZLq/LwY8R+NKW0mD/i8BuZFpnpWE/9YB30SOVGIHwixeTgeNPUFN2Jz8M0ytrW3r5m9jYXq1SrkNrKdNJ1zGAubWdCunpGD8drHCbez1Qbtpvdg8qM/QFf1PXX8xtYjQtaIH3CkC9eqp20IJJthoXMvHwN14waaAZ422b5FB9bx38HlLoYohSO14rAGgRM4f02bYseTaRx9F8MkuYSVCfQ8gUO4xzTuGvuRFlbNv1Q5va1MjcMLbtSlxmJWvjqgpIbBFSiGdZt2Qb/Gh+I3ftk+Fdk3hK1mV23fsExgRRwYvzlAcgsZ7C8jpNLTeKgU83SQeSfDIWfAfxUN05wkXBKODvbJNlDB3p0lOdxIVvlROX19XeAEFx8aY881EY85CKhRSb1Jr4kR50j9oZ507IUlfvJMLkj90EgqFKLLtgXM8fMoHLswl0EnvXZGli+ghKkj39tRynEXg6tbyj3qNNKbrvWgRviy3WiPaHzeOV97xqittkiCc/SjRfbRMYRU79gmGVJl3cYWyaKMdOBTH7FOjIrvrbTA2MWCuuAEXFVIkB6dd7ptmEjIraEUGSHoLvwUEvrOfiWAHIgUj4/A/QNnZ3pTgTQQglOK8cs8jkkWFncQkNKpLXkNPxIm/TblU0thhzFMVYY/ZG+zi+0zv8pyWR++ntczL4etIPBEhbAvM7GnmHqfUqU8pl/KSu7TC/+WspirvukkuS+IF7cW4/6SxfLp5seyu3gTAH1MutZ64bqQNbKrNn+LuazcJpGOMbq1xaz2gihsdlR2O3aDDDLdiaQUwnrhEWbtk+/iOl23plxObpWxLUeyuPkKei894o5A0TTHgTa4cvleAGmyWiPKoU4f7BtNeKrMWTW1F2dgbejmhmNqOG8Iaki6fCEMthS/UoQkWmVWuz1zqbYlqbIn6mvUEgcVKqQDX/hcE7qLEepOAoiYxDFc9ORaQnNHo8d9VF+vgmXN2vv0OVAMXHAeojc6FQEDBc3T1Mc7MEgs/W7w7OJdw5MSnZZocWI065zuKzNOHHfi3LWRaspYp2S2Xr+TN6HBWR/QcDsccfnq8f+y0YJcfnkOkCLmYM6qrK0j3WA0QOV26BT/Gf8IBhKNXvJ6aZo1ysj0M864XOmDzW9zj7V3hFHvgPyGZQMHGvb+U/uBvFEgxOFpajum9GETl9FavYlyczMuSyEj12BRQbFV0wFlp6XsUTLfHjL2gqUpKLvIpCJRkm7puTrl0Pmvf+Q/ZsI4/tTPoW0qUmTfFvRQzAGnsmC9Jk7+ZqMB49OviUhpo4Opl8pXzQ6EupLFi5PIbRe27pwHacfHJVMrFTfk6J+qTK2qvFCFq4WKcu8PWU6Jt5e+ZRmXPUbLFVebTKfvTslW6NPB2wBfeqp1NlH8z7SLiwYNWMMQwFsnNRcCz5uNlv5PwMhKtLXnk6gtxG/yTI+OOKBGcY7sgTjm1fv7ZHrQP46YATZiKz9t/A9QStXW4V9LeigkF4b3gk+uXYLgGm6KMmrvtx1GvAaAVHguC7oz8pzDAomqp0ecQfbrfRceW8+SCSbcLTi41CjsYyVqnJlDElkXEjpcTZtq6H6Y4wpvSk3SRwVlKVzasUy0AdUPPK9LoICYKofJqRBe75Wb3/KdZjvDbtPbvtfAl1J0YbTXcuu9HnFFE1BA1ZlKzXk6CIJ5Crr9+mAsZTLj51gynplAbArBcRjWbhQ4mg91iYOZOmdgQyf5j4y7dGp/xZefuxBWhIrKgiZW2+i3NlXbDAMZUQSn3/+qnUhLnfN/7l5MlN7BwhDGXhMUTLtBIbeh7Cwtj+ha5Aomm+ZQ6BS4ql/qMsTkXzQu6hyut92mowgLhIseMUAHxaTkp049ZxrX8qw5bY6Mv8XPtZDGScBjgyWIvXewQ1YVgb0gHrncITN3LY9Bf+gtnSHMi5o2EACHLPwdUCoG/f/3/3jy+gv/4tJVMe7fEca5T8NCCFTisamHKn4xfVmr3XeCggoV32Ll6kk4m3FvdTLs0yDnNh+gVOr5nEomCIfAyU2D4ImCU5MeOz/KtSR/F/pdOYSdVJLHFwIEl0O8NcBNylfBTDN/RYC2PM0AgCoi95fqiGhmqBWdSYtc+sYn7xKDqyEsfv7jABmWF7S/EwUsra7BGvR7isDlm3pzeS/vswKcKqgZWryiwz+pKoVGtClGuvgkn7uCQ39sdouiiyWc9Wg3SkpmW7E6PjfQLUXylr0UbJZ/eOz7k9gEBqKKh+Ic6w8Z0gXoHq1LtfBrIH7VTzQQGQ7HhDtfi824fS3a3F0+zx7lCH1I1IFXCHkfNyfDa7KHGTRuOeYAbjMc0slXCO9AtX5eNd73WcPwpZsswH7SllM81APyYuApL2iFQGFxqIxK3iO98ZddXxUnP/LW58R1KY7p3AnuFiVK523it7JQ1ssreFbGxsOTENzg8+x8VXh2bONV8qLeoowi3lj+9QCd8aLAoNnyXSC2gW00LMssru16YZI6yRDctn/QjJazMgzjBYRy0wwC6GL4AUSyD8t4u5i74YN2ZTN6MzacpIGtou/Ti9i2da32cwDhxxONL6iAdTxOpgc0d8VzjqVOcD33OaxeFlm+dkOLkc3nhA/0E4aqRk87+gjaBmadiSvkOzj1JgRO95tY0nmxJMQnsFXFakFNc7AsJGU9K7Hu/nRZPU+O1tH9GiMnlauW8AGMUmoe4I06obKulxwRPTTr613q994Z2jFL72O9JhWzdP49le9D3FWjqI015RLAMvk06VqZl1q7h9vE6IemTyFgO34MB3uHSKoGUvfarVj9WStPcLWT/P8Vir40E6S3AOYFjgtFPXED7H74X0z+o4PVjBSgcOIWm55ZDGDcnCO56I9q20KLXUnlkRNFJ+Va9ak+uf3XNojxlCLEcXYzYLh/m6Fis8j8m5BuKPVnWw3a+MpwczPQ0D+tqAVTDSfOIVwAKORTqYM3eZKehjsoVDpB/5VrjgO2PKqib9OS32pFu/P7SlJovz358VlSiAanO7RuCCLf53vynLaNJOJxifxjJiomBLjGxdNaxjbK9bxpzJZ+UBWJ40eNu02VfOPFMDEGM6uvA54QFljYPox6FuYxyN+H3V8i24uCX25LmD5Sw87ofR5X/9MIMX5ozORxFCS9WI9MDx2JB8ULOh7ANOjgfgufqQiYsC5N4fwF/MsDWBrBx/y0gbDV+vp8hoDNx0ItUHws0K56cSroQAGgBgR7EzUo3hOHpSpHOHS1ix/QbDKchMyR2htrBDT2wuHL/docestX3QTHGVbfuHz83/fMoBsRujZqZET5pMsM9ZHXkfHSjrS39Y2K/AWtIqC1eY2WSdCmOdPruoY3VSi9tfM545HyUXscXoiuUtndtlUuNClLKO9jkmesls2ibuYxVzJNu/eLOmneJjTiqqtWGLwV0i19CBamhwYYt/wQpHyqHyCXbOGqgvRKVfFFb9dHsTJciKxE1fy3DaCDY2wxj2qdH3XMo12fVcYOX47kfkEcEGBnriATeoajMVLqXPa+SQ1qfqQqo466T2PELtgf2PLEN7kxkpp34YRtnz0Q2yorize/Imbg8H4UM7EiKY8FMwwOXu9mvtetBxsQ5YuR2E9CtgKu2vu+RspfaW3Ie2AumH8puswXVD9a1FK3ZdpX0EjCbTeTHgcy39zxLzQUz4sVOkSfwcQnTmP7/ln1+lgFTiEOteusrU/tvkpmIdc2OcUQ64GZ/VdfLiVqTf/zfmJLW9ArmQNAkWqSmqELkSlO/UvCGU7LDdorQB2TtR5MX2GViYOl52t7IOqTVAyN2dlOddRc8Dy7aNyp6dnP7IVjodhWtQQiXsr0mT095wSGZZBLmVHM2qGhrPJZYkzGkcmMW7x+5o3pbHCbnqKMURBgZrW3EUNibi6h1+FshZDCAA8MogaCtk/M1yyXVB2vylJcanxsMbdlNwQskx3WsIerHWRc+rIaFxrvUKw2W1x76qKIMzuNIOKO2ht/i/sDQIJhLlt1zCeev4HOeoXxRLdTGW6AXJ2yRHKS7+LJRmcHlSaLiPLs+9aB6xY8uBQR0odEjNfJOpvs6pRL2PKZkl8lecPf9IWyD+uAu3Y3Ai5Fl8Q5IIA/fOVI/+llIsGznK6uD4zuh7MYLTfzPPXxzxuY8hTfuL4AHXheFic2gubMfh3OtRWJGX/JizsQrmEtc6JCWjb8Po3ZKM9TKY+/lE5ITI0Kf/gTihQMQKsYuGU39JkgC3WtWlRaKMS81xzkLzCMF8fO+RNAKlnUjfA8+nsoSzGcRgnKN5EHC2CHj1j8IpcliasTZnplc5yBDBZCljYs2jhrPws4QPdoSsOfTflRlxS+CyTD/hqcofKI6/+gOptHYK+5wkutYuhSd9Yp0q26r1+eP2seYo0VYvJvmX1MxRTbF5q4NC9TNMFHhTVgdURekKijYwPas2FqT1KxrqKrggdBuJTMMddUlwCa0KyuQqYbf4kg7BfgmcG89v18i89dCReTYDNpngXyZOsWnpqEr2qnDaQi6xqUfYtw34N0Awdl1jAzDBRGNxzcR5+Lh28+vv5qy1XVqO58HUOsnxRNoyK83JNzE/9rWMoChI6v0k+Ra+OHtXm0gHmxIbzC91/OWvi8FvrL3UNu8p2ilrgopqOqvEjmPE8OOT/8Pno6WszqPZHPYq3fwq4Fkbqmzr/IUmD28OedPKPIhsQepalT2lbRci9kAK4VWoUPmUwkF31LrUI56d4xfj6/LR6tjdGdghPn5MbqFLzfmAXcut47bkfbFJxMCLqWYXCT/cPPjpC8uUp03px00lGOUll/TBe7A12jgWDdkkEqZaavj1jXKb3EHDLPzjE5CPoqHLWpzTJuClDun4bEpC7E9z2bsSm6fFAAKL76DDFC0/90Gz5xbwAS1+KPsUtCsEIm+K2lbsBQZ0A+ew/wTg6Ttxup+f9rK0P8Il/18VmZnz0Fv5NiMYVPPY9AP1rHIgmMbTLmMqQyApuqrNbOshlYvTpoXCiSSS+44eQCsvjT66FJKsn/nk0Ybm9Xplb/k6M2W00rtRbuf4J2nNbaUok2nbyFjM5SvU8olPju+f+SlvFr/fFRmVZwceBj5YbOKHSB7qXTBqkUn5S/MrpKynRPlMCi7CpucxEbuYlqnHkLcxdHXQTbMcfVVglKkrObo4XgcHkP1777oQ/vgzIqdq8o87nf0QWiyyOejPPVwnwbUhNPAfZ8MO2biC5W9p7NUrvmYuX2YuPTUc26UvnXNUIYodGwgzZqdvBTC70Fwi9vaF1lg2r+pz73jzlmpuyLawi5jXgXIHgoUic3Ai6NQUZtYuKzGo2019mTMYbosdsW3ZsGDZLCCXfMvyOYZ3GfmPdSiHspmPD+pZEDmjEP7SfhoPkMIMRlFw0wFAWhBnrwzSX1dAq1fZfc5i6EEevt1TV2X2zIe8UobDf+31WPaUfduDQuuXj3/crRBBfxwcjZeTQS56woa1jLbIeuzWs+gHVatVk/QMt4UH3HvsXDOCqOcOcZSwXqUZkZhyqwFxsWjRl2AiCyuCq0t8nLwB25hr+WAsgo2gHzt73NGIzmVP48luFsJtcl2wfNrpxnhDNTCjIT/O25JmXWDpaY17S+6F0mKAXY9y3N/Dp80UCLz633Yti4Ywel+Sontfekyqtav0NdpIkWADynFP5rmIwlwGjxLDdmq5fOgFap8YYNwTR3YYPDUMXZ++U+YftPrDJL/0l4N3hf8LYc3sZlj9fyUYzDV+hzSkyaVxTI2rkrmeduFGdm3uKr/ou+ZbOEAfnt3V/vP88JeeTdV5W9yWWwuXZOwh+Ov8kL+AxFcQ6BC38jVCs5e57qHVnaRZVN+i+WUIxgzVXdz90lzOwX04tjZG6cMuG1QjFE5Ehw8Kl33LF6jlk0BRsVY63yY/UAA7dLTli2eGTZgIlT0einyYodmX5J2+AOALeVcUcSxEtzi8L4z2CkScnpn3POahNHeKulSu7SO5hW3YnqffauL+P+VQ5Mh+nPpB98wQohL8igpc845ybWcPLX+PNkyvOVRlWa0GqtgKZLYdftZczVq+lX1FxFftqd3JfatKfZ0YsXuPdXudct6cJ58GgrnOgYp36IFT08yCCCiJpfskvEtsDW76bll6wz5BUk8Q37Pe95cfxzbD3yqZt/fDXZVGjlIrn8mDSKGZWrnrEWRAKpo9Z8ZGMypF7D3Y6W6anjItUf2/p9yi1aghF5uH5/7KHLxVTHwmuoJWU9lHycjx397AnI90iIhCFw6b8Yp2WIsRL/ynyl56TDsW08JO9326tQzf+ORebpPQJOLsMBb9sUHHRIwUhLaKF9p0fA7KBP63/IDUhVp6hfOBDeg/DHc9CkUCKmrYqzwgmBZfUpNliJOsXm8OIw8zvb0Et2JmnniiCn8vDg5AaJCc00u/9iXXS2Toa823fFr1JK2ANFTUI1VeUoDDEaDAXO4Ajx6Cnq14Cgcw0B68HBIC4wfFZFExdEEeoE2kGydhfL1cQOBmvrjP3zWMbXSDBIKhNH1xuM3HgYSRgcCbD3W3aVagyHeIcwmDTwoDodMRCem6U3LBCb5jHXuuZ4gA301vG5xesII6XDxpwPPf5MJb7thgWOEaE/qTe+HGzE4vh6ld4wDz+ZqT4z3/Ea5w36Ptx4uISirWs3eSYkUzmmaaS7gWkd92byE418STURIBzT4lHAMHExnsp2bP+TX+aYfkSfG+HEE5AD/RnQlXVSx/i2lv54gxLmEgrcVytN+wrAVUgfV5+wiR7vH+t31hQFNEm2mXNynNdaoqW0dU3+RjyE8UeVtvW4LlbUnPFNb23uWkH8ezA9JzIBMFHzELmUvIKUDMN2KT7WUeTx/BOExtrkIS10KE6qOmKeeYBw536Plw1o4oJU3Xz/EiaYQ4/Ka+fqPNFks2/OuyYi8+0267vwibVNLfjeS5euMw+gCzWRYN7VTXoyEH/58as3J2+hVIrVq1ypjk/jzTQG1lRPJfygGh8UO65M6iHb/UPvKnWepx+uauv5tHvoSH+KSqU4Fgf0GszXGJ8Kb1PSx9ULnkrxIPn5DPzzFF3c1LU3Hupq3c6n6iuStWLz1vmLVK9xPuVcvsW71namLAecISD0o/JNSu6pSglJiP9K4+vZaxWxGkx8YBDMjBMIgOBG7KK1eAszPum9H7+Siz98wSd1xCV35hd4PUIm5WBeuPg1cHz0LdBV/7hT3FJhv3xOqAp6A+mx/JvnPvxvpk9LuMeBdS0PPhg5tRIGdzndLtnRaZGTwgGGQsitrrEDInxW+VuXoGXatukrZimIMq+aKNefzp3w6ujiOWKaQiYeqiazjRB/dsVktPGsTOIFllJsLgpEPWZRPQ8Z7AUhj/+bX10I9FAbjcSm1j2duN+AzMBeA+qCb56E9oo5ImUQMnZVAthN7cWZ06Eog/rAmCoaBFhz7cP0ghGf9Z4oNec/EbEd1vZKxG4/IgZ7GG2KqIqahCfdY/XOEgmmlP/vBnHmIpmG9QMoQ6vtuSpfdDvtUiW8IBf0TERSbdg1Hclz77nKOl3pw/eE1fBZnz1xiseBEYsy9DzHYYph90LDyMPF9jGaP0wCboSHaJC9ZDsoV/+1auEDdByNRPmZbulkc9O89LCBqqn+mLUtWJNSIiEvEcvrn9TfIwjlKyVVmGkQJRIzbkrHlxC4TkFa5GYYUHGiutEbJVp34UEjnYIGb2YG6z323ZdLs+b4dCb6FCJgjXhjSSHGMJoI0P2aPo0rgu68yFG3f1PbvGvdbLLo+FpbQoMlTLgw2p0paF4enXK0q7GWa0OCseTCNuiIXFf/IGH7wlImk2BX82pWi7z5ya32Y+rKwtEznRs5INhrunbG9R78plrliAHI19zD0po4WfbXpofynn7y0DMG66jeZ+t5dZsgG73sksfLt9STM52kWsYPF51tVl1l09+yXQB2MFVB/y5TDQngNtJ2wIFYJUqgeO50P9Qg5ut8LNDxi/TunTdgXKfdSjmu0cDqvvbb0Kvm3ywScPeJyHUiFbmkBlgkfDk+IhAug9WCMCMzvdhQGYiPC+nPIOroM4NqiR4d9OywjiTiw1JSMBp6cZiybP9967a9VgV/VdtV8tHbeYi8m279GqrOj9HaHoqdIum2FUH6oht9RW3d7nd+OqRagPsPenW6YW1cDG11dOgYvDyozrsbWja4smu4kod01RgWF80MmD36okmjNH2PQE+dKDdbCNI1FBxl7Ybc3T/vBFm3fqTv7CIhbEnzojNzYQ+8tQtYribRrBJYCYgURYzGNCHTa5Re6/O4x6pCfM8W0qgL3Pd/eZa60oyiqXAnUuTr752BB07ixf34AB19Y+wmDK0UOzUm9C98Yap6qieb/YuXz1OSlQByOdTqj/u5fdgZk+J8TiMPukFVhrH73F8c9SVwDd6lPQ7sctS2/EQu8Ez+iSNEsAZo6z8x4/ZD2Xn6X18N36iYKyQCfycsSoQcJ+VIrKgPyBALh6dTCOHqpV3aq7Tq+ukMTG+QrisKltG8qQXBuiUfY81tLxInp/x5rgHf7ld1vL6B4vAcFSsrtbWzoM8lrfOgH0GALGDGYNxRX0EBBMCUzjEvzk6/+sC5g/sHxnhMxu3/fqYTlErNyVmMyNGNb8YZfimD5fYqnXpDFdv+5Ty6mZ1udSDpczmx/hOKdOUguemORqN/TnNOl68fHBl95qywu2aHUp6/Euse2o0FQcMOVUt+tfDud67NWZpKbT8ejyDHdCkmJYSNe8FiL4iH6vofN7EoG87uG7ah0dEn2eYOIAVThoRMrJBwWL/hqIqSwYGDv62CpQYSdgJCqmlXhYT9KWODuyAtV8puvfapsiT0Fb1I4pVZhmfK01dAL+fRcyOg36y4virJBxSFNuxo+Zyrd0xqivDJmZ1hQZiYnU2eHW74wIW9eqiCLkbjU+aSU3qYsm0a15ZZTAs6aEoLPbUzvuf7LiMHBInTlZWOVW6pxIJfxj7/gKC4nlwWKLfMsZcTx3TcfS76+cafFamVYk58JQlNykH4WAcIGdRqhkifHVPwptzPEIKGpV0dIp9LZUNrtuDd8Le+ypmSXhPgXMRVQx+jjA1RprGKyV2azxdPXvIJX9nXIcm7kG7okJcJVWAdOtU8m7UPWY5BoApNYwPkqoMZ90Ao4QPTFb0aNSDPBfhjTI1rDwkBq46O+kPl2+MA0ljLy5i+tU733f3AsX2XjoGcL7RYOphLnz2agjq+wSXsYRr4gM+DzYtdAwvn2j060mtSoQ6/2Zx7/OVGoT5rS207ZZIoSlACz0nd4k2DmRzdv5d/x4o5Qi+ZQTc8GiyYbzX7km/EBIxrrF2q0E0MHogq9ieDxVnxU1VTrtrm68g6HiIcMCerxkgRqeypcSGdJyp3ig6sfTWupCFH7n6IoHbfuyn3W0FawvNph+O4Qwu/G4o+p2ZaQVbNZRuZVgHpxP+oBiu7bKWfn4AVjB5jxfWoaO+PFUz/bmL/0U+LbpmB+UQnzg+rO4ctcy12WbU9cvbQbzXTj2k9qzR9RgIzhIb5wY4XoZAbSwGytJnsBkYVeQw5v5nH1PQXj758ABT9IcIMbk6QRhD9TOBg9Cv1GoTIxIvX46sprS+ASEuY2eO9iFVl+mwpDeInOLMSGQ3WdSyOkUJcURBo2A9qIzDtK1saEl/E3UItJoqZkbDCtc3j/aQkhmxKfLZTAB2aLjDzQpMoxRPFSFXFCX1TM0mOnG3xb9wNtknwyMeiCQ85bq5zEsAf6eIODkjrM+mTFrtb+IMfFdJy6kw6pjhxAt8gsmWxAxkCK0iUcZZ9qae3CRUmLfaq7CNlAtNlb0U4nfieITyehL8MAK/QTGX7R5u3l4/Vsx2EcBZDquDkhvFnRx1aqgoVTN5QCRC4xBJEzsKFm2rbNHi1azJ+in4QBg+9sF7VOQ7gfkQIHBpo8t72woH881CW5jy5aBL9WQHIyKpHN4CygfuDrXlMAUINmSPDY9AzvFidBfIwteJ5EEgBfw9W8BJvqmFpS/s7AlNfjg5zlpQNT72UhhCSjSiNsg9RTRkt7QJ8NCZRhORTRNdbU0GEKi0soB2VGCDUBfYnG/J+WcLdgLXyu45mZTmBHCYfzJG3/FXFHLkMNcW3q73blyDTKKGCGsUsOzLJjwDV+W6hJ7sFl8+0nGuU+y+unlKzzAOoKSqlvhYvXYNvmdeJuVTBv5iTyrAoBS3w/Z+4OFC5Hc3IrTVxmnXE6qj7iqQ4tVa9sFcqgLTfnsrH8VRvrvS1mIa45fpGrgyzbpMXCdww4jM1GwXqsiHsLZJb3konmoF+XpGRbWr+UVmJDVaRT2m6zvinvGkdgOdEBKzXYs2TT1pa8RWPvgvfZhoSoNtHp9PFy/RVg/zxqvym8TfWItyHWYv/ALA+UKzONraM6BHPeU3MvRvsrjVOFCksOggCz4XpDcfOqXO6HMYVN1StBQUoMqYG3w5R+lr+AgETqYtzgP8BFJI6FXkEY1AYSydyF5EgfXYL/1Y0NoTXLr1eTwVJUSTHLlNoClMJXhXcTy/XG8yI1eDvun3Pv3EvYkjvPLPghVPHw2hEx+KGOKy2rC3Ux3Wp05jBPkhFTnk+ro8MSOqE8B+Ju7IbwYLHiccpoYGZahEXWBL2MMJSLhSRyq8yHW3kRuRM2baRynScIdoaAKUByGI3KDHx6yCrPW6wabbsGeG+SuqQbz75WgkrIoy7p6HrHvWqOgu3d1iENZHASit4UIMAKLRw/hVkINwXn5B3gKqIoy/O8X8eczFwSWMTSi4OrG+OBAdy99IVbEw50QnohwDuOjvRoVshhpzRTVx705lWT7wuk9iUmlCIwKq786tnBXhoIrYH3pHg4WmTSZ44jCn2ji5u83XrbvaENZW1X6ohf/RtcPBl0LOqx5ZJ77spY/7OvjnU8xM3hWxLbT22NW8KDDDPgPp4G45vaCRuFf6PNYP8orNC7z7G2Dzibld+w3PzENsfriYVdng4xmkOT42Q8G/rHmjfoXdj/pN70C1Ptu1cB9P8sxohL6RSHLewEb67UTjE6t00vCLMjHoNNIXRCS+KEzrrOd0bxJK6+nkLjc0U5wGl25iMLp7slCdT2pHm8VdNhe/DjzrHd4PmDHht+0OS88+zqeV1OQJ0JbgOw1k26YouPfZjv57DFjKEBXs4JkJNrZOWokXLhBBxoFT75jYFEx7GA3vkbPJt1tyRIoHiJCTIhvPazQyW3FVSVf/yKwqzdwyqz4WuYFV8i+3n2lXUldgnAZSA+cQPgZ3mRWl/pgt5FUZVHRJOwX8iEETNRZMyh004ONgvCTIWhL+faAXzjq8o6eGh2CgRC4nebUHojjwq5sUGOuMcLa6MGAwXH4BqQSv7FXc/Rq/pqI44sx43HkbW4ikMI3sBC/hNndm52unYrlquTmEttCZx9IcSqY1YzUqCWjwTxJw5Inz7/1GT5RGXQlPsCOBPyFLI9KDFQFO4XY/x4LML3IkxWq/RITRbIqjZlOL5H46R16frp1/r43BDeP4KIwU1rEka4Ex6WjoVU/UH2c6ywNXkAsosYUsQsVYvcVZ/1bnaf4lQwH2PxIyBhHEsl+W1bYxHBUYkCp+5PJdhaEnB6zPUdQ+u+Im1jd/ddc8BinbrVmKxVvzKBICLdKw1RZTgZmL1SNbktNUgaOhaIjzr9of6Aqn9TKl+ZfjGgQJ+zQbA7B5gnjbv0MEyWyLHvVCPsQ2la+ppn776ZXtQOvEvEXZzFELyTiwAUjbhpCDfXP+YqyoQze6h8aBLZcBqxiM56cNmnOFo7YiGu2ElAiOwdsu/JqsnTwyLxwpWEQOoZCfq41xtADKYNDqroVa8OfmLnnq11SS96QRjjclQelqzhz/gjWgjfQdD5vfIIznhAqLt3HW9djdL9kFuBGZA5IR4NK9MYHWoPMmMe3jU6YhAIH1jJYETpd6m4LlF4yAZc0KAgThEAMQts88/Mw6MNSPhlwlXrVpZag/dDiPEdUndtobeRjnErVU34lnfNl4sxddyACCuLrRBU2oBgp2Go67jNI3g7FHI388/CAmoPCUAhv3FrmqlXe0GpEgO4TqfUI2Mmmmt9wvICbxeQl4+efR2Y2kUvNef87T4qGui4kcdI6p7N32eqZG9487gg0+t2EbLxLHtuXVckL3+QfLV57YPq3L30frDWw5neZy88EXsSEB2rp5tA0vxVZ1TcAFp+i0xTBvkwyvYb+5/jPaWONJ0xNL0sttEHei24/MN9rYuC5ZxEMYj/wSvfNb7AycNDI3jPc9ZsFuxdTufIULBgDUBvuHm3h4xzyp81ndIDHXqTcx25gRqHyqhq5UASVrSE20g86oDgPtOqkKJw9K876ltukfnRPvSYvAsR0YYQ5v4jDBqw08p/yGrcNkBx9unsaQ33eI2DER2yDdZI9cPvysB5TV339orf3igP2GjtUW2/EbOEVB4hkmtREOGmmSbzQsZDnuQHgI8yFyn5wc4lEACZ0kBVQD6FvS6DkfR0a8bHpfv51+VRzooWwCOBa3/+qCyyeNq2XFZ3jRRrxVTcGVUcEiez8CyvCqu31tpQQoCEcBHNtBdaqhOIzba5SZ0Ev0Kkm18fhDXOUyy3KV+C7lRd7fR6Nd2L9eLFZZlvt/YjHt1KbKB3gO3DUY97hGtWROfs11mNsp/0MU+pgq7T1AkrrHA5kD6i8OzNytAK+UGN/ao651o3rgvn4qg6eo8N7ZYNLBATrkVbjM4qBjHPpaQ83sdblv7o4p+NViKmcnk9ZxO3E2U5pRBGKjzy6oH3rDSxUuqj7dbGy1CDdUK0WbbpkkByfYVwXa88EYvWXpYxLhWTYlkaY7JSkst9YwMj9go3HYUbngjheckiVRuUeUTSMFELkhcpZtVfECW0l2PfgojFCuxsBY6Ob5dtLSWcBSRfPF44/UQYFx6o2TIdQKPK1/EI2AFwXE1j5KPST4tT+FeRzGp5gnSm0qSLfLeuT7+CMdtxST5wOk1Bzh8u9Z/bcbh4JWKVSXJ98j0cj3h05AptuxW4oRfKaPEuq4r7YSPL5ZrRumRwGfh2rZlYI1v1lkj+GbJY/ykBSkH/dSVHLQZL9shwlg180wgjNlU0NP7hpSrx+70AdjELBC0SOZXcugDFu2dGbQCJcjzeDO7TE2MmKOJDSX8KHkFzvZIPWw8cS2NQY8lHKbjVm1oOXyohACs1zureawZ/JS6FRmD8MJFc2pUiLGeCj7MS1gascd3IlD6LOrozIeKbXgPsnLN3OsEUw1M5URGqkofLDHBqMqiat/tA+DiHn7w+q6l87Ni2InGR5Zjcv0XyB8roXzhjNin+iCMpwhfsKD9A/PWVcBDWT5jhmdItDCrlxnEP92TKUqXWezczHIbO0vR8CODUEwr6VsKQPuefINTfzUAhORujIoPjQDbLifc0Cbj0XjtNjFKJgDnu6EcMSKhKTPWtbqYDXqx6rajVHcveBuqyMghfQnfMDbc4ukV/4+c59U86Em1oj6KtbMkvpn0CKePYvFNfVd9qJoeUnDC30Znml0YEXU19NmL3n2adFySrZ1oBs1JdIlNlDHROwPqy5ZJQpFVNURGo0218fDILhCJ6jG6VIAQADPilNmaZlofqEqYfxosjcRcEJIssC0+WhCTm0ESGF442gUbDZl2a4j+g/RpyClgJZGcrR272L8aGavw6OULnm6XzQV3IJMBNB8cqbrzLiCFMTAGuXPWkPUj4FJ7c7PU5hql9gJjCbrdSQcP986ON1VRk41OG3BDQKcRO+isWRJGBoQgEjxn7fWrkGFbzQ6h21CfVtpECLDDfqCnIjFqlUJA0apBuLE5Np/Ssk9HJK4dVT/I71WMOjV0V1F4VEVi1Tu+uMi/QFzMvCOUCLrNvVyv3dcb571tgDhiADml/jxIciRlxb0CvRHV/meyxUXekknNSpCwZUprqsl6jFD/rZIRFRwfaZ+uXG9owQo3m4yzqlsDHRJQW8QT5DOfuRThPZeyx1eAK1oRFYxMfQvnTgwLC0aqB9gN5KBmhBdxR2QNx31J8wEEjOa7CixbHBeZh5wKOJUd4r5p0RPuf+N9sSsqzhBlKarr6BtilYcuaT0aLfEMmcW7Ai/2Zvc47UXtMkHxvs3JZSR/ranZ+7F2nn1NDqabyDNjX8coZLkdsp+CXWfrDJ8QN4ADiqhaKvoHeUV0t4u1gcWs6ABIh2GstXsuDCCWPuYLi4n4lok/RYSnPcilCiigS3iiHNXZOQNDDV/2TqNWFe98X8v/CkkbSnXQMB3wLHIJM8NYKzyjpG2r3HXhCTWXr4oCG5jSEtksKNtIZ/rS6R8a8x6lQjzx9t5lpcIGOltaw5jNLcxW5t2s4liz8yRoUnI0j8qqL39qhwUkZwyXKHjNNIHPM8At8cGl3Nt706pCXi+zujBUqjl7R7WfttTC3LxCvv7JcPBrpocfly2x6U1EbFjYjlPeL0jncPUgEtfq9RVQyR6WzNU5IqovekZAZRwvrOPSh8IuDkDH1Ld4htGWCvWCZVBBCCpS/Iqkuiqt88kisYrnVYfaD2ZrrqA9coIR9uqZ2hXfYvSeYIlEW7iIFi3c1yrdeh6IN6G9Yu2V0tYEPHELgi6aE9qwZwfmfCOU2RHxjRaDFsy8JTkbw4oU9PLhVpeffTWYUl8doe676qr1Cbo5iiGyVNch7bHBSTnSRuzkUKkgFUmGZXaqtAbTFmYIzyTq5luQPmMPG3ns2qllNj5gTQc8Z/mlYtOsBmTgche/fX58AnN3ZetB6jNY1xtPEBcB9fZ+qB22TDa3ybfSu47xiDehE9E7zwCQmfVZkbCv9NjEjER7Bk3EF4xKSPJK/1q+agPcBCsX50IZeyAZ5FQ1jpE0R7hcNRVAbuddoTHJBBfxZjdHO2q3imr53cQoeefOHEGIyV2qqHZeY3eeSN8Qyp27RF8emJZKq89i8+24O1ghAG/B8caTBKGevNmsE8hiQ+CyYJyzFO8o9edg/2eEn+yAGg9QP7OScvatCRZefYXy7UL91etm5D2yl+Nt8zDq7c/YuD65o8e5uJaAqDPDmy1kF8818uiKM+wSRiZaQ3t8qGSefeO//1Tkq4mrN2qlJmTgetmai/YejXdOVqkPe8y9MyI9j7w/TF0AqJ4t+1XBDp7XGo41ZHy2GFJQlNkdjrulVCobLZOHLlUJqqKW2KN5LIiGESNcnqu/vweFBzd7oFfKJrPIIKwNVpqCSeGjXB3zxqZznNX7uT8mvdh0ipltAioTZZxzvhZof29PTPz5mm3zPtgRb8/vhfjk2HVY7cn3DKdlXN9KRCodw1cBua+kHHSMu0JOQnucth3k216giSSeIPuFuON1mBGXbg02L7KB2pgT42a3xbs0vJ5Ehvx8cbXN+771c5ITa1LboQX359Cpb9KpO6EsAEo3QxbvlEOjCewcVX+0V4C1mlBMZ0WAbP9Kn8GKXoNeVCjTlPpQ5zqFH+90HNaRJm1rqz8MOkOzznvzMcsqtUT0kWSynIpV2Ul5owDxw/wwJ2SZJ5S3gTb8Hoada7+dHmam+fCQPocd+wvQ9HnwQobQSS2zvk2sxgpHth6rh4qSmxmeu2u2rVYFXddreOtmqKAwaRCaVQFmM3+fsOXQx966fhmOmYJDp0+32EhgpqPLK7W76FEdzHCE4jZyDCZz4n83u4E9j+S+rK2lnHxAVDEFo1CG07hglt3PSXwrrZJoXF+yDnPZFUCy5MF4SgCK8Pyqc+VpVez+7S9DfuorxGWmKhQ/hmneIrJembhY8WvUWmIkTE98nkQB6vqkWTbXQ9yY+sQyidKhBFFQOKhnxcUrp4Vi1aQYKjxS8n1hXTPvZKuoSYJDRTLpPzkHyo6QN8rjpWxD91og0OdFq+LAHbR1z6qfxfmei0A8Q7vQYHfhyuWX1C36HeNeZ9wKAr/bPt2K+FkwlaJ6KVMnL+0hdrzLGKr8oCY2WW0NsHDDiEmXn+C2+AESYFr4TbKNgUd3E+M7gF0JCNbKrdiWHqoHxmmOv51s0LPXvIUqtEzUK4SzbHHVWFq/hafl8fbk3WWtbUWxcEQ4DXgvtb6lBx1aGiKp4BsXdWjHIvpqlgOYzFeaGNnzal0LyMBL4j6jFbF6l6/uedkFoI0dttIkyyZh9oB4XsaLjsL7fuZ/+y8A1ibgw1YEdG6T6AdmPcTIR35pRX5DMJbVq+UshN75bjQwCJtsdVJGCbuHt7vyb517waQ7UnfY6u1OkKnAsEUwLMYkOt1j9ay+GsMxuRYDyD3zuprrf1/wOfumKi4qiLoHapZ5wVHPR91NjVvjI0Agt4Ewy7lwURB+9ePRbCObFwVl4eQKxvQ5bMi1AhuqLT9cfEHyrQzaXiiGisE5WcBbkfdnGMqejXSj+zjgoWtrqegC4qFbacGH2X4M4SQE25KcI4rFxURFxThvKEeie9UDRcCFN0+Z1RssgLq+zib2fkzH2nqIn1As7stv3nS5aPFDfYnho91BVKGGGNdo7AEvnlA2JOIchvMKoNHEGpOTon9gbyRg7HL2POseIys/wgyB8mM46yTps6TxaIsJs5Q+bsp929kziePifFHoRaZLP/Nw766ggEeFB4BTJH814G3GFQv1Q9VYmxsQRLj4fLLxJTnjo9QigAhGavmyMO/izXvXmoqKimxGYNRiVIRyRUhzxMIPH3/jtDS+MQVbDwUMoRQk5t3zF9fbJskRf0yG/HtpvjpVDR09R13R8njbmfqHFk7PpHeo7e0KbGqjIzvrdyT+X1gk0Grwbw8VCqiwXpg6baGdaxrp+gs+QWdA5WxS3aWF1l/RNhIGd2SBydyjfoIxmsdMOzcr35pZJMpNIjnbMZay0PJ/H0bP3jGUIfnoKTf2yhrPZFtvGfWkjzeRpTA5eaY1elNSC56pakW77eAVS+4jEfo9beeKG203RcpMwZwDxUgfmwFTv23wmyNXJDg+RkGdbdQSV03wxFWbevsUY6mxuNxtDsipQebVMjB7OXnHWbFbm6KZV7ZNoE/d+arSsL/AzTNb86pnXc7T4Q1fqpG3orn/t0zV61NeX9wg4ZM4ms0Ar2wNQFf5x4XBUSaHDTrroFHgQU0w67X1UKWGljI6i7SmzLBtBIw2CkmWamlQzyzfFiJlbSON8jyF/jKECLWFCozgBRuknoSRdqy2hSNyyC3t8jaOAwMZvJ/8B6xIRuyS0qQi2mKGIYP0wf3Kxq3dCWBg0omYUYpLzNHWbEHvza+wW9VKn9g+mDaR1ChW9HqU7yTNUxHaoekJKNKpXpH9368+Ep8bj/hu1IlVkFcjIYPQrY2kdCzVTusHaF9tUNL8nvhdGoerWmPK+j4D8a2fzpnk+98M1/Z1vveN76CbASzrjaB3VERJ0NaFznIOYqIrIgxPDTfdareuMhb94EIN/OQvydKdkqBr9ArX8p0wyNvXMxcNSYhBjNuZHFbeUW+yB7omp4sfLNyrwSJzef5GSdPYmatd0eVh/BQIq52Ehzeh3hnawlpzQcYwmWGz1Mbt/cBMGQAKGQ95c6sw4s9IRzL/+2zpD0BZDUUUYZqjxEjp8sQdTgGJudzwItT+tDD4qSS+OhWMudjNmPbNcwDNnXefDu7zSH4l3IUupLzpwYaFN6XfEVR6bY5ilo9D3hvYj3yB/OWyOg0Ms1VDH/0YdOb3/9X3reD5UpiGUBQJfPMOpGA7eSEnS3OoaeLle0hOV6fv82tNykwjeVALit49VT3+tfuakSCdmEm6LK+sdRJ9w7VL9egnMfuLm99Yd+zmzj2fYf4wCiZmgsJ69V+vsAJ9DXZ3nGk2fSmPhZKT1EY2mLmQDznL6heba1gDSYTWWUy9a5Pk1iaHG/QvLoe7mr40DxPO9dR5EkAZsohKVI8lt9edPMXG65aZEKyOJuBFc+Ki4kAaNm8IMI7+ECJj3mfoKELyptjuNO2dhmiRInLr1mgiZcl030pYB2bvqD0/MsNUGpZ3cKJQTvjRTtbGYEvxsKErytyxfbwX7s8QZxIzxYuKN9mmd4XFFjrxp/VsXmib39ibhCbUVta5NtY25So68KuoYWu+Ge/AZnEmFZ8bYvhJRx2+4p0GUg/WKN55nrnnIdwp++FPdjmw6MzjZxVPQiaDrEv2P5KXhfrP9T3SgAdenGUGuXVPT+LL/30AkgK0aLyzdWKI6uVy4+CQ9/DOMiYaheaeZu+VlOUMPyXDkWsFQSg+eGfNU/Ya6QoeDHpUwnEygstAMPAqag9Lv/LKJpCx/vGq9Htkdy7TaB1pxFAnGfaSnJAdZRjB9HNri9FbBpg0YG/pNrSeYRNBPiBS5SEFVPmWHcuNiWz4eZch+SvCTnNRR81v4QO/Y/wUR/sHjwh3++GbtrfceR2YSVyWIBSxckOgL+RhKwgxL1WBjnMdIp1QhvKs9JSVcmbhd/e3DpyKTQT6dJJ5aiIEXbk5chVeVGLGg2h0q6yFN/DHfTbxkdN2Qnf1ZQ7vLj6CFKLhHreGgvkpbP2Yzl7oImw//PDJdPwmDoUuo7hJNl56GGxt/4GdbW35I2mKgZtDZ7jyVqBFVQ0wRTuLZdQqj7eXEsg5HwNXlQAXsnohAxBJi/fp8vJVrxyOhbseAlviqCUwWNXbwFlCWDYqgDr6R3E0rxPY654plYtz23RYAciHayCMznsoIYSK5V2Q7JLsMI0Gqui3uyo2LMRpGH4gQPqrKFAJtUQSeKpXLvwC0dMOi65otUXSaKaMNNfgpyTGNqPpeYzwDHFLclEA3K+vLg0JeomVWf/D+64rEv/gs/XgeRRRSi4IVQ3P72TZn9/wMlcPt66vkzwkq2T8LlCYTG45LyG5yHuoDCVvrgJqBKTNFFPNEl+ccw64sMLNlNJEzp3zR94+8C2nD7NGyRxVysQlQsRWv2PMmtbxi/XNovbQv8dbJ8WfXFL5joFBukicDrxnuXXYVfsmWr1H127eRRA6No3qxNR7+ZCbxCf5+r6o02aAN7HJ68hdGuz13hl/paqwsEdlDfUQYI7tGbpV+Fvar+idvunvIJVNqLfzh1dFQOIMLACOr64k7IVja1q1cpUslO93NauM8E8p9NpkZnJ1cliS1xVHAKK++ior/E00W8yZ6yD342yuzLchfGL+c8qL3M8iUPJgPHECyAUMKeFBRM1SaMWLY83rY/IKBRSuq5P2GByS/5qdIQFKoQu6cbd4u7/MWvclIT0x2+tu8E5ueOXre/6Vl4aD0JowI5ciPFh9NjR8eaOY/gI3VjpokOPO7ICXY/U9+GxvXmxq1o+INwsRIv5QvqoR7qXGO59HmoM3miwdIPCsu5mM6+VdfpTUJhttRM0b/dlqGkcTATDtks5D1kBncSHkikM7ONB+Lkjq3wgIxpPML6eAds4brMyb83YIDf8NwonSwe4+kb7QxE34EQeASfxtoRH9PP06GMnRnUegM7xMm9rNUpOPSrAvn0YDZPaZw1hgOqsowBCSNuhcMkLiMZxXTafjJN47V0hb7lpFbMeewhMqN6C2BCAttVczhSYGtfJP9rITNm1yAvRXa7xPSP0zXLpKu/SndkFzIfKOkRpnlcIzt1ne8R9ZVCMB0ZtQaKU7UiQJ5vUDjLXvpn3lJDBhDUEEuX9nRde1XsnMTQFNAqOXU1FSYhvPQwu1sIHjzT9QK+5GqK8WRaRfbCZ+kPYkst0bQug49xac1G+4UDXCrESlBzAmlUwPOBn4DD2fgeaGOVp/yhGHKjpj7P8Avo8H8XqPeFYCnmOfGLbZaE1b1pIyNVntQHJg7UACcVrmNeN+nLhR+/z06obfhNPCJIEPlCQR5miX5dVzSBr2RHugqjWFLoEd5xPVpDcBfv/fOngGcI85UkLzIQN+BfhIWvtOhLt+CzcDJMoMGAcK8s+MPKJUVWiHxFMpQM00wqq6PoSnkNnmVqiog97ZcrXs6K3RmJFsgVtgZNX8/Y4F6QVVyxc1SJzbs1qVU6pFyYIaL344iw5MOoArfJVuFXYpzClG42BLSHY2jpQ/KzC1kT3WTTP16oBQHhydAVwAZOkIM6HLCe5/XwRyU3Fcwh9mkbsA2VDHtm/WqY14IZ2/H4MeD/53LCji+c8wzC3MERH4b5ncROIPkERj8SsTsi9jzuBsd1GgHgZcr3yDCv1D/GQ97dsFU9poMi+1nAeLmk7gTYvpuk8lCMBkwqpWFqtcWa2aJ6waBIo1ErjBXERKqjnctSay5lX1XnMpO98kPK4WtX/sKSsLa/8mBJWPTundLjO3wwthg6G1eqM9XjbVnNWBaXrMd5zXjku6nyk7eOFe/K9ewVAng4YPdwL5xdVKD0iY2HVcPd8SSJUv5ABigz10c+0z7DCykC1jAAR8DkCE/uN7UNOz6dNHHbyCfcMZW3tI5dZcaGUDJUkIGClr1O1pt6eoxBzPAnreGRjlRa1roZ9NWCus2FSWY1BuBYr5ZmVkBoClb91GC62Kbhs/d0LcwjcWzkXw77SMj7Tg4DU8FbxpUbHfRzk31fnGEHrWX1w8v4WOjduEBsNyMgkbf41hBXrpbkYeRzUYk9yKwm5hgQkZ5CxMfq78y4rqBgmZXPu2OpEt3c3xwj5bYI2niC/NkfTG0s6CUDsJvNw/nKMTtChvF0wJXzeGTKEvVsbmntj2GjIcETifzyniWyftR+v50o7Jkyzz48ebewKrPzQ2zgWooIyGMO+Qbp5EF0cqA7RmrLzAxFO9r73kNgu8sgYf7cROTxhScv7GJoXwdPSChr6vXyJzWR3lu6wH3Y+0Kv7CGBpXTJ9zu7aYVkZzR/HeyCussUIbN9l4pFB0E47zAp0YWFPVdPbWCFk5q9GDWhDWVG1oo+C4anSoxqUseMqqqq6Pts7Ly9AVSbKM3BH5D4Xbra2Gdba20j9CiwYpYjJxE5RKHkIxLLjwXfBYANJ6VRFuvX6Wd4UQgFlgXSAUlnHcPNtgjEnbD9KqAwnHru5Y9Y3zNVMjmK6bmUCLsH2NTEdm/zdm5kr1nukEjZKXZJFysIYNlqy/cYncrCtRIOTCbCP+iofo8HrQ1rInPbMJddc13VNDmTmH1/7a0Jb97fVQ1NMTjKXOSH+s1UEtk2ig+drJ+g9hAWvefpIauNH2Kx5ynNaevjm2wOn/olOfuhBYG83XgYxh+wwqXKBzZcnIoZR3ibPVo3LMQW5nYL6w66drZgcVukAKOCwTdjsXq22S8SgprO8A44afjpNawevYO1mMeZcoqs45P3AfkmFYuPlg89dzZQ8QP5mQgXorMrBt4sbGfj93lCUdoYa9ZgXTTwi9jwDYq7uvPjMFuaU8d7Ya0gum4CJjd9X727IXCzPUYwdTq5lF7hV1sscHaxYmmQVbrNGaFLBUfyZhGNq3XlhfPSN+uVDqmR4jwROqkKeX/f3K/DUhdjG0ayCmOHS0y0QApmZiyxRrV7nCjWi8xBMI6XCmH1ohD1mMym1Hwsu6D7+W8S0mkGr/VEeNxRrbqFAx7n21XqtsHUYq3lhMA03sLEiG1q9eegZ4c0TJgM7Rr0eAGTWKoiQBO8ef2IjdVzaL28MxrQcxbHlC9lhs+19OGo0qca6GegJ8xQ4NRgsL5exJlkBKb0OabTuvuxLSPIF9axfu1rgnX3Qx9ku/5GiaD4wCDtR4YNZD7vR5bXhESmnPOtulYDunkIFfpvOB4VhbVv+3hjSnMyl/QBUPDqb1Ff6sYUBqZ24p9D9KnrHXewMcjwNz/DN7OlcFQOUCB4c/IB7DR4yE6Mef455RKdzlID6rWgi62YyNrcjuJ+QVyP2l5GmKrOAqCDfdGIg/kdXtQeDQIFCrJRQa8HbwIdMMjRQPFEsgeiFnIg0I6tWLWZquHq1tj9CpetYCODbzMngdNn5svc0JDCP50ZfzcITSLbs4vynDaullWCwowS0MAJTSmZOP+KDv3GI2oczz8MPOc4PkyDre0dlMIIIF5x3b3vFHJIZ20SWUDyu5lrb0+b2HsdtO6j1euu5iYQWn6ErJxd/1jWf+CpBXA1eUPyOYZNHukuPc211gnSygRXTIp8wediG0dIrbQdLn6Ritj410hgTDjKfwgeYq+89m/87gAco4Mi0VeiL0nugWveuG1kO2S9PTDHUXTWO5JQ5V3tNtzZ1u1pcnAJ6Vs8SbXRQkmTUTiOCjFwH0TBqf3CabHATK1h6KW7HSWMQf/w2NDHt9641+pZPQyrZ2JWquRoMBr8zy/pCxI1grF83CuR7aB8PTRFhvWGG6RDnKFcPSdMqoOWTO9+tSeGWC4yQhOriiGTRU1/Ng683IjSIxZo1tcm7qSbfgnTyZpGkfVuHkrqomZNYp0KNI7bf+ZplpgJpmkmqFGb3qDigAvcs7frV8uROZ/mTKT2UI9R2945gvk5fHqZQjdpdqCol/8B+wC9SM7GuVK3TCB8bZa3xvS9SbdaVRO6l/oLRM9sSls80FLVRShSlp0JCNW8+Vm1TPSNZMk466oQbWYVa++Z8ZWw19c4tNdHhcTBIOa4rAVfomLwi15Mnw5V18C18Bi867P19V8MwzKHLIKcRlzMFtr+REmnbqWAM3lft9SiEt2GQEDQwkxkxSl2aB49z8PQhDIglGKWPh28rtfAPUOMI9q0CQHN/9B1PhLiXoDFEcGNGoAzUbz16OhF0heVRPkzwT2y6hxT6w4zhURLMOqOcchvRbqemTM4WgagWF7l39q4A/0auTHsYp9Epv0IlqprHm6Vy2NS4U9qOyia6lRwPeHKw2RId7wx2UbCgW5Lg6g1/BldCgrMUZzmbDhfVnoZrlzV27WrX8nPnEVwSF3QqeE80bYMLel6Wd3Yaxi9ZP195b0+p8V28fkknZfczwcqo2YZT2ddrvJWsHGNO5XFJl8aLH/SmpoH8F4G2jhaZKpDL6VBZWwghoNGD90tzg14HCEgvmzOgQdw4ZUe/soTnUzh4R9lldyEI189BaTyqOlWm1hUIuw0Gfn9Vw3LeUxcHbu90j4JaxxFaayiModoCIGMDehC4JWAPTy9xPFrWEG9WE9a94tO6SRIry84+OqtvNU3sDYF3BUkspTitx2nExS4TPQZDD3Dgdf4VpGduKvRzEn0K8+f2rt05DW+hCLUJajmFkC9S+rPVOq3Ox/4R8iAn6S82Ntv94IjPvcBwoq+p4au39mfPZrjIa2D/0G+jlMGt/eWRggg5Pghh3i8u0dZeOPooIEIo8W7xpILSM0pA2wpNcY+1OU4aNEfXN7Yz0iQKY6Adr6rRj/pWsBKfkGbB+ax3lhQOXepiLYC+dEHYEB8wUJCqwDF6pRf1wKJKfqMmB5F9lM5n8mnlwZIZ25tIaWG25s+sSwbvO4efu2vBV2mDFBEOBx6Bl2Df7ZSzttcUdr3/OE3AS5tuiDZ74qSZcl5o23xGLUP2bk5pBq9uXisG8/cIlEkaDLGij20iXsIcPWVezKdZb8ycQH6imxShY47lgb9P4nuceHF3hWAoeW92R87w1p++yjymk1GYfilqfYIt9brlZjF2YyoezDkaeXmEqVRO31iU4xUg2GhoR06s0yd8pj7BHto9taKh7Q960LD+yUyayQGBf/esx8tKTN7xVxXDmalWzjcyU4CEm4s8OSXUwU26+5s3uinBJNQFoMiVCqff1X0MH44dfGzS5ivejCx5BxUCehWTczhGfcJwK2/PKo9C5zCskzadwoUcX11O3nG1WJVYt9iVbDoxof497NZ2FO7qDdCZdZwwNteWsdXjX6Nod9a8v8ni/XZWjKo9hsocoKDJl2kkEubOV0aN0i6lnPYTz24XX3JQuEq5nNZF2EMfvWWayh+1I42pA2nPleqLB3ZEihQl6hQob5q436TtFT85moCzMeTe+NNdTIxrL0TDzO/Iyg4v7lXLLMsiPIWg+v+0k3yUlT5afqVW2xcI/GVcSta0aJHCn1JxP7d7dLUdqFWZ9/xnTUGO6TO9FC649eE7euo+KUqUbD1lurH0No4Oo3Ri8vovQlD3IP8zf8D44MFRHakgVcEUuH1i5GYkpTlN7IYsK8YGNlXsBH0EYfg3We1Ir2zaHkyeHSu8vypkyKtp/aU+NK+TNqG8d0Jw9k5eQk7CUkukorMjDVln3Hkjk0H8pPM+tFnXoyC+zGB6p/0x7wCe6x5tU2XV00lGkweuNHE2xo9f4xPahcuulPG2i+mAvPoLwi9sat/ZQA1cvF1Bady+K2q5sUZswdRqM+ju6f9moP6frkix+1UpOpgVi+Oq2GYUKCOJEg0pM6GDH5XTPROwMBGrXTdmt+1TutzLvuriLQ7yUXB+87ojE5bqP95ZCDcf7SwomQ0wKq+vh85qH9PxXz2Fd+6wKwDnNg4K+p+vzC6ku1sIo7N1hESBv+CsVm/acS+yTBExdU54+sna0jaP/5M7+HmHIVddsyh0B7XIeY8HR51gJfZ2KISqFCaRcLITqCNtDnellHnO4LgyX2NWPwef3wrUpe0QiIU3AijrxDV9LWkwksI2EWQIhtjISnXeE0hfq7T3Sx0tK3dU1DA8fefMM1WlAyZfZF6+zcDRy98F3l4GOOSgeS77fwzPNLvF6D2dnuAFQSFrvLQjg+TI7AshjeUvjwjrECpVTkiIrKrEV0rirVUtx0jo/NFzL3XYHpqmAOuppcvBuHiWILuNP0JwK0i6BE1jYw2Vn5H+KaVhDrcjBn0SJJb3qxFiwIJmYGLEOKGTB8LEzNpm5GUUErB+zDWSdpYZp31T8PMmMHFjCp1bsHQ+wB+3BrdRqfmIKFo9TYQSWPs6br+4M1iCzS2QgktnmtLjucnBPmKqqCCroVhErTU37U48bUIEwvMKxUbJd0U5c5XDNtdlSUyBm1FcFhUrrUF2Ko7SXrdDM4SlMycYY4VQkepFgZP7gZol06vporlyEQ5qc05AMIj6Ms1JFCryro6l7WtJGQ1Q5Y6vTTCUBij6sEoh9jfwBJ11rI6HU3fwZS/qHkjkARilHV2yblPvwRExUBfd9daf/gcCryrvV/pORYXEQrKYYzwCNyX8L2rQSw6Y45Q7LsEkxmhxQ4hLsDMEJQlAuBfTjeaMxglAskJAykJjvYRSgD4tOvZJeepOt+cz1+c0v8lLGCwchjD9ekwvwRTpLKvwjqbaO3gzuGfCmUbs11XdzJ8PqpFJD4b9H5jyTZltt34lDEmohCTwdUR14vEILB3QBWJ/+owL7RtNiR3ZllNneTDewPp1Solo1YUHGu3tTSUZhZTa7ya/M+MWHO47S4t7CIj/DVwDnRF93ouJR/XXW6PQ5C1E1DKKI8d1EdLhx+fd9mxw4H2rFV2HzcL1IbTIs9g5gvunq1YTzV51tOwSeSAjTvMXrmE8A/CAJB+oE1PZmxnexb/S7+3qE/UOovu489lauRrLF0flDYDytFdmp+L2skqb8gAlWprelG50IEtBell4WGDQHG9sIqzZBr63jh0EnJlLsYMbwhQ1wWXFEKTCUyosOwf/8R4IcmPq0Vh3/ipbZe7A4Xq1dblAHelWnZxFrcTQT5PsaV8VzmGL0+FK64kAyDzFEXL7kiUQG1tahfSJnJe2mINHxqic0uJIE4GlCDnbppupf7Y7LFvxGs87OaHYt+ypIokDqNmaTpb9lARQPQI6psvFGdxY6Ap3sJVfKx6GD1GuFeIkumuwIsk6VSSsSrPBKwe7GOEjSQHLMxkdDE8sN9L1OI4yzDuBaobQpcAdLUKZZdtSPVAZs5oRwN4EoI5TEOZo80oRlntNsNj1sJJQsx1Lxy8zq6oi0cX7OvNFd08zUiCJwgYze0Sxmp3BaExFCCjII3dAeakYMNrvSb7cIZh8BZUwx+etQgrTHTEtmZp9qgsrIfyJyYLlbw451/TBdRXpiZQgsDpbK0dMLzCaMFyW8FftVTwLkBAv0+5idsMGXXiWTCWgYEIxp5IZCzO+p3y8fr3GbfDuUIU5DxhxBtpwUglTiBtzqhoeaawEK/uG00bWo0/gMrd5ev86YVPbunZ57/YcnYpbmyoBxnMaywIFef5uxTAJJFEMtvwNMig9TNSDfrfF2D8TK5hxmLrAy2VzDMTl8EIPkYt1aexFtBWQQUpaXrajgtnT7Gaxa/5CgL1IP8NOPHrqnVWzFEWsI/6DdzFKB6M2QFl8XDVm0cylbzQuepZC4bQPuKY/Pie9Cvw3nu+8rY5Qz1ESEwO2PUD/P2yq6vot6aAfVt9i8oYFm4gFE+KpyNYcFk1hT+HjKtDQUJADZ43nlchVPb/1afynk5pd6pUIcMAJqEPGyLwRho1+J+mGXR9Bc1T/rLgrUTES9eC7+N0S+Rzjq34sr5qn5lgmZhLTlMyfDm0Vw306xXahl81wrDoLcMrhbFesQDn1i0/hMjVo2WgBF02tGFakMtxtCJRMbC68Tl0Zb3VbZbYjNu7tDZKdQ8osWyu1LCzAlHe6SUvUvzGX5I0Iro0Wq1pC58UTm0bPNn+KuCtQrjJag0vc4tbcnP92fcgZhIwdwUcjvwJkCif8eM/YWyubjE9zL77D2j7K3tNV+Xt9dRVqICykK0PrLXX8SYrfNo0hge3Pc5QvBwddBWaVcOcANFGoQy6VfDbZNAvzSbYJs5f4GtVvTuk42X0oHksm14hwuerL3oJqw6GHIg21Cibgp1hW6JWhzePLKU7eB1a/oiPK22sed3FDLuGMuHwY6s41EV2PwL+K9BaR1DI7Dg6ugAA+vnQEWeJjxYtY9Rr1hGQqMLKyzJiui8h0FY8PhfF3wLLsSKGwJcDxJQI4zYTQrcSMV15j3g9cTKkzh6HGdOHPZo/B4+g8P24bnYRKnpoH18aS11S61xwEAzmeYiRLk2fM39c6ve+kuL3orO2nLyVoLwj/W4B+SUe9bZjrXDW+02X8EucBNCg9phEYseWsfhetYe1J+Y0gF5aJ3JpYWXVy557M59YxIll3EFu3NsMzeP64WKcuutyvuWDf/AYcbmqE846qOGqAErIWWzs+z/hVw/4824zFHfGGP4m3//Mu+42JisjRQHLNlg1S8ioXp2oWbVLxSM93NOKBItkD26u4rFJArne1Daoi/ZIjiwdmwG8IFUtoJUcuHEF7Os+Om6Xo8/7xyHjvDwHls9mS8+FwMfuITDkyeP9d96Vsa/axaQ35VgNh6bGOqXKE8ubLENCWpWu+cJ67Ux3kDte3f3mtyHAXkSceqZIpCMRo6+bqZuyWPoq7mHuuhgL1ihgMlEGYJ8WcF64jx4aGfRemhzFiduRdEkckfhYHib0vQBG4ih8gVi6kAJxi0PRuarsHxG44V811WECKyAv/S7nuleDGm4jdsGdunsgNq8UCm1a/gC608+UtHnQBQ6zZ44FPtKZ0rLHsUkLqyEXuwrTHUiXy2lRthUydqYGlLljPCwrR27nxkyKpQMt/wqKAQYm+jGK7xmcfjHI1XCSiiagFCJ800gGD2ZNKCcKCypyh0COLxxh8S8vckA+tUPdSFZaOnQvITgMhy+I9Jy32qcMG6Zct4n/Gd+7tjSoRQTZ5rdsPEoCQPNWR5Lr5aBT1pe6PXscRLhKODKi52NikGQshyh6GWpOxNIRDE+vcZavMHDBsSgmunCkRz9IRzs6BGHO9XFktatqTy9ycDMS2E3vGtJM05SaVNZH+0kpXRZ6uCNArf5RmqCgnNed53KGhpLShWIoBfsCL38hgWvXXLpx3Dm9jkXYsCZ3tSFkk9axUjShJhRRofmlSAxqqi2cpXzYP+Vs8duM50jkECNFR5ZTvhYd5lG7lvPSjmpN/z6AEZdoowXrsZKjg0IEzxd4PkLDQjKDTQySr10zBgXl501WY5S3LsVoKk+6goyqI4ruYINxC1NDfdeS/iAKVetHQZHWh015Zcb91NxZY1gh+r4RSfNiEEscKJG0nYWkSwfK7H9G6FZdEltw0oVwg1vjkef5fgknyg3jd9GtcFRz8m/7w+HBsLy99xPabdpNG7Pc2cQlaFFBz8SpEzlNsqkHtRnNoWTufZmnffJotjeNuXvbe5G5AIYzxo/6silErUkrENiEkR2n613V2cbJz0mrQZLjXV6UC5aopjvwRuvR0ai2mADVxXvJ80+OY+d73NTkH7Li4kBCdxG1V4rpDTOlqZIRvEln7hcYibixnMzODUAuNiGe+nHxxEKeJL/zjzl/cy73bHaPJvFcyNu3yB242JtP6/lfIpUhuys1vWp4E0/tCT6aKBL/yHct9JdMvaaECAINk7n3xktSp7tOXdpmIpsQ/Jh/YXL8ifztut7aNRQLm7ai//tpXtgnzzFmbRru3MfPQ2l63Rv0LafyZk8wR9l7J4o31qKCYDFwYFYK2CuA1hKUOqPF5ZuBNLcyt7WpXpmOVJxdL5WKL7NIOiHJoXYfE3gsirrLzZbWtTT3c79APAg7SsLJG1uIroCbuNeit2i4is4rpNCSV9kyHr+D0r3s8vvsfqFRJrzOu4c2zHQRkzkHDTQPTd3WP1TPm/Abv1wgdNRWcJUbd9HiUHdAi+ynSJC7vhwMcpm1g5UjZ0Ow0yXdL2yIIq0nrOK3WzYjDh2ldm2958FiEelmtIQw8027d5tldrE4GWTCR49akza5NLa219EYnHVd6bAxfAruGFxoZonoCckg5y8yisW4d+eCI1TA8UYrolPEU/benu8Wu7L94gqiGl9e0FrswSq0V9AQARQ3AEj3HJ9QDNwbEtEj7dReJsJVS5icMjzK8msOKiAFNbvUHJY9gCj0BC9vs16GSrJSNvi3nwAHTpHGLOdpfgl9RjMXiiH+7Jfyv04GzXElwtV7eMZ7AWJPN4JP9EweVBmRFv4+JXqjcJ3f1JdvYltbc6tsVG7EApTmNNeMvsziYMlk7ZYJYRpXK+6EJ9DavGgixF6OqdL8A3yT1IQrFHaF6tjr5GSBGKEQlYCa09fqQqOEUlVuNS5tenQZ8tTn6XpEn0G1Rl5lgAgH1F0uCjii79KE77i8khsBmHB5mu8ayNfUuxiVZEwuX4vkBVbF0AeFMH1phiMJetgdW8+54LfqI04M/cTSCk9BdJgE/JY9M0EpcMN+Op/+r1+mxoODprM6W+HWT5VgMk1OQWR2G3I4SwH6OXNUFRxEuaOhse8/voKoXlGFVzedPDamx0JRmm7+CoEtjwkndu43BTSIFEGwc1/S6u4YeDrrWAyrPpo3YMUBmMkSejTh4Fffpql3dXqcmYJxuZBwIN9/IGEIW2prZ4nv/DFCk9THN/KU5AJSfoHJlOHu4f/IDtxxv2LS6+Qj6b9VSq8GZjltvvGXqlIwi7c0MIrd96F2GgKoenM1bkZK48cOP6/ec9sban568xFMykYsvCisaCl+GoiPqjopPEMUvloARKEMPbtsfxTTweUf2MNysJyOJs1fhd0lNn/dhez0NwWR2sVbJj1ZyNKx5isFSmPMR9baquZWV5Wt3OzloA/eLdP4riyUYagPjlbpfOT5kPA+Z/hZ3SdMLPxAaYEIalsK8CbZsm24cS0cW0Qxy0KW3w5ca0XOckVnFPpGVI3CENL0BjQWPystQ6a+p2ndwAXdYY4dFrkkd5DW760njdk5MP55xn5PtqcsBFy0aQGIx+u7jN/LJbD/wLQD2M9wiOulB0d8neHDqLqCUx7myV9JCbLds/Us+ba7fZ6Ez5hwLNmLNXG3Qg5e9bMQb++F1c4ae1lrji0T4zDgNkyZY8s6sap35U4TXwWiSzGRDDrxD+BXdGtGy0aeYcZKm7YPKMpkAkDDTiZiWQ9DqEllk0aUDSfm0CFw0aZC9OazNa7sHaiKxLcqRuEt6RGLJYVSvcESQcd6AOVqhTj4Vvtksm+CAZfMv18oglG1S0JNrPT7qWX/5WUww6ctPMIMxJOi41mp6EB6U+Fgg3QT/CSZP36UwJNiZsCSCSsQbKyvRc1mggnU4PFbooHQkNqk9ZY9Qzo9AMmViswJIs4hzWpBGyKrL7A6twVnxOnCk0BhPGkqik+mgo+PrXrVQyawVwvkcyraGkqep5/kc82H1kyMU/llKrMDfL90GwGmDMcqGz15d/IMGLN0yP6t1oGvawkKT9d/TCtedVO3m9y3xohO3cTN6nUL0xUoQ3jonJuqGwPjZ/E4OXET84bttnuLG0SUZRMFTnqbdqwIH92ed0uBWP44fUDvHEgFNSVumFw2n5Bgsl8wrI8aVroFvAA6FeKZ9H3k+IMQzl8xSIxvKtrajT9B04/1NlC9CvJBVr1xXy3ic556CarAYeI+9EeB9jtJdv5gN5NgtZ9Ob7pdmQEQicLj6LB/dYmIJ94XdfqTBXIfLeFw16R7Q4Uy2UN+u8Nke/uS2bm6txFImLv/Ec+XmQJzRRvj+KQ074wDuXb1Dvfm4jeju+Vkne51+mW8QG0tDNwMY0SGe5g04rvmlj46ohKOBq6KWvfStoK04ccnEgLqoFMC0eqoUHTwsaI6h3z33hXw9O4pE48xe0eybDCYOJR4LmHMBmILit5IeYbfXrg7Q8W00a0BlhIZAq09XKBbJ9ofr4t1ujNbPB0lc35emKQQEYaUPt4ftTASdNKJSXjCaTlB4a9SbgkOYZWh+wekdJ0jlGLfm0hh1HCAAfBqpbG4yDMx1mFASUCeqcyeMUsYVBUuq9rkkJ9mk5L82x1xrabNxDylPODkvna1yIAvHGAGrNpaHE8opNumIrvjn1t/aqnDBE7Mm41rj1Ffd7NhITZ3E02CXwU8MnxgXYiwHcLRpUnnGEb00gy03ogpISSmbhcHmrGcSKqYv2PSd36NBffgnw/2Ob5mj8kd9QHmBzHFftq5aFNIfkPgRFCswbDB7cv5cEoehQeM5mKwNqemgPGAlrjSszXUTt8+lbqrlPN/B7ss1S2jHH4SeakZXaFuglnty+eUMpc5nD0SD+Lxuj9v73iAMTgf0Lb2Ru1zoQsoqw2TXVh07jAj4FfWIx7b9hPrgiJsI4q0u3jjclUNUAVsnofjek4rF8k7SzUPTNpsTyV0hDRFTiXyxrrXtj0WZKpNybkiw1U0yGY5AYqV5xveIKkf+sRpvH5HcJtSFAGBCD33RszBkkW6GSfXMJ4X4tvUzYaRe6SISoXQXEjEeLPdYJzBGUdKyZVgDM6gOMiBFdlXz3pfD9VP/YaGZng6XxYMdSBUf1DY0VIRcR2WWni7/3XdsYfRQRdG2QZNBG9Zh21uo8ZqfZVgBta4agGxUj3UisECdU3FZJ0Azy+n8AgJqENbtmw7GQlGI3R5MyZeVWHrp8Th3FkA1HTvOf2SwwOmIf2Jua08zkGfnylQD5fjl4zkytG9o9KQ2TKUhP/pZ7riX0p585Rfj+SnSDpBa7Mc5ubsd/P25MFgu52YdwyTa7Vl7LRUBb+qXvf7w5U0s6qSH1xwxGyPSc2OHrGzbzBzu0KFVsvo/bISfqu/l77ax8kqem8xpH9VxwcG84YFJYVUAjJH/paCsFWPBCUy+QBUUdls242eUz7qWhBvXJnwFECVAHVsQ1oVQcKHbPBe6yAhZPCSH74Xj6rNuYGCy2PZBLPJQ7feQan8OS8Tt8mbkarmDJWzfmwlN9bayhwN/q3karChlBUaSl7sQ0+w8tZ0jB5Sorf4lC5M8KtHmZ8pmxm63wpp3wM2qHzsoWceQpEBexNswMeaSCyjCoKxNv4IfQvrRuUXJXKahE7Aw5k2y0qrzUbYnBph5bAn1yNau7+2Q+tkjVWs5PdhkrO6uEfbLFqaV9j4DegTtQVDfIHokPkkplzGsUXfdERxDnvpJzvvOI3UHZ/Q4j0aD2WLBuP+KrFQVpsYZSWj3htnD26R49TJ81DK5nzt10FgOM2cEbp/IQkplywbpqB6WRZx3Aw0u08UzOsOyFamcnMsNuYi1yZY70846jKfuxYBN/4gxf0TBfIvhL+gneEsnF0p3i1hFVaFxyMd3pUb3tR3TM58ej4ArXXaleaFcoixAfLvR1t+hjveDWQV3ZEyoWk4jise/xlR+CUI71opw8EBu25+8WZLrqO8ek4eeMH+VzxejYQg9rtx9QYRjlmqvP/iCTLpfZWjpk3cYKkEd60hcuR+L1UgbXuCVl2zQoUu4wCOnO30mgEIF8JA/kVUtZNW1rElR1yUymaHuAk1UfgkB9EB0LzQHRoWCr3bR75C5i9X/oDvSKFxxdFw/QQN6ef3eJWt1jRfY9QGUdUxG54MDFRMX00vK9zVagzn77mFaE206jbX9x5tSFPAaGL0Tz6/rO50Wy43G9DMcp49jlWjEJPaZr49tbgFo2oImqvHwJ1J/+AKeSJ658sPv+BcKcfW6Dwk/4CCD3eVUplLph7zOhHuWqSge76+Y4EKTcoEfd9YmOkYQ3uHMmFHLQ9q5RDiE1s3eGfKBXGMVyp0O+BzSR+mm0pvEV4+SU8XcHNa6aW81MDpiFvqlMbFznT704+6nembuTbvlDK8IKMKOOLLp6+iCV0A4nHmDC1aAQ+eha6iHkaF73VS0WiHFGeE/9WXK4rLcxcvjPGwfTTYYWIwPQxiazWUA3Zags/wTgGT4VhU/Lpsn05fRLGTd0Vv+1RuLfIu40ive/PRjpaO1dF4/qVpHPftLnfrys8n9JMn18UES2g9aYCLpbmprStM4WWYsNg90j/rQaNHV+Q8tddAYvaHrPaGrmNjJXXivKp9YZlejO9xZCfgWP4xfUQOaK+/rvY2JquL3LhJgOuA0mzb9VCm2LTNxNtmMDmCoVNVhw2ZSuFvbkOrjdaVS9LjyqgGS0QDKOS7RhK+BH5PeWQwgj9CBn+HyKtAoJSC4GoX6aGLOecOBRIbHUg8opemEGCXpSdp3iN2uKpBOoQ/W0Di6nTJwuHI8b/dhVOYOg7X/ldxq1qDuEFg1Z70vGFlIddK3P5LrAXURkkXyHoxZd9Wc4R8+emqFWhC+c0gSuuPRb+9khy5N+cmVxArHJ8vOP9J6ihVs7Y9v4t3hh/1zXp4F98xizLKgrxUeQ6Cr0gcDm/Vau/CSLSJe/Cknm9BwspsK7b5E+Ke9XpAUw75KZy/yeB9tACafJgwH8dPPReWYumHJVAwGL8ADVNvR8wHHTf0/5PNPPDlpajXtsqEn1cI3+z+jFAv7gYYSMcJR2QOHqv6fU0/lF6GObVLQi8CIg4t1RWyZ7YP7SqADnNirXg7jcpBerC4gKaQzouGE9SvpsS/9rmoaKFrjPFJn0n2/EarywrtCutz5pW/aJN9zfnea/dfE4sohO6Qh3zEoQ5qUlzCEtWIbAPcDjoWbvG7FDuPB5auwVCvp/Fb/p6TubTALOXAbPBMNU6KWIEpejH9ZSEcPUiYvMm++eawnbLflErmb8luEyFs1mUvL8Wmr1MtNVbDO1h/Ja49vHMLY3LMKgxohNwJ5fwvh9c5L2Fp57Q1UL+FyOq6gLcRGJeOPiU8y6H8ZSpOPPNcKd8TyljqrC48kiXwD+n3Vkd2IfSd1TUtSbOTlztFI1fvEwI4U8VIrhPDPjemM/KN4c1OfAebVrbD9rJhukFl3216ZJUQWVeOHoAv9JkQ8VfrnzX+pp7kuYYR84Jf/TsHi15VFqOlSKBCMkKex/85bfYpuF66GCaLrMgez/uqr7G4EMQeNgHo0efIuf8PR4kWuBSas6uvTIt2my9VkHAudTIxi6UwzFZUD+QNZevmjNIPd+BC892jmlfQzWqVoqoWvelwo18BZwckufZ8x74at7isblMvxsXX5GmSD5SmvZiAZP6OgEAMK8S2O8fVVumlc6OZYuYfDnq6FcS4zD39D01EkElKtQMMI4xi2UYL9adGq2lUeC0+C2PEtmNOiquqT85wOEToXHt5fdppjXZHy++YonkEJ4bMF10/hCR7T6hsWOL+Nlx9KpvT2Uxbygh9iKZmy5q8Dt03BQhbb5yFuKVom9mrhrV1f+qPecsZR0A82RWDjuK2waxgyxKIBuNNUfTvhaaJ1mAOp4j2ISaXpuQPP5Xu/lzFkowzup0rRxXOMrO37dStrxunHYzCrveooBd3syb3s0VG1sNFbtGKlKRAjzJA+XpyNpcYCbfiK+fSmiZUGEjyac3gNcCnLQRVYf3Iywb9/ZRTH4SKpxYvbUQFRcy4Gtu0sYPGv0rskgbXm9no5oApt3+aRdk+/1otb0bM99l0k+W+jBFBfgxilaJiirC30rkPiwSw7qlCCyxs84aQN8b+kra3OT0CeUGivALU68zA/56ae0yZ7g1VjcN1pdWcYltfOEmEpg7TEjIkjJgBSrKnzCqq4Vc/PtGkIDqyjdvt4oRky/3odGZsokbJzihYo3+god8tgKbYvddc0Ky0pvJIyToJl88poWTYiGufRKzyOwYKK52BjyciY9TxduEhqs8I+8D6Udf36gQWwRj5IkglSTjKWHDgHgmmufYUs+zCeF/5cmXetlIbMOqk+33CmfHvtTvr11TIEouLrO0WAoCc4CJlzTB0NPBJIUiFoR6U/WjLcI8/rPhTtEfWX5eNJjOu8YkSdn+BXERC7wt26P6ELouu1jam09R/vZ8ZWyk5HBJ0/yIAECwLfUloQTpPq8uX4mGVrIMDn8NFq1UlfxtgHjZtqHgsD8Z5iynmUxEOBiiyEjoqQBltRaAa1ZtOk/L25p+qSz+nzi3gEoTU67+RNX2kgSDVgZMtxTvB1MJq9lDOsemxm3vEPVv6JcQgRJKjqkQUSzKnbW2gtQlITLSOEVCk7lfXeo5d3jRN0TnOef7yfsocNH99Ivy+9SXmm5Zz91/ymUcgnR+BHcoftJxurQXE8brptXTcN9dNX5ddHPqhiwN3Gm9l5pu5yfqxCI86goYJqy6HMoHcNbv/2/JKqTG2nrTY7u61Fs0TCSWhtmZ+RoyVj4cLesR9spbaqIfN6T1sWhcHtCXRvj+/5gP8AHucf2U/EguUY3+dpzaKiNwrfkrUUDB+BX5CONEPVfexs+ygRuJGghyrg7QlhEHSFdUEBgcvAiTLF7I+WYoamUYCrKGbE7951IY9FaWvC9RgpzUpST15nVqEpHi0KmBRCZGaYVRwuwHB+gGiPNFlk1EsI6neaBsJuFJH/U2NCYLevPenBV3/4Hgq0iG5nqAODjuJKsIjVci2S4GRlWi5cHTgZdG2Bf3vEkxEqn/M4bTOtu40jotbuVwjxZO1PT5Eqtg2PaJwXxEAZ5GgO6UvbjJreCMA1o5guajKVYAQA9U+kBi8rhIAMdVM4YKUk7sEJFftlQCvGIjYw0ndvm1HC+Nq1tq/Z7WObrXdm0es9qQVQbjco1h12T9R5aNQ9BiteQWHixfkzy05Rn/2K/u61jHAD6AaOFLDpFgy4jWeY5WohQ3HXVpslsOnMJnSfPnzuoz3nlEACAb59zLkaY/zEW1OB02IPB+qrNMscHcUxBmFRlCAX4elMHPEX13KZmLfZX2gpHte+GpaiRM7K8GpnLN9RAT2Ac7RirXKitbnMpUTEdfGP7xOut5uDsIWJn1Rc44qNXsDvfcLbRxQ9OucBVPvsKt+GP8c8zdS/a17R7eh/qqN5ehmiwPNpvHGdMcOn2VqpqPnBJm+SZ6TDQDACv2JVg3+56AfFa69pA5bs+t+IJqyp6Ost4CCg3u45GfYY4hAmzmTpUt+1ozCNbQlXDCF1a3+7WPUG1oTG46SxRke0RUEeawRlYlCGNRdx+8GV4juUQEZQkM4jh+Xs78JVN3ljg68WJaUrhNtDJ+7j28nHOWRE6/wp3jeiRpQ6STMVtCnQF5WN+RYQKrWycu5Fu/qzzczNNgWnOe8/mhkr8QxBAAtiSmEMwCf0XcTQC3CaC7iXM18XnJ87Xs4WXiQzp7ejZdWIUiggbDgh+an0ihMZDv5u9V42U1OstDr6OdrJ0kEPTajzYmBA9WtbijiiqK3P76RsBTkT+IpbWdjRHKPxMAHARgeRJUBMfp4AWsFSBJ0KJQ6anOH1IFCsvMVU9KaM/2kLh24nzSo4CRzpTTDyaHEo5SSb/4FizgfscOO6GkWG7NDnwDS5ufK/aqKzniQMhnUyzXcigvae28Nkpmgk6n9YpJOcMfIazE9ewtBy3X6mL6Su1kyn1SNWIIiu0LqKPwLvnpaO5946CtO29v7YHrkLlhTyxRyyLHdG8XosLaSnREZ/XAA/UQQyHpSoZSKSb67TlpX1LzvjrT7b4iPBMT+wuUVU0T/GfhvcEmtpZJOOwTtDr41PFItBu9ADVVH898iqv7xmrqDflrY38gd7m7Dlc6er2IsrdByifMtASBSQAfsTf3UQ5G/2KOqxYYoOAhjCDbDuyc2QhMwdPLSWOhiSbJ6+cgfPCZW5Q7clXiJObAJEdp98d/kwJvozQ4C8hqiSu3h5jfXbEqAuDbNrTNjUpwtxmuBEfIPTVKEGQzSWyUJKf48wOmAaVIUtIRcwmjH9wnW0hwi+hw8tp6TZX9V+HEKo2dwJMaDts7SCzA84ySaWceme0o5/T13uWH2g6AF/McP3Qgu6vzOsgBZpeZGxnO8Me1nBPugmLnN/C4iGLpJehKi7ob6CImeALHhc2iTyaM5ZtyAn3CpePEfl0/TBEJ1GOFcCxKloz3MFkNbgsFU3Ux3WIqZL9t34Skb5ZRiGAFLOYjvV4hKlpgVzy/YKCnwyrWddw/vxbFHDrZl4UWJmNSo2FI5LQgtoYiUkMlQSdf31dwg6obJk90aKLiiuzel6WwGaukjvbGO3gQkw5zYSeXHXf6572rHOtgSesIrvRz5WMcxRWnP0KzOvWMlF+Qvzvf39Z2KM8rKjL0gmodG0M4AWE/44omY/7g7cJ1yhAcy7CN7MD3MKBdiUjipUHsJVTrGeV+1sRMyYjgrsgo2+Vn+ULpiSt0mEq9nSgBib00XbHch6grIbRXsSVKaqSjHaqQeSqI5BHlq2xKeLY/mBxU7RVg/HU+yptrFCEqxeOv0ticvfxuJ5MEBDdxB+7oFPDZPpRwVrKSTBRY7zly72tD2dn5V2DyDMHuTU2cQslOiCdN/TIDOFnLUh2hgfNZbFfVqGpDIf4SWYLoPwcBq12hjhukvYUSZ9Ffk2B+PO5ti88UAAPBGcED6d2xmkMtbmkK5JuXgnHLVTmnhurC2YubacCOdjcWeXYWf6rwG3cTrlupgPlKvIqzaMkNkx1mGq1TRVMX7aiHhJpX+2zwGwjVDK2s8qzcn8C6/UFIV43477azxexHn2XTbqR4dkpz/aLe5BmhVGqnO79XURa85VWUguGazVtrqd63u6z7W4LwqesVA5mQ8wHciSRSVSUcbkF8nDmcByadFgd531yuJ3SqyDqJHLxfRwCzTFP1Qr/8txULw5jVJPzo8IbB2r/OhLBIOKNN+nCM3H2pvz+LcLW9TNUpAwQcq9p9xMJ9L91t4o0WgiY1jVykisjxaHeOtZ0rvI460ykJ4x1SLrjvfPFb7ROD9hg6eMMn+9mbu/q4CBEu8oTvC0g23Jaxo61UTsos/LNAozolKnvO+KpWpZSHH5A1VgJeUmVfdBOcLmFj0z5G3hRHpyQxswkzXj9AfKBe6zbAHcCFhVzyaeOYKKAv3rG46/GbI+RTRAdq1WztNBAKU9+DmaB+lGSFfFRmTRMy9lZ99LMGns+QdGYMbhKj24UiVz4ipn/6PSu2QD8bDy5dA4fMw/XqR1Rix1bWux0059IqK2W3jr/1FaP04fctV5txEhRC0QoLe1fkP29DULS62GaePQ5hzNZNg7/7KRATLshIjt7XetI051eNh6rGzkkCKOmgrHx5HaNdP2s91yiv39H8RuDGrBkn0lc23TQ1UZ0JAF9gThly8W1xeDlI7qGGGdJO+maxoR46G0Onsf7K72Nqc9dae6UcxgyUSxokzDnpjurAMEKQMZkvtRBEvqLuqR1YF9M7zxa0DReDfr1d8sitM6/lk9laDkQMMD2kopSm5IvPpqvQzkDbXsWgOH2JvC3EmvAUTgdtEQECdPDkGkr5t1+nAa6rzdwyNu4/nI+WZieZwAZUmyIkmkrIm64bjm2eaK3sIze5jY99vGdRQztYXbD38DPfw3sYX2BmOy0cQHfDFGtgWq2jIOor1gQ4Ckoq0zfo+qu7Q6aTxRmoyRk1TuWcg2K+iM3j+2wR6gnZEZVs/vnSumeCzmU65TNmrQi3TF//lheW5gc03hOMl2N55fWykfCyAhplw/C/svZDZMvEMIkt2Rr4IeUAH462cmaCrfqivdNzBmbdVTJ/+sjv1jtwxTH8vMxxMZIO8Pfb3/9of5nWnNmXIgFCVuRc3FNG3SwP81BQ6gMafTsmY9cFZPL9OXsEIBk4ZJLS7ID3bT4ty9Z1DU0fxdHYNzPkG1fgCX46jo8HP4XFtcQpd8q6Vr+nAn65H/mEucvsk0AOn4FTNiJeOiEuy830Tuqlvs+lE9Ddgm+0SotP4SC7QIFNjyaWe9tzMRCQXo8nDa9YcNg7hXGsu9hQdJTr/TzOOQZnEhMN6+UBv0nHexOYQneo++Ky7dBphRxWCm+jkQDB/CZTMhtEWmgCYvC6SvHclQ3rhl67GReB7nEd0tZTuuTLyhgyrcnboSGkHysNiMIonR+/rosvMs6zJVEiV5iccEwkZzhCySWWDEoaRNXhEtF1kjsQeUO+sbtWFhp/4AF4iyFWt3mI0CVRSTdtzcPe3m7vvU693zN05cNhtwiE9b9GN+aw+4PhQFuEDSRjl++BaBiHzM7Kc58xjvTbR2ewjKiXkCIl4HKLkR8hJjW2UyXQIsITJs+C2QgY22/rmq/fHpq9YvsoPLCxZXzTKIPcKaujwgOczWMK2SB0tClxYzkv80EIIHkxlmy8pHBo2oXcV9HNpGy1GqlmQHVbUzSJ1+OuKy9zxOWjVxxI32FOjPH8Tg5DByVcVPQ+0gAao80qBZeewpb6qVz6w1SGaIAxA7kzlCfUCqLEuz1k9ZeU8DXeFKFjIjnJpAye7QcOejlTaT52XIhAv+ZwQIEpokNpS44IUh+xmchkepxWR6gtvG/GYPNHNYDk3BEq5J7xZciUEeYO1hH5kheq1f1UI0tkBTDHo0ibnSS813srJOJy9ihn4hbFY9b2x9ZSQ4L8I1oxKbkgzz9Mr4e0xUG+UR+1Afo4MZFofpWQyCFr9RusF/UEUSLl7hswkfuHNkWid9/h6pfDk7FIbInkk3D1w/LwKndNEhwBybEcUdCUMHDIjPfKOEnkb5MJwY43D70RJWMrRRxRO3yN3Axfo7qtTGYLmAhajWNwnjzfp+9FahL+2wSnHRlIpG4eSf73Wmihd1QYc8rIVQ32W50WnFVYIup7IDQ67AtDmbo/a8P4NYIwiuY/CY6ztDIEFPRoyfo/SpCVYDnfyMWDvpcamFNbP2aYG9HSR6FjWP1E6xTaMJRxeC009FgHke2fxBOegBA09eoTUFb+XH5V1uaWj05ODUZ19tJCWAyhckNcjbVgq2yeO7M61asDNHq1wTj/pCKFovg3bmWGoIYWybOZ0ZdjQrStAOMGo5B8+v5vk1cjf6CiUKPJv9Wp/A1RyaCSNf4AhbwcVlYdN7RQpP/Nbeg1aiC2AqKYNGnbjxDheN8ijGyj+5kj9cnd4iWinRDAf1n+NArJMKAsbcFx3UWcC9y8dOF7LmekkJuUUQP87L17KySZMflTkImBsQqhKZUCD7xiENdlCqY2fFvchlUeJkG/FFy0jW4vuuMl5p4cgIGbKHROq9/EcwC/y6Ch2yD3NJ2W4d+RBQufdGpUj1xdR/6lbPooQNyg57fEKMvRuJ9AFqdyrGBaKlyv40sVByeTXtkuVtoPfbwfn+sXQdAM0iJTVxNGJyU6c8WdlrtoKfpvSjdWIcfQsUu0BjigauFknRMey3LJPCcHDWnPbSyTq7KLgXst8i428j2+89EsVidasI2jr8kkXZn4OdERAAHNFXeeYXtUco+Cvceme0oWPUgaFn9v2eXxbTGEoOOB81h544wXlx8vhprb37vcmLos5hhJoaBoabnw9Oh4RJ4BNyglhzRYx4TzXnKesRNgkG/9Pv+7ppAH8DEYbjheeORTliXjp84PqTHt10iX6wS6/Jan2hnvH9XefXouClzHXn8yq7PKmtrKO0XGMJRLca3AYq+W48erGEvXVw89eWeUd/312TSO6p/ePLyH8xGz466H+o29pAMI+JUD4P+vdnGYnml7hBE4eqLeJPP7/WeMwSKZEv1zRd80rQAp4chLlXiAAh2ZHTr9V0GSOU8knpLndx9ThGuua7aL6S9hlYUklvUnPQre+nhcG3JC158BXPATi5uzJ7YXsitugdAx1x+zL9POJ93aJgbclE+/euoZ0w93bvLBYgUJ8FFoPXwuBLSpeYzckvnPFEjsuqYXMWApWBk9WJU7Jon+Fu/HTg7LazZnEBbmHXLNjBOtCYm9Hv5s/xBhFTJkzrRdSfCexjFEu3W/FK3rrMbCmUtHmAL6SZpjDpQKMHECjxwAB2mgLDUn/okoXZp5eV8gEY1LAskdtdESQHUg2QPjwwSvnJ5p7C8vfsqZIKVNgbrQ9rSuzYduAob2qxDuxTVswDNJAJVnopQgY+rhUE1qzCK3ZSAXrSoRDcqj/zDM6LopghxoT8DnMx4J6+uAD4vDRhKKt2ENry8MaOC/lEcYAcS+eE7WVhj7JVSny4MYonqcWuh9nmXp5xs75cUC0LPD2B70ABicM5z+A3+YT2xZj2QAGTwkx3unk8UMA9FpRmtdAxhVSOSblgfmnBL3N0W+yw+xFm07iP+0SofqKFnhTi2tVHIiHO53hBGs741aKW8kyJ0y90p5PmBGCVQMaCwdAMDLZZGCUzCnheatjadXOJ5DShqgJzNBc81CrsdFsGoBdMBg3GPxiR+dzKyiT57Zx52Ei4RMky1hPHyF51rN/M+pwyF2X6W1yMQ7ZrX/9vM4ncAu7Ty9QVDQ9gVy7DRLlQLi4RI2T4pw5K7miiQYSmCPVX/Ua0z/lVVp1SmyFtJIZ/CBXL0uZlsSX9OwHb61rQNSZrZizVwD/4xc+LztzciFX7YSRDdMGrx/q9RSVNbBHw5ece1npZlsSX+ZK95SFlZ/sxQxVgN9jij0igg0laqdRpRpvf9Q6elFShy+/gEqPM0OxkLjy9N3Nnyx0jYHG5ESgSOhkVuFyDB8GuTdE32yzQO0VDIqjEyc9NGtZADQbIlhqS+DUqNY5XpOOVFzVLC1k8nm5zvn8+b9Y8x6E8Z9OaRdXDsjgPx+BOWxuzacHzJeo5+YHi58vzWgpuoaQOJ1+b1XWVwMce6FnR3aWRVowkpplNJoMdfhb+asGTmWW0Pj721OPw5Ds+bE8fabojV5Ds7QbWPJcSw+BC1QMAAwMYm/0NGYf82c0hmldXsCd4qteESn67cd8vfzP6cH5X48RwknUQm0RlyaPPAWRTB81N8NpNz49E7k7kL4ZxP3eOxLK0itxGCKuGu3RCOjk8gSTiu/zg3vkZHxBcD1yh2aYdq44xq8jjZxvKCf22dHaOh6pqRwiCzhguwy/FVBdUBd73nlZyReBFATeu67C6teCtvInUdRtFer5tuFjXh8AvPP4Ot9IiprbcrCz4rgwM9u2S9JoC0SvK0YYQ+hI8m5vSxF8JIdxLNkZFdhCA9W8rUsuBJ1yZzuaNUR4Y9sZeCk6DToV7Y8qmj/VNOWLWqfVSP4gVMayThMtRnCFonE1+cc+UM1m7fxAl2TrF59T28Z4u5AMqPO/0eJzEvjUBgVFoknHPfwqvzTBa7gnbbFlQJR5Rw9OcwJKz7EGhX6/Ox/syRPFQpdIDhwOaGNfoiKdhEh3X2NztagWfpTfmU3YYLgTFs5GRSHGNRumxXnhK5lz5x/yntAmwoJTHGika8Z+lUP5N4kWrb5Gd4OzEoc1mHvX8OekHd6EyLst14Q2jK7vRXZO73i+l964bsUnyLEmQtGcLwZSIO6AG5WUYmlchq4IEa+G5RxcmeIkIKlwISh8VhHkSh4006Bby4O3MkHA9n1f5ip9HgTd/nPaZ4lHeM0D6ft2Tr3y2VfrY8UfDynluGpzi39zYI2SqFUFl3AbK084t7TEr+OUUPCp4BTVEKbxjjyBSWLGsNVtofOY998pOvIB238BZwwCdFsuLjMQ93eE23VyK/OjBicmOHb5PsVz7pXunppX1L5aIlgK1vgJiX2ePGEH/tVCbKQzEFma7CnjYk2zAZsHZWUDHa3c4Wxn9sWAoB6FvN8NSpf00DyjMJd8qaCSft8z/tMi2TrjgmtXmVJrBFa5umv/t8Y4OyKBozzPVNOA528Jl22TKIAzMRrsUlOnBS2467QiC47U6P8TnuD+jy0zjwecoVmtINW9ZzTe567+RV9wPjatpy8f1Yvq5Iwytvmp8aBYdnusYeEwVUe/+As0GFULAuvPVURVQsLH8Yi3TW0uIcsHqf9oDjgcYcAPlg0fQqHxLYWhq+4MsIMf1FA/viom6cWzxguZ7n43vN0Z3zKxtI0ls0uTlZZj/+4IbT18mfudjHJEyhIzZQdgxkCLqsNIpFpJZpbYCxp+p8c97D3So5i43KYXR1TkNAtehv0UMOrsxFyYVokaHQ17jamX8/nrJQ2iFF6hfjzP5D2aoaBWvN5kyumk21SoftyQeKGui8000VNzSDi/qq2L2ghyjbKl0S1slCoXaRXwqTy9hisOwGpE8NCxvGmWdiBZkz2mSVjhTKpGIiuNd/cHwXmmi8Y6EcgX+s0EIPt3w04pTzi9j8NbP01sIT71dhgwZYIxH2+Rnwdvq0yUtUZyncOqMqpFuxVikH7WYrpC+hOK8zRnyiVIfIismm6u8dsrlx9fvwtoCPSxduerIj9j+XX0x5mmPPMhue0Zot3ESsuxm0N7HwfDz3p3Aeuj2wyWtjhvwWhZSyoahmqF/7WP4RW+eZcTuPBt9ebe0os30lqYmXSD9qNaCAcY00B7s4WUVQRp5kMm2yPfV766CzbwrTGt2gMH9XAD4FfAMyBHqJ3hC+YDFz23p/1DrnS4opgDDs6a832ZN3DpI6E+pjazqMasf0FF2nDMU6EFP47rNIGaOr14Xo7ePvwWlsw6DF/WVcrd1j9gX1KI1phE+pl1wsovlYzQ/MuuqZABC3Vbhrk/ntC72z11GuVSYJfwoomVc0ohQFPrQgpO3AvfxuLT9BpVvVuRnYdGBJgiw27CRZpoah1atdg3+fCPui0KNwwzEuXYr8sRFEMjx3HC+e759AVgMN5/QgPuzVBgcI7FUz3dmfB6aD7mIhGtbGTo280FoKscqe2Vot/VC/kyJBRH8xBep86FHo2eIJ2xkFzEYNdyKqD+6GuqkSRMyV95vX39VGWBJykIIQqylYYa7nAa0/qGT799v4eJ97LnjiUD3uLvhzzd9KkoIe9nuShfYb12Vx+Zw+Uk/UAQuEkR1zCznRKrsZQ0vp3PhUORZB6p8CfIwhE24yDd17ckGUHK88NB4df/1eWmDqK/OoZsBu1udt0nkzWCS/fFKEOMod5NrlsWPgKvO1o1ASg8b3Xwy4AcHrxpQhwTmOHX9ZWXZqPq/ejW7ztJpLhtU6tGkCu/VPhlyBAFGsAc4No5u6msvphFqLNUhLe37dW3SidZcnBoe/zNXxD6Arp5oP7ZTMiAiFd9Fs6pjS9J9xsGkGR2ZPxD43RIkx7shJRTXMeerktJUtmOO9fxmwexLpQ065EHXqCt5WfCFHnd5QPnBVoUC6MfqtpIYlBFvhTQyHPd3KGh0KZyJzT0Od/5rYzD+mH8QD1a3gh5/JRxZKYoAVxwbWZMtaeLnlavTbMAztdC8DKCqwneDBJh83UMO3esq+l+cSliW9BRrAa7Afym4cxzbwB7qi9AkINnbiOj1W5dWmVk2LMokV+coadT1QIzaYVqfia7DWeUxeShEfgd92q9AIBPCOaUURgAnZQZpxmeKM36po7lKh5LX35gK7X6A+AaRYPu8vTF5uJ1W+jVlTABf6TmLXTubOXO7ICvTuSL/80mEK1orGtutJxCyqNpKpjSAflvK6bPAv1do6HlUDbeI4cT9Nx6EJemtIY+jb/YTJHJozkVv+de4jKosGFz7JZEBYI6AJoWw7oTJ4QNJB0tojXkIN10QBBcdN8N8mlQqD2kqGc7TFu+7RMMqJMtjlD7vJG2xS9TSUiY/kRw5JxxzUZJjiuD38PH7s8sT4XaybAjRTQsD2y0s5Np1J5TWx5ggAS0fZZR+01ivuTkjbzJWrYfmDieTabQ5mW01JBrEx/cGug/38YjQ94YZ5WDMFTZJX37hhG5zAYGb6JInZXwcue85GdS5k1aryysVWY8VsZGNi0Ta+MuCQAk3W1SNUs0vLx6DvyAy3iaLF9DnlgNmMlFrhmtAWliKt0MF6SMXLqzsvqboU+nfJriPGA1ueWtsBFEWwxu19B4lShSwli87K3T+S7Yk7tPR2Mtv19W+syD1FK8f6vcXr2EPuvGChQUEE05ttIqvsqa/8/Pic6ABWjq/PcBDq0iPxwMVF6VJgyb5y6/SzRehWIPkWO+jvoSzawWSiVZfDIkRaV8q/O5J4HQj41wdQOqAWVajROW2lQkTLmpmsGX2kp8/lT9HJx5ZmMaOi9EIOG32nIZ2NEcLBOMwpAuizxB7+b/bcglDIHLaVdsA6NnT02n4Vn/MOev6mJ309w1Mi9rawZvgCMcSw2L4oZBB3IjEJSW46Wu9/51adpecsIGV/st9CWVidrj8pd5mk2XIKiaq5ASMaqt+eFFUlLGHMEeCOrcnbDhqZJT1CiKB7CEqTw57tNoMmRer7FUT45t4k0FxolnKotoxMMwcAdjExHzcGIKIiSR9cP3ilMobulr1xNsHNSfvRsKBH0pqiX3Yzuzl45QsXtOgU7qGMVUARMxECmCLJFyUYaFqFDzB1rKdzEu+aYH0iRv6vkZR0TDqGDJ4vDxGsDVNmyPcEewH8Cvcy29HuPaDSxY8kj1Okq9l5s96Wzlg8wPIQi702TXBGzA76MixoqZNJnJVu9tZUl3KQ6tnSpGU4PCHIfnxJEGnBFDln365KuJ8sXpfjceKXKM+uFlilIDuHirw+QPRP1OYbbHqMRuHXHavNhBfLXHFQPRfkCBFlYSE0wYgdYDNHNvHnM3HykkBl+j53LsoWMM90plA2URs9q5ugFu58ri6lcU6ptDc3d+jEi0D6eM5AaKCtmZsSL25qVtCkMCbt/7DY2yPAJTXfy6Y/HCTh8CjqwQO1p3UlAChPwISWVEtJq96fd+NdN5Z3Be3ShZPV5r56717YJhiaGJ9zxVq5ZJOnNOheNCdFuJaJk9tWqkzhiguyGmziwMCwCloqxqMJyUo3fV4U3MGmBG+pTGJa/wx3vUVnePfcdN52PdpM4DPtf9/70qMrnvxb7h9TO1xaMlhqtuhd4aO1movvlOOyDnUhVSxNqBpCcOj6Ub6bdRBFSWeeBJf+IB4Y+lIUXC/lDRzKk1iFlga+oR18Xl8461m6bZ4YnidlVFXw36wMMxNrkeZ52YwWuvUpQjir7enT2mxMSzfutIH1HmfWo7xm1eQrxCV15H682ubjzUV28Qq54eZgTaEzSCr0dS/8v/wDZDtvPtbaI1/nrv8OiZnNWhYyqQDJVNTGMMH7Vk8Q9/i1JaYT8QXVP86fTWpNk5qKNwDOswu27rEsvla+A9wymXXTqOCfFSLZivPI2zMaQ4exUrMz2+viEJk43nIpgyocrpCkW1oPZztp1szOQkcCr2eePDZhJpZjJDEP9egYN0Epp/8NBaFLI/M60Zk81qXLxaNhKD4JeyqQujciUJ9gMCxpHH9EaHFFYRZG20hNCrNs0GiTVDOmS3bwiENU2PwavOMGoKbraxyw2DHXawM+YQOLUhxR+OD2gpsvLm+apRvMEeAG1eP+ANaanjvQsZyD7mNO4OpNNihOSRztxYMhpXGVtNnPySETJMQTEkuRbQG+Ee7ulOQErHZyp9pRuRp/PBwIb4BayCREksxUcsR/tCk7IiRSdQYzz9Cl+m7MefGO4+sXpYgrJS+TZdflh641DkZA8Y8c6vy+5NMkWX0dowWqb0C06mGXhLEyG7Wk4ytzuMW3CvfSMNssQ0LZnR4sF04R3rbRqnVoTzTov5IkrQBu4YlAWwJhfMGY2cl4fOzS2tx9glmOuhCrFxaMzkQ9V1+Ja0874S/jdwjTh2tltmE3d6veRbtsKDpwVUiL7UArSHDekLkYodO+OgknGODFp2Kcif1jn+VQANYsCA0a0dMZ+Sm6Dj/jB93um2IzCLdkhaZLjWo70AG2S9ozw0YZVc6FddnZAfquiTG3QP1qcDsp0bG0o78s7OvjvBToijeayw6x2/sUfjcF65nxEMwpSDsf6AEcwHkw8T1H2s3CfOvfaEs4nrkrwKCf4c/wSop6/QcEYHLh40iAXcQ7a+/7KQ9UCtlHFLVbvjNae4MwPfbzWx/P3YmNVdQkmmChSkPkZCL9XR/OWPku9TEqVro9VBOV8GKx7fh/S9rWSXrjebkx9rSInkvfXesf+aGwNk10p/0lKx2pUoKp+OO6OzLZnT3LndRSuRMgbUUDVqTMrPrUP0rXu1OSvPyrerAZEtV1ifk/L9ZdawD4ApzN9CBDBZDD9KJn7YfbXwjffiP6Nzh1oHryoF/UoY3Zn6v8Es8UaAn4FsU1hkP+KSqghS0ZnE4qit91FmazXWYNPka1tRS5qdQl0arho3qWtxZLNlNBMN/Lcb8iLYcjG0WoPd74osOcwMfYj5SEMQIXjg9ihynRu3m42dWLcYfR7732KzFExn3l6mZ4mDd2hQ3K7efej4ukuO6RW06g+DX7Kya2koj2uWhcj/wAXqujCq/nfCZbmT4ngP9Nln0aqUm1D5t+vesvetSx/sZUhTjyakKhWwvAkeuT1zDea47L3afnr14fJMWJqa9cX/RwMPTI4Vo833P5i/5/BEkPAA0AM7sBE1/lSbPuGjNS/oYgeCqSxrhTd/VpEvBqBh13/s2fa9777PGaCu2xVRmDP7+qH8st8a2gjRQGZ0Ap7wJbwU7xxDxKS+2/hpn0PXAyw+PaGVsog90tAGqAvg363DPdM+zI05gXzhH1o/8r4IF1PSPKjf+alX26EotimI1St0APnLLoKbuE/ZDbgrB6gmMXeT2eqsMKtD/WTGzjrw+lqGdW/zgdw7m/9SJ925AT3ipa3d8729WjQZ2gkilHYzuCpR1ptEQZQW4lftoz8IbrtU2i9muAa8pNfugOXjLwpp1zKxWQOb3Q6wGRnzeaZUSVn7eS8y7P5URpyd3rIV0xCqo90wi+UpRfato0e699izxHef8uIb4/MKWUNu0R8y0Jlua2J0w5KJHhRga2SrZM8ZQ11osrnVadBF3yI1OxiYQrFa3zkrieEjm7WHXDtSfzjxMB7Cxzf8vesCLqkJ4P4DadIjp5lP8RWNilRAzTm58HlTqXbp+r/1PnzH738THyj39m4TyJ38q/xIl4Bq/xpFLF+68XaAxm+gdaY3qLJXCut+sisQRCxGcbnkrBOV80RgzQmDY4c8fwooaEWBjSNEgY9tCKh9V8IDCofDfbLoQOrPQ0amd9+cF4HdFqmC3JllKzMpvk7dKo70jvo/pHE1mB4VBRMqrPTagOLCOFbbswfT/DqtEo0/ScyZGc6uB4hnb61mp8QPb33T5dhgdPcLlL4S0KHgAKYF0vJRDsjAmZb0gVsfKjwzt2gZ9aj7I5GRGdxC1oY5c55ieOMRT94lZpq1dI8U1JsR/BhyYAxebxkDJkJx6/64uhKKqrqNClLIYDIgGvCp6g3/PeRVXeC/yt7b/QcpEGBznGtw9SyTg5d/iURTVpPiJlgk+vUz7IebyZZOiodHYmwZ+up7vQZsshmNeWvAeeoXmboXuwT3ZhzhFchSdjeCA64A/ncmJd4ea2JnTV0BgJcBXr9OIA4ka57AuID3WGlom/kJ4qpiZmPX8sxNDeNj3ADazZ8rOjrHoEcMP1zVmbuEYw/dWVCvnoYlUJOhCrI9tb5TzZdhIj2HgevUJIEPVSNzN4EC2VqAH6NCq0VCru+vaZbpBb+3TsiaON0qlkXGff1cox9o0q0M1tv2a17JbsP6D3J1CaBCoweTi1KGP62GwvCtd2fdV/u/AMZXTV6MbsezyRlS+tkGsVXT9qPUPLg44wIeGlUwC/KNmVKFQBMmZUs4fYwU7cRiHIaIvnYyqZ5kuslznwTkh6gKo85tq99LNXXM3KpEPQKjQ0WKpn5qkxkckqSBRQuNbrBubSD8Ixlz7I49+AF+u+19teJBHI7qPvBhcGAvDMuwH9JggJj45GDY/8hCJweIGePkqKyE7PVO2db+W5Jq2KQ2/H5Z4tS1YMK6IoY6D+KyMZ5l8PCp8X24melE7qPWZShwiUPJaBhXPRu+C2PtzsWxFqMvgL1Nkxl3LcbCGgY6HFgMPbcG/lGGjWCkk31BbxQkwF6iNTErU0a1TYiGRI45x2WjWvYgNQJRBdFLCyXBY/wZVRM2FCB9/EV9IUzFFfQ262QuI45Swn7f4Ooga+TEzoVLWkAQD++hqj12HKIpTEuSyd7APks4rX9KL+5DQHentv11qKzfJIIMo3/U5ucyEr3lXAt0UACazuVo9EuAlcgPr0wP+n1OfaFTMCcD5PHYzfCFQ+PxS4D6tVI15I6+0OjAMTQukUjICUF1kSOF2GXE+7Uq9p79qNaKSkU1QsEUJTaxVhah0GX3x7lOtEznO3358hmPpqd29isoiI2oXMKsjmBOZXUeAAqWKt7I1iv6yckfDd32TpU2j1iZl1Ll4TQrVX4K76zEBneQoR8nzixKZI7uvWy2YDbQ0lDspMhFMjk8+mjzbIfTwmGbm1NXpElmDIEgacOYoieN4Qak3LTemyUNvYzNfKT4yaEUJDvKIpC67K116WhxYi4Xg0Xy0wILeX/8qVx/vbbHsF/qyCwvVnsvLzK6CIcQApuQmB6Kt9OnT79joJanAJJxaLCjcDUeR/q4BMrJrnotLIecbPa3rY6mex60gH8W8n1lbIupJkV9Dch0ZS/rc2VaRTZYNus7zC7i6XBwfXorWTgJoOvMJZyYj5J0zHdu51J3/+GYpmbSRR09MAIqjIXWZw7qJzaVbbRgvV5wHAVHFnHBNQIr3+PZTxIvM8LhYGGk/2V1ZMC8zTTLtX6OWnT2/yOlyT+7ic2mrrJ+jliRjPGoPyB+chOvvNWUk8i3+Noeil8mbQbO/vObAqYJqiiVTwSN1NFrt9uKrp+YNthZAvbPRD7nL7hdUVOO3ZuTx4oob46TIr6tqWqUh3S5UdGdcufRTF5f0JKiAWlT99+aHTUFiqybhN8hFQahXhdEdmPJJ6xx/RnomGiA+mDAoZdxtFpEGBTKhCbisYzDCudfPt8/Nul8adQK9WUUu+nfjeJJpvdnOc1fBxU7LOsutRafdpE43IIaxd6fVxz9YOHaxuEH9+dbjpn22nLvAsGDkFfJpXhlNJHywNucsrziBYM5keluYc943L/Xx9I4t7SDeK4RMzhIbzkWPgHxtXjSC/wpuYIophwAIbuxGQgxhFakiD+LuNafvhy3VjgWomcdyp/kMfIANYm2MLAVSiyqbvZUgfWtvUTCuFDB5UqUOTrwSDU+CxJ510ePTK5BFHalXH4ln2X6VqlNCSqGhGGnitcoYvmSL4rmd8XGOFrd4YTabF+28cGJP4sL56Omg+R/dbUfpN+ij0fXd2FY7JcG3UFrvzI+SozCC/DgC+agHGTwpsBCLKAhtDAvlco6sYcorfIuGKzZX6+iz1X/RuN1odSRfVY4ygfo2Q0yDY6mm2riJji6FdvAMgfe4wKsfYUhhNew1bwe0ceiEA0UXm0dXx0vJDjnEENRmt2ONCzKccdIk4Vgq7N+6t0hNebJjXyd+zugz5Cqgd2KcDSeFls1ZDo4C/UaJ+N9R76ZFg4qvPCsDzXSaoZuVFI1SBd1gsBC0XlSc6u5jeUd7ee6r9lIBBR3rP6L52ce8oa3ylZxUSa1V3W/5YDRn64Ino8nuiYqkgTPL3grjcFDS3st0+eLV0NvvR5qHDqhOmwusG+o6TNjL5hdziAINSx9vLqMpVwtkA7/KnptbqcRNUjJtQR9Z0SU0chrXEHD5AdYLitafgRWwS+g6aJv8l4R5GpWN3EWLqexGJilLKJEXzvJc5l6yZ/+lX/qQ3/1j6jartWgR1pjBUhEGFjqj3yULlumchK5h4Qbc4vme61mmRv6VuhTgcSuhbPMHVcn9Uv8KeXWyta8808w/paEdmC41mofIvJ5OqcHsWx460VNLuyzUXVzsDxZY7FFJyH12CMeq4noTT0UwOPsuiRJDpL7KjnXpXlYTRq68oT1sy1zOQGQIKueqcx9+bLb9riI54vhkyNRM13bcKYb4LS+kmJZcUPVuSPXggPygcsleKLQG49wNvIZyct6T6yG4iYDtZH3Xu5i5a4nhi9KEF8z2coiz00h04DjW1SQ+/xgU5byx2PCxLBy4wGGPQCA6a89mXpqV+5cnme+rebR+wOhYPkKNMurMIsCYytW0N00sZp6Bw2qpmoCtp35Ko9Hw7R1DxWe4mqKMT2tBpLj+/bms9Av9awW//I6+CYR341y2jN9e6mIWhOYkikaGpfanQTPE/aKZnTrwsNTR122mNGgbx7OrtmkicvWv6A6w1qVeHsW4npvjCd5WX8TZyhh9kKXjbTTwJBx+QiFOHLtyZ4KPTTzoa73oohQ3MLvlqdmyK6PrMb/c900ha2zoU1wh3itfeZLDHFtopSV7N5KAguXpN3V2y8Z1csb2VV+Hmod3oZWDERdfr7Q2ZqDZTlt03rDLWncd1I4zw0BMECdE2k5bGxXYnLY8g/0oLZjbb81m4S8zUjmHqQPTgzEm/ePrag6p7cd85lIcCkEMBDMSTDau/bbZlTiGNqbJ5LEWEOQN1b/ozh9rMHj00ha8Fc5/e0YAnyIOn9etxAWbg0ejxjHT1koQseH6gYzDRgbtM7BBqLwQ3nuf3G3nov2HWlLwgnXYoPYHAXVFhFjia5JcHSZf4YXDJxMf0vWD+nWNouRGu3zcMYcx5Hm6tUHSLRaff3bHG/qDhYR8wzet34n5+trGnrNCfkWQEd79OV33sS+GPHA++f3rMJ4ihjFZc0f3ijWmAdEkSaPvNwXCbPmTivp0rvAxFiM01eaCqaFTUPxg0+PDrgipZxr9ZBurGPD+uNMPbklo0YkrtbvEh9C6im3grC6ekgJ1+GpH/zW0T+M14NkUQr7Dn2U86Qcx4etZEIha1vrlPrqRmvWWSGoFCYKZEWe5fTkiiciL+0ZCd8y5t5YORBWOJpJ6nzPhYx5ZIWMIQOs1EG7mI1BZqMPzOta2/qf0wGCLeYxUy32ziS6SqjlEG0jZkYkXIVJ1haJslTLtKaMnW0BkAg3qk3okiIiA+sYyfdy+pwbwOeFw4EzXaztZb8TzCS039/tqBAIhYgwBgmBtQZgbYuqkOegfSY35x5FHvsas2cvT4zVEtekixG9EFyABp87g2T+DYGrRM5jkj7rpT+oynL/vgTW27fJttNOeRT84dpS72vDE0VCOOhFzXi2YOI1fff0xHliQWIb7nwGgkSMFUG24JkhuefzzRrh+058OnUAp15CI7Xt1r7K82muVQiEErx5i9I0vjBxcAxowsgUrh3pDICscEcFQ6Ds0X5MSpsFuQ8qx3P2YE+2j5FkTZeSSXTYHxR7exrWaEhsMlGtb6lgkv4NPZHT3/alyP1oplsnICpFEgJY+XsHip4UBHD8NechESwKL3mf2lKF7A+32z0TXUZYDgzQKvhby7/eQ/U4zGcInpw8g5UZM9sym9rscGB2xduT7PNlnU6IdXlrEjeGPGeQuVHttKggZ2HmS7B4ekFCp9PB+O0oP61E9NGVr0d4A0WnvnnWYQdY2Tar5WjwIeDBgkizCcJeT6U39ejMKJGIG18nO6X9X8HaMu1lKPzNSaR9Hv8p6h4e9ObON+VdpAyyB+vE7bwOSd3FLIzwAnIDpBuHx1IXTQmKu9ESNLScSkGHwZygsJs2uAljAp11jkRG5QGxkqsQmPe1LE+2JBOVASkMnsIU9onugDTenCspUC73WaCQ6bP4LCic5BD73xUAzB+RbbmxEA2H3+qe04TQ+3UKApvKbA6zo+NfgAzCxM9DnJU9o1SlzLTidw/k0Vhznx3CzDW+P5TyqT652Nx9I8d3I1j+diBvzA+Gwg+s4FzW9LSa9jL02q6qHFe7Ub9EA7i4PWWH2EDTgMy1vQdc1YsPSCM88MsjMdszUkdycx00rLSGn7kTAbS5pmQzLEmUXe0Q+GMos5hIaEimNaJmdp6umAzDhq55hpNeVFsHNQuUYWTIjqgAiLhQglS6SDNY5gdNdy+cb1DFkF4etMxvPEYqkENRkxJkrAaoYppw3pOXh1gFpYTe1Doe4ZYhalBsu/K+zRvDh3aIeimcFVlY36TS3hXzH3gE+zLYPQQW337AAhsE/oe76+aYYMiEt+MuMcy7rhElCpSmz/8c4NFUYHlDLCNd111nES+tNpD5AFWq9ozwyQF9gYBxQD0tQwmD4ERdcI+LIze4hj7HbrMIRuVIiyD3fe/OWbSOa2rs14ohN06yY0zZQ/JQ5KBJfXCfdTi9g6h6/NCnioEHuTSxOMjnfpscflgN9d0jiqGhcv7VhhvYSrDYhUT852Ctxkq4aHV2XA265BOjUVWYT1Lzof//uE1zg27MsAtjs07KQb2wlyfENjnId2tWro5JOMbcbB1xtEhAgTIGMF3PN0A61jPO6CJ3qGuxx5QKCWfvh5I8bPKJENxLPa/TI6T9oQKc6rvA7qJGGmWG5x57YmrhwLJEmKxHGcoyOT5RsBTR0wtHVP2xM2jWh1ehgI3YuMDRR5fiCFbgCyCUunKilJCjyhYkSjygmmAQMzhdjXO2Fb/S1cMbtPW/b5wuC/SdyJwMV7ELWLr9EUm0UBLVTrkDzxQMcvCF1i01jnH3w2x5kSEj4vM6GOnP8k5uj2GhCUHT3R3gPQdRDZiyYYN+UH0BNDNtrAISNt2Gg9tOpjdqggcptDIWTNrREJzISF865vjgogXyeShd2/vgjdiasz0Wk3kIoElg8C90kBWEFmxElShRifsiqxPr2trTzHCTcjlP8aRVyeqNZEGOm3uaF7xrAzpaOGysj+Aaf6jke6z3cEPe4d7l10Uurt2Vn9bBZuhnoi/K6d6lq2cZwrtCTHBe3EwGWK14wmCiWK8KZLunZ7CmwQ+vYEOpN9rvBFk82+51pxCTHnSyqFdULbPOrfciCQLqCwvTcKuUc9egE+fsPs5VJTVbj3XE7rGKztY3ZKVv8d5ge6bEIjFtmZ508Ztg5cjUs8AYrgXUPGbKNIyn+bk9sbA1xYVVEpdbYL17orP97dMhk4wZnXtlEK93fN932tbBFya1QWGCJyH3bWvy0YRZnAGTm8A3NNDusN4+O2DFn5x4htabwSgv3mU5S2+wr9vLtAik/nfiqOOplgwbq3A1B1KKLDIPh8asBJP/7cP2IPKcsA5eUROkqqJzPWUzjP/Qj0WkksFaVaaMMs5593jvGwgNZ6op1FvzGYqhc1Bp+EsHQfdyDk5jaNnbAw8PLPBc8Es1vJ+q2PwisrnpQvu68GO59y3dnHJ0X2l+6LjQeKmRDi+KbmZmta7PPwgRBuVOX99MCYSNm6rkfXRdjN+CsCsjB3Yk3ASGREaZ/owc6vViocUYGtBqGp9bcMyYw6AWPrxh4ENT1nM4cxG1q5+f77SCwRyTJD8AhKo3fQ4dwzqNz84WomCQxsjexnMxAkNU6IoAlkuj7LtG38kUQmx2r80Ko2+BUAx2q6Tm4uTQ19q/7nS+ss2udeEqCaEWt2G31WJQ0+BCt20EpAU35I+AfDtVINlmqWWlnyoYCrfxLdboWAskcsrjOVAVhF3c99z++h5THi5zqaPsJhBnyR/yHscZjTFCToOvWxOFzpKtDJhttRinx0WTEdV8ydTQie9H7PxjWcw/Sjt/WP1XrMSHlR7IOvIDy7rp0HL98OAI683825O3ayE02qpxJFMrXwSBawfJTeBqfKBMdW01+aySurha+BrowCaFW9KrOTd/fo0T8ShVykk0k8hkLxQoD7JSZXIuCMuortorNCuhcKeSevgb3xz/wnrscWNgbIOJEhTk2qhVfW1FejTuQDmF/aMPUPGbLp7PIKgAPk2bHrJplLQ30zcd3jdMFOQnI6hNhxoyc+fiXhC7k17AyCA7/ca35XNpDL/cyzphOEAjGpH0Vw0MMGSt9OvuV5elmjRS+CUhyyVxks4cLsgHmeTztEo2Z5klj/sCZ6sK9BrHOj1cGC67WU6NTRlFwZhrPl4u8GNa/YT9Axz+9RpK9rRIptsMO2TSaeIOjlKUbX2y0Jt49ax2WQaqqAANfoUZzozlen1Ska9sLDZOd0bsZO8ijjUsink/NdDivDICJsOK4D9/U3xbnEm9Z4/ZQmhsd3U67wJq1Qjbf13owE767nhUasLUZExht5iz4YOTnvoNJO8QtkMuDd12oznqFUzHhcSLX2QUgPGpBkwGo3lm5r0YETIg9v3DfXPXSnklS+Uuz1jyQPL3X7ZSSKKIrpBXLRGlC/6c1g78Yrqb3IaGxrIq92oguNNlGplEJo24vz5fIjh9yxIRQS/9ky2QrUq2YZwPGNrrFNGjP7qSu+z0LIQfJT0EI+NHeDktl2RN0eBSCm922ZLjqVoN6nCalew4n23GdeGfrnOjkYi9gvISEbZ2RQyezLJVlZbZ64+0uhVBXCKsFj2Benv+PdumjDwcmn40OMdaS3k8ncBEa9UkPgRkhbCQUS3OVcsnZ+8NlNaPzxJambw5hd0LRq3G89R1aXzZEMQZhuhtgjBFj+qVZOsRSFNSlzZd6EbgcSgEgNvct+swkc3Sa5ZwYaUtsLr5EqTzMoPoG7J3XUHjRdiOAKOS/0FFPoL2CHZTW/iNhxy28+QHjC9zSto4X4u+WBBl3TP9BCvt2NKO/KPgaorMCB+m4P3T5Z1BenRbxjgn3kt60tS6DZatiT1lmQzoJAHb6lSocSg3WUwiU04RgdyusJxS3yGusQKYajYkk52X+B4rhDo9MZ6gPGlxM9+9/nS+/ghbjHbrYpDnXjiaR5N3tiXDNi8Pi3Guz129MtKP4/S7oiqbM8C9Wre9PKeIjs4fTPPlox71zhG5Rxmt+N0sEPmROajF+81naotOSj7HQrEmVm8xdOf2q0fAl1K6wzLAZr0072onnj88WgwVS2ysH4eQb2ZWGQTK3DmxoFdm1werZPmXrflxo32x7P1Q9STl6sG9H41CPX9G+xOSnWmu2pMHjdgeiE9FzMXowFI2giTQxVtns1VYIhIrAKjBy615x7QBj1WW7VZ5Hexdbfhd2CxTB4Wh01oPsg4FZPEfG0pTWkqiRDtUzJ7SyErPf940UZefZeDenj1w4bi7uSrSTZKa8fL98eU9pHi4RZ4RTjp7NLCmBQT3EwNB8MPafJMz55Ax1zqJFmcLGeQc4e1sayIFJBcAgyRMPiMG45DnoBLmzbSKERRucnxSEEBxA5UsPGzP+KxIHoK4K30suYbkoahZ6KQE9W4oGhTem7QpW+c5RVghLbLycx+WfzK3FYfBmAUKe64xYA/r15otgbp0HVE9LyQnmcao1S5GUWhe5Tv+jxbZQYYE96f7P3RsJje94asAQ40Nj2BtETWMFqJE00cDN4La5iVxYFrF2+c6dWXRgdNbMn+8cQrSNiLHOTwJaF0u2VNAdbB0GBi0BDTIZy64oAwmz4ky8Hrh4oUjvCl955CkbBs+cQKD0fWQv9eIl+sQ2ZYJ8BetJo66F/4r7A3W6S2GlIWfJEpc3Bz7f0P4DzGdaU2XZjmAGkMoZbLhcKB1nx6RQ3izawxDprjIpmGFAhEyZARa814fStxX1a8yog1mZx54OTtzniug4jpzXfk/ysfBVX3GUibYp3mKhaos2+AeJNxw+X5uQFoQL+vGJtY7ae4DuyCQ9iZ6iDLukBZ+N3nhZxGCTPkCKgzEaFA4Monqhv8L9iV4odXPoLtaRJVcwvf+Q07/VHaLrtQA1haVlZ+qE1vLSAU7tzv7vcTiQjkwSPdlGEs8Tj1vTaG4QN6fRyZxojh+LhGyia5Cvw8BLvugjRAm8+EqT1lGPVxFpexqqdDGjtdtODeF9lD8awCfLIhZ9xKmqDGbhkKobMavMm7MmQxpcTUbcEappuH0bRON6IZsevu/+XnlO5FRLwODYF0P/fQnsJFtgfquf1zomH49zRor2KvLfhpEW+vVvOhtMUpFfcR86Emue4GRAdARYJUOHp6o0V6UAzBvYtEWgeXfm1gqsoNeY7Kgq3xQ04+heJ/8C4Lz0e5uBOfoRJAVgFoPYjKZRAc6M+HdveZ5ENtY78zgHz1kNJPOTR1m/3mgC/XmZ3gPAEuuqjXcodLdTcqS0djPVKww3ACMnD7KlZZ+MuFpMhpm0gJU7HvqahtEA4mXpcM95kGmyHN/oIWyuUw+I6s/Hl6ZdEpiMRQjdY/eMkMnRkm5kYCybJpjG6F9qxCkBGcuoGyRQEoFtJ6joV/ISNAkbFa48/CfFaI0kRBfysYW556bRdpayJGit1IYXyhTKyHy3wUE66cvdM0F1GJsmytDys6ZRr7TTte6tEVGGNtgVBxfcbsES3jYv+yR2cY7STp/mUTF8hgEC2GEBKa7FHzqp4GVN5Xif51Je85DnLNiXv0wo5PrQCsvyaXpuauWqKrSwipfCvQkwC9FNGMXyX8Nm/7smE8TnaE1BofVfS1/o04SOe06KCA0dmc7t7EM63eajDqtg5/oPcgcbiEkC67p3CfNVCoT2KMv26Feye3QvYEQAJyug6DJA/cmc7kS2vdcToTm3IqbdDrDWPbl6Dbc3vq50ttuzCsL6iXHOLSUYMRe4HoZnG9vtfYBTQT6wFOqG/UhLmW9pAOSUG1bpKSOd92fUN0cXxIMc6ftHwK43rCuVbsowTj0+7d7Iky2ddkRkGB/EwgusesHy+xeaEDqAP7hL6u79IqHYkSPlR+990nScliGwvhJxfhzSSMfLLuPraNau7n+LepeVTlTObmlOuYTOHJ+dXM9mZEjfmTZS5oIMvoY7J1heG6mvR5kDBehHQYw1nbPPkAznBTAwfslp1Z5TgGFifUezIQrI/caQ/dEnN63QTzu5HBRJ+SIMcn9th2dgk8esKV98YEk45Bp6gS5M3JtvYc9BRWBjEe83BjKtXaVQYwxKSJePJtmv72KKwarfIX8/+erTHqdcb7JwTC5Xdh/WT/p3ZEeGte98bL9oqH0/26D5mP6rhhvOCHkAfLsudMFq8o0eM72k1JluVzCWaBBsyBshV9fGjELi7tJjkUyYi82daBd2QWZk2XTeZ0DMaoN46XvmPnBftAvk/BVCevtrtatSHKWqTUYYXjcbS2Pgnaa/FSL2pdYK/e+t7lJD3hVX3yw1l6zD0ZhdMG7TPeT5jcmh9al/nUGOfxFqe56iUV0Ow0zTZHeqKAaIm4FxU9OySVWRFWr9Q12nRzxf4+GLoP8aHLTOCtBt0IkeK5nKjYi4UZdUBqzzMzgV5Jl2c0hgnEPc7btc0CN2jFVoY0IAYRqPkEZ+AhLl/qXM9EmbTFH8eicy6SF3IPAG6kpia16L415QaP5SnFxBC+y0s5Th8WzReX0HgftCGxthnTrNN18g2qzUww58Bbvbky16mcPCDdP7U3nW79l0GLXCxtZUq9BjQgcCexhJzEqAa9lkpF9L5YWsRqOGk3FzMPyD0U5Tfv92ukaTkI/Hyg75yRQVtBZeXFLOGBHblehW2zIFbsYnAPwn80t98FpL3e0OaCMlzVlEm6wjt5r9UJqeN82YQqJWD59Nhmxj+U0ZxxOSuQ95NUNeBue0tD7++ri4bovJyhEvCzarcoN5IaBxDWCzew4vL2BxuaLUDRoLNeIadabVYwZNV4SapZv05HMQ01ehP5ktfdBhQP/k1gur2O+S4LPQw4t2s+CcqxBDdHsbYFisEMmq0D5C2bjpaSO3QxBdosHgKJioDuYM8O/IQnxi7U1gBrgD8KshG/II5BdsBj6XI/AYAak3L4rshXy01ZQwgyf+vPNEhxHgnoRhD1ytBNPs8/XbbtOghKKTp/KxZR+pqVJTpSkI5dQsvF4Bch2v+DxUXn42mdgAFI1ViCD+I1nr2WJODrLtU2kxY+4nOfdyooqPZDRQgiGI/YRN5rVKUGEAgeBZHnJ4YK+FwR+g+jtSE8WjDBHboBYPe8CTBck9LKibXOCO4hP8kj09p2kKuKwmpOBY4CDUYChH41Pz0Vza5tiJI66CgtiTmsKJ6UNayQOGQZ7cTi8Iu4+FYhFhP+u7vUg35XwZ5PKjg3cywpvX4+RBi0tMn9GO760Z83hd3FDwogwKRyLbsnQJsyray/I8NaDxLVySK6DBHCMka/TMpj4WACCeCcTAXRYqRMOuz1gccI1BXBxgcfjnUmD5v6wd/igSEL8mEmBot0l9ETtgjM6uoj0Ysrqj19vw3ezTdBvTMhtiApCM2E4y3kXbcD6nb7ae3v7LidXB3MrQnLXJpIZ4pK48rJ+rpXLro9frTJ6ZmDIUaCCJsV9ghy2Sdq5GAr88bfiGT5Njodak8ImgFpPrT4Uxzc5bHu6o5NalwkLoL6fZgacXWFgfezMW6+E2toT4AEAIDBMw6cShq31IpX2Ih+/vYFVRpXELURGMFJ9QZWBJORDEnkmx3K9JqZDApkrF/ifDJgYUftWIn8SUd1Vk3fyj9UFh6Q27op4k265ruzAWwgYK4A9sCj8fMmwWZ+4ra1HMoX+gN9yL+yNfDCH0cI847Gd5Yfo9dbb1PRx6jvaN7LNtyyBANsORulj5dEpF4H0NwwNO8v8v22y3IX3/5OXUNng0o2GF6i2Y/F4hXs5iRjlIJL2kEObHOctIqfoIJUUSt3YCHHCmjzu5/x3bQo+1e1U7giACU9pNwxub4E4xljMifVg8sH3mSCLtZdtV6xcW9MsC3ATwJxezkmip8wSCa9y8XdzKhKgv5FRMsGJbWFTFs6MK94UELk6+oebE50lXme8uHvnYqyAoDWSB2Uo3iOtwPj9Dc7hJ/0yoeVI3skuZCF2pz6krLA6vE9WmF6S1dsAOcZlT+Yz7Yhu0YwFJ4IXyI69KQLT4KoZBQIyDWYbs3DdYtPg1+lKgZojAHbbgwZZWuNGFTZ2QdFR+UP491wxdWRILwSzcVSDtbGfyyqmWe7OJwf8+llRPZP2m+zC+1zJtSgMNALwBwet40OtdBZtRgBeLF1ySmoCBfRZ48Aq0cd+XRzrJaSvLB1Ug4cwM+1lotbLzRzY/r52NKjfta2Ecx5UeJRqg0v59zhxmokkVek11zkOlPKrgcCcGi+5xLOlvKxBcZDDvAkFY3hdHHfYG9u5o5hfDiE9pnLJud9J3QSNeAlw3uysCiW88QF+XVNQiG/elYBtaupGEsiQv62+135ipgqD2wBrlUP83np51pvap0dL9qoD6DkJk0NJQgZnG8PQHXNqaoDIw8wJ3+Jm6R1BJL8XZeLg89DQ3pw8Kd2peqoZ3NAtSZ3xUDov3taeTe2yzNYpCWn61vjJpoa/Iw7eojFwnXBkyHN4YSsBv8PQsVDfXfGchHOcrrcUAe7Ynqc8nx7pWm6PSheo3u2onaTH0NA7rlMJ7rXo/7gBcFTXjIZJI6CuMXrLaLJe3w0OtjJjnQGYQZqXczyolNnF2SFhfzVKtdl2yVUXzT2PxmEGJGl2pL12lHhQFL2HJVcHPVZX5Fs+LGx4iIsAYrUsvPV3Gb+8J6cOAl3IZWOHaa+syRo3qSfrfZ56RY8qHX+gOIzBpLZ/KOtdwJHCuTNeSgP+OpSPYuaEgzMnIQaVAAZkTcJ2FMaTG4NmXJyz+rqMnZBlrWD8MSMfMyB6Bg2HdGJMV9ogjh4a9ezHorTcxVYAnw1jI+Kvl/0/0BbqiWPOK9l51PlApa6K7XP647Om4iwzV1DOyfllfrCHnsgITtbyC5KnKAiYpG5E1i4qk7MJDRKgWgYlpoVI0S/NtpKrCzrZtQjH6BU06mrAZWABdlCxylJ9zozxxQC2+aib6hfVObtzlr1r2o6qPtdagm7q5MQykeZzLH16YB9aAXhjgefAZYeidtg0lXd/b5WMnMzheTAy2Jddbcod2bcK/nqitQMNvg1rHzjWmT+VhAGvKXuMZV3ZiQD1gUmoUe0DBTeAknSxEXEFmrX0qLaX+txCSdgM1S1EyjB0YJ3J1KzLtu5VphkPoR0DRw1IBzTNBXPkab8MgR4AYFO/ajWkdsycKNk+sYl0zzo05f1wwVIPlKt/ALRr8NTcFivj7bPOLxuUr3lSxTz06RbnfuKJM04MSejQdP5XBJD9M6lYAferOUrdEM3xf0Ra1l+p/NF+BfpsyMbDPTdy272rvrXikTenbXbbm3NtflgVQwFf6kI8rRxxBNOD/GwSTQbaGXU8r35wUNp1NX9+WsVIR8dxqjqbR6xYImzCVq79iBCACP0rFN4+DcMpPswUu/g+iPcbDkFt5Pg6UZhotqXX2sY/1Ov+6ey/kcf1yd5oOqokP3TidyF7BsOzhAjm4DXi6FIkVBTpaarPrDvr0oirRz4tgfmkDrGg1/8yNh1yC6HNSGRcX7v4D76HMFvRwprpXoqWLP/8l03LCK5xPierR1Ntt0qt8xuuTZl85uEpP6ZQ2+tr0Hqo13qHfMP80XanVPCII20KWuY95A13fgjC00MrmZnT2q7IdGQpEAhQtG4j9s6uKPMN1Qm3EgLRaSf/9yrVQE4h0/x0hYTIQwjOxqFK7PyEZagLEjne9Du4GdRm5RX0K96fzEzCYDujxfBBUaMLHj/dLKj4XZoJqtFGTZTigumg5Y8rnAgoufpPk20RbK7gQ24CJTDIyswm0tI6zyCQlbNVoLpP2K5Lqhs75siNRl/NsAKAlljUVEhq9FqRsnlqvgso5r3F6wEbEeFGmzAWkw1GK79ol+qkLrqQ/yFrmH09OLAV0yGJEb/N5wBlzozpPGn0DI1U++zbOsBmArr0jEDj1yNWAccWN/DrjZkZi6ysHik1qedywU/Kc8vJw1hekCIpNQt4SqTzG6zx93kqi8Kz7G7YbUnu/0eRMhlGNgLS/fietHpbx7S7PhjAfgh1VrP2X2UnM+iCUPYXiIrFJMuEq+233liPKUfcJGFhCZKLQILGsGg7lfB7CIYV8A8T7zdCXyzLPK9Il6YeYEVrxN83QcWGZs5rvyd7Ido7QK3OUN6ptfHd4kTr3ivS9KEzxfE965pNsiYXACBmhKS75vXbklQsxkgxce6ZRJYDl5tgE3MKHah0kTd9H8sFHMYu298sY9tmk/SYcACcR+xODnhlda+Sb96A4hcOE3KYitEpILBZEVz7Pn5mYgZs/w/bq1Qm0RCf9rioCWM+eJALKQRHz0buSUBUX0/iwvdNMTR0eepjvGZd5Oo+9XyCVKWZJFv5b//+ERrAk5Q9dQb7wSA1pEAp2JsBQsknSJTkCaVdkIrvPr3zV4fbv8hZVc9gCN6vSMWcJsSsEWrHYP6cLHax3SKFmjxyZo9piNudVlq8aCcMl6oKbjbCsht1561YdG/VRhBxQ5c2yAlck71TOxppAteTz/Ynd+HT1LFFNKzyJ7kAZkvUNbBZ4IrZd6XJXxu0e9NnYWi80Ye9N8alijGGIRsFO3ndBwlUAneCNouTj/2zAGW0RkLU1RUDjs8nlyZCeKKTlPj6I8Iulblz3hm6Jw6L5LjGru7VflIbNk09clcmNRjbOLLXDSxDQDCoI3XDFJU6mbCFDq4f6zb6GBmxwK5kd1hA3F2GUCo02KHxecP/FvTg2/HILoXMvYYqZfb3NmPfmbzf9bjm7iqNmSIwrOFS9MP3DvyET+eChk3pxFiGi7XIODJCbSRu2qhkrYg9eujMEsaB2ZGBcDTuNQg7Juz10Uukh23OqZXVKBshkfq97tyUPD8UxxuJJz8l4ThJWDRS/vJxOMtBOL88W3z6wU9CcUISQHelqwXU2j3xcSNY66B7EBRxI+Pb3w5rMWxuBdRA0A89DlpaTT+I71rIJcr+eRpPa4v0af4Ce9bXjGA/5c22L8J58eR6fHm59xwEVZHx3FPfYXeu7JkN6tJd4VNHUMSCn6QPL0+JAjKtcdLy6hS4SlG3sZnNDeFNEsPd2TA3LhRPQOcf3/8Nr+lo/aU3xH0FUmjyGzcOSpEVBEKdudDZiiVqWRhY3AjC2cqmLtUsmPy+sG+KxXaenhT5ZpgCLzJH1RhaLBfBYErupFLmdWQRuveDtkIFUt61CFabTYXvfN1yLY7/hoXzWLFp/N7Y9OmNb+0l8uFWfDf46jgpE5chYhXDU8hqhP4Pc0vzuZ4K1NgrejXlklDGy+jVEe2LT6iCi67pLLV9tiUL2gh6SvFUO8bU8RcJ3gePf9H3FQ4EL/EGgsQnsj0tGjluHb3stR/T8hNrisTY+pj7DNk3j9BxfJ2rBjaNbiHD9EMyv2BSqZGzY6k/pxKFOtd+GcyqkUdq1qCp/u3tRwaAEbcpLJApzkqPaPoNhUkueumLI3iIkdW9FfhYWBDqkzhTupEzAyWEO2Uxba+D3W7Z58ISmapc31HSilbuARonkosqrXzFKVFjyYhi3YNiv4GfTKzTjVK9ymho8ICtMh2HyDOtZtZNBT6VfxLU6tYEcxxxZ5pjznBCQCAC5jcs48VpdDdSVq+8EjNKF322H3G2fDkGoOTBp230a56U1o6j3AmahCo20dMDPAdo23oAb6EHtZJ4706FXevNqa/BoCbQIlaegCpDsKM8SuC6TEMkL7WIlwfSfi0b/nMmFY8cDa5oF85bMVrMd1rwfxDOd34wDz2a/a3a9KyqQzJyo0MOMJ8we/hxaaslhTeLvJ2dwfhcEMTxB3fchzAiCvEpjSOhYRmLJkModVwjpkUGX4EMGT3dq8QXo/UaJgZgF96BW5hajZgIHD34adYHqjN5/DoK9VQT56Fcqy+TIvejQgIB7bIxM/W2HuIIZjKK/GafeqHYAU3efXKA12t8NPt8WVybsVd2wgpHMrgfGoRy46gUV1Ys9q1bNLMRUmxATCAu/p9ohe8ug026QSVvbR9hQVeivIYC/RLoOC0MBqByZZz7GCM5TUV/qOBrMzS1hTdxLDa+LoCKjU4TXj4DQMrmr5sREyqPPhFGEtLriZcBJ4fKVjYYcxPjJvudZuPSNzcFFmPoYxOczwsLv7+wC38bdxgeN7UH8Glk2aPMl1bcxOiGWBsD3Q8wlgIbxl4Y2nR02hnhSKe3DvmzSNKfZ8g+RB0iYwIq3deqNY55GOrCmMflZgkLBDij+n6p+4cQdytHBFeo2+Odqv44cUBhzOjomSq9GGMAGxO6rSU0oVBEd0yQfRG70VUsb9K+gLjprocDMgdQTL+LLyjGJYtkeFfL/MEoj5jlT4UbFRcFhbgmX3lih2ayEQXeLha89cyXZ7KLgdJB0NIyP0lWamsTPxOOF9+9f3EVnN/sO+pQDfnvG1/qCStuZzox6eI9mHoqIYB382N2ZSTA7VGzb2xDJHa4O6hKPPQ2CjMgufiUBBdtAqYhEQuCC4eH453YhlRcpafVM/z5BBHRezIOqVdBhdYyEN7+H2sSx5WBq+7rZqNBVUAhYZfFbjcGnuD0mddpMGB75Sc4u4UjWA+DSVaKZbB6r5g8E4ixw1HbTR5mM4c/ystnKmO/vFxEJee9z6lOrnYdS1he0rXvJVyVSSUmlV8zjzRUlmWAc5DF35f/qYwpjjcX+8oZoF4n0eh51E+XYaJxbIYxRCGIb55h33Lo/070nkaxqSAy8Rz3pH/AG6nsaYaRcPG1YSGa1kIUXJGInnr4BlqWsEEsgwrT37nwHJ+RI6sNyI5ZhhEgZGOjthD9tHHl9B5TxNK4vaC/aKkqXxnU3e6TCeTVNlbya3oamFpZFJxwXbArz3cpvoQKa7o1+Tvm3S+kV5qeVc80jNgo5vTG3hi237r9eXvToGSLnWsn6LxcCvE67lWExJgWDbj46VfcopCz1UTpCW+SBSVNijR4XkKWcQU7J/YCH2tOEqoOlG+3IufzAh4ZIIro+kROubEAGD+uCGeFE5BNAoSbDEuLmjrKE10rmr//v3lXzUCqUxJHawVhsVycDImd1fk/CSApvlh+u901+Ggb2vzEbpRxBAuyS6x31rLrQXXnRN3b4hRQ9YrJDoRTKiMjAp9GMVgcsiuh/8ZRAe/qjUfyk1rLHen4tAxTnNi0xgOy69X9DyosdVHOUp/q1l1gHEyuO9b/39XYC2dIdFRzfn/4VRmWq5sM4gLBKTWICiw7Whgw+PLItuIP2Ov/0DqD+P/G09BWOJBrDS2nhWRY4Db//qGFRcgl0zcoa0FM9OI1gXZ/pnQ2bHLqPXAUGGKbABcR3CZr6VFp9jpuImkP1D6Db6bE3P3+Ec/rMxifDaRVW4qHCTQYKnEi2mEkvyf3uMfPgVti4ec8eIpe5M2AAP9lXvQT1VuD9cjHV2uUW5MydTfvxIKvxbxw0Q+5dhcd9q6dNeDlMX+PjU9GOoAcEhWzSOn68KMk6UKR+TdRquItDETs65syz9uBbIEneTqzfziLNmPo/BI87JahC1vz93YzMlIH4p8PASYs5ji6JGp8SPYAS2LlI63B1Wzw2OzMic7JrBlfApdaC6/JMf2Rn7aSjPuXM2+pR9JwIATyMe1bewCbD8dJx8hy/TprbCNoQARnZDPHz1d5wUafNM7jynkQMC0xCbiwiKmxPctL4RgL+F5NNKWeEiUwfhLhF6ygqrHAMDJ9jjVojq8F7Mfes+rM5gyYephI8MxhkTInFOm0L23wbQLr9XNuw/ruz+FCzEjoqc+7Njxouy50VIDnsrLetLl7iFsobDG9ePvNoLgfvj9bhbODnFU52IrJqXlcOsYpXc5jtZaeP1HPcRbxNUazNgAX9eRg6Qzp5G6epwrcIbtelKvTO2h5nfXuOqjd454cjzpaV4+6QEa2k+xs9bGgEryeRW7uj5FhR89vYol55YeRQR1bZCnrMq8EFRmoWZdev3Trfc0dEx7HZzdgWgV35i1cRDa9FVHoh99Qiv3bq/fFzHqWXFrmcGGn1rrfG3blTMbg7sZoKTwB8AnHT2IqD2TvsGbDMQKSP64z4k9exsp2lGtFwVu6do1Td5NAcIDFD2XfwqAgl995bt5lj6i6h6M6i4TLJnLFioebfGFd6RKvTAeZaJW6+dxCF4OAwnFeFbQ3LGFIhT4vTY496cMXL8nmYtxFw3FeQ0R849d2FmYxXUJjURLaiqPRy7X8dne6lkHrTbBKZagbNnxSDYEAhMTqZVKnpPsL4+EGsWH9/Xzh1EHsE8JIPSDeOxMzZQhCdoOISoXNDPenvI8D0r16shdD6ZdsCHjBTwzE91ZFEvd8XZkmSsm/fWhNC639SI5I0tMzZZpLYtn+4Vjy0jbP8m8kyM2rjzB6njh3FCnxO276wT1oWjBLCGjDh5qsrIeBA5ljZ7lI1WeU775AL0QfSlxAJ4aD9X3KjqsJgNSbvY9Xoo6gww9M+21vrRHVWjzyehLRsucLAWPgbbVr/3Viil04oYO6bVfQqpctiWTKIuxZv4shlwlTJcSeenVMUaiXpIskdxQS6l9HnHA5plvpNXvLy71W8lzSzueBkyuMAgPbs35jDK8YU003AvfDqbogC6B8xPF9mr7QA18VnbdPYsVGngqRtvpdMusVDUq/+mfI3WD28tVTN6VBkNqoXI676dbHnh3eJGUxMYlIRWtDYn6BNf7bZOJcRXnbeDIpZscRNSuGgp1Z1h8wzJeU0vEBHAihCTxjt3qM8HUXRmGHBQlu+dBHlczgWceCD47EZ6iQw5/7DWkQ7k3PgxRH53FnX9Ob67XARqG+PCUYq2EsFB84ESNWxcgHnaU8WlIZHi0vYnjqUVJBPD63OH/caRpaJs8O8M60tmdmRNdYYtU0WzIEa9IDQ+KIdtH6N+YsYDR92398/IFVijj2b4NXhkdBMoUCZ3eL85Xh8gCt/QGoUwj9otRTSjFNJXUVWMEZl7Xwdx7YU9ifz+ZHlJRmE1w1m/mu3Y1o1SRxNHwo2KwU98SjeBX1AqOXMD+7wh67oI0bjg0fZCt79l0phvFg7haftHAI5+hMDBSSADCP+Zgrai0RmqToBRvyG1kN1R56OjHq/k0wxg3FoZBVl8AD6iDMNfkCzAlwCUcqwt3jH5e5ZYT2UYsxuVlUOfCEsSAY1r9NZhQR7BhQV1nkm5jveQ4NxNMFRPwbjMOCVn86CPPr+mROlrN0Ds4IPkUraLC3EaWQDpEVraVDLkJZESX12+8PTUz0DopB/txm96A4T/Y+P8hm2o7jAQibMVDH1HLYF5ZDVb/1Hk0TlPucv4PpfhOTP+C5DHd8EXj6UI/EygpNTtyN9jov1qqafFurPvTy+fG4v+K8X7ezYrF0BZfFpFkIzeMOlcbne23crwvMStXfYqx/U0otEHsNgq7iyy5b3H6230YcSyoq9yiMMWG4OilNuJLfkFTDnOKCxEQohM8CO4f0y3fS1uz6NyWlctFHjQ52R7WpU9zZqaUU/GDy6Gekx6GjEP9/TTLgpStV6qg2cH7KknW/Psz0Tc9tihkIXInaFZ2DuxhOt+6c6Pond+EyBWj/C0NxuKw7cv8/p1xHAcUQjAe9Gv0H4zzOdtoubTP0mvpCte/tycPwGu76q8O+Gwu+UxsrMbFjimgLXOidFC1b0keVTYstll5+SmJKez0jn1UXoN3X6WnPxPImWB46LPKhRCQIl9H+krRUF7/TOaZvTf6z9LrDtzLpy1AnFv1tIVfvCAPMEs6qjqEuVS8WqWvhADrhdgcd8bGdLQKTMc4XxO1Pg/v73xyhAkLorwLYrAgIAcB3ydahKZZ4AmW7JkJUvDZqvonOPQgQ+xDZYEHxTYoJL9cV/OsN94I4U01kRZLaITQ/JncXphd2Ym4HpaEOmNW6Y1r324jVl/0CHTfa8hcVicTqP5AI8Zr8dnXlZ/z+7y8EuXQPs08cHorbzlPNBrAfLoq80xasRY9BdfPcXNslq7eIt6CaaMEccvfvfIAUDrd4T5qaNWj+9sGhaWYcn9xmf0XLFEDBQgM5yC2Dv6AHb9R5sIaZWiOOF1oXJfS9vkIyO2Nuvya+KMnI+RFZDZncXg1mrMm+fIJZ2qk5oUOTEWw8+1jvgtEYywH7t/klTIekKjIVxe12j/XnwKLD//Ip2hHFEnwyEjwgbXQaucr4NHIE9SjNc0xNduIsfBQb0+tHx1WDYNFWyZSvGpyIM9pJd5bmVsr82qfZHHu3nKkK3nvSApcEoNDA/du+/8RpZ0std5+1OztEVmpKscT60ge221D4g7d8YehbpvMGjR2B8t08Riud0cjLQyhffCk0P5ToSpJ1UdtmiPcboPWpp178LF9DauJ+mpQfESj/CIGicputMcLSFXOZk3dAl8JX9XnpArTaRFIpRGZoyoafh66LvMRId/e1yohXbPoe48s+csTo081r5FYenjqn6eguKegcCh0KnuSR9VX5BlPxi+SiCzja5fBhQPQe2HztgF1F3J2/KrdXZ7SEJ+3lm0wTCF63qLoWtE1Tb1dDV0ccGe53IUv9lUwAowbFvX4E5y/IPPwaaKiyUYvFcTwris1+RsitLcsYtnI9eCYNIRnpcGX19SgQelWWVZzLAGZG1AGnz32wrYr7DK1WGUfn0aSDdyU6KF5j1AAtd50oyvi3EIKC9fXiWH2N91KnVFlNX0p/z+4ahCYoUOWLdUOMHZESrGKcNwcGLhsM56LS75rpUZkM+0T6NTBnahwuKSIFO4aCg6d4EDouEOvTCMwbc6OucmZNYN4s9FEpkqwxDH+4Wip0EJj2HrgeNXO6iSvt7iaeHonlXukKUlXGjuyzkJULzaMM5BPJdZp69jDw1bQOOD5H4S7M0yTaUsfTdJfQwbZ+BAUlbOc3ayRRFSxNcjijipOACuBcj4QWaoF86KXlNiKR7hHzlxKk7tk0Nh4LKop4C7ucc676qtEDmsKa8MgSLfJUSYQnrTIxFnnuj75hLrDvrU/MPRTwmEFj1ebP/c0vA8faycoOteE5dOdZbMPgAZIP9hBCudrhVBQlAwnsAJ/H4UccU92jF83+EwlibJhOcVp/6KJckSwnjK/FFqYLJpXKvHES+o4oXkDEIRYvjoVlvEGyLgP5aQpiOAN0/n7Yhx4tZ+BGhJinlVnJoFZWRxZHtqh3OacSnx5qw8kuPMEXxF8+WC0tX5JIBx29lJD1+qMwG2RspBg2zF0YkLZEjPE/9jMob6OYQ4Kiufqw+g5u10WOby41wBFlFri1kjNQEGT6KgHlNQZA3mg14friYyrZ8iKoskaTk+NqfLdSyV8/u42hE9rOxTe+wMyMIkH+7ZJu9WsfBz3uQOE19We9KBlZlV+DW50mjQmegpzNWxrxvGWhjQYa+PsJdf9lzIUpFyAXOBEYAUeK4S3ZNkAD4HJhCSN+JT0XKgZTWLcF9IP0OamYhdrdWQlz1pCvRbhx/sYnIETD75oPlVHCQjE7DjORtlvPDi/kbOO6sz6jZNSrhwKDMPeFlkT3R4+4rSXHDeXMBCNzdiDRs6B4jPPCTqtqjbW4O/trg0fOKT/bH3c5tvoDI4S8XWSY1b06TpoDslD/yTCxcvhudXM6rqw58ZctErzZwEbPtJKmkmkWuMUqgPsRsiL+4w+lT8JF6Q76XumhnsY0Byr+QhXRmrjt0qoBshmnV7+g/i7g0QV5sjDCCZidhtsVjzOQ2tVYJAU5d/2ZeY8xNc6JczaWGTk95yPq1iNxBB25uIzstN+2RekKILpEmgkXKZWrsW5rS2R0nbv5gRknvCm83QIk37dcWJzu8dQEazi4V0iLZHWeLDO6TLu470pi2cWWJhlU9SXy2VgotWAS/LVVxm76hUliYAjx/TCfep3AEY386YaC5wojAFyZTVbLGXbGC8tzoPoSCk/Em/Vkv4UfxyPHn5awakRHOvD6EUQBSdYM7xPP+HGc/jTgsoX1qtLnXW3BMarcyXSJQ72aMPDgSHoBetA5w0bO/UlHtENRQnxRKMPvLsylSS/+H2aeWbCI/ymdow0gApsqlK6IQ7tZwPaQ+o6zeaYoGpEAkC5Yr60Nulju0m4Nfu3Sw/YDO1FI7XvCWz5rUmNZ8Gh93g8Dv9MqRLIFb/Js+S1tW+/k0IMyMUTOWjRa0yL6rZa6pj+fi5mfrVKBpTtWgZL1erEr2wZ7dRHXoqjcke/FIawWuuW4uOp9vvpqWMAEfoKsaTt6Oy2LqpTXHatiwMkffMJmF9eMEih7i4BKfpWlnFQQU0mhpSZ9RF59mkLv9SZDtpHumTDSXjuJ2qulQ78RQEU9B54zp6uI+4soIz6LFhu34YVZSmg0nbCgjV2MqoJO+e0cVVigjiiiqGSWyrR3FsszVaj/1PgNhwmHqnfNjNGvyWmUyl37UJAQQBFQsg1lT4RorZRzSWQXiuzp0AWlpBKE1vWK0zgzZqZtTiTFmwFlDZkEnRMvDBRdXOo2JTtmHK9oGsK4jCy77QhzjyP/dBLG1RcPEct+Owy4qk3Vq0XsWaK1DVxKCRU9eGmUlZf62DKxKAC6j5GNHaBoMQddjTJSpVRnOh+YgpugNSjBM/7DKFhwBSeothOuw2p5EGzTSXBedDyW8vi1rgVLy7mcb+drC0Jl14gGJlwNdjL8m3+6LIgN0Jab1LfJ9hu2QA8B+5p3j54pTzSNMngBzLsohPZrAlbBgkQNypJNsn/nrijCP28LQFEX1WQq9bbV9kw1VDD3GAY2oVXSV3HG8KX7e/SmOagx4UK/SfzQvaNdq5KClrDhNWoN9Ibx5mLUQB4byLLovdj8JQoGnQX3mFr2NY0LFliJcuPwL+AwhIAD1hCo0Gt7zd7YP+i5vwv1lMRXQ7GIq5dPQP0wh5B/hgu4Yx3Rq4G0RRqJjAXy8W8AlT4Bqdih4hiPX6axY8v2Jbaf9Gm8nwFEEIm9ux66AfVYJKdueoO6IRtGaz+OqYfA6eM3GeDj1x0L2TNApRoK5X6B7YLLYcMx73wzudVuyOyBVoNU9TjPx04aY/A44aboqdZN/sBudWMhG287DDTerbQS72iy+B0RichQenUxt2nhe5YbkPBkidILUqKJ0Jy0+iNDfVIs8XeEQ8PmUrhbq2Jqh63FPe4vZkFkHiOr65T5B9NXESzJYeVEogp+6Jr1VF50iuCjZylKa70/LbVXKsG9kGD+FdLSneeJm2ucYEI990jrErXnnqf5XJB+wC8Myl7AARVZIIAxH1J4lf24gIlUUZH2lW8uFVA7dEuXYkvn7NOPPjOpbadWMJd1tN5Z2rwHdZwVVVu7SlzICSPGvQ0mwKvDht649oiwAgQqsWlXU1Tt5q0stApRdmlsPOYbk0L25um88kHM0Ws8M7Fw9ZZRUmK/yxyNQaH1snLr3LFrH/fNvQuI/rsF4pQNgZbtEjemTty2j2xANaOvMox9uHFZnzTQhvgU6BGSQWeYU6v2t9lJXDGGU5NwxGywRxzom397/cSwOtHChLUyoD95kPaRThPwy6zHtCcax3A7zI4SERmImxBzHRHCQI57gv75myaI0mJ5CHgY3Lwb0RW8rhA7L7Fv7rAIke5t5VzX0VYHBwo2VaYh2F7Rl54AfM9SkkUr8Xcu8igOkweBipZNneHTcHb5H9IvaAvE2wrCFKGmPF0gRW7q5cgb4cfH3JriH91m6s4+SOdIZPCJX0LobTDTdWgnUM0m3AMyNY4ZzgFG0kbGP4wo0wkdYiqblNKaz3RKGqJYo5DccjnzXLdtmMCwOFhNyAKcz23zJ2lYuKy4bd/ypH4iISan0XNfXCIxA6U7bX9UbPFZagPbpc1WMH/kXdAu7bXa+jLc9vCyZn+UoioCoQplmsHJHoZu6r/upKcESdRme8WZ2M9cQPWZfYPxTUpAhCKvEaapqGhLZs2llUr+sBLiVmYhW4oZZbipe6LKJMqPFjVhf49BnrF2uMi7V20XxLvhn7yJAcMUKxxv7ve2dEDS1xuTHs9MYywmGPHiPyr4mu9zk9kVb+6RQZ5sa2OIUa7KdIF7dt/XJ8XYac7WwEvCoXg+wG77PNKbbxnsMH8yCPK2swgfS86GZ5cWlBeRp4A0NlaOGaajw8wzX/qjZahTrcxzxEECPs0O4z4sOWhlRvRm5VKXTIWcsj0Y7Uk3dQwJytg3BvMVPOgqS4to798qaN9/j+7TRKFF7cwSYdokm+Hu4n33lhJ9AwTouLf/Ey3It2XwCHYgnXTThp1FU4HTvp315QkCH3zgZSzhxe0dcqMKs1u5IhMd6z1+CRW0P5phitm4bFR+yQZP6mG8Z0Q1w1k1iXKExxwa5y3yX6qcmwEscb0Sy78vKnTABGhunE02nR5OZUNjDe+h+ZMfXxZz4qOXuq94h3h6HoFRAzJ50nLL9WBm2woNDb5jvwNykYZKBGiQ7TsVzA6HNqotwMawerthuIbE7iB6e8GQ6L6F3sVhQW/3ko3K1AXCPd8Vzj6huLY2Ud0JHTUsq2CnbdPyr75Jr4NXPSOvUDYw2fneGKKWXdYKuImh2O58A8GiblY4eWsFiuARMSo3Bfb+E3p2t2FV2EDYhqrT9UJhWfTerkqcd/Soni5f0SeDwcTF8CrLheOr4XqYqcxftTXk3aXaN9HsQDT1NtKehILkruARe2Sk9O8Qm2RWBulKNMLp/ah3szjEp9B8Eldso7Fr7pLm3lLD18bomJO78e+OCCc9HMn/O6RjHqmKzVkJBDQEZOQRRcn4RHHWlWb6Yz29CUtiKZSoxNBF1O0MR+G8WEv5d0bJFhD879EjbTE9kY39FvCW4IUkLkqwgWU5DfJiprUvSCwK4Ew16PmypS6pNQ9jop9aB5niieAMXwj2Rn62hDelKYZWN48hF54C4gFQ1uI/nPsyNzal7jkHTYgC/floYTVkM0cyZAE1kP3itq7UuNlTRfym7+P8dYEg/kfnTiFJAeYb1y2XKXgIzNeiRKGue0GnumKqYbU27VzF1MhX5TelJWxZDwQFVZfD9oEYSMrGtFCRevyPu1doxmuG5LUEKfONKjCnhkvckJaXVYfYAMwxvEOHuh5CKHsYj1Ar04Eu8jX7q9ae2xxIFkHzVTOYxh/cNJ3zxEzZte7nHWZ8zZdjtUamKulCeLLG/lG60mg95LLwXbgVNNDcZ59b28S1BqBV81gWqEOZi9yjnRbbsC0reRIjjuIiFDIWQmsDAI6rEDvDdDbcliPeH5D5iTajC2snQbpRqXJsdwobyumYmsySoZXW4XiRJBSgw5S1sQSiAZZ9zLKCWSRvdBQ/gM1uTf81kZ/aIdApb5AE+itAcfiHg+/cVIR2c+q3XpsSQj16ONfC47/7WQ75bvnNmfYwC1I5zU5CmALW9AoXdERyGYHe2FLtEsufNJy67wYMlmb/4czXExway90EGtzA3SGISsgxH+4NNoN46bzDeUPUcJv7yewsZHpN9FZbQrZTdVVV31tPXlbcpIAG9dNFd41dCLKIkClLd7ar258tbQVT0jQ+GX/EO3RvE55PeAyy9qeoDv7HUl2tW3IOO5YGD67ydth8OoePBW925rdQXDpsb/NEVoniMD4Y9z2xcNRkWIuhH66rkyr0sJZQGUQhzs1h84YeooejTRN2K3cwwzj2KjLavn+t/Lua0YZHjaHC1H9K2jxXkW0OAGjJHmYSInj7T9bSEIudQPyNrAQNuKjVN8+vCOI5Y+ylxv/l2jzlt96PGZ8VfqkANnHA50IHR+74fE4lWDxj3LdEx1EwBHcmVvcbRpBJV1LJaGODoPYM6kFDMxpetf2r+GcMQHZXx+IchaZiz+petRve4TUkI98dJtcGl2vxVck2z2pKaX2M9HooyttiJPCVUHCg4zDC7DB1OWz2EGhgkGC5XkNwawbnEKcGXqfa4GUYyjnaOq1KnA7QA6giRpNjSJyR3yaKqewx/F4I+tp1+RWLqxqJe26TrRFyi9emjQRoVfKoqnHZFLQPAn1HxplDzAEOhDehBUAi+OupqTL0QFFwP0c4VnBSb31uQMeRyMa6KJvTPIrjg0HQ9OZN3pHXOIOvwww/UQp8byzPg4ZTPX5ekIXKURyV/5z1Lh2E/oQUgzAe/qtfaBb6lZkGt1+A0WjPPkBFRHVuyf0K6elObgBJjfRa2vdhyAvkc1HYWbzLdgMyCjGPAZ4JvHKYHaaSjwz9cGhJt0nzDzhqIRQKIeu7nnYiq+BqHB4liVmQFK/nxFVjFg3ngz7WZxXENHUzA1Sc8Cw/eH8/PnB0agxxiKYC4xVE+fzgv2jGvS+kfphpJ0WteVIdsCpqZE0M3JLNr3WZE+DzWu9DRxS/9Q5yoymGNFijzqeEI0j7JzdFaHa44AvMlRxuJn6iioFIJxZ85ndEEFm09C7O3E2IllXwA7SelX8N8Qycgm3lpUyLWwONgJylb6ul7J//osNUtKMQnAUATzXNDBWBlXGqj4Tq/G0re6yCxw6OAeRLdBDTr36tzBVLd41RMYD/Scj5uAczNGs/5sgTOo1F5wSRZG26Tdie5ckNEXyNSMyL9fozlXWsB5qbkeevx4/afJcRbvS+0RvMdKilEgzG8ykXEGlrBb2U4SU4e/RCruTiK8QM/2oWT5+DASkz85Ct9XmbacNNWRiM11JjSZAtlc53ey+oZ7psGE7MAnU/EKtcd8LewlGa0HmaLTqZ9rtFSyRcyPUuzSs5vpi/D2SB05UXj6rhTWfcjLFc9KABhATcpJ545Kd7tyfJu5apeeajj6eq5BIC4sH8hkO0D6AeyUHxqjdG1unakdYOS/PYrbXqGMuyB/s28N/8efui7JeZy1iTBYXjW/PB2HYVubM1G8atospVZ0pGnFEANrVhDWT6mrgYGksr9lD86pA6z1wFzMXjua26GsjnLQPvwXLM59Flg9P8+ItsOKSKo+yHz/o94kFDQseF21aJL6kHOU3bfOl4wpSt+24VE096xfBQa9TRpyx3UxmW1rxhldIBLCYAZX7YeuZtfgc8SJWm2xIyNJ3jmiz28Oizg49gFztmh00cMiTxKLtYpT1bVnL+fTtk3Moqfdt9DS2hEiX2FDF/zUyktf7gLorZ/r/oM/2pD9xNdW4GOcnznvK84DJvSWFc3oYHm0S88GzZXgR//xAWZmc4nvHKkSwbDVsQdAWFfjrPK7KUoZzLfx7Lp/pHKFzjZgQ/oSInyk1Dew1hNS7bVhSN/btTtozyYabXpjoYoU2vcMXATh4UtVX7y4qqBiqGqGw7g6b/H+J4MPl1a6GQGj4UCIzOn+KWbLBpGY+2F0XAiCOV6hitjTdPudOsNcQFo8SQNPONfBBWJXiu/7oXVhR7QLSRTZcGlUk6i9l9Vz3hYSr+dILmadBW2ybz8NB5UcOtwnv93cRAiVK91HkMZujhTvMbcLAE4IaanXE6d9AvuTFzK924bDshbgNxJ+YNff89uj/0GTTUykVNASuN3IAcUJWIonqy4YtGKBuTwAMzhQzGZg4f+vHodj5sJcQLNjLSZZoaqYJKVR8pV4eXHpgv08xGgV0Hcx+K2mxnTaUloGB41n25VBO3mYNSZ7CD3vRy1ahGFZNV0u6U0H6YsZyMr1CvfRK8nPLQWQ8mGhEsJHiALJWlptFV+x4dI78ezsov+GZtRoJ5VCm+AAlaehNyNoU634eC1BZlQZv4zXfQrUvQjFqgDgJZDk5qaobtp2sLnEEAmgfP/AWAhh6gNWmGoITWLCRs9S/Q4a2UQVqs6hXWcns5krpvR0xdET49I4jSeJUslRkySHaer+HMU4VM20AWVebmqdd7q/hwuw0Tk+v9U8ugoAD4LDupRyXICd52vXs7Xy4Ydz4dJ0NQaAI3k8Khe95j4r687HHADmWD+p/u0ypgWy5Fu3y+rROnvfLbFQJxm9cgBD4dI0YiMKQzyVyxnUlQkiRC1ic3gtN/oyvqalqni9dOw91hfwfFgdS6zQHCHWBRkuhRU2oLHVyBLbQAHOy+aMqcGK0vOxYRdUcS9hzdXs1S4+Abl40QheW/id1dh6oRuERYJJKdLB5aCItnM/cDyzwKQMAsaDZbCDIU3qWJAnwpalgF1uhmMuY0kWVKL+7KVizlxK4gElKX0128L6KSaKvFX11HqvNG2bNoVhDrLfvJmijIkMM41OxTNjyoiKu54bWrfqDZZ6Z9Ft3Fc7X2FORyizf4phD6Sl+0huI2XYtjAgf/dou1Dedk1jotZsRNt2rR7QV0G3tT3PTwBy7w2SBQY3dpO5KLsDn3XYN9EdzaXLt1KZqWjjw0KAoJX/S12oTDAyRwwYnsiIhWtdnJM2BU5a/Y+dIKZaEqpxXPefbS9sAaJMh0CyKJ5U+ohT6S9+h1FMC7sysiYCyLfHk+V453TX+j6wg8gj3LJlipWvloNJ+nL1i53bsHp7rZGq3h7VCq09Hx3cIuy42tKZTB4s/awkMXatTKg0whtcHHIhKEqb2vruTEXPGzgX91dU3G9enzNaexi3W72/b9BswYuNAFIsbYBGnpI9lxl0FauiUZi7R57WB+JGjnluBGmq2KgzDz8WVujtnwKa7s3IJuJKooE9D1LHN+O1QiCTCfh43NnusgTZDDSsk5tbS2vwilxkGwLLsHE7LRKSFyi9cfjtBXhAnpBM//5jzpMb+JHHxhvKknHUV58jxuwkebhDk6gzX2A3bU+nAqfx/gO67c7p0UYyqam1lCvx1oGR/EmXszXYLAHPgoLZQkVzZbUd2FbMftV3A1Fzt9Bf177CcDs3fGmEIu5qZJ8TAKXFZCiXPTo9mPpUuMvSpdKMUwVR1E1izWyqnq/L81iYx36BtyJfErc0ECP++HhueyO/Q9/mCAYpVgDxmnqDfXVP/klMHUFIZPXNn5xxUwHbndmd+OzcSJwHv8Rdfd5qzawf12a0CaXKJMkv4FqwFgqSsOEVO2tKXojObzZggF6A6J1bi9pSftcMrP9WnepIPpAV6j8e2darDlzgloc5hOjxOy1MGv+T67KaBvqSiJfv4T+3B6zDIjYvMyiSHu6CJsYWcO7pc+29mD/C20oSGIiPXRjjzrDUHpAiY1u0Xcw8++hjbeosmvdZtFzv65hDH8fqGWTrwbEdYlOHEIi995fxiGNMSAxn9z9/X5zrgaKBorFk7O3YMNdSPVLfBjDKngUG09M4J4X8ds2Nhb19FjrBXMebcHmuX/QTnNUYecBX2oY/ZIPhxOm5FVau5sSq6C5SMfvQCBUNi9W0rlQakrq0s7mVoBphacbfIfX3EtJ5mQE2FjATT00ZA9fPoajKhc8FN2+IABd+Bg04oRomb+GnRllttcTN1m+5OaekvDjyyPJ04L9FSSSwiWPcvTv/w/SpGXPdu0ih6UZMcHXRVt1sTp+LdL1bpcYaCWKz8IufoliIWluBXtB7n2iwKVY87tqw0mGkN5l0zj87Al60stukUKmcUxXhdWRP+hxtkudcqEGT2Mihl6dl7ui8mMqmmWJ2q1xfU7qvoYAVpiwtTnhBXCsV2a7EtGmABkRZygIuk/tUNezPnV5biMv9gWAb+IDvRfW4w3SREmXPEpI6swAG5nq5dGKVeGiZwlWJfC/bVeJiparcH/X2dunFhOpkXzbDTrldAzePz/M1VR1ASlHEUwzPKaJ6G1Ajv43CzY9ssF6SK5fcgc2nwb7kv92VnZqG9UICH8EZR95g1P06sopM7Tum2lD//SNiTIIzHpi0iME4+z9XA/5AttknzHcjbLo6ZGFbBqF50zl+GX/vaTXYhzlMUZmtHZEQoI5XGC9WQxJMB2jBbygKRQPmkWgKGL0N1wRs9gSrGKAQmkea2nmCkhB3IwpaEs9BkAa8hR5L/6vR1omsK0NGhGLMHfdw8OdOXVsX7hyjM9U2awBFbNL6oMESmKwnhqSjQU9ek+aNDKvm3ln2DnoJDh4Ycy1fqsGQHgK2UdamfpfhMWuacFW0mvOuhsf0/KtSVp4VRCFXKq7r6SHqAldTjfcAVv5yRTyLrmvQQwL/g7QUeOc0LAA5XYu2aZULXqV2c7ORzOpLZ9WGNyXG4ByM6WGQplV6b+IKPVcr3RLeKLZ/n6Dt7N/OdykCqkJ/6J0r8bKq/5oOptjwOCGr8Cg1GAxzfnCwrPWu685ovqas5RPBt4eoRQH5N4nD4vwC6RrwT6NaY1CbGh44+rVbS4tewziisIgITfC7uD9W8rhExHKbs0mxAsrRPajdDVuYSdjS9TsRo5SECOuP1wBx4Yg8AH70jsDZZd7jW4pAjnLyzV7XBvfwjVHp9Y0FKGFNlTF/06SYPVUHFwKArtRTJk/i7pOmCnf/KT+DmBfzWZuBiXU6M9GzZmBKp3lJbn3J2XoYfRUDpViPbBYEjI0rKPo5KhnvI9FXXZFOTsYEH65IsSGtEnIFbK73j+1YX0tpGt9A+udZS7w6gVw1nNBI1u4wOueb/pXjmCdsVPJjsHS/Fw/jqsqeIHD7udMeO8huUzaR3AYA2/jSSdY7++m3cZpJzbHonayaV7/Hm6NJHtr8AiiNizQBWnr2GYUVvDKjfurkWDUv17MqNOn/UfDjLP0Ra3pjF3dwdxti55Fj+3pqqCUmbich9cJW7mvcSUw+Shm/uaGCkjov21HhIwn7VjhlOhkEU3ADmPXd7UjNtrxTsPOEkxAKS8eun/24fDdIe5pMejfgB43lfua5FsEGm7T+Rpf2KtaWlKgSR0NFrEGTjcazRyJN1FDSEAYURQq1iKRwohS3t+FvxGq1ffHUtvz1i/OpGowu7EB0wmuEMTpDdgUul1C4AuIJXinaPAGlPVI8u0A9rAJo46i4z2YtDgdM0J9oR1Qy9RIxkm4z/sosssLIGPfzGPRcHrE8CnHCCXELp7Api1QRWJYdczhAS9Sj0PK6ZmRIJNgSlIJU7RdLgTIWUfQufqzZmMKUV+t8HbIOT3rEhetntxG1O7hlduygiXKJP57I/AxjQi6iYI4cS3aRjxKd2cREiBCGmTfR+/nJtdLPT4Q2BHwBtQ8pF4/SWKr3Q+N8EYjI9SKNlKnMGcY2hf42bs4B6GChWKXp6pbq8pJ0UuFZkQOXYrh8dSQBL3qskfSivT5S2+LlTGRxUlUzowM9QSutkuIacnRcHtMnijtQ9437+4w28kacr7M7EIjSZ7rFtzwhFHjw+AhDJH5FJivSEGYt6UbW9DnacUJO8QixCOGVz/6jDw2SdNkg48R6jjm1Q4J4Exs7Oz+SND7UUVJgsRF20zCCswyq2suK65yaIb/6QORlis9MAy8PC9FfI/uDAxKiSMrfo1Bi0kZy8WnXK4vCSJo1Gj3mcoOC5lb5zyhzHB5ZMtBTHOb9gQLq69WXeUqiLodqugQDo7BR10nVWte3GoN6tdBUPS3EdbfunDyUyUWLcEDmwNy+UKqdBaLv3cvTa1kaNKewKbLf5Z8mwZ8HIsCZlhZ9ptyuWE6/WiDWWKLnNee4Cm9argCeP9VRiEMRMyRo2Q4NcrsrVBgng0OUdu6VgxBSdItvyDhpM2pxrrStwT6egvMTW9Djr5ttUOlXP5LSBquqiDmih0GkN/K58aBWQY54Tfx5/Y4lVN9wDlUinV3jf0Kj+9+JM14t4ypPQo4xrZnud5IGRROnB4rfM7hKF8JFHJ7kd3LmlarAzasCUQEGC/WeSp7UelIcDs9e8p6oNAwMSAADLowMxGfqv6NOXkrPDPqsreTV9XcgYmLfBT/P6HQlnDqZmbwiu3hV5pcSmLmyyuOJSYCHj4NmKstU2epp13ps83Dw1IkbGjy+M8vfyKlSSRLrLPy9JutkTwvy/gkes0uSrdPKcGtRAiAFiTcHa6eXI0GaXV+UyeQqzfI0st9LVPTl+wzyqA9xBSfm7leyQLH1njnDnMlVKK+g91PlBMNXO52K08Pyp9ZQiXbF0rXv8yGlrvN6x0Y+AAYzlv5V+WxyWlQ+GTzfXevJZtB9pXns+1V5rkqaaVI2JMrAETGRsbdL7zs7gLTIaCR1tl0rx640zXU0l3ULRsVj0780nTzz4Naie4ybeR1C/DojvUDjbFj13l4gGLl+bTwZZYdSYO7pYhOESFGCFze7oTMqu95rWN0Yk0JXJVzksJB5D9F/JJgQ7zTd144VHuUrJl+3rNwSk26l/ZY3cFV0hsq2MgbuI9iad2RpPCT3+RVDY+trvH2kjki7F0gv3GWukbkrM/tfvfYLzr0kJzpqyW0bOW2Fa1iIAj2SmROjTEwqIK/8vOIxkX80p+RU3REqsga0TZ3CP/b5Ic5kPALAuOVQA7fwu0JMf4YEOIsuJMHtGQWaDwRzdA9618RT6cm1h6RW6OtAhifgoJkRwdcp1NJVhkfiq4JLcIJVwpAb7acAban6AAxA613lr5gq60y0IR6LL+RAUpYgVokR7fhuR3jAeMrjs71A7IeHpW0DuPm1EM+UoCEyZOJ7y31SubD1d13YWUAgMakZaxHyav+A28GL5BiBo/U0uasUpwXxjWVrDISlE71UMaYz0WWd7vEEZpIizmHsYqYUb9mfyG8ydP+2rtMlvFQYjoLeWRYhK8Ntfxiu+hY28wIw8Su7TUJAjjqembVf22sWBPv0fNOeZMwKFc15p3WPKoUYwnwWBQ+KVbZGQ+MAj+B3zfh+H9T6F3mypyytM0N4UIulcc6H6DRyz7j8zxjI4i0oSje7o3usdd0wgE2A+UAQfZhXDTnmCN1jUoCIftgMlHKLoKpd/ExSGZVZ5Gray8t0aqXK1Q9y6ebs5VTsOroVjZte0zcKRGI/TpZ5kjGsnJgpeJARkQi8dNS7Zjrk+MShVQKUePcYhMK2l0LfaWPaHzoryYCXNOWDIsNHIe3K59/EDhO1FXXYhL1oROBE2oqPe69YoHsX83xoJUgvIymskU038ms8cXkY/h8mI+q6bgECT1BfwRMkZ3VvoeLj3Yt/ncQgv9RpKLjPtC9nYxhuMQvvJxAMLUnPUGbZkp46ibszOjH2pHWMITeSsFQzh8/bqGHbGd4b/ouegAvxpRgiQUvJaXWS+dVa36KNzLJ36ddKFxDEqQNWLi5PWsGUsQbNZlxewFdqsQFB0BX46XLEk74RwyhebybGopns0vqHbgMPU3RVaL57E1vNzCZUgjwDHSsHviCBKHb9M1HDpb94IN30/PvRAMA9nogKEfbKCdNT+NrbZu1TutWwBksEWoJZ0t/7tEJpo+9qc9aoKh7ANhIPFzUQSxXQvj5+Qym5vazEAssifse+Udew1Ubw2FqJd7meYIeg5PD5DoHsUCMZ7+10R78KZpUhhFVH5vLOIdbV+st34jQVsk1faKxOw5BXAzulmddMg0Sy83nK5QvgKblFUpQp5PqL3AZevksc7DjUptMkyvOki64tdmGIp4Db/oeU+uKfy9mhnYkD9JuQe7uqdwdv8kNeK9S3LZqg7x/9kY48oq4BBOa/jCoylu6USc0Ag81KXwNLEHMzLUubaLmg3QqGOXC2pw2VCvoFBsCKhP0bprkwBuyNOpaj69AeVAorH3Dl4xnbajEGsl6R6ZvvjoJ4k5cXWtthj+nzYhvd4JlzZsPYShG6qhGzQSK3fYHLiRTQlSkKgnlOP6V9WsI3d7stNxwkTc3YuIhbKTQqnMDtl5NAXSyA6/X6mUUYKkxISdMhxzGkKBW6QSAELfyVLYwNwyl0fE5NxfN3no9tji6DlERoA7/gLgb296v2R+231ftGANVXboJsw1rl1ScuA3RFCKwzDZQ935TUrTA4pO5MuesL/pbQcuWFzEDq0itAEYRK38usiyTNsm2Ia6u2LQRJRO/bSY7O2E0W2vBofT1ES+za4foiVAQuEBQT80RY/KdL+ja0bwRJ1uI/K91jOo8FtXjylVLWawFlV+KDKdlEEAR+ve7LbeRR5/UiWn+ABeqXakbOxTnfvc0nUtQPUVoXdwJLIiorvloftveaEugoPUkNd4JSFp8+uYDb2wuDY6qZxHuV/MIPVhDKQC0x2eK9s5lselevQRCFOK2jj7RLAHLALiplzaRsY1kHW8BJ/4WetJm6ArkCOvDKufDAXMlUu2W3Elao4f1UMK/gDOpLee/ExnftKgkRRuY4hrYxVUQGzjXOWRvsB5RshVMr0Bm4o3FN9bB/3TMvlMA57a6xb03zgOonPXI9NlsRYA+tLyL8Uwe9pZOCLBh2hANu5AQlSj8kn2OotvmNlpzKyhjCyxFZbSaan8cMkhdfqS0lGxuQNpWp7aQnubyW9pICQRbTXPyTrvgMX3hMs1W9xPqZC3bhGbLi+Gr6OkIjgTCFfyiv+G5PcnNBJbIKsQzJUHyQxH3hcWhguXB2YH6gC3eR5N/99bCjsnF4T/YPkkSJujA+jSgvwm0RbkG7udUr6n5X9Juh3yiVy/+vjpnqzE+GcoE+sWOCgtWUcXDEfSij2Zvf2cIWhno+N+ytZ0K6d3hiGjXEYW1DoY7pus5Q3Bbgi0Q4qr0TOry0OQGfAzJhs85c/h0RU4eeTeNntvHoWzllNOCB6BHWW7ZUw2NJMyJLwVo3IYzqlw/zC1W7tkXPJuuyw1ASTCx25nezwCLaBt05PvGaC9Zh8wVX9zNEWIiU8JG8pLI8g4BE2eWj5yZg9ihP47Ytf0oGDqLBZ73ftmzjMagjLIvw8grG6fKm9ep+b7zPsiEV8SAtMqd1IZObhMcI0e2sdzZV+AeKstXLXXBGhubMWeABGa/6JVZAWzS55TNBmnGpZQ96jNjT9nCz+gme9Gn/KhX6CTPT4PKd2U7kVoTgSrMyy2bCp62SKtbA+cttsa19MJmrvLZi4kXY0lPCgznlyk/F/WmSYn3ChrXo536bO0NSCahRuQ8mv7m5w8hAyzZuyGh+b4auYKEv1508JkDTVDsHlcWohjgd4O37jBRyV83rda7uppE8MBOQFTKKikJH/Y7ajOTtLQ0nJu9Ska7/u5PWyF0Bmq1rrc+NM7VAysDPLV/bgEMDj2IoGPlXvArgzwfthHBVedt1wOUuNA4+xzoOQQV7fhE6xNhhRxnMf297RA5rgzZmWCWPDD5ZH0z5oFMAGaCLYqaDXy9prBFFJ1RyrAL/DsCXZGliipXpgESfRgyQRQhSDmOE1E8LtgN13l714uZZ/bYoXsN6YqP2yVYgARZ1v5kLLGrd4m7hyd1gjz3wpff6a1Xv7w6h+pLJgWr/YakZ0FPIVLe0i2KV6ZqkKnH4fkcuCw2xjYIjoW5etDCv/Jdl/l9ffxAn64FnKzs1DxuaE8btI5hk0py9mimS6k34x0EdWw8nakzkI1wjR8k80u+mlTtR1HrytkHuCXqzSIirTzkwaKjnpd14PFL3QJDgAVCptwLMEj64sQO37odyktYL/hihAjLKsBmPxMh198MMnDP5Wny88QvYJyNFObvz796uqq0B2THMsFeizkhTmWZlm6CM5Zcx5jYD7Dg146xXM5zwpRu1yR11giqDw0CRxX6ut3/qcGGbbBsoQ2wHF1C9O/0leA3dcvIyGV3F7pJQe3TF541CUwN349X1JeXe+I/taSSyMDSrSMVAOem5hGE4QLsjwn8ETIpE9FjyqgnvGBOE+cOaJbBE6KNuqDjU+OWYKApXXVc6wXpb6LAtoamcszojU8YsMiShhLtf8Lcz/CLxx5X5ToLN18kLuqMp78ZxYdqb0r0PB3naMrNt8hZfuJRi0cBFq1uHBitvmsTe3VxWWS0Jp9wjEJoHjUDkpcnCBukvNlMPPdeDSAWZoka49b4UKyLgLn9K8YKBzcF/28zqFp+U9BzBS8YcQpAiY68ps79KZXYYlvBMy+jw79MpGmxpfked6H+xk/bHC+4UvPNOEMQSPgEn5Z32w4wahiJdwRP2Jbts3Gi81uuUDQ+kROFhy4ixg7hNm6H+QEPiLWYiObCdBBGygvLiFynU6LT/goUeeomPAE5amzPXftyC07f0Iu7a/M+YYowXdGctVChv/lLw76eUSBM1XNV5LDmR91e1j98qCAhihcuM3fuEfWcWZdcvsslET7Nx/P1vklDm/QKuAw3iHB/cMgpoTWY0KihCdQ/NHlgzr6K0VAf309Al+klV1K/PQaNocrJORbhuzCLvuuU2iyuRHuSWYOkO4dmb3VbtvKRdeCm7ltfs4/0WixYJTVDf/8KyiVYuu++jG/nTaxEH4QqqLH+frX72IBhBp/mrnMpCH39oxz2k/EZJ0uZF+inQ8MLSp4B6f5tDOcUeqTgrnqtaSiSERBOCK7GXbQe0PofPuXFGlMTLbQ6RkovAd0tNw7E6/R3piC06Jzj7uStugqHcTIqnNAZcvQZah1l3HRaryZSi8vXDUfu5Ekg/TLKUwstnzP2q9SsbLJwFqADpNjto2aKwD0wetmUunWQGgjAvcGSxr676BFS3wvCC8FNsSdkkkKxyOEWXq7g7gw8sHYM6GkOpQNbqnGLTec1QAnvpyXcKup+TlEDwomNwOvRk1A5O9LnGAnmHvgcHMuUwWwuQ/+4KhTKulrLuiWMI2DvWSvNsDnMKxuqiOL7CYSy8nJO4up6C66vJn8r0ymJ9DrK/VoFDNkgGZPQMRR060SQIrpgnWfzYivOGEAS1EgxBRqsmsXsavbzRttr2hexiGtQKRYGVHlnXNnCUJm7X5JyGdxNOGMwdNFTKvLTPOxKD+V7Phb4rmmaBZojynNS4da0AYqZbowmeaKJE2AMuC8eiMXf+I8GcaVM4UvtDnjNnWwWCWK2pIRK2V3s6jXCKrFAiqpmvE3aLSS5ewzi+rx8mjqdpGBTqZYnK87A5kDLFviSISR0EisWPSo6n5sIoXH4XNhrZLJ2z1htTCSCapNYQGVaNqEy2eYBClBquN9aKN+5DCww3twyJZXWP/8oN41jL988Sd9EQWktifKINfEgKFcugeKx+rcv/+NaV3zs+GRw3Mi1sIsujJGKwFeAW1kB1eXlJwMUXOJ252vCGQusN2eMQG9nklbELv98AZq1Utn/IrY3wI0VhlXRiAroLfjTizdYggO0ydx4rsFbDDgR14FSj22TaC55zPO6NVgfteSRasm7BpLXafUzPlmHwnEu0uVfRhNm7769dY/xfr9KtqcCbVG4+NmoqtwTL12PVY/hX95cO9FGxFUm8dqUTNlvb5xP9uO/eC+xUVXYgDJ3XV1QsRdqTS2YUGImXzHaGRwuUdrCWcmuTf2YSTeAWMQ+rFiNKkK1nASHr9g6QRaJiXFcL8r8k9QPyeejpNDAeygUgVGfPqG65vPxLCchemaywUcGqWoLTVfNzrcIJasJeEcs5lNBVTzmE+r8OH6hD/KR5DYu1CVo5yKPknLI6C6f3Yp8fCHOqUroOczWymg1AoXMPis/WPaNwtOA0BeaHcxsOSGEkV6pUdFTca1TXRcQ6Iqc1xu/EtB5lvyTrj1Umfsvf0jNy2/L45/DlDP34T1FR8nTXrs9synGpNa1LzGkGRGK3NM2Tpgv0Rm0BU1enxO8HR5fEad7f0PxDe0jDqa5eEGGn7/BGY4h3sTv53SgBfLQgNDc5o+6L9ZAAhj9MH6hQUXL5G61XHyLuUaDGoSGuLe7RDvti++OPCye74eunDHsA3mvGtc4/yezqZN3UycoNKTOV3cKMBxrq33rKiAUkr2q58j/YfFl4pBC9TEOjj3QrllXZdFN2QNdZoSQYNTHvkSjg+SKD1E/ZSOK/dePwqj9LEy3L4/Fl1wsFQRxAFO1EHSMmn7LLJSw8BdBSMF44lMQsBlxSiscdQ377NQm6htEqaC7njZUxBIO8pNTcGDt5MbHE2Kz3PRYc45toC/tVpxY31iiNlxXArpdegkR11e3D4bIg1PgNVLK2b+A2EWUukp7drNM8j9C7BjBJ4RmcWdtj3XMxMUgJhNuvzjYMgL6GJjiMGSxB/07o3gDad13douKXmgPYtYUjr93zCOMGxbXq8puL1ydDyJljxEs/2TaSoVZivQ6JwZKUxJuEy/rETLCDm4khwygNaeAPTc+dhZBTqA23fxDk6biXlCG1zxVj86BbUVrC9ZLkyUJWIGciMhfQkZd+hwt7W7okRcluDgK+CtqKGxj0zYvgANgk+v6PuEGoxr/zKMO/bllzAe6s8wOyX06Mzl0MkgP9RtL6IzNJgCEm7jVRBpBANOXeSSyMzkP39k+nUJ2RFb2bvKT0QpnNGMx3f8E2zu2tNZQ5KQEiHYhiy2UgOxW+OUcqZMCf03rKDK8NOAAXbLx52MgsXkexjI+EqVomf7yDLNha05DiAttYoNwE9UjZVuWL9qwpAIZZBVC/IbO1k5C/uDbxp1wLu7B42yfD/k82ZG8pyBnmRFdEYQUq2bI13bjtVzKirWpKKg7UyllSkuQTJbzrGUj6MGwdx2dIoBjlb7BYYByju5drqr8nu5ExwrjFPo5I7hze8Q+C5UgzCmetYE0wNXS3jsodvWa8dOT7yKwOeHoElwsZZ66S0HqLLokLY8/OsVE+fxpbvHyIRsyNcAsd/A09/LvqppDVEDVIGTC8MnKdztU48xdwp3fiJl42/aKkoEqhGj5wu6xbAjGndNQSgpOGlyjk/nns9MNExU4wg72j0jpeLjqLsSBsSAu++KDh+TUEsIA1lGZIZ51qanupkwEckcX17Ict1pDcufp4eisL7ohz6QsqZBLWKXg1Gt0eqqrYTkXKb9vU02x/a2Y7eCQmeOIQm/6QgH7baljThof9zHtj1wA49c4y44Oh5TFuv/FkmZDsEVXSyE/aMaJzwrKebcGS0aBwcoAHfWJKuG00ziOBhbRrVcMOC/Hi8Y0YX9CKlebvQ/Ziay/hdvOPMVJh1ipwAMSNsAxa/5Khe+umPquYpfJTdCBtJU3tLBq0PLYd4RW/or2fPAIjGc7Cbjl6XdsWLVuLJAgqaKE7zQcXVAZDvdi8jUtPTEvMUqlm1sG/DgxNme75JUaXu4DTSXwQXKFf+UrDe01kgtNqOAUk5hCj6cd8JZdvmIcmBvCJjWm92WO1BF6SMMO1edvmBpLfCDeohbZzaK7hY/Nn1F19nDTIfrOsSpv502TQWwFMy6orgVl4mUycyhu1L+6WlNlTUerUla7LRAP1Vd0Fg2pAzTXFEblBZGmz4RS24JbaSTPmCNdaAf5zbwHiq/AjZC7FjhLjRC8c0Y17MuBpen6oeG6Cs0djJLR94gxKCo4esrp+O8xr7zUh5FkZ0zxw8m+qqmpq8+9PfGrf1LMl/fhW6l4dK6wNBdn+f795aCwILbA/En5mP+rWF8rR2dgaqlVoc8v5XQh9s8qIgf8B+3y4KfamZr8HfhhXFqoc2KM1fY6pNtX1hUCpqqMA7XT/18XMrVBdq7FVrMr9iyDtGfTCHEoafchGiuyp6y7f+UpPZJvDm36urflVKeqbMMhSv2xIVzm1/E4tTF5BfJxOtWN+6ba03owWy7Pd5KzWEgqk0VBmW8bHCYptDMgfcx+kXrzg+jGaZwNS1DAEDlj/w2VNTacfiQlXtWi1v12pyp2f4pHF4Z+K2J4cbv1UQ/ImJHzjE9Vb2Mue2lTfGsLmZeEjMqYEd6XCSJsZEGGUGYIc9HA6Y49JZVncyb2PzdLnugCnU5zmF9mNZz+GOPrKPuQLayQVsZYGFN43i67CodjZgW02a7ejPlNmCy8EMeJtzdJ3lEzF+9/UTwbd0ZS2K3Wug0iuw1s6h5kRSbNeuVEYanlqU7Z14ISAXdv16koiVpPxZGaX95da2P0lrKYNwTk2BLioE3PhYdZQTVLUcWe5spFSM+VgJjV5NLopVfUjST/VJ0viyV6BS9zufZd/XbUbowqlcqS3FOnF7Mi8YANOI7JY0nFRROvXmDe5DD/8HWnmkhK0iSWraQAKBtro0w3v4EQnct8cuq4t3xomynrzrUhnIMZayL4FBKRUCNqZU6NGWR5oIKLMD6/7LEyLvIiBCT2nPI784nrbDFqL6Ol2LMsjMZygBgDxBrJvW3lnnkKYrcjQ/roTMbZafl0rcZKRSI1jmeU2hHl8XjDT3KIy/N06cySFH0itrwefAsJOZcTqow7Z18FT5tQ7MYtDZecI99ly9KmAlxTv+zSe2wxF6OGVl1T7ni7D3Yc8ihWiiiUQME6kSeX+rhvv9azHmYuVLpluuYCjQuaq4XRFA13dzbf04FERzO+aeXXuQ3mRQztjxFSzN+EJlaDiiLV+TnXS25HPN0NzmKZ3AVuzWQhVUvBb3zUPUZxQvR7EZkHPL5OGqqMPx8WKoyP95+Syf3aMJjnkqgXACfP7hjXL0MG4mMktS3NJJS8IAE88D06A3MJswVyAZLoAC+AOQnNS0Sg4K8TjE0i+55gvkm1qT2Y/Ek0GrhIVRnR/Mkq/PLAySvsxzFp82MiRVBGkocDKfqZUG4G1BlT2ufWI2Vsr4G3b1ndT7ukE56j2rWmpkfm1FjDh1rn4kA4OI8bgSD+057XzIT/qIA/tCs0n3ybDyifX/mLIUSqe2hIePgbSKjnjFvfWCr+qJko/rGbgMrWyg+Fqv3qdqblzIHQjgPfmuu9U7UHeT63mo8mLsqgPK23pkZAlTMEZ7Of7B0kgiMk0ZgVsTmVYn5PRooRNiLjBoUelAoPk4Jhm281VP5F+FSJnXc/N6xsIVzaR8S6MyWhXf7l4ZTnUa0aQAz9e+L/HiqnBH3gFcnX3YeirnucKUEJa5qUeI/TjMaIWNvaov3DRSMxCr2fqZUNqRIOWoLpgBRSYDPj6fRe9NH1q4+sF2UavGJ550pQxTkX3RChVTWe5yowoCCUSkMxPU9Sfv0tIZvhESZrYqUeVy13TB6fHyTr3x90aSu+509PhdSGY9Tq8ZkvyWWCY1ObumPObXEvtlli5zBjf4Izl17HX2oa6UQMhxN+JK48tP145i5YSqDiLxdRu4jvGMJQJleAYiQG4KiiCpWQUt7UuYihJ41w7T44seuGk79KPrGW5DIM9rrT2AizlPJTcDNm6VyshfkiHMFPaa3LosPgbYlPfHqf6J3nu10r8Y97Hgq5x98TeOIFZ8nzc+FBO56rjg8Em7KR1PSXLQHFw+6Ugbok5G/44JGDvNg4/MLxOL7rZSzI1F7xXQulW8WxYpbBggmMoyscPfB8sizWKEQ6GY1QV9Scme5ol+SbNfV8bfbviTLGX76Nj7/7XJ+f3ex6JDGSvbpqC12ObEGYQLPfjeVPByCjFpbGaDW56av9goI1mX4AMDRRQdrTM0AdvrnnSEvjHw4io+PvYsI7WMmx1xIJvsvouhIwIYmE5BKWHe63HDzzYJQOu6WHJmmIxJno7bfKsUlaA5qYIDYEidEWl79taWIgIltkfQ8cpVllaLxrSpmSB5LGlKdKCy0TIe64sSuRiAjBSVQBs50riE5LCBFHAkLVoHqKpjOisTp+qlHGXZZS3b29eDIlwMEp/Rin12eu/S2I9bjyQa8sk9BzWP2hY+mnHsRf0mhkwwr+e699hp7CQutSfoqNp++Th2pc6GcaGBG5i3sx6Dj6zwraOJ7whc8AdhdMIuND2kiUUv5/TtS0KFHOZNh9wxmknLAIVcjQ2dO9XeJOYlzabcFFiY+N8jtkC4rUCN3LMJCOycrQd1pBYQBbOjrC7eLgx0yf1Uhu3L+WrO/eCXPU54TV1gwzg95bZM8/qKx2uy+W1gi8+3PQ8WFHkpyAxRWnxIMTK7BJjNETvZ7VYR/9r6PSACtmdSxVVaLz0nRykJYNxqQ75vctacuYo90p980t8pQukTalEez5QVREfnnTL3w9CZk55QCkwEjrdDa63D8vqzJ8zhQ0IJzzVr7lvpDj0DFaBavHRyMg5QSXr01D1kZayoOZ6LgafNpvrSBxSnHY8/zrvJwJh1l6DHl4P775T2XdmnNeaGpAADmY9JnTeRr/YCtEWnfQXuWDp8k/pJlK5nj+xn9BZ9M8aioxrbQ92UuhaH2yvhUf9LRWDjcFPos+aaSfWIzW/hQKVmIlxZOLli6dqLHGegp/OQ7UibYUZDDcWkzxQ0h3W/5cotwaYZ1fUj+wNLEj//AhJSMtiKh3nLHndr1It0zlyuKn+pA+gk04WGB7ALUrrIfSkqBok3C5sTrNE+22aZJoCog+sbfJYM+zNHlc4QnUNNlD0ihLLwO9im90kJ63ysajt3MZVeNM6l+VSR3Gtn3sbtnY5a6FWJhIWgbvdHzesAVVUeQzw7OL/0BYYIvIt2/1xS8jgMb+OEV1sd8HTqpuz0PIIbUmVVz+cX+p86bkK6Cg9ZGoiUBJxbF15a49Xdv+jQuyCvHMvaTgPJ22737HYp+oSdmL3cgGh4cs16EhKTgGRDUtkOpkqibS2863hgxmnJCgBvaLLdsfpPQ977sYfh2sOvviV6PgKq2exXrw0hMgkeQ3UBq7zm/+2FlmZ4wv8QBm1j/nzMbHGcQq4qENmr6AODBbkwXdmVPP7PE+vnSeorgCoabaB/MKnqLGEeBT3SErBePoFzsm77NVx/hkxMBQkiDsl2vmB0yhwwkCKm5YiBOw92adPWU6O48Oc+leHB2E8Kt5jCoQpwKlwNmV08Y/hV5/zev6wK92DXu/1sANnxb7mBYkwVOKERNXp1r7++lWxHn6WdKiSYxlqzFt1tjOyNYb9Bj+RXl0ZRieojJ9fIzFM2o/GOJ9VjZSTh1BembgI7l0Hk7tcvwASR75+pYrlYraKU078O+mNlInnjcBO4eDIhqVCP+T3PlXsH4Hz05QKi9WgmOixg1I272NvNWprOHrqeC1PzvjaJOCqYy9998tdpPE9BPR94bghAF5mFPvKRhMsD7n43zIFcU4nruY+lz+p0KcmOg1fwOPzGMEH/iKlPxew+Kl5LbXDI/YJx7/CGEa/UiC5qDxHgZ0lG1wcr4spfoJC4Js/FGeCUnwAEP1yMrFyOLgLFIouI79oJ1lZ6DWXLiqwWYsxv952cxTeAJAJVoiSf/3X90ifjdhSldKcak0uFCBut/Xh6M6uW0f/eTIEWJI5+pGLaBnGROU4fP0k3YeI+MZ+wgj9cO2GwG149yihwI9MkFBA7YoJexb27e5FdhPnkA/fzxmldO+saNWCXmmfIPs3bzVu4lbOXVf8wVJNx+5A9m92lI83ycQUKc+AC8cncyiTq671gzAqiCDTCNeqLud0WvxwIwzsWNRi4j6b/+LT3GSZytN/NBYB3X6id/3D8BtsHqVNBEPHEK4nLFQ5HNvh1cRpltj2C935b69gJYAAZphlUoOHFthQLTTmUMuhCbX0iTQiy/segpj9GuGODsBz6lP3HA49L99oZYIwMNYnvUusm9AqZ3a2ANS9qXwJJNzACsoGaH2l4vlPGjH7SOkbcmax9GHGe3leCccE9krvDtq/nPl3Wgis1J/UL6SUftkmVyQTRDnBu+arV2ez+WLjAy+PnlegFj/IqIXvPUM4vgUD0TbBv8Ahsuil2wKzAsgwTQJwdo+yMAoJE2rh4Qf90XEN3LVWyMQRdjwEBAE36sGSPPDz4zM0SiWH18VlZdYCmyDqkfWIchY21XtHyQVUct1WxGSYYVphLuOzPwPt8kQDtHIPh0M44JvGH8IAPrJrawFmSDIgbbbcJ8x2NxKtixJavkR6Kf63fUvWy4bXe9J3KdYeQaQlHML+UFs/GaMq8oHaUBDiI6YoO4UFYZ2/fYCB8iS+jL9UmlOHu0m1Cqnn7D6QaypBCYlgD18pALoA9N9knx5X/SdfSOVCZpp/l+2Lxo8W5CNXzdO5RszJxwSC5qf6L1KLqx8N+3C9yyWkpHTF0zgg/KIw6dW4qclN4dR+9JU27ZfUsCjjbnWgtO5RZN2JNmmHmkyiZt6ymGXyAdMsYKHKlE3hcZv/Gv/B/xIhOq/qVNonTy/VW/DjNnvOE2CoPgfxIDp5bp1ASI27IrEMZLsyM0DmcT46L+lwH0hn8VF3KNFJVUDlVwLyPKVbax2pqsFfiHO6n6CSO4+eYikCrGaYZf6Bn3f4iCDXHa5QBDpXmPJLnelk7ahcjIkjD38uQ3XgS5ws7dGyXU/vkPKegHzqUP2OA7ELt+2x2McjcrnToxJyjqzpO6Z36BqvZNPZS1LTCBxqa7BKY9G4lUP+6DCxj9cdrN2yPG69tCu+RF77n9A+Cf1rPwcKUablbdggSi+a2Vu3kZHSYT+2AX7lK/p2zbVJjwjuk1BUktPMzC3ivQC2CTgWOiHLkmdbRhDpTMivC9pGAnjgkEbQfsVivnsNnNttxG+sVcgTgPVRDmzWUxCVo+LBrsiB8m9cnYcZDNugYSNbRWAkalNeVu7NlufCu9dhYTeySnSemZRc43+Dz1Ch8IOXOZZ2/MmKghJpze8TO9KpxN3T7YHwChtuh62YfQOwREQbqNlHZSj9udcA5eYL6TMrYzfDXRAele3JzreNyNejZh9iM9/P1PT++pIsvuh5E/zwPXRBpSDZ5Lhzf5CoggGJUhxA+vj8lg4X9FX8y+HwshQQzcFtPB8A5Pi3cjYHiuPcMOKTf5LqeEn7rR0J6FnX8DYdIZfKIDpp5iw6si242QfIqwIVW3xJ3d9XYtcj6hpxKHx3bpRfI0h2oam/WaBj78CMK0mmFb1JeTeO3jQ+/pjbhKruYhvBomAV3XKh7nUdVzLX7Ft87eA3bis9NSaoFmhXyWS0i9B+pSnl4fggZZWwe+DwRcaqs2cNKi5Ns9bMEUv+ETpOXlu59b6erAKNby/J31hoHgHCbhoR1Qxd11QMG+UC7TKFzdcvC/5Of5MPebEiqHnZXg9Xw8UF0pT6iPqcHFV11Ti4ZEok05Wcv+VjoWfIRwC4VLeLkZhJVhaFhOFbjx+tyPegWjU1r+Wvh/rZtpdmXDr05A7rX/b0R0P7PsyA5nkwBFt+zY65VdUxFWecAis70b7qCNPaim+PMQPhOJsa91dkPV3j6S2OYcbotrOqriMVot480dQwvZRXzNa7X2QbPBSX2KSJkJSBs7eXePz/urnbjTwfdTJYyI14YsPCMls5bwYdI+lsK4FBf4UZYeUEQ4U5UDbvQG6yCkbk3FLxDN1siF9rYAz9noJk5gPM9XD1usMsW9l6LrvH5JAhQEc4S/qVytYb17tqH0HWX0mWGvto84OKlHDVXVWD9Nr1CPB94wHJZVhUtRf1SNE3cRndj9aIZjeN2p+cblgq4CtTZiJTmCfD/AT1Ds4JRWIvxN0WpU1K21jrKG8/Kzzg9WwVL1Lxq9Hu3JI1XVywz5rFjVbhSKiaAHmZzZrdLvIaPfoDs5ETD8ClYEDP6tEgsbcvYPwb3kvFwe8NjBT98RGqzpkAwWQ15cEz62z3FQsRjwh1i/2SgJtagxpIATgOn0tCRh6kXK2pzmTSWRooA27GjoBXfNW2npvq+8mEitQpjkomJU6u0GADmLCgOXzTbkI8hQHRgVbpPyQ8gt+PV3svSOqknMoN0tx9vc9XV/soc6jawgGhMF0KzH5y/ErgwBszh4egMqpczX8kTIyDwkf9CQyh6Ya5dFjmHbTkzWxw2uLsEEnX40ZfS1RxdiCmXfE6wF0LPPBAtvDCoCWaH7nXgkgRYnBAkMJX4Vi2OI9pDE2VapZso9CV4Q83P7ANxTFxqmEPEbkDlVzqKac4ooxecw32v3jgbT5JCWooKqik4WviPpcfeo5UuU09CAMvpindyZ7uetraQM7Lj0U8fMj020tj5sRIVYrx8eqqxWo7FC/dBGdf8PfbB3NholCR23EXp0dPlCR6BK17hEJNwmzb7epDDdVZnEuExC4vmiGvP1fEM09ai5jWdeyZWY6/ToNe5ICk5ar4YVJctTJSTerHlDlq8Db3Fsk/Ksw2iS18asC+IuG9NgcmSU/GUlmmSQucxTvi9K8c5GWkEnxheRhvdAE2vobD8IrptZrQK0mmhqo5A1bnglHVmQOIm+EoiBGzJ1MK3PznTQlNFX4XPHOQtCILBqff2e5LPzK8xVp6D/eZtUkAWQ5l6EsTHGioZy0rZK3FGlPFKoJJ4AWUTOFjRWDl9b3W6/JaleZSAfjVQY1XC4vNyJAg+kMdm6h5eNpgv2eeTauh4BT6aRVhCpjnlDfrSnTsZvWO5Uq/nRQTHwbkZKvXUhHOoa7gO55Di6pQU+rlIRaAPEwonpWraQpkhWow4y4x1nrF7laLFBaaAwgODL8AV5JFybWeJHokFmRO0gSkhbTjMqpBDCG+KYy0/gbO3yzKk4u9xRdD3oMOjxys7osHY2dCgb5GyQLntIqCMnVN600hO7A+tF5BE65yLfZB6UJUQrmx/vTc97O94uN6D1pKOWww1eqJ/oWXwvbibPrJKfz9AfiDpYv6lfJfVkGF6PR72Y7MUjY5tqP234R+gYSXjetucJWGdDrbaJTxbfQcEmsP8ivNx05H2xJJulVehzLqleC6hlWZX0EvY3N9ApMPEVhKtpVdznAOXgFjgNnOylwAymFT58yldaxeHumAmONYkDRMgFA4MtpCxxOM4G5fiyutNdIip2e28WaJgZqY1lHAEY6yUn7RYJWSVBcParjvENXl9yEpNou+IjybpYnh7x4dOmnuSk8HOiBL9y4uC68SRjLJN2is3v94cMVMpBiRwYlmYkhq/Wve5wruAMmq/qgxgqq51MOcgaADJZKbqCLTu/8adArRa6sGeIKYdLExmYtzDL6iWqiRJD960Pid3wUiV8O9HSApjnvEZTWb/B96QY+Sd+T7MBXrRC/K70k9zRMhlwC1DRc9nlSwQkkkEB2FOl6k632Yw3SlFHq+V3B3jF2SHfbJPG6ygXpXL2T7yjPVIOEmJYerTJp7HwSBPorhc620JVtu6j9dxp0MNddSZd7W4z8KIucMTsBoJwn8+R+domBxHulMps7WWCCTYfL5qOmkibz0uRL4vHcBUnfWikbaFxBB3uDAPqKfz6ob4TGarlBCgjHnhoxFoVbB1pfdNdao/mg4xsjOlIfAGnsubHF3BcGnJrEQ2Vu/fu9tHK7lqYOj/9a/GasBjTvcyiIUnFX+UpisrxK1M80Or5EfI3mZSK9vuZmLAHqzPQj+Ij+1XaJcJzf2JSKlDhouWW0MaIywNbBHCGAOWPyUQ+87iAktlF6lFHIgXwkq3a19DXOvzawHLTgBjLmhM9Jf47Zg6q1i5IqOAJvi6No3lIMzivi8rD0NNCFgaY2S+yRn4XfzAm6l4AH3VXo0A28YK8/sFEvtGet/XtU1OdU08G9Il/OPVAP6V5W/k0O8W8Fqo54rtd6RYZzj3uVU+JjtsLkJCPL5BGw241eG3mTslDAIpbNtkH+z75Ue2qFDazxeMwbZIlCOxG7bUDaWH3kA96vBkAys5/wtKH6qxzyuZvlNqqaXRYDWWm2Qls8eEZDvh/3dsBcr3eYtBubqRMtPro+p9kqI1eMcGKaNAiL2cqcHKGXqNvFLdNlCpNk/54BrbCjuAEOeZIDBGxftX7c7G5+zQAyDU0Na61wXLkGxcvhKs8sqqZaAldOxyuJjv8LD4tRSj8Z1w5psfr6y+jCPsVgKo69rouPSTKF98V0xaORpfL/mmu4R2I8jRPbRk3cPULx/2vSJiJuT5juIhkCMvHc6glmF/sAxF9bUtnyaIxZ6pd4kvdDF69chHQnBcCUreHnqusAWuOwlamz6DWIDWdPwZLZhJKlpv7zenFMb8TxCgot+/4avWj/xJB+HUaR244hpHQF14h+feaa0/kST24AR279RCMuxmk6SB5yhCDF0zhp3kLhktn6QCXP2zbebTAJW8luV1C9cyCQxq89/ZcWTmmw4X+EghY18jnrZa+C7XDGTm0ysKE6vzPYWqsC93t6A90vyOeWttvJ0j2MAX7FwB1fdF+FMmXk4neOZJ3m6e5a5WYdgu/5LlBJ09hwez/6xC4vpOV/lFJW7k8hoRUcyPRDrBcPX87IL/77CoKu0y4H2IERymrUcKBDRoUVZcGuiYwBfxkJtq9X9J0lys+k1eZ85vCAA0TdS4VNf2H7SkL9oHMHd/tx9gqbm/IkSPNqWnBZ25sxGPutdmtb88TgFHVZfCrk/hIV+xSlWYVhpPJaLjrhACD1e7DANdgjiX8fCP83vEhrYwFS5g6ExuPRx0msDBw+0A7uutZcDxx8/HzClkHO4n1LJJ0GRudGEe9Jy7T+aKtvYxqa3f3V6BEecD0cmkJjU3a+K/wRXa4Tsmecm1xqTlOnuF0QyUawD8/cG69g59Rwj7FZLS0/shVMJ+EmU5TiydYQc5sckZv+Qc1ADGNC8Cg0f0BlAIZiXIlmZ15WIiD3fndgXpL6Aw1mScY7J9DhF2gcIe+CtNBzTsOVQZQfA7qjsIbiX3YNWx8FphcC/0BynitCCxMjoQQ+XZsDKHF6DgOggDtQ6A3zKk5Wwg7T7mtrSamIO3lf8Z+1VY7fqqRPIjjV5o5+kWlIDqkzqgqxtxTldrIQ0GTTlE66G3OjG4jMRXs4A4Y2/cXYfOB1tOfz0jtCNOLIAqqFx3tmW/Vdhp0vJHgwHLS9Wy4hVxwwBQYW7qrfNTcJt27AbsyVyDFFDg5xsYA1RA130z6mzn0asoC3SsWFXZ1uiFa9OeO8D2rd3nntWZ/keKq8Hj0tDZJ/KkXIXME7k8geiX8LSuScHTxqGFiir499nGicHFJSaspN6av5Z8tB/Lc70kgEzAfR5TEGYGxXzQbo+n954BmHuYEoAUdJ5FZ0x8qo0nZBI4pKks+pDsN8u3KK2IFDJC9tCRiDM3ynHJC8PX/tC9p5q7l09jG7rx6iJXbM+Jtl1mq5+UYBAvsxqCSAT3rBRKkVh2KTlPQQX3zgP3m4xQyyEJKQ9UbYHytHp+4SKrZFzK9hfPDA0QSTl+4hzJrYq18oxsTwospDQfGyaNt3V32FJ1LBObwofiIDS26B1m2D4kj5dXhmGBLt2aTZtQZGqKmv2JIl/6hV38i5vrLr2RmgFdyIANq37pIjJGI6V1eIpl9gEbVaBzeuOXYamY4r7gie33lzlMQfm3fBA+aeP2qhCVxKyTvJIJi1mQJGjxDLy7O67AqnhjglOt4mqNni1P8XrasE7ua4P5S7acxfArU6gJqahrgpzwz80O11zwylE+5x7OzsujFjPsx9sw9rg9gm7cgjXUjZ1QnW5D1EisAb2vLFFiKcZFatFoyewH+3Azd6Fe08CI9TJb10CxFDh5Je5TV2UJklG7+SZXbeF3MtWe4NVNDKHqhj/Sz8ubFmwpzSKlRknaQrM49CScZ64KGBFHS9B8Mzvh/RpftVomWOtsQYszA0X3GGMlRtasFGD8eWpd0WjcSqGJTtel1fJPeR3UhPZKxTPQZ+miEcbOwjQeUky7GZNNkSnLVqapP+G9xi6Z4Cpleg45dB+aopvreaM0YDHZgffwLzo9wwyCfwTic+A9tBKvmU4B+nhjzJ11sWayKb9HdWZyr3TqkFw/zKr6nnpulIJpfPaSW+HCIA+BzCGV9FS0uTJyrCb2ocRqEwLWs3xIIGCt+YNmaEu821vL5YCAy+vuulaxojmayuzRoRjN66gR0PPQy1LIBR1TH9TIMbEpuaFvg3DCLSe6Pb3yshwKZVWNUTLZ9I1oJS711V03s5pbxq2okUIV6MakL4hn02sFL7M/Kli64D6F3my6FX33aNhGXAzygREWP9vA0TknnWSCcqRcZT8BxuQN00T38kj/Tsaymwbm315k0Fl/ttWtdnVSgLyzXC2Pi5etAIf3ivnGFoyTNYZxmmWjmHvYNBwyK3S+lqmluz/eLQBkTe/YEQDC6HrKfYp1RlV420xl5JoAuUgA/7DAHhhguIawFTQCQ99yj/LL9eb3SsICUBhTaX5zpft0TFSYzf6c/g6H548NhlFD2zR2H7UPPHUwPTaPu5gBMRtWro9rsc7qJjO76x0TcnGcIfAWdI0UzTro6luSzIY0Zi3CUdwyOWZH8rYWu85l+l4WCIp99w/aBDl4/HVm1IsgX7uXKc0kmkJzdtc2jYS72yrQm8eE/oRzoksbzxHhADvp82iscojIdk9OxBTKIOPAfCMc9XhJtzGqMNrQNSo4GN1LN59jQ7u+2/8ZBlZHNmnCfSHFFQx4g/Jg5v/3oJqFhA9J0JsC8JZMGRvIL2exdXU8dAuoGI0TcUfsJWaDt7EBtx3DRfm+iHOMFC3OlOE9hSd6+a6zFo7deZKoV0+4Ui8E34yKG4+pj7b2/mwjTrBW9/z5TUW+KOku+zra5YNiuMVfPNOq4Hqo1iULyVAAyE5BOvYCkf/hwcbf9SRqKNF8FrSS1lrLq6fdH+g0weAZQMXvOeeNNO01N0cefA6S92E+YURU84ZuR+jIS8z5k2u0L9KzXXJ1Auo54+YU58KwcoPPRGhkYbFgfTUIPBAItUgw0JqdtDV3tQu7QAp9X+qr5yV04/vroCVHvN+ZN5YWioGU/oH+GYI/Vlrq0770KIVhtXEpftt4RgcbLa6rWyZEMLT8pUjiWCwhfj0GEVqWJJca3yVr9S5Q+Zj9MjdVnW9mgMIzxvZ8sUAVmp35K1K0Gniqc8MofTKe6mQAJ7wrZTblCVPoLIz4Ono6CCXG/xlNi6guf9y7hNAamJNsRpDprlX97y97aN4gW9EtKaFgBYlf5yYu7Z6h8Mdcp+u0QP+y6N1xGnMNao3gS44JmpbjdZyvRH1/QTBaqa1Yg0/p/JATig/lsPEmOXzCcJiczYdxOKyLu21f9yNU4ZoYZLejJtxMoo2vbjHofYbHMxO1qcr1E8QHGicRaK3PcrwI5sNjVm04IJsqy5CnZFjXi9dTwMUfB6weZ+bAr4SM5bSkzvKpihI1I/N5nKMowgEtAfLJmmuxMUpLPm1ggS2TYcWU7MllB53j90Yjgm8xFM2vWU3bU+tk+ggUyVxzwNO8rcDWPuYUW5to+0gYVCkB4QQ9htf1rvg5wbtWDdC5eoLt898gZqtGtVzTMrPhgjahKZfTKWNbrVvxHJK22OevJnurVC+Ge9HUgU3v8GArtvHImnIZZ0rvIVABJLQR7QHGJFem8h16sUGJTlqvRXH0s/jPfalSjV6QopEbIbo30rAStnvTCT1FTVAzpCgBoaXZ+fXQpHz0UJdJBIEH6qOeLmS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a/tree/specialized_tooling/webassembly/pdf/server-side-webassembly-building-portable-and-secure-multi-language-ap.md b/tree/specialized_tooling/webassembly/pdf/server-side-webassembly-building-portable-and-secure-multi-language-ap.md new file mode 100644 index 0000000..10bf71f --- /dev/null +++ b/tree/specialized_tooling/webassembly/pdf/server-side-webassembly-building-portable-and-secure-multi-language-ap.md @@ -0,0 +1,15462 @@ +--- +title: Server-Side WebAssembly Building portable and secure multi-language apps Danilo + Chiarlonez-lib +source: sources/specialized_tooling/webassembly/pdf/Server-Side WebAssembly Building + portable and secure multi-language apps Danilo Chiarlone z-librarysk 1libsk z-lib.pdf +source_type: paper +source_hash: 3ec57bb279cabda12cdc7177ecfbe41d5a5b40b2fd27b8d5c3d19c6276a44f91 +tags: +- specialized_tooling +- webassembly +- paper +extracted: '2026-09-07' +--- + +Building portable and secure +multi-language apps + +Danilo Chiarlone + +Foreword by Brendan Burns +Afterword by Luke Wagner + +**M** **A** **N** **N** **I** **N G** + +**Compiling a WebAssembly app** + +**Executing a WebAssembly app** + +**Wasm is architecture independent, so this** +**could be an x86_64, aarch64, or other machine.** + +Native OS + +WebAssembly +System + +Sockets + +Interface + +|granted|Col2| +|---|---| +|~~granted~~|| + +**executable, by default,** +**it would be able to access** +**any capabilities from the** +**underlying native OS.** + +**component.wasm cannot** +**access this capability!** + +Licensed to Ibrahim Mohammed Mohammed + +_Server-Side WebAssembly_ + +Licensed to Ibrahim Mohammed Mohammed + +Licensed to Ibrahim Mohammed Mohammed + +#### _Server-Side WebAssembly_ + +Building portable and +secure multi-language apps + +Danilo Chiarlone + +Foreword by Brendan Burns + +Afterword by Luke Wagner + +M A N N I N G +Shelter Island + +Licensed to Ibrahim Mohammed Mohammed + +∞ + +For online information and ordering of this and other Manning books, please visit www.manning.com. +The publisher offers discounts on this book when ordered in quantity. + +For more information, please contact + +Special Sales Department +Manning Publications Co. +20 Baldwin Road +PO Box 761 +Shelter Island, NY 11964 +Email: orders@manning.com + +© 2026 Manning Publications Co. All rights reserved. + +No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form +or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the +publisher. + +Many of the designations used by manufacturers and sellers to distinguish their products are claimed +as trademarks. Where those designations appear in the book, and Manning Publications was aware of a +trademark claim, the designations have been printed in initial caps or all caps. + +Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books +we publish printed on acid-­free paper, and we exert our best efforts to that end. Recognizing also our +responsibility to conserve the resources of our planet, Manning books are printed on paper that is at +least 15 percent recycled and processed without the use of elemental chlorine. + +The author and publisher have made every effort to ensure that the information in this book was correct +at press time. The author and publisher do not assume and hereby disclaim any liability to any party for +any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result +from negligence, accident, or any other cause, or from any usage of the information herein. + +Manning Publications Co. +20 Baldwin Road +PO Box 761 +Shelter Island, NY 11964 + +ISBN 9781633436206 +Printed in the United States of America + +Development editor: Connor O’Brien +Technical editor: Adam S. Chalmers +Review editor: Angelina LazukiÊ +Production editor: Keri Hales +Copy editor: Kari Lucke +Proofreader: Katie Tennant +Technical proofreader: C. Gerard Gallant +Typesetter: Tamara ŠveliÊ SabljiÊ +Cover designer: Marija Tudor + +Licensed to Ibrahim Mohammed Mohammed + +_To my parents, Beatriz, my dear Nonna (1946–2021), Grumpy,_ + +_and all my friends who supported this journey_ + +Licensed to Ibrahim Mohammed Mohammed + +##### _brief contents_ + +**Part 1 WebAssembly for architects......................................1** + +_1_ - Introducing Wasm on the server 3 +_2_ - Building server-side applications with Wasm modules 17 +_3_ - Enhancing portability and security with Wasm components 33 +_4_ - Interfacing Wasm with the underlying system 53 +_5_ - From machine learning to databases: Applications of Wasm 84 + +**Part 2 WebAssembly for developers.................................121** + +_6_ - Creating production-grade Wasm applications 123 +_7_ - Introducing Wasm containers 167 +_8_ - Scalability for Wasm with Kubernetes 189 +_9_ - The future of Wasm 218 + +_appendix A_ - Required tools 229 +_appendix B_ - Deploying the SmartCMS to Azure Kubernetes Service 233 + +**vi** + +Licensed to Ibrahim Mohammed Mohammed + +##### _contents_ + +_foreword xi_ +_preface xiii_ +_acknowledgments xv_ +_about this book xvii_ +_about the author xx_ +_about the cover illustration xxi_ + +**Part 1 WebAssembly for architects.......................1** + +**_Introducing Wasm on the server 3_** +###### _1_ + +1.1 What is the server side and where does Wasm fit in? 5 + +1.2 The advantages of Wasm for server-side development 6 + +_Language agnosticism 6_ ■ _Performance 8_ ■ _Hardware_ +_agnosticism 11_ ■ _Security 12_ ■ _Standards-based and_ +_vendor neutrality 12_ + +1.3 When and where to use Wasm (and when not to) 13 + +1.4 How to navigate this book 15 + +**_Building server-side applications with Wasm modules 17_** +###### _2_ + +2.1 Understanding Wasm’s building blocks 18 + +2.2 Understanding the guest-host architecture of server-side + +Wasm 19 + +**vii** + +Licensed to Ibrahim Mohammed Mohammed + +**viii** contents + +2.3 Creating a Wasm app using Rust 21 + +_Creating a Rust library project 21_ ■ _Specifying compilation to a_ +_Wasm module 22_ ■ _Writing the code for our application 23_ +_Compiling the Rust code into a Wasm module 25_ ■ _Executing_ +_the Wasm module with the custom host 26_ ■ _Reflecting_ +_on the “Hello, World” application 28_ + +2.4 Building on Wasm’s simple types with WebAssembly Interface + +Types 28 + +2.5 Building the foundation of a smart content management + +system 30 + +**_Enhancing portability and security with Wasm components 33_** +###### _3_ + +3.1 Benefits of Wasm components 34 + +_Cross-language portability 34_ ■ _Shared-nothing security 34_ +_Only-what-you-need security and improved vulnerability_ +_mitigation response 37_ + +3.2 Building a componentized “Hello, World” guest application + +with JavaScript 39 + +_Setting up the project and installing the toolchain 39_ +_Implementing the component logic 40_ ■ _Compiling the_ +_Wasm component 40_ + +3.3 Building a Python host for a JavaScript-bundled Wasm + +component 41 + +_Setting up the project and installing the toolchain 41_ +_Implementing the host logic 41_ ■ _Running the Python host 42_ + +3.4 Expanding the example project with Wasm components 43 + +_Improving the WIT file for the key-value store 44_ ■ _Creating_ +_the SmartCMS host 45_ ■ _Implementing and running a guest_ +_Wasm component inside our SmartCMS host 50_ + +**_Interfacing Wasm with the underlying system 53_** +###### _4_ + +4.1 The WebAssembly System Interface 55 + +4.2 What can you build with WASI? 61 + +_Creating a CLI application 62_ ■ _Creating libraries 64_ +_Creating HTTP proxies 65_ + +4.3 Converting a Wasm module to a Wasm component 65 + +4.4 Managing component capabilities 68 + +4.5 Composing components together 71 + +Licensed to Ibrahim Mohammed Mohammed + +contents **ix** + +4.6 Expanding our example project with WASI and + +composability 76 + +**_From machine learning to databases: Applications of Wasm 84_** +###### _5_ + +5.1 WebAssembly Standard Interfaces 85 + +5.2 Machine learning with Wasm 86 + +_Understanding ML inference 86_ ■ _Using wasi-nn 87_ + +5.3 Running Wasm inside databases 97 + +_Making Wasm UDFs 97_ ■ _Getting Wasm into SQL 101_ +_Running Wasm UDFs in libSQL 103_ + +5.4 Improving our example project with wasi-nn 105 + +_Setting up our project 106_ ■ _Writing the ML Wasm_ +_component 109_ ■ _Updating our host to use ONNX for_ +_wasi:nn 115_ + +**Part 2 WebAssembly for developers..................121** + +**_Creating production-grade Wasm applications 123_** +###### _6_ + +6.1 Creating a wasmCloud “Hello, World” application 124 + +6.2 Adding persistent storage to our Wasm app 130 + +_Modifying our WIT and wadm manifest 130_ ■ _Modifying_ +_our application and redeploying it 132_ + +6.3 Scaling our wasmCloud application 135 + +6.4 Distributing our wasmCloud application 138 + +6.5 Another off-the-shelf host: Spin 142 + +_Creating a Spin “Hello, World” application 142_ ■ _Comparing_ +_wasmCloud and Spin 146_ + +6.6 Implementing the example project as a wasmCloud app 147 + +_Adding persistent storage to the SmartCMS 150_ ■ _Adding_ +_machine learning to the SmartCMS 156_ + +**_Introducing Wasm containers 167_** +###### _7_ + +7.1 Containers vs. Wasm 168 + +7.2 Wasm containers 170 + +7.3 Running a Wasm container 172 + +_Setting up containerd and Wasm support 173_ ■ _Building_ +_and running the Wasm container 173_ + +Licensed to Ibrahim Mohammed Mohammed + +**x** contents + +7.4 Wasm and the OCI 175 + +_Packaging and publishing Wasm components as OCI images 175_ +_Inspecting Wasm component OCI images 176_ ■ _Pulling and_ +_running Wasm OCI images 178_ + +7.5 wasmCloud and Wasm containers 179 + +7.6 Converting Linux containers to Wasm 183 + +_Introducing container2wasm 183_ ■ _Installing_ +_container2wasm 184_ ■ _Using container2wasm 185_ + +**_Scalability for Wasm with Kubernetes 189_** +###### _8_ + +8.1 Kubernetes and Wasm 190 + +8.2 Running Wasm in Kubernetes with SpinKube 192 + +_Preparing the cluster for SpinKube 193_ ■ _Running a Wasm_ +_app in Kubernetes with SpinKube 197_ ■ _Scaling Wasm in_ +_Kubernetes with SpinKube 199_ + +8.3 Running Wasm in Kubernetes with wasmCloud 205 + +_Preparing the cluster for wasmCloud 206_ ■ _Running_ +_a Wasm app in Kubernetes with wasmCloud 207_ + +8.4 Running the SmartCMS example on Kubernetes 212 + +_Preparing the SmartCMS image and manifest 212_ +_Preparing the Ollama dependency 214_ ■ _Running the app 215_ + +**_The future of Wasm 218_** +###### _9_ + +9.1 SmartCMS closing remarks 219 + +9.2 What’s next for Wasm? 220 + +_Confidential computing 220_ ■ _AI and machine learning 223_ +_Compute at the edge 224_ + +9.3 What’s next for WASI? 226 + +9.4 Staying in the loop 227 + +_appendix A_ _Required tools 229_ + +_appendix B_ _Deploying the SmartCMS to Azure Kubernetes Service 233_ + +_afterword 239_ + +_index 241_ + +Licensed to Ibrahim Mohammed Mohammed + +##### _foreword_ + +Welcome! I’m excited to have the opportunity to introduce you to _Server-Side Web­_ +_Assembly_ . It goes without saying that the development of web and browser technologies +has had a foundational role in shaping the modern internet. Even today, as advancements like mobile and AI have reshaped how we think about interacting with internet +services, the core technologies of the web, like HTTP and REST, are at the heart of +these advancements. In this book, you will learn how WebAssembly, which started as a +component on the client side for the browser, has turned into server-side technology +for service development. + +I’m old enough to remember a time before browsers came with programming +languages built in. Early in the development of the web, it became clear that adding +dynamic elements to web pages was a critical part of the browser’s evolution from a +static content renderer to an application platform. The development of JavaScript and +HTML5 changed the browser from a static content renderer to a dynamic application +platform. Over time, however, the limitations of JavaScript as a programming language +became apparent. A variety of concerns, ranging from the need for other programming +languages to the desire to share code between client and server, led to the development of WebAssembly (Wasm) as a foundation for multilanguage code execution in +the browser. Much like the Java virtual machine bytecode or the common language +runtime, Wasm is a virtual assembly language that can be used as a target for compilers. +With Wasm added to browsers, developers could build dynamic web applications in +languages from C to Python. + +At the same time as the seeds of Wasm were being sown in the browser, a separate +development was occurring on the server. The V8 JavaScript runtime that powered the +Chromium open source browser was taken and transformed into the Node.js platform + +**xi** + +Licensed to Ibrahim Mohammed Mohammed + +**xii** foreword + +for server development in JavaScript. The motivations behind the development of +Node.js mirror the things that drove the development of Wasm—particularly, the desire +for portability of code between client and server. Instead of developing a new assembly +language for the browser, Node.js took the core JavaScript engine out of the browser +and made it available on the server. JavaScript (and, more recently, TypeScript) developers could use the same languages and libraries in both places. + +With this history in mind, the development of Wasm on the server is a similar migration from client-side browsers to server-side execution. But if you examine the multilanguage value of Wasm to the browser, you might be confused about why there was interest +in Wasm on the server. After all, even without Wasm, you still can program servers in a +language like C++ and then execute that same code on the browser via Wasm. The motivations for WebAssembly on the server actually come from two additional requirements +for browser-based Wasm, particularly security sandboxes and lightweight components. + +It should be obvious that code running in the browser must be considered untrusted +and therefore must also be isolated (or sandboxed) to protect your computer against +code on the internet. Core principles in the design of Wasm, namely, linear memory +and a capability-based security model, significantly improve the security profile of code +running in the browser. The same value is present on the server in the context of hosting multitenant code. Consider an application like a game that enables user-generated +content to add items or other characters. This third-party code for extending the game +is inherently untrusted, and thus we need to isolate it from the rest of the code in the +game server. Just like in the browser, the Wasm security model is a fantastic way to implement plugins for untrusted code on the server. + +The other value of Wasm is small, lightweight components. Because web pages and +Wasm are downloaded across the network, the smaller the size of the component, the +more responsive the web page and the better the user experience. This same value of +small modules applies on the server side as well, especially in the context of things like +function-as-a-service, where code is dynamically spun up and down in response to user +requests. Wasm can provide an order of magnitude (or more) of a speed-up in terms +of starting dynamic code in response to user requests when compared with traditional +process execution. This also has significant benefits for server developers. + +I hope that this brief history of how web technologies have shaped the internet gives +you context and motivation to learn how you can harness the power of Wasm in your +server-side applications to power the next generation of the internet! + +—Brendan Burns +Co-creator of Kubernetes and Corporate Vice President + +of Azure Cloud Native Open Source at Microsoft + +Licensed to Ibrahim Mohammed Mohammed + +##### _preface_ + +My experience with WebAssembly (Wasm) started with server-side applications rather +than browser-based ones. While Wasm was initially created for web performance, I +focused on its applications in cloud computing. I worked on SpiderLightning—a +collection of WebAssembly Interface Types definitions that simplified application +development by abstracting away environmental complexities. This work evolved into +wasi-cloud-core, a set of cloud interaction interfaces that’s now part of the WASI standards, where I contribute as a champion. + +The idea for this book emerged from one of my talks at WasmCon, where Manning +editor Jonathan Gennick approached me about writing a comprehensive guide to +server-­side Wasm. The timing felt right—Wasm was gaining significant traction beyond +the browser, but documentation and learning resources remained scarce. While AI topics dominate technical conferences, Wasm actually rated higher than AI in engagement +at KubeCon EU 2024 in Paris, highlighting the community’s hunger for knowledge in +this space. + +Currently, I work on Hyperlight, which, through its sister project Hyperlight-Wasm, +enables Wasm adoption in public clouds like Azure by providing hardware-based isolation for multi-tenant workloads. This work has exposed me to different perspectives +on where Wasm might fit in the server-side landscape, particularly in isolation-critical +environments. + +What excites me most about Wasm is its promise of true portability and security +without sacrificing performance. The ability to compile code from multiple languages +into a secure, sandboxed format that runs consistently across different environments +represents a fundamental shift in how we think about server-side development. Wasm + +**xiii** + +Licensed to Ibrahim Mohammed Mohammed + +**xiv** preface + +enables us to break free from vendor lock-in, reduce cold start times in serverless environments, and deploy the same code from cloud to edge with confidence. + +This book reflects my belief that Wasm’s server-side capabilities deserve to be more +accessible to developers. Throughout these pages, you’ll not only learn the technical +details of working with Wasm modules and components but also gain practical experience building real applications. By switching between languages like Rust, JavaScript, +and Python in our examples, I hope to demonstrate Wasm’s true language-agnostic +nature and help you see how it can fit into your existing development workflow. + +I hope this book helps make Wasm technology more approachable and encourages +more developers to experiment with its capabilities. The future of server-side computing is being written now, and Wasm is playing a crucial role in that story. + +Licensed to Ibrahim Mohammed Mohammed + +##### _acknowledgments_ + +Writing this book has been an incredible journey that wouldn’t have been possible +without the support, guidance, and patience of many people. + +First and foremost, I want to thank my girlfriend, Beatriz. You’ve been my rock +throughout this intensive writing process, taking over household responsibilities without complaint, showing endless understanding when I had to work through weekends +and vacations, and always believing in me even when deadlines felt impossible. Your +support means everything to me. + +I owe immense gratitude to my editorial team at Manning. Connor O’Brien, development editor: thank you for your exceptional guidance throughout this project, +your patience with my questions, and for fighting battles with publishing on delivery +schedules. Your advocacy made this book possible. Adam Chalmers, technical editor: +your insights and thorough reviews elevated the technical accuracy and clarity of every +chapter. Your attention to detail is remarkable. Gerard Gallant: your meticulous work as +technical proofreader polished every detail and ensured the code examples work flawlessly. Angelina Lazukić, review editor: you orchestrated the extensive review process +that brought diverse perspectives to strengthen this book. + +To Courtney Kimball and Manning’s marketing team—especially Aira Dučić, Ana +Romac, Robin Campbell, Divya Mudaliar, and Sam Wood—thank you for your invaluable support in helping me share this book with the community through conferences +and other promotional opportunities. + +This book benefitted enormously from the numerous reviewers who provided +feedback throughout its development: Advait Patel, Beatriz Gascón Lourenco, Sergio +Britos, David Justice, David Paccoud, Denis Saripov, Fahrudin Paljus, Heng Zhang, +German Gonzalez-Morris, Ikko Ashimine, James Haring, Jeremy Lange, Joe Zhou, Joel + +**xv** + +Licensed to Ibrahim Mohammed Mohammed + +**xvi** acknowledgments + +Dice, John Guthrie, Jonathan Womack, Jorge Prendes, Juan Camilo Cifuentes, Clyde +Kallahan, Liam Randall, Lîm Tsú-thuàn, Luke Wagner, Mario-Leander Reimer, Mark +Fisher, Max Sadrieh, Michael Bright, Miley Fu, Mohammad Shahnawaz Akhter, Olivier +Ducatteeuw, Peter Hampton, Piotr Jastrzebski, Piotr Sarna, Pradeep Saraswati, William +Ko, Ronald Borman, Sander Zegveld, Shantanu Kumar, Simeon Leyzerzon, Thomas +Lockney, Thorsten Hans, Tom Oeste, Vojta Tuma, Wayne Mather, William E. Wheeler, +Zachary Manning, and Zbigniew Curylo. Each of you helped shape this book, catching +errors, suggesting improvements, and ensuring the content serves its intended audience. Your collective wisdom made this a much better resource for the WebAssembly +community. + +I’d also like to thank the broader WebAssembly community, particularly the Bytecode Alliance team and the WebAssembly Community Group. Your work on Wasmtime, +WASI specifications, and tooling provides the foundation that makes server-side WebAssembly possible. Special thanks go to those who answered my questions on forums +and GitHub issues as I worked through challenging technical details. + +To Jonathan Gennick, who first approached me after my WasmCon talk—thank you +for seeing the potential for this book and for believing that the developer community +needed a comprehensive guide to server-side WebAssembly. + +Finally, thank you to the entire production team at Manning who worked behind the +scenes to transform my manuscript into the polished book you’re reading today. Aleksandar Dragosavljevic facilitated my connection with this exceptional team. Keri Hales, +production editor, expertly guided the book through the production process. Kari +Lucke, copy editor, refined my prose and ensured consistency throughout. Katie Tennant, proof manager, coordinated the final quality checks that caught countless details. +Tamara Švelić Sabljić, typesetter, brought the technical content to life with clean, readable layouts. Your professionalism and dedication to quality are evident on every page. + +Licensed to Ibrahim Mohammed Mohammed + +##### _about this book_ + +_Server-Side WebAssembly: Building portable and secure multi-language apps_ teaches you how +to make use of WebAssembly (Wasm) beyond the browser to build efficient, secure, +and portable server-­side applications. Rather than presenting abstract concepts in isolation, this book takes a hands-on approach: you’ll build a complete smart content management system (SmartCMS) from the ground up, with each chapter introducing new +Wasm concepts through practical implementation. This approach ensures you not only +understand the theory but also gain real-world experience applying Wasm’s unique +capabilities for cloud computing, edge deployment, and cross-language interoperability. + +_Who should read this book_ + +This book is for backend developers, systems architects, and DevOps engineers who +want to use Wasm for server-side development. While the book primarily uses Rust for +examples, readers comfortable with any modern programming language can follow +along—the concepts translate across languages that compile to Wasm. You should have +basic familiarity with server-side development concepts, containerization basics, and +command-line tools. No prior Wasm experience is required. + +_How this book is organized: A road map_ + +The book contains nine chapters divided into two parts. Part 1: WebAssembly for +Architects (chapters 1–5) introduces Wasm from a systems perspective, covering the +fundamental concepts and architecture: + +¡ Chapter 1 introduces Wasm’s evolution from browser technology to server-side + +platform, exploring its performance characteristics, security model, and ideal +use cases for serverless and edge computing. + +**xvii** + +Licensed to Ibrahim Mohammed Mohammed + +**xviii** about this book + +¡ Chapter 2 demonstrates building server-side applications with Wasm modules, + +diving deep into Wasm’s memory model and the guest-host architecture that +enables system interaction. + +¡ Chapter 3 introduces Wasm components, showing how they enhance por +tability and security through higher-level abstractions and cross-language +interoperability. + +¡ Chapter 4 explores interfacing Wasm with underlying systems through WASI, + +demonstrating capabilities management and component composition. + +¡ Chapter 5 showcases advanced applications, including machine learning infer +ence with wasi-nn and database integration, highlighting Wasm’s versatility. + +Part 2: WebAssembly for Developers (chapters 6–9) focuses on practical development +and deployment: + +¡ Chapter 6 covers creating production-grade applications using standard +ized interfaces and prebuilt hosts, emphasizing best practices for real-world +deployments. + +¡ Chapter 7 introduces Wasm containers, demonstrating how to package and + +deploy Wasm workloads using familiar containerization tools. + +¡ Chapter 8 explores Kubernetes integration for Wasm applications, covering + +orchestration, scaling, and management in cloud environments. + +¡ Chapter 9 examines the future of Wasm, including emerging proposals, ecosys +tem developments, and the potential effects on cloud computing. + +While the book is designed to be read sequentially, with each chapter building on previous concepts, experienced developers can jump to specific topics. The SmartCMS +project that runs throughout the book provides hands-on experience with each concept as it’s introduced. + +_About the code_ + +This book contains many examples of source code both in numbered listings and in +line with normal text. In both cases, source code is formatted in a `fixed-width` `font` + +`like` `this` to separate it from ordinary text. + +In many cases, the original source code has been reformatted; we’ve added line +breaks and reworked indentation to accommodate the available page space in the book. +In some cases, even this was not enough, and listings include line-continuation markers +(➥). Additionally, comments in the source code have often been removed from the listings when the code is described in the text. Code annotations accompany many of the +listings, highlighting important concepts. + +You can get executable snippets of code from the liveBook (online) version of +this book at [https://livebook.manning.com/book/server-side-webassembly. Source](https://livebook.manning.com/book/server-side-webassembly) +code for the examples in this book is available for download from the Manning website at [https://www.manning.com/books/server-side-webassembly and from the](https://www.manning.com/books/server-side-webassembly) +GitHub repository at [https://github.com/danbugs/serverside-wasm-book-code. The](https://github.com/danbugs/serverside-wasm-book-code) + +Licensed to Ibrahim Mohammed Mohammed + +about this book **xix** + +repository includes all code examples, the complete SmartCMS project, and additional +resources. + +The book primarily uses Rust for Wasm examples, with JavaScript and Python for +demonstrating cross-language capabilities. You’ll need the following software to run +the examples: + +¡ Rust (versions 1.84.0 and 1.85.0 or later) + +¡ Node.js (version 22 or later) with npm + +¡ Python (version 3.12 or later) + +¡ Docker (version 28.0.0 or later) + +¡ Various Wasm tools (installation instructions provided in each chapter) + +Detailed setup instructions are provided in appendix A, and each chapter includes +specific toolchain requirements as needed. + +_liveBook discussion forum_ + +Purchase of _Server-Side WebAssembly_ includes free access to liveBook, Manning’s online +reading platform. Using liveBook’s exclusive discussion features, you can attach comments to the book globally or to specific sections or paragraphs. It’s a snap to make +notes for yourself, ask and answer technical questions, and receive help from the +[author and other users. To access the forum, go to https://livebook.manning.com/](https://livebook.manning.com/book/server-side-webassembly/discussion) +[book/server-side-webassembly/discussion. You can also learn more about Manning’s](https://livebook.manning.com/book/server-side-webassembly/discussion) +[forums and the rules of conduct at https://livebook.manning.com/discussion.](https://livebook.manning.com/discussion) + +Manning’s commitment to our readers is to provide a venue where a meaningful dialogue between individual readers and between readers and the author can take place. It +is not a commitment to any specific amount of participation on the part of the author, +whose contribution to the forum remains voluntary (and unpaid). We suggest you try +asking the author some challenging questions lest his interest stray! The forum and the +archives of previous discussions will be accessible from the publisher’s website as long as +the book is in print. + +_Other online resources_ + +For additional learning and support: + +¡ [WebAssembly Community Group: https://www.w3.org/community/webassembly/](https://www.w3.org/community/webassembly/) + +¡ WASI proposals and status: [https://github.com/WebAssembly/WASI/blob/](https://github.com/WebAssembly/WASI/blob/main/Proposals.md) + +[main/Proposals.md](https://github.com/WebAssembly/WASI/blob/main/Proposals.md) + +¡ Bytecode Alliance (maintaining Wasmtime and other tools): [https://](https://bytecodealliance.org/) + +[bytecodealliance.org/](https://bytecodealliance.org/) + +¡ Wasm language support: [https://developer.fermyon.com/wasm-languages/](https://developer.fermyon.com/wasm-languages/webassembly-language-support) + +[webassembly-language-support](https://developer.fermyon.com/wasm-languages/webassembly-language-support) + +Licensed to Ibrahim Mohammed Mohammed + +##### _about the author_ + +**Danilo (Dan) Chiarlone** is a champion of multiple +WebAssembly System Interface (WASI) proposals and +works at Microsoft on cloud and virtualization technologies. He is a passionate educator who offers free lessons +on Rust and WebAssembly through his YouTube channel and is a frequent conference speaker at events such +as WasmCon and RustConf. + +**xx** + +Licensed to Ibrahim Mohammed Mohammed + +##### _about the cover illustration_ + +The figure on the cover of _Server-Side WebAssembly_, “Marchand d’Outres à Pulque,” or +“Merchant carrying pulque-filled wineskins,” is taken from a book by Claudio Linati +published in 1828. Linati’s book includes hand-colored lithographs depicting a variety +of civil, military, and religious costumes of Mexican society at the time. + +In those days, it was easy to identify where people lived and what their trade or station +in life was just by their dress. Manning celebrates the inventiveness and initiative of the +computer business with book covers based on the rich diversity of regional culture centuries ago, brought back to life by pictures from collections such as this one. + +**xxi** + +Licensed to Ibrahim Mohammed Mohammed + +Licensed to Ibrahim Mohammed Mohammed + +##### _Part 1_ _WebAssembly for architects_ + +### Wmance for web applications. But today, its most transformative potential lies beyond ebAssembly began its life in the browser, promising near-native perfor +the browser—in the cloud, at the edge, and everywhere that server-side applications run. As WebAssembly matures from a browser technology to a universal runtime, it’s reshaping how we think about portable, secure, and efficient computing. + +This first part of the book introduces you to WebAssembly from an architect’s +perspective, building your understanding from the ground up. You’ll discover +why WebAssembly’s unique combination of performance, security, and language +agnosticism makes it ideal for modern server-side development, particularly in +serverless and edge-computing environments, where cold starts and resource efficiency matter most. + +Chapter 1 explores WebAssembly’s evolution and its advantages for server-side +development, from its sandboxed security model to its remarkable startup times. +Chapter 2 takes you inside WebAssembly modules, revealing the low-level building blocks of linear memory and basic types that form the foundation of all WebAssembly applications. Chapter 3 introduces WebAssembly components, showing +how they elevate the developer experience through higher-level abstractions and +enable seamless cross-language interoperability. Chapter 4 demonstrates how +WebAssembly interfaces with underlying systems through WASI, teaching you to +manage capabilities and compose components. Finally, chapter 5 showcases realworld applications, from machine learning inference to database integration, +illustrating WebAssembly’s versatility across diverse workloads. + +By the end of this part, you’ll understand not just what WebAssembly is but why +it represents a fundamental shift in how we build and deploy server-side applications. You’ll have the architectural knowledge needed to design systems that use +WebAssembly’s unique strengths. + +Licensed to Ibrahim Mohammed Mohammed + +Licensed to Ibrahim Mohammed Mohammed + +##### _Introducing_ _Wasm on the server_ +# ~~_1_~~ + +_This chapter covers_ + +¡ The evolution of Wasm from a browser-based + +tool to a versatile technology for server-side +development + +¡ The key attributes of Wasm: Performance, + +security, and flexibility + +¡ Practical scenarios where Wasm excels + +¡ The limitations and challenges of adopting Wasm + +in server-side development + +WebAssembly, commonly abbreviated as Wasm, is a construct that is akin to an +instruction set architecture (ISA) like x86_64, aarch64 (also known as arm64), and +others. In the same way you can compile your code to such architectures, you can +compile your code to Wasm. But, unlike x86_64 or aarch64, you can’t go to the store +and buy a computer that contains a Wasm chip. Wasm is not a real or physical platform. As illustrated in figure 1.1, it can be thought of as an entirely agnostic hardware abstraction layer over the hardware itself. Wasm can then be transformed into +native code for the specific hardware it is running on. In fact, one of Wasm’s goals is +to serve as a common denominator across different ISAs. + +**3** + +Licensed to Ibrahim Mohammed Mohammed + +**4** Chapter 1 **_Introducing Wasm on the server_** + +Figure 1.1 Wasm as +an abstraction layer +virtualizing over various +kinds of hardware + +Wasm was released as a minimum viable product in 2017, and it was created to meet the +performance demand for increasingly hard computational tasks that people wanted to +run in the browser, like 3D applications, audio and video software, and games, which +JavaScript (i.e., the web’s only universally supported native programming language) +could not handle due to its inconsistent performance and various other pitfalls. Unlike +earlier attempts, such as Java applets—which also aimed to bring high-performance +code to the browser but struggled with security vulnerabilities, plugin dependencies, +and, still, inconsistent performance—Wasm offers a modern solution. As a W3C standard, Wasm is an official language of the web, alongside JS, HTML, and CSS. To address +the web’s performance challenges (as illustrated in figure 1.2), Wasm was designed to +let developers compile code written in high-performance languages like C, C++, Rust, +and others to run safely in the browser at near-native speeds. + +Figure 1.2 Compiling +to Wasm from various +languages + +|compiles to|Col2| +|---|---| +|comp|comp| + +Licensed to Ibrahim Mohammed Mohammed + +**_What is the server side and where does Wasm fit in?_** **5** + +With this idea in mind, some of Wasm’s design goals were established for it to be + +¡ Fast, with predictable, near-native execution + +¡ Safe, designed for efficient, secure verification and isolation of untrusted code + +¡ Hardware independent + +¡ Language independent + +People quickly realized that these characteristics could also benefit the server side. In +2019, the WebAssembly System Interface (WASI) was released. It defined an interface +that Wasm runtimes can implement to enable Wasm to communicate with an operating system (OS) and run outside the web. In a browser, Wasm relies on the browser +itself as its environment; outside the browser, a Wasm runtime—often referred to as a +host—serves the same role, providing the necessary system interactions. + +_1.1_ _What is the server side and where does Wasm fit in?_ + +It’s easy to get overwhelmed when developing server-side applications today. Unlike +before, where you most probably just crafted a monolithic (i.e., nonmodularized) +application that ran on a single server, today, you have to think about microservices, +serverless, containers, and edge computing. And, with all these options, it’s hard to +know which one best fits the needs of your project. + +Aside from managing your own hardware, there are three primary levels of abstraction for server-side development: + +¡ _Infrastructure as a service (IaaS)_ is when you rent, say, a virtual machine (VM), and + +you are responsible for managing everything on it, from the OS to your application, while the cloud provider manages the physical hardware. + +¡ _Platform as a service (PaaS)_ is when the cloud provider is responsible for manag +ing the OS and the hardware, and you are only responsible for your application, +which is often deployed in a container. + +¡ _Serverless or function as a service (FaaS)_ is when you don’t have to worry about the + +OS, the hardware, or particular details of your application—like scaling. Instead, +you write a function, and the cloud provider manages its execution in response +to events. With that, FaaS has the unique advantage of being able to scale to zero, +meaning if no one is using your application, you don’t have to pay for it, which is +achieved by the cloud provider deprovisioning the containers running your code +after a specific period of inactivity and relaunching them when a request comes in. + +Containers are essentially lightweight, portable packages that bundle your application code with all its dependencies—libraries, runtime, system tools, and configuration files—into a single unit that can run consistently across different environments. +Think of them like shipping containers for software: just as a shipping container can +be moved from truck to ship to train without unpacking its contents, a software container can move from your laptop to a staging server to production without worrying + +Licensed to Ibrahim Mohammed Mohammed + +**6** Chapter 1 **_Introducing Wasm on the server_** + +about compatibility problems. This consistency and portability make containers the +backbone of modern PaaS and FaaS platforms. + +Solomon Hykes, the founder of Docker, once famously said: “If WASM+WASI existed +in 2008, we wouldn’t have needed to create Docker. That’s how important it is. WebAssembly on the server is the future of computing.” As implied by this quote and its +context in relation to containers, you might deduce that Wasm is particularly impactful +for both abstraction levels that have containers at their core—PaaS and FaaS. And if you +did, you would be right! + +[In 2022, Docker announced its Docker+Wasm Technical Preview (https://mng.bz/](https://mng.bz/vZQa) +[vZQa), which allows you to run Wasm workloads within their platform using the same](https://mng.bz/vZQa) +containerization technologies. While these “Wasm containers” still adhere to the Open +Container Initiative (OCI) image format and runtime specifications, they use Wasm’s +unique capabilities for running lightweight, secure, and portable applications. + +NOTE The OCI image format is a standardized way to package and distribute containerized applications, ensuring compatibility across different runtimes and platforms. Originally designed for traditional containers, this format +defines how filesystem layers, metadata, and configurations are structured +within an image. By adhering to this format, Wasm workloads can integrate +with existing container tools while still benefiting from Wasm’s advantages. + +In the upcoming section, we will explore the advantages of using Wasm and try to +answer why you might want to use a Wasm container over a traditional Docker +container. + +_1.2_ _The advantages of Wasm for server-side development_ + +“Write once, run anywhere” (WORA), while being a very fitting slogan for Wasm, was +first said in 1995 while describing the cross-platform benefits of the Java programming +language. This shows that Wasm’s benefits (for this example, its portability) are not +new ideas. So, what is Wasm’s differentiator, and why should you care about it? + +_1.2.1_ _Language agnosticism_ + +Java allows you to WORA by the fact that, when you compile your Java application, it +gets translated to Java bytecode, which, just like Wasm, is entirely platform independent. Then, the Java bytecode can be run by the Java Virtual Machine (JVM), which +serves the same role as a Wasm runtime in converting agnostic instructions to platform-­ +specific ones. The parallels between Java and Wasm are shown in figure 1.3. + +The main difference between the two, as you can see in figure 1.3, is that Java +bytecode, while it does aid portability, is primarily designed for Java and a few other +languages with similar execution models (e.g., Kotlin). Wasm, on the other hand, is +built as a truly language-agnostic binary format. While over 40 languages can be com[piled to Wasm (see https://mng.bz/YZNj for more details on whether your language](https://mng.bz/YZNj) +is supported), the key advantage is not just the breadth of language support but the + +Licensed to Ibrahim Mohammed Mohammed + +**_The advantages of Wasm for server-side development_** **7** + +**Wasm is also** +**bytecode!** + +Figure 1.3 Java and +Wasm’s similarities + +lightweight nature of the runtime. Unlike the JVM, which requires a heavyweight VM, +Wasm’s design avoids the need for a complex execution environment, making it well +suited for languages like C, C++, Rust, and Zig. + +This architectural difference also means Wasm can more easily support languages +like Python, which rely on C-based components, whereas running Python on the JVM +requires reimplementation from scratch. Additionally, Wasm’s simple instruction set +and stack-based execution model were intentionally designed for broad compatibility +across languages, unlike JVM, which was originally optimized for Java. + +Another important distinction is that Wasm is an open standard developed through +the W3C with multivendor collaboration; it is not controlled by any single company. +This contrasts with Java, where Oracle maintains considerable influence over the language’s direction. Interestingly, Oracle also owns the JavaScript trademark, but this is +purely nominal. + +Wasm’s broad language support, in theory, sounds fantastic, but in practice, it can +sometimes mean that the support for a particular language is not as mature as it is for +others. One reason for a possible difference in support is that, for a long time, Wasm did +not have garbage collection, which is a feature of many languages that allows a program + +Licensed to Ibrahim Mohammed Mohammed + +**8** Chapter 1 **_Introducing Wasm on the server_** + +to have automatic memory management by the fact that any allocated memory that is +no longer referenced is recognized and reclaimed. This meant that languages that rely +on garbage collection, like Kotlin, PHP, and Java, needed to first compile their entire +runtime to Wasm so that the runtime can be shipped together with a Wasm application written in that same language. Let’s take PHP as an example. As illustrated in figure 1.4, using PHP Wasm without garbage collection meant that a large chunk of your +Wasm-compiled app is really just the PHP runtime, which, say, in the context of the +browser, will run side by side with JavaScript’s runtime garbage collector. + +**garbage collector** + +Figure 1.4 Running a PHP Wasm without garbage collection application in the browser + +Running PHP’s own garbage collector alongside JavaScript’s is inefficient and redundant. A better approach is to enable Wasm to work with the host environment’s garbage collection infrastructure. This is exactly what WasmGC—a proposal by the +Web­Assembly Community Group—aims to address. 1 It introduces garbage-collected +reference types that can be managed by the host, reducing the need to bundle a complete garbage collection system within each language’s runtime. + +However, this doesn’t mean the entire PHP runtime can be removed when compiling to Wasm. Components like the interpreter, support for dynamic evaluation, and +other necessary features still need to be included in the Wasm binary to ensure proper +execution. Language support for Wasm is a constantly evolving landscape. Throughout this book, while we primarily use Rust in the examples, we will also explore writing +applications in higher-level languages like JavaScript and Python. + +_1.2.2_ _Performance_ + +When it was released for the web, Wasm promised near-native performance. This +means if you compile a C application to a native binary and run it on your desktop +environment and then compare that benchmark to running the same application + +1 Steiner, Thomas (2023). WebAssembly Garbage Collection (WasmGC) now enabled by default in Chrome. +[Chrome for Developers. https://developer.chrome.com/blog/wasmgc.](https://developer.chrome.com/blog/wasmgc) + +Licensed to Ibrahim Mohammed Mohammed + +**_The advantages of Wasm for server-side development_** **9** + +compiled to Wasm but running on the browser, you should see that it performs just +about the same. Does that still hold true for the server side? To answer that question, +there are a couple of scenarios we can compare. + +Wasm app vs. x86_64 app +In this scenario, we are comparing single-threaded benchmark applications compiled +to both the x86_64 architecture and Wasm. The x86_64 binary runs directly atop the +native OS, while the Wasm app runs in a Wasm runtime that implements WASI atop the +native OS. Spies and Mock 2 showed that the Wasm app, on average, performed only +14% slower than the x86_64 app, which, all in all, is close to near-native performance. +But in practice, apps are often containerized. Next, let’s compare a Wasm app’s performance to the same app running in a container. + +Wasm app vs. short-running workload in a container +In this scenario, we are looking at a Wasm application running on a Wasm runtime versus a containerized application running with Docker. Long et al. 3 showed that Wasm +runtimes running a simple application that reads a file and exits perform at least 10× +faster than Docker running the same application from cold start (i.e., meaning the +time it takes for the container to start for the first time or from a fully deprovisioned +state). And when running other benchmarking applications, Wasm was found to usually perform 10% to 50% faster than the same app containerized even on warm start +(i.e., the time it takes for the container to start after it has been previously initialized +but is currently inactive). + +This, while impressive, does not necessarily paint an accurate picture of how Wasm +will perform in the real world with more complex applications like, say, running an +HTTP server. So let’s explore further in the next scenario. + +Wasm container vs. Docker container +In this scenario, we will compare two container types: a traditional Docker container and +the newly introduced Wasm container. This time, both applications were written to run +a simple HTTP server that will listen for requests and provide a response after querying +a SQL database. Sondell 4 showed that, when stressing such a system with 50 concurrent +clients making requests, the Wasm application showed a worst-case latency of about 15.2 +seconds, while the Docker container showed a worst-case latency of 0.881 seconds. This +means that the Docker container was about 17× faster than the Wasm one. So what’s going +on? Is the containerization of Wasm creating that much overhead? The answer is no. In +fact, when running the same application outside the container and just atop the respective Wasm runtime (i.e., simply eliminating containerization), the Docker container was + +2 Spies, Benedikt, and Mock, Markus (2021). An evaluation of WebAssembly in non-web environ­ments. 2021 +[XLVII Latin American Computing Conference (CLEI), Cartago, Costa Rica, pp. 1–10. https://doi.org/10.1109/](https://doi.org/10.1109/CLEI53233.2021.9640153) +[CLEI53233.2021.9640153.](https://doi.org/10.1109/CLEI53233.2021.9640153) +[3 Long, Ju, et al. (2020, October 13). A lightweight design for serverless function-as-a-service. https://arxiv.org/](https://arxiv.org/abs/2010.07115) +[abs/2010.07115.](https://arxiv.org/abs/2010.07115) +4 Sondell, Per (2024). An evaluation of performance and usability of WebAssembly containers in cloud comput[ing. Umeå University. https://mng.bz/Kwgg.](https://mng.bz/Kwgg) + +Licensed to Ibrahim Mohammed Mohammed + +**10** Chapter 1 **_Introducing Wasm on the server_** + +still almost 13× faster. The answer here lies in Wasm’s lack of support for multithreading. +That is, the Docker container, when stressed, increased its CPU utilization to 11 cores, +while Wasm, due to its current single-­threaded nature, only utilized one core. + +Wasm’s threading proposal is still in the works. But, while still being a significant +limitation, this is not the end of the road for Wasm. In fact, with a modern setup, it’s very +common for multithreading to be handled by the Wasm runtime through having each +request handled by starting a new Wasm instance entirely. Regardless, Sondell (2024) +showed that, while latency was left to be desired with this setup, the cold start time of a +Wasm container was about 160% faster than that of a Docker container. This is particularly true because Wasm containers are much smaller (upwards of 80% smaller!) than +Docker containers, as they are never packaged together with an OS (e.g., Linux), which +consequently reduces the data volume required to be pulled and accelerates download +speeds. This is illustrated in figure 1.5, which depicts two Dockerfiles: one for a Docker +container and one for a Wasm container. + +Figure 1.5 Dockerfiles for a Docker container and a Wasm container + +As you can see, the Wasm container starts from scratch (i.e., a special empty image), +while the Linux container begins with a base Ubuntu v23.10 image. This means the +Wasm container includes only the application itself, whereas a typical Docker container +includes both the application and the OS. While traditional containers can sometimes +reach several gigabytes in size, Wasm containers are typically just a few megabytes. We +will explore Wasm containers in more detail in chapter 7. + +Is FROM scratch possible outside Wasm? + +Yes. Docker containers don’t necessarily have to be packaged together with an OS. +You can avoid specific OS bits by using a static native binary along with `FROM scratch`, +which is not entirely uncommon for some Rust- or Go-based containers. This section +just illustrates with `FROM ubuntu:23.10` to display a commonly used setup. + +The improved cold start times are a big deal, particularly for serverless applications +that must constantly deprovision and reprovision containers to accommodate scaling +to zero. This process often results in frequent cold starts. + +Licensed to Ibrahim Mohammed Mohammed + +**_The advantages of Wasm for server-side development_** **11** + +As described by Hall and Ramachandran, 5 serverless applications suffer from the +_cold start problem_, where startup times typically begin at around 300 milliseconds and can +be significantly longer depending on factors like networking configurations (e.g., service meshes) and initialization processes. In the same paper, they compared the performance of non-Wasm versus Wasm serverless applications under varying access patterns +and found that, for applications experiencing periods of inactivity, Wasm’s improved +cold start times led to 70% faster response times for moderate tasks and 90% faster +response times for basic tasks. + +Plus, Shillaker and Pietzuch, 6 when combining serverless and Wasm, managed to +speed up training a machine learning model by 2× and utilized 10× less memory. So, as +it stands, it’s clear that Wasm’s sweet spot in server-side development really is powering +this new wave of applications. + +Later in this book, we will explore for ourselves how to write Wasm applications that +use HTTP servers and machine learning. + +Wasm density in Kubernetes +Until now, we have mostly focused on the runtime and cold start aspects of performance. However, another equally important facet is _density_ —specifically, how many +workloads (in our case, Wasm instances) can be packed onto a given set of hardware. +Greater density is crucial because it directly translates to cost savings. For example, the +company ZEISS was able to reduce its Node.js Express app deployment size from 423 +MB to 2.4 MB by leveraging Wasm, resulting in over 50× greater workload density per +Kubernetes node. This higher density allows for more efficient use of resources, reducing the need for additional hardware and lowering overall infrastructure costs. + +_1.2.3_ _Hardware agnosticism_ + +Another great benefit of Wasm is the fact that it is completely independent of platform or hardware. The bytecode makes no assumptions about the underlying system, and it is entirely up to WASI to fill in the gaps. This has various benefits. For +one, it means you can compile your application on an x86_64 machine and run it on +an arm64 one without any changes. This has the potential to save countless DevOps +hours that would be spent creating pipelines with runners to build your application +for the various platforms you want to support. Instead, with Wasm, you build once and +run anywhere. + +But, what’s more, Wasm’s hardware agnosticism combined with its low memory +footprint makes it particularly well suited for _edge computing_ —a field that aims to bring +computing closer to the sources of data (like mobile devices, vehicles, sensors, and so +on) in an attempt to reduce latency and bandwidth usage. Edge environments are very + +5 Hall, Adam, and Umakishore Ramachandran (2019). An execution model for serverless func­tions at the edge. +IoTDI ’19: Proceedings of the International Conference on Internet of Things Design and Implementation, +[pp. 225–226. https://doi.org/10.1145/3302505.3310084.](https://doi.org/10.1145/3302505.3310084) +6 Shillaker, Simon, and Peter Pietzuch (2020). FAASM: Lightweight isolation for efficient state­ful serverless com[puting. 2020 USENIX Annual Technical Conference (USENIX ATC 20). https://mng.bz/GwXA.](https://mng.bz/GwXA) + +Licensed to Ibrahim Mohammed Mohammed + +**12** Chapter 1 **_Introducing Wasm on the server_** + +different from the cloud, where we essentially assume unlimited resources. Instead, for +the edge, we need to support a large number of different devices with limited resources. +Wasm perfectly fits this requirement and has the potential to power things like self-­ +driving cars and smart cities through edge computing. + +_1.2.4_ _Security_ + +Wasm is designed to run within a memory-safe sandboxed execution environment. By +itself, it cannot interact with or access the native OS except through specific, tightly controlled capabilities provided by the Wasm runtime. For example, if the Wasm runtime +grants the Wasm binary access to standard output to print “Hello, World,” that controlled +interaction is the only way the Wasm binary can communicate with the native OS, all +while remaining within its sandbox. As illustrated in figure 1.6, this is in stark contrast to +native applications that have full access to the native OS system calls (i.e., the fundamental interfaces that allow applications to request services from the OS, such as file manipulation, process control, and network communication) and the native OS memory. + +Traditional executable + +Wasm-based app + +**Sandbox** +Capability 1 + +vs. + +Capability 2 + +|granted|Col2| +|---|---| +|~~granted~~|| + +**“My app” cannot** +**use this capability!** + +Figure 1.6 A native (traditional) application security model versus Wasm’s capability-based security +model + +This isolation is especially crucial in the context of cloud computing, where supply-­chain +attacks are becoming increasingly common. Supply-chain attacks occur when a vulnerability in one component can potentially compromise the entire system, as all parts of +the application often share the same environment and resources. However, with Wasm’s +sandboxed execution, each component operates independently, significantly reducing +the risk that a compromised module could affect the rest of the application. + +_1.2.5_ _Standards-based and vendor neutrality_ + +One of the compelling advantages of Wasm and WASI for server-side development is +their foundation on open standards. Unlike many current serverless offerings that are + +Licensed to Ibrahim Mohammed Mohammed + +**_When and where to use Wasm (and when not to)_** **13** + +tied to proprietary platforms, Wasm and WASI provide a vendor-neutral environment, +allowing organizations to build serverless applications without being locked into a specific vendor’s ecosystem. + +This standards-based approach gives enterprises the flexibility to move their applications across different environments, whether on premises, in the cloud, or at the edge, +without needing to rearchitect or redeploy their entire application stack. For organizations looking to adopt serverless technologies while avoiding vendor lock-in, Wasm +offers a path to lower operational burden and increased portability, ensuring that their +applications remain future-proof and adaptable to changing infrastructure needs. + +_1.3_ _When and where to use Wasm (and when not to)_ + +As discussed, Wasm can be pivotal for serverless and edge computing. Particularly, +it has great advantages for serverless as it is, on average, 80% faster than traditional +serverless technologies. In addition, the same low memory footprint that gives Wasm +the edge with serverless when paired with its hardware agnosticism makes it a great fit +for edge computing, which relies on executing code at the edge of the network on a +variety of devices with limited resources. + +In addition to these areas, Wasm has also found its footing in + +¡ _Mobile and desktop applications_ —Wasm’s cross-platform nature allows develop +ers to share the same code across different platforms, such as an app’s mobile +and desktop versions, similar to how JavaScript frameworks like Electron enable +cross-platform development. + +¡ _Microcontrollers_ —Microcontrollers are tiny, single-chip computers embedded in + +everything from household appliances to industrial machinery and vehicles. The +microcontroller field is heavily dominated by low-level languages like C, which +can make debugging and porting code across different microcontrollers challenging. Wasm has gained popularity in this space for its ability to serve as a middle +ground between high-level languages—often too slow for microcontrollers—­ +and low-level languages, enabling developers to write performant, debuggable, +and portable code. Beyond immediate development benefits, Wasm also has +the potential to improve the long-term maintainability of embedded software. +Modern cars, for instance, contain dozens of microcontrollers managing everything from engine control to entertainment systems. The average lifespan of a +European car, including its second life in Eastern Europe, is around 30 years. +This means vehicles on the road today may still rely on software built during the +Windows 95 era. With Wasm, manufacturers could more efficiently and securely +update and maintain such software over decades, reducing technical debt and +improving long-term sustainability. + +¡ _Application plugins_ —Wasm’s natural sandboxing capabilities make it an ideal + +choice for developing secure and portable application plugins. Its ability to safely +execute code in isolation from the host application reduces the risk of security +vulnerabilities. + +Licensed to Ibrahim Mohammed Mohammed + +**14** Chapter 1 **_Introducing Wasm on the server_** + +Honorable mentions also go to Wasm’s effects in areas like smart contracts (i.e., blockchain) and polyglot programming, where it serves as a bridge to interoperate between +various languages. + +On the other hand, due to the lack of multithreading support, Wasm’s performance +can struggle in traditional PaaS-type applications. But these effects can be reduced if +you use a containerized application that is orchestrated with Kubernetes and scale your +application horizontally. In chapter 8, we will discuss how to deploy Wasm applications +in Kubernetes. + +Wasm’s true struggle, however, lies in its ecosystem maturity. Even with Rust, which +has always treated Wasm as a first-class citizen, the experience of trying to utilize an +external package (i.e., a crate in Rust-speak) and finding out that it is not compilable +to Wasm is a common one. This puts the developer in a position where they potentially +have to choose between using the package they are comfortable with and dropping +Wasm altogether, or instead, when it exists, using another equivalent package that does +compile to Wasm. In the worst-case scenario, the developer may choose to set themselves on a quest to compile the original package to Wasm, which is often nontrivial +and most likely heavily deviates from the original project’s goal. Another significant +challenge stemming from the ecosystem’s maturity is the lack of integrated debugging +tools in most languages, which further complicates the development process and can +increase the time needed to identify and resolve problems. + +Wasm is constantly evolving. While this means that support for a package you want +to use may be coming soon, it also means that many tools and libraries are still maturing and may not yet be fully stable. However, ongoing work on Wasm itself is actively +addressing many of these limitations. As new proposals are implemented, constraints +that currently prevent some packages from being compiled to Wasm will gradually be +removed, making the ecosystem more capable and versatile over time. + +While these limitations are real, it’s important to understand that Wasm offers capabilities that simply cannot be achieved with existing technologies. Table 1.1 compares +Wasm across key dimensions. + +Table 1.1 Comparing Wasm to traditional technologies across key dimensions + +|Capability|Wasm|Traditional technologies| +|---|---|---| +|Sandboxed execution
Cold start
Artifact size
Portability
Memory footprint
Multithreading|Built-in, lightweight isolation
Microseconds
KBs to low MBs
Compile once, run anywhere
Minimal overhead
Limited (proposals in progress)|Containers (not as secure), VMs (heavy/
slow), or language-specifc (Java, .NET)
Containers: seconds; VMs: minutes
Containers: 100s of MBs; traditional:
varies widely
Docker (platform-specifc builds); inter-
preted languages (performance penalty)
Containers/VMs: signifcant overhead
Well supported| + +Licensed to Ibrahim Mohammed Mohammed + +**_Summary_** **15** + +Table 1.1 Comparing Wasm to traditional technologies across key dimensions ( _continued_ ) + +|Capability|Wasm|Traditional technologies| +|---|---|---| +|Ecosystem maturity
Debugging tools
Runtime performance|Growing (many packages
incompatible)
Improving but limited
Near-native|Mature
Well established
Varies (native to interpreted)| + +To stay up to date with upcoming features and improvements, you can check the status +[of Wasm and WASI proposals at https://github.com/WebAssembly/proposals (Wasm](https://github.com/WebAssembly/proposals) +[proposals) and https://mng.bz/z2V6 (WASI proposals).](https://mng.bz/z2V6) + +_1.4_ _How to navigate this book_ + +This book is structured as a gradual progression, starting with low-level details and +steadily moving toward a more accessible and streamlined developer experience. The +early chapters focus on the fundamentals of Wasm, giving you a deep understanding +of how it works under the hood. As you move forward, each chapter builds on the +last, introducing higher-level tooling and abstractions that make working with Wasm +easier. By the end, you’ll be using Wasm in practical, real-world scenarios with a much +smoother development experience. + +The book is divided into two parts: + +¡ Part 1: WebAssembly for architects (chapters 1–5) explores how Wasm works + +behind the scenes—ideal for understanding the inner workings for debugging, +system design, or building a solid foundation. + +¡ Part 2: WebAssembly for developers (chapters 6–9) shifts to practical develop +ment, covering everything from HTTP servers to machine learning workloads, +Wasm containers, and Kubernetes deployment. + +NOTE For a detailed breakdown of each chapter, see the “about this book” +section of the introduction. + +Throughout this book, we’ll also develop a SmartCMS project, applying our newly +[learned skills as we go. The code from all chapters can be found on GitHub: https://](https://github.com/danbugs/serverside-wasm-book-code) +[github.com/danbugs/serverside-wasm-book-code. If you run into any problems,](https://github.com/danbugs/serverside-wasm-book-code) +please open an issue on the repository—feedback is always appreciated! + +_Summary_ + +¡ Wasm is akin to an ISA like x86_64 in the sense that your code can target it + +for compilation. But it is not a real platform and instead just virtualizes actual +hardware. + +¡ Wasm apps can run outside of a browser primarily through the WASI, which + +allows communication with the OS. + +Licensed to Ibrahim Mohammed Mohammed + +**16** Chapter 1 **_Introducing Wasm on the server_** + +¡ Docker supports running two types of containers: the traditional Docker con +tainer and the newly introduced Wasm container. + +¡ Wasm’s language agnosticism makes it so that more than 40 languages can be + +compiled to it, but that is a double-edged sword, as support for a particular language might not be as mature as it is for others. + +¡ While benchmarks show that standalone Wasm apps can be 10% to 50% faster + +than containerized apps, real-world applications can struggle due to Wasm’s lack +of support for multithreading. + +¡ Wasm, when paired with serverless and its scale-to-zero requirement, leads to + +80% faster execution times on average when compared to traditional serverless +technologies. + +¡ A Wasm binary is completely independent of the platform or hardware where it + +is built and can theoretically run on any system due to its hardware-agnosticism +property. + +¡ Wasm employs a capability-based security model that restricts the binary to access + +the native OS only through specific capabilities made available by the Wasm +runtime. + +¡ Aside from serverless and edge computing, Wasm has found its footing in mobile + +and desktop applications, microcontrollers, smart contracts, and polyglot +programming. + +¡ When targeting Wasm, it is commonplace to make sacrifices of particular pack +ages that do not support Wasm. + +Licensed to Ibrahim Mohammed Mohammed + +##### _applications with_ _Wasm modules_ +# _2_ + +##### _Building server-side_ + +##### _applications with_ + +##### _Wasm modules_ + +_This chapter covers_ + +¡ Building a Wasm “Hello, World” with Rust + +¡ Understanding Wasm memory and data types + +¡ Understanding the guest-host architecture of + +server-side Wasm applications + +¡ Writing and compiling Rust code to Wasm + +¡ Executing a Wasm module with a custom Wasm + +runtime + +In this chapter, we’re going to build a simple ”Hello, World” application. As illustrated in figure 2.1, our app will take a string as input and output that string with + +`"Hello, "` in front of it. While this example might seem straightforward, implementing it in Wasm is far from trivial—Wasm doesn’t have a built-in `String` type, so handling text requires a low-level approach. + +Before exploring more advanced features, we’ll work through this challenge +using Wasm’s raw capabilities. This will give us a deep understanding of how data +is managed at this level and lay the groundwork for appreciating the more efficient +techniques introduced later. + +**17** + +Licensed to Ibrahim Mohammed Mohammed + +**18** Chapter 2 **_Building server-side applications with Wasm modules_** + +Figure 2.1 High-level view of our “Hello, World” application + +_2.1_ _Understanding Wasm’s building blocks_ + +We begin by rolling up our sleeves and working with the fundamental elements that +Wasm offers out of the box: linear memory and four primitive data types (i.e., integers +and floats, both in 32- and 64-bit sizes). + +_Linear memory_ is a contiguous and growable array of bytes that forms the memory +space used by a Wasm program. This model is direct, meaning that the program interacts with memory in a straightforward manner, using explicit byte offsets to read and +write data. It is low-level because it operates without higher-level abstractions, giving +developers precise control over how memory is managed and accessed. + +Unlike most languages that contain high-level types, raw Wasm requires us to engage +with primitives directly. Wasm’s integers and floats are the bedrock upon which we can +construct more elaborate data structures. While this might feel low-level now, we’ll later +see how Wasm provides mechanisms to work with richer types more easily. + +Consider our seemingly straightforward task of prefixing a user input with `"Hello, "` . +In most high-level languages, this would be a trivial string concatenation. However, +as mentioned, in Wasm, we don’t have a string data type. So how do we represent a +string using only integers and raw memory? One solution, as depicted in figure 2.2, is to + +**These values represent characters** +**encoded as bytes (e.g., '101' is the** +**encoded value of 'n').** + +Figure 2.2 Representing a string with Wasm’s linear memory and basic types + +Licensed to Ibrahim Mohammed Mohammed + +**_Understanding the guest-host architecture of server-side Wasm_** **19** + +employ two integers. The first integer acts as a pointer to the start of the string, which is +stored as an array of encoded (ASCII/UTF-8) bytes in linear memory, and the second +represents its length. + +By storing the string as an array of bytes, we can manipulate it as needed. When we +wish to prepend `"Hello, "` to any user input, we encode the prefix (i.e., `"Hello, "` ) +and the user input as bytes, store them sequentially in linear memory, and use our +two integers to keep track of their location and combined length. This approach may +seem rudimentary when compared to the abstractions offered by higher-level languages, but it is precisely this simplicity that gives Wasm its speed and flexibility. By +controlling exactly how memory is allocated, accessed, and combined, we can optimize performance in ways that higher-level abstractions might not allow. Moreover, +this method of string handling is not just a workaround; it offers a window into understanding how higher-level languages (and, as we will see later, Wasm itself) abstract +away such details. + +_2.2_ _Understanding the guest-host architecture of server-side Wasm_ + +In the browser, Wasm runs within the web’s native ecosystem, using the browser’s features and resources. Similarly, on the server, Wasm needs a host to provide access to +system resources. This role is typically filled by a Wasm runtime, which acts as the execution environment for Wasm applications. + +To illustrate, let’s consider the structure of our ”Hello, World” app. This app is +designed as a guest application, meaning it is built to run within said host, which provides the necessary runtime capabilities. + +For demonstration, I’ve developed a custom host. Let’s call it HelloWorldHost. This +HelloWorldHost acts as a lightweight intermediary between the guest application and +the Wasmtime runtime—a prominent open source Wasm runtime known for its performance, versatility, and security. + +The architecture of our setup, as visualized in figure 2.3, operates in a four-step +process: + +1 HelloWorldHost injects the input string into the linear memory of the Wasm +binary (i.e., our guest application). + +2 HelloWorldHost then invokes the execution of our guest application—specifically, +a function we will name `greet` . + +3 The `greet` function first determines the amount of memory needed for the new +string. Then it writes the updated content directly into memory, constructing the +final greeting in place. + +4 Once the `greet` function has done its work, HelloWorldHost re-engages, reading +the newly formed greeting directly from the linear memory. + +Next up, we’ll dive into constructing our guest in Rust. You’ll be guided through +writing the code, compiling it into a Wasm module, and finally, executing it within +HelloWorldHost. + +Licensed to Ibrahim Mohammed Mohammed + +**20** Chapter 2 **_Building server-side applications with Wasm modules_** + +Figure 2.3 Illustration of interactions between our “Hello, World” +guest and its custom host + +Licensed to Ibrahim Mohammed Mohammed + +**_Creating a Wasm app using Rust_** **21** + +_2.3_ _Creating a Wasm app using Rust_ + +Before getting into the meat and potatoes of our application, let’s address a fundamental question: why Rust? The choice is practical—Rust’s support for Wasm is the +most mature among modern languages, and its safety guarantees align well with Wasm, +simplifying our development process significantly. Don’t worry if Rust isn’t your first +language; we’ll break down each code snippet and process step by step. + +For setup assistance, follow the instructions at [https://www.rust-lang.org/learn/­](https://www.rust-lang.org/learn/get-started) +[get-started to install Rust. This guide also includes information on setting up Rust with](https://www.rust-lang.org/learn/get-started) +different editors like VS Code. + +NOTE The recommended Rust version for working through this book is `rustc` + +`1.84.0` `(9fc6b4312` `2025-01-07)` . This version is important because it ensures +we’re using Rust edition 2021, which keeps our code stable and compatible +with the tooling used throughout the book. You can check your current version with `rustc` `--version` . To install 1.84.0 specifically, after having some other +version installed, you can run `rustup` `install` `1.84.0` and set your default Rust +version to it with `rustup default` `1.84.0` . + +_2.3.1_ _Creating a Rust library project_ + +In Rust, when you start a project, you’re faced with a choice: build a library or a binary. +As highlighted in the title of this section, we’ve opted for a library. This choice means +our code is compiled into a reusable package, which is exactly what we need since our +application doesn’t require an executable `main` function like what you’d find in binary +projects. Instead, our project centers around a `greet` function, designed to be called by +HelloWorldHost. + +Before we create the library, let’s set up a consistent structure for all the book’s code: + +1 Create a top-level folder for the book: `serverside-wasm-book-code/` . + +2 Inside this folder, create chapter-specific subfolders: `chapter02/` through + +`chapter08/` . + +3 Within each chapter’s folder, we’ll create project-specific directories. For now, +inside `chapter02/`, create `hello_world/` . + +TIP If you’d like to just follow along with the examples without coding them +yourself or doing all this setup, you can instead clone the book’s code repository with `git` `clone` [https://github.com/danbugs/serverside-wasm-book-code.](https://github.com/danbugs/serverside-wasm-book-code) +Some files in the repository are stored using Git Large File Storage (LFS), +which is designed to handle large or binary files more efficiently. To make sure +[those files are available, first install Git LFS (see https://git-lfs.com/ for instruc-](https://git-lfs.com/) +tions), then run `git` `lfs` `install` followed by `git` `lfs` `pull` from the root of the +cloned repo. This repository also uses Git submodules. To initialize them, run + +`git` `submodule` `update` `--init` `--recursive` . + +Licensed to Ibrahim Mohammed Mohammed + +**22** Chapter 2 **_Building server-side applications with Wasm modules_** + +Finally, navigate into the project folder and initialize the Rust library using the following command: + +``` + cargo new --lib hello_world_guest + +``` + +This command uses Cargo, the Rust package manager and build system, to create a +new Rust library project called `hello_world_guest` . The project’s files will be placed in +a new directory with the same name. + +To keep all of the book’s Rust projects organized, we’ll also set up a Cargo workspace. +This helps autocomplete and related features work more effectively when opening the +entire book’s folder at once, making navigation and development smoother. Inside + +`serverside-wasm-book-code/`, create a `Cargo.toml` file and populate it with the contents +of the following listing. + +Listing 2.1 Workspace **`Cargo.toml`** file + +``` + [workspace] + resolver = "2" + members = [ + "chapter02/hello_world/hello_world_guest", + ] + +``` + +With this set and after creating the project, you’ll need to navigate into your new project directory: + +``` + cd hello_world_guest + +``` + +NOTE If you are planning on using a Cargo workspace as well, unless specified +otherwise, after creating any of the upcoming Rust projects in the book, make +sure to add their path to this file. + +_2.3.2_ _Specifying compilation to a Wasm module_ + +In the `hello_world_guest` directory, there’s a `src` folder, and inside it, a `lib.rs` file. +This file is where we’ll write the code for our Wasm application. You’ll also find a + +`Cargo.toml` file in the `hello_world_guest` directory, which is the manifest file for your +Rust project. It holds metadata about your project and defines how your project should +be compiled. + +To make sure our code compiles into a _Wasm module_, we need to edit the `Cargo.toml` +file in the `hello_world_guest` directory. + +NOTE The term ”Wasm module” specifically refers to core, or what we’ve +been calling raw, Wasm. This distinction will become more relevant as we +explore higher-level expansions onto core Wasm, like Wasm components, in +chapter 3. + +Licensed to Ibrahim Mohammed Mohammed + +**_Creating a Wasm app using Rust_** **23** + +Open the `Cargo.toml` file in your preferred text editor and add the following snippet: + +``` + [lib] + crate-type = ["cdylib"] + +``` + +The addition of `crate-type = ["cdylib"]` under the `[lib]` section guides the Rust compiler to generate a dynamic library. With the correct target settings, which we will discuss shortly, this ensures the output is a `.wasm` file compatible with our Wasm runtime. + +Your `Cargo.toml` file, after including the necessary modifications to compile as a +Wasm module, should appear as in the following listing. + +Listing 2.2 **`hello_world_guest`** **`Cargo.toml`** file + +``` + [package] + name = "hello_world_guest" + version = "0.1.0" + edition = "2021" + + [lib] + crate-type = ["cdylib"] + + [dependencies] + +``` + +NOTE Parts of this book were developed using Rust’s 2021 edition. If you’re +Rust version 1.85 or later, you might see `edition = "2024"` (or, in the future, +later) in your `Cargo.toml` files. + +_2.3.3_ _Writing the code for our application_ + +Let’s edit the `lib.rs` file next. Clear out the default contents to start with a clean slate. +Our first task is to define the `greet` function. This function will accept two arguments: a +pointer to the beginning of a string in linear memory and the string’s length (as shown +previously in figure 2.2). The signature of the `greet` function should look like this: + +``` + #[no_mangle] + pub fn greet(ptr: i32, len: i32) { + ... + } + +``` + +The `#[no_mangle]` attribute is critical. It’s a directive to the Rust compiler, ensuring the +function name remains unchanged after compilation. This step is necessary for Hello­ +WorldHost to find and invoke the `greet` function correctly. + +Now let’s move on to constructing our greeting. We’ll start by creating the prefix +string `"Hello, ",` and then we’ll convert our two integer parameters ( `i32s` ) into the +types that Rust uses for memory operations: a pointer and a length. Type this code into +the `greet` function body we wrote previously: + +Licensed to Ibrahim Mohammed Mohammed + +**24** Chapter 2 **_Building server-side applications with Wasm modules_** + +``` + let hello = "Hello, "; + + let input_ptr = ptr as *mut u8; + let input_len = len as usize; + +``` + +It’s worth mentioning that even though Wasm itself doesn’t support a string type +directly, we’re able to utilize it here (i.e., with `"Hello, "` ). This is possible because +Rust’s compiler translates high-level types like `String` into low-level Wasm types, effectively bridging the gap between Rust strings and the byte representations required by +Wasm. + +This same principle applies to our use of `*mut` `u8` for pointers and `usize` for sizes. +Even though Wasm doesn’t directly recognize these Rust-specific types, Rust’s compilation process handles these conversions smoothly. This highlights again why we chose +Rust for this project. As mentioned earlier, its strong support for Wasm makes these lowlevel interactions easier to manage. Still, we keep the function signature utilizing `i32` +types directly because that’s part of the implicit contract with our HelloWorldHost—it +invokes our function with two `i32` parameters. + +Next, we can calculate the new length of our string, which will include the length of +the `"Hello, "` prefix: + +``` + let new_len = input_len + hello.len(); + +``` + +Given this new length, we access the linear memory using an `unsafe` block, which is +necessary for any operations in Rust that could potentially lead to safety problems, +such as memory corruption or data races. + +NOTE A data race happens when two or more threads access the same memory at the same time, at least one of them writes to it, and there’s no synchronization to keep things in order. It’s like multiple people editing the same +document simultaneously without talking to each other—chaos, and potentially corrupted data. + +This is especially pertinent when dealing with the low-level aspects of Wasm’s linear +memory: + +``` + let output = unsafe { + core::slice::from_raw_parts_mut(0 as *mut u8, new_len) + }; + +``` + +By invoking `core::slice::from_raw_parts_mut`, we’re effectively grabbing a mutable +slice of memory from a raw pointer and a specified length. This allows us to write to a +specific segment of memory (i.e., from `0` to `new_len` ), facilitating the addition of our +greeting to the existing string. + +With the memory accessible, we can now construct our greeting by copying the + +`"Hello, "` prefix and the original string into the linear memory: + +Licensed to Ibrahim Mohammed Mohammed + +**_Creating a Wasm app using Rust_** **25** + +``` +output[..hello.len()].copy_from_slice(hello.as_bytes()); +output[hello.len()..] +.copy_from_slice(unsafe { +core::slice::from_raw_parts(input_ptr, input_len) +}); + +``` + +In these lines, we first we set the beginning of the slice to `"Hello, "` . Then, we place +our input string right after it. This operation, though straightforward in high-level +languages, showcases the meticulous control over memory that working with Wasm +and Rust allows, all while maintaining efficiency and safety within our application’s +context. + +Overall, the `greet` function, with all its components, should look like the following +listing. + +Listing 2.3 The **`greet`** function ( **`lib.rs`** file) + +``` +#[no_mangle] +pub fn greet(ptr: i32, len: i32) { +let hello = "Hello, "; + +let input_ptr = ptr as *mut u8; +let input_len = len as usize; + +``` + +**Adds a compiler directive to keep the** +**function name intact post-compilation** + +**Explicitly casts the i32 values to pointer and** +**size types for Rust memory operations** + +``` +let new_len = input_len + hello.len(); + +``` + +``` +let output = unsafe { +core::slice::from_raw_parts_mut( +0 as *mut u8, new_len) +}; + +``` + +**Calculates the new** +**length to accommodate** +**the "Hello, " prefix** +**memory from 0** +**to new_len** + +``` + output[..hello.len()].copy_from_slice(hello.as_bytes()); + output[hello.len()..] + .copy_from_slice(unsafe { + core::slice::from_raw_parts( + input_ptr, input_len) }); + } + +``` + +_2.3.4_ _Compiling the Rust code into a Wasm module_ + +**Constructs the** +**greeting by copying** +**"Hello, " and the** +**provided string into** +**linear memory** + +Before we compile our Rust library into a Wasm module, we need to ensure our toolchain is set up for targeting Wasm. Run the following command in your terminal: + +``` +rustup target add wasm32-unknown-unknown + +``` + +This tells Rust to install the necessary components for compiling to the `wasm32` + +`-unknown-unknown` target. In Rust, target triples (e.g., `wasm32-unknown-unknown` ) typically specify the architecture, vendor, and operating system for compiled code. In +this case, `wasm32` indicates that we’re targeting Wasm, and `unknown-unknown` signifies +that there are no designated operating system or underlying platform dependencies. + +Licensed to Ibrahim Mohammed Mohammed + +**26** Chapter 2 **_Building server-side applications with Wasm modules_** + +This highlights one of Wasm’s key strengths—its OS- and architecture-agnostic nature, +which is what allows code to run consistently across different environments. + +With that set and now that our `greet` function is in place, it’s time to compile our +Rust library into a Wasm module, making it ready for execution by HelloWorldHost. +Navigate to the `hello_world_guest/` folder if you’re not already there, and execute the +following command: + +``` + cargo build --release --target wasm32-unknown-unknown + +``` + +By specifying `--release`, we’re asking Cargo to compile our project in release mode, +which applies optimizations to make the resulting Wasm module more efficient. The + +`--target wasm32-unknown-unknown` part of the command explicitly tells Cargo to compile the project for a generic Wasm target, which, again, is what ensures our Rust code +is transformed into a Wasm module compatible with any Wasm runtime, including our +HelloWorldHost. + +_2.3.5_ _Executing the Wasm module with the custom host_ + +At this stage, our Wasm module resides in the `target/wasm32-unknown-unknown/` + +`release/` folder of our root workspace. For easier access, we’ll copy it to the root of the + +`hello_world_guest/` directory: + +``` + cp ../../../target/wasm32-unknown-unknown/release/hello_world_guest.wasm +``` + +➥ `hello_world_guest.wasm` + +This step positions the `.wasm` file conveniently for the next operation: execution. + +To facilitate running our module without much hassle, I’ve packaged our Hello­ +WorldHost into a Docker image. This setup requires only Docker to be installed on your +machine. + +You can install Docker in one of two ways: + +¡ _Docker Engine_ [(lightweight, CLI-focused) https://docs.docker.com/engine/install/](https://docs.docker.com/engine/install/) + +¡ _Docker Desktop_ [(includes Docker Engine with a GUI) https://docs.docker.com/](https://docs.docker.com/desktop/) + +[desktop/](https://docs.docker.com/desktop/) + +NOTE The recommended Docker version for working through this book is + +`Docker` `version` `28.0.0,` `build` `f9ced58` . You can check your current version with + +`docker --version` . + +So, finally, always from `chapter02/hello_world/hello_world_guest/`, we can execute +our guest application using the following command: + +``` + docker run --rm -v ".:/data" +``` + +➥ `ghcr.io/danbugs/serverside-wasm-book-code/hello-world-host:latest` +➥ `/data/hello_world_guest.wasm 'Dan'` + +Licensed to Ibrahim Mohammed Mohammed + +**_Creating a Wasm app using Rust_** **27** + +TIP These images are pushed to the GitHub Container Registry (GHCR) and +are tied to the book’s code repository. If you’d like to inspect the image, you +[can view it here: https://mng.bz/Ww8l.](https://mng.bz/Ww8l) + +Let’s break down this command: + +¡ `--rm` ensures that the Docker container, serving as our temporary host applica +tion environment, is removed after use, keeping your system clean. + +¡ `-v` mounts a local directory to the container. This step requires specifying the + +path to the directory where `hello_world_guest.wasm` is located (i.e., obtained +with ‘ `.` ’), enabling the Docker container to access the Wasm module. + +¡ The final part executes our `hello_world_host:latest` Docker image. It requires + +two arguments: the path to the `.wasm` file within the mounted directory `(/data/` + +`hello_world_guest.wasm` ) and a string parameter ( `'Dan'` ). + +Upon successful execution, you’ll observe the following output: + +``` +Initial Memory: +[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] +Memory After Host Writes 'Dan': +[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 68, 97, 110] +Memory After Host Calls `greet`: +[72, 101, 108, 108, 111, 44, 32, 68, 97, 110, 0, 0, 0, 68, 97, 110] +Hello, Dan + +``` + +Aside from displaying the final `"Hello,` `Dan"` string we were expecting, HelloWorldHost provides us with a view of the linear memory at several critical junctures: + +¡ _Initial memory_ —To start off, the memory is empty, signified by a series of zeros. + +This represents the untouched state of our linear memory before any operations +are performed. + +¡ _Memory after host writes_ `'Dan'` —After the HelloWorldHost writes the string `'Dan'` + +into the memory, those characters are represented as ASCII/UTF-8 values in the +memory space. Each character’s ASCII/UTF8 value replaces the corresponding +zeros in the initial memory layout. More specifically, HelloWorldHost, arbitrarily, +wrote the bytes at index `input_len+10` to give us enough space to subsequently +write `"Hello, Dan"` before it. + +¡ _Memory after host calls_ `greet` —Once the `greet` function is invoked, it prepends + +`"Hello, "` to the existing string `"Dan"` . In practice, as per the code we’ve written, +that is actually copying two memory segments ( `"Hello, "` and `"Dan"` ) starting +from `0` (i.e., due to `core::slice::from_raw_parts_mut(0` `as` `*mut` `u8,` `new_len)` ). +The memory now reflects this change, showcasing the updated ASCII/UTF-8 values corresponding to `"Hello,` `Dan"` . Note that `"Dan"` still exists at `new_len+10` . + +Congratulations! You’ve successfully built and executed your first Rust Wasm application executing in a non-web environment. Now let’s take a moment to reflect on the +challenges and limitations encountered during this process. + +Licensed to Ibrahim Mohammed Mohammed + +**28** Chapter 2 **_Building server-side applications with Wasm modules_** + +_2.3.6_ _Reflecting on the “Hello, World” application_ + +The completion of our “Hello, World” application serves not just as an introductory +exercise but also as a practical demonstration of the complexities involved in Wasm’s +manual memory management. While we’ve managed to perform basic string manipulation, the process has illuminated notable limitations related to the following: + +¡ _Handling complex data types_ —The exercise of manipulating a simple string begs + +the question: how would one manage more complex data structures, such as +classes/structs? The manual handling of memory for strings already introduces +potential for error and complexity; extending this to more complex types would +exponentially increase the difficulty and risk. + +¡ _Memory assumptions risks_ —The strategy assumes a certain memory layout and + +availability, which can lead to overwriting important data if those assumptions +don’t hold. For example, our code assumes that we can write to the memory slice +from `0` to `new_len`, and our HelloWorldHost assumes the output will be written +there—if you modify our code to write to a different memory slice (say, from `7` to + +`new_len` ), you’ll note the output displayed by the HelloWorldHost will be incorrect! Additionally, this approach assumes that the length of the `"Hello, "` string +remains the same. If you modify it to a longer prefix, the output will no longer be +valid, as the shifted memory will not account for the extra space. This risk is further exacerbated when considering the interaction of multiple modules, possibly +written in different languages, each with unique memory management assumptions. Such interactions can lead to unpredictable behaviors and compromise +application integrity. + +These challenges underscore the need for a more robust and higher-level approach to +data management within Wasm modules, and we’re going to explore that throughout +the book—starting with the first part of the story right now! + +NOTE If you’d like to play with the HelloWorldHost yourself a bit, the code is +[available on GitHub at https://mng.bz/8XK5.](https://mng.bz/8XK5) + +_2.4_ _Building on Wasm’s simple types with WebAssembly Interface Types_ + +Core Wasm keeps its type system intentionally simple, relying on fundamental numeric +types and linear memory. This minimalism is a strength—it ensures efficiency, portability, and broad compatibility across different execution environments. However, as +Wasm adoption grows, so does the need for higher-level abstractions that make working with structured data more intuitive. + +Enter WebAssembly Interface Types (WIT). Rather than working around Wasm’s +constraints, WIT builds on this solid foundation to enable more expressive module interactions. It serves as an interface definition language, allowing developers to +define software component interfaces in a way that is agnostic to programming languages. This facilitates smoother communication between different parts of a Wasm + +Licensed to Ibrahim Mohammed Mohammed + +**_Building on Wasm’s simple types with WebAssembly Interface Types_** **29** + +application—or even between Wasm and the host runtime (e.g., HelloWorldHost)—by +abstracting complex data types and simplifying module interactions. + +WIT enhances the developer experience and broadens Wasm’s applicability by introducing a wide array of new interface-level types in its interface definition language, +beyond just the four number types mentioned earlier. These include Booleans ( `bool` ), +signed and unsigned integers of various sizes ( `s8`, `s16`, `s32`, `s64`, `u8`, `u16`, `u32`, `u64` ), +floating-­point numbers ( `f32`, `f64` ), characters ( `char` ), and strings. Rather than adding +new core Wasm primitives, WIT standardizes how to describe and marshal (i.e., convert +between representations for communication across boundaries) richer data representations via the _Canonical ABI_, effectively lifting and lowering between Wasm and the +interface layer. + +NOTE _ABI_ stands for _application binary interface_ . In this context, the Canonical +ABI defines a standard way to translate values between Wasm and the other +environments—whether that’s the host or another Wasm guest. Think of it +as the agreed-upon rules for how data gets packaged and unpackaged when +crossing the Wasm boundary. + +Beyond these primitives, WIT extends its type system to support lists (e.g., `list` ), +optional values (e.g., `option` ), error handling through results (e.g., + +`result` ), and tuples for grouping values (e.g., `tuple` ). + +Moreover, WIT introduces constructs for defining complex, user-defined types +that closely mirror data structures found in high-level programming languages. These +include records for structured data (e.g., `record person { name: string, age: u32 }` ), +enums for enumerating possible values (e.g., `enum color { red, green, blue }` ), variants that are like enums but their properties can hold onto data (e.g., `variant error` + +`{ server-error(u64), not-authorized, unknown(string) }` ), and flags for bitwise combinations of options (e.g., `flags permissions { read, write, execute }` ). Additionally, +WIT supports type aliases to create new names for existing types and functions to define +callable operations. + +All these newly introduced types describe values that are copied in and out of memory. In addition to these, WIT also introduces resources, which are things that aren’t +copyable and, instead, are passed by handle (i.e., similar to how file descriptors or database connections are managed in operating systems). + +Finally, WIT introduces higher-level constructs to organize and group functionalities. The concept of “worlds” is akin to namespaces or modules, organizing the exports +and imports of interfaces or specific functions in a cohesive manner. Interfaces, which +group related functions, will be further explored in future examples. + +Revisiting our ”Hello, World” application with the addition of WIT allows us to imagine a version where the complexities of manual memory management are abstracted +away. With WIT, the `greet` function can be more intuitively defined, utilizing high-level +types directly, as shown in the following listing. + +Licensed to Ibrahim Mohammed Mohammed + +**30** Chapter 2 **_Building server-side applications with Wasm modules_** + +Listing 2.4 The ”Hello, World” application’s WIT + +``` + package component:hello-world-guest-wit; + + world example { + export greet: func(name: string) -> string; + } + +``` + +NOTE We’ve adjusted the `greet` function to return a string, eliminating the +need for direct memory manipulation. This adjustment highlights WIT’s +capacity to simplify the interface for developers by abstracting lower-level +details. + +So, instead of having to create a function that takes in two integers representing a +pointer and a length for a string and directly manipulating memory, transforming +Wasm’s basic types to higher-level ones, you can write this interface and create your +program off it, utilizing abstractions provided by Wasm directly. + +But there’s no need to rush into creating a file for this interface just yet; we’ll dive +deeper into that in the next chapter. That said, this does lead us to a crucial consideration: how exactly do we bring this interface to life? The implementation specifics can +differ across programming languages, yet the essence of working with WIT remains +consistent. Once your interface is defined, it enables the generation of boilerplate code +that navigates the intricacies of memory management for you. This method shifts the +developer’s focus from wrestling with low-level details to purely concentrating on the +business logic, which, in our example, is fleshing out the `greet` function. This significant shift streamlines the development workflow, making it more efficient and focused +on the core functionality. + +_2.5_ _Building the foundation of a smart content management system_ + +Throughout the course of this book, we’re going to apply what we learn by constructing a practical project: a smart content management system (CMS) for children’s stories. A CMS is essentially a software application that allows users to create, edit, and +manage digital content. In our case, it will enable the crafting and curation of engaging stories for children, making it easy to add new stories and present them. + +The SmartCMS will be structured with a straightforward frontend where users can +input and view stories. However, the core functionality will reside in the backend, which +is where Wasm’s capabilities will be harnessed to full effect. Our backend will consist of +the following components: + +¡ _Key-value store component_ —This will be the repository for storing all the stories, + +which can be persisted to disk, Redis, or others. + +¡ _HTTP component_ —This will act as the API gateway, allowing the frontend to com +municate with the backend. + +Licensed to Ibrahim Mohammed Mohammed + +**_Building the foundation of a smart content management system_** **31** + +¡ _Machine learning component_ —This will serve as the story generator, using machine + +learning to create new stories. More specifically, we will be using a WIT interface +for neural networks. + +Our ultimate goal is to deploy this system on Kubernetes—an open source platform +for automating deployment, scaling, and management of containerized applications— +with dedicated Wasm containers. The architecture diagram in figure 2.4 visualizes the +interaction between the user, the frontend, and the various backend components, all +orchestrated within a Kubernetes environment. + +**instance** + +Figure 2.4 The basic architecture for our SmartCMS application + +Let’s begin by laying the groundwork for our key-value store component with WIT. +Our initial interface, as seen in the following listing, will focus on the foundational +operations of any key-value store: retrieving ( `get` ) and storing ( `set` ) data. + +Listing 2.5 The SmartCMS’s key-value store WIT + +``` +package component:smartcms-kvstore; + +world kvstore { +export get: func(key: string) -> string; +export set: func(key: string, value: string); +} + +``` + +The `get` function will take a `string` representing the key and return a `string` containing the value associated with that key. This will be used to retrieve stories from our +store. The `set` function will accept two `string` parameters: one for the key and another + +Licensed to Ibrahim Mohammed Mohammed + +**32** Chapter 2 **_Building server-side applications with Wasm modules_** + +for its corresponding value. This function won’t return any value, reflecting the action +of adding a new story to the store. + +By defining these operations in WIT, we create an interface that can be universally +understood and utilized by any language or tool that supports WIT, ensuring interoperability and flexibility. + +With this in place, we close this chapter. In the following one, we’ll dive into the +implementation of the interface, bringing our key-value store component to life and +running our first component. + +_Summary_ + +¡ Wasm’s core is formed by linear memory and four primary data types: integers + +and floats, each available in 32- and 64-bit sizes. Building complex data structures +requires utilizing these foundational elements. + +¡ Rust provides good support for working with Wasm as it can compile directly to it. + +¡ Server-side Wasm applications need a specific execution runtime like Wasmtime, + +which is designed to enable interaction with system resources. + +¡ Manual management of Wasm’s limited foundational datatypes is intricate and + +susceptible to errors, emphasizing the importance of more sophisticated abstractions, such as WIT. + +¡ WIT offers a versatile, language-agnostic type system, enabling the crafting of + +complex data structures and interfaces to ease the interaction between various +components of a Wasm application. + +Licensed to Ibrahim Mohammed Mohammed + +##### _and security with_ _Wasm components_ +# _3_ + +##### _Enhancing portability_ + +##### _and security with_ _Wasm components_ + +_This chapter covers_ + +¡ How Wasm components provide enhanced + +portability and security + +¡ Building a Wasm component with JavaScript + +¡ Building a custom runtime for a Wasm + +component with Python + +¡ Implementing host-side functionality from a WIT + +file in Rust + +In chapter 2, we built our first Wasm app and compiled it into a Wasm module—the +thing most people think of when they hear “Wasm.” But, as we saw, a module on its +own is stuck with only simple types like integers and floats. You can build more complex types from those, but that means both the guest and the Wasm runtime need to +agree on the exact memory layout. For any real application, that’s a headache. + +This is where WebAssembly Interface Types (WIT) comes in. It gives us a way to +define higher-level types without all the manual layout work. But WIT isn’t the whole +story. It’s part of something bigger: the Wasm Component Model. The model uses + +**33** + +Licensed to Ibrahim Mohammed Mohammed + +**34** Chapter 3 **_Enhancing portability and security with Wasm components_** + +WIT as its interface definition language and serves as a wrapper around Wasm modules. +The goal is to let different Wasm applications talk to each other without all the manual +memory wrangling or those fragile, implicit agreements about where structures live in +linear memory. + +_3.1_ _Benefits of Wasm components_ + +The capability of components to lift from low- to high-level types (like going from two +integers to a string) and lower from high- to low-level types brings several key benefits that aren’t always obvious at first glance. These include cross-language portability, +shared-nothing security, and only-what-you-need security with faster vulnerability mitigation. Let’s break down each of these and see how they come into play. + +_3.1.1_ _Cross-language portability_ + +Imagine you’re developing a JavaScript application but want to use a machine learning +library written in Python. How would you do that today? Most likely, you would create +a Python microservice and possibly introduce a network stack into your application to +communicate with it. This approach has two major drawbacks: + +¡ You are forced to write code in Python, which you may not be familiar with, + +increasing the cognitive load. + +¡ You are adding the complexity and overhead of a network stack to your applica +tion, which includes not only network communication but also the serialization +and deserialization of data for communication between the two languages, such +as using formats like JSON. + +As shown in figure 3.1, Wasm components allow you to write applications in different +languages and then compose or link them together to form a single `.wasm` file. + +This means that with components, Wasm is not only language agnostic—meaning it +doesn’t rely on specific language features for compilation—but it is also now portable +across languages. Languages with completely different paradigms or memory layouts +can interact with one another as if they were one and the same. Imagine the number +of person hours saved by not having to replicate the same feature across different +languages! + +Also, it’s important to emphasize that Wasm components are really nothing more +than a wrapper around Wasm modules. This is useful because it means we don’t have to +change our existing pipelines to utilize components. The only difference is the added +linking/composing step. + +_3.1.2_ _Shared-nothing security_ + +Wasm components also address another pain point of Wasm modules: the lack of a +standard way to interact between modules/components or with a Wasm runtime. As we +saw in our previous application, the way to communicate with our Wasm module was +to directly interact with its exported memory. This approach is error prone because + +Licensed to Ibrahim Mohammed Mohammed + +**_Benefits of Wasm components_** **35** + +Figure 3.1 An example of +cross-language portability with +Wasm components + +it requires the Wasm runtime (or another module) and our module to agree on the +memory layout. Wasm components, on the other hand, follow the principle of shared +nothing, meaning a Wasm runtime/module and our module do not share memory. +Instead, they communicate exclusively through imports and exports of the Wasm +component. + +Let’s walk through the example shown in figure 3.2 to see how we can achieve +shared-nothing interactivity. In this example, we have two components: `my_app.wasm`, +which imports a `secret-store` and retrieves a secret from it, and `my_secret_store.wasm`, +which exports the `secret-store` interface containing a `get` method. And remember, +both components are completely isolated and contain their own memory space. + +To start, `my_app` calls the `get` method with a `my-key` value. That key is copied from + +`my_app` ’s linear memory into `my_secret_store` ’s linear memory. From there, `my_secret_` + +`store` can access its own linear memory to handle the `get` function call. But `get` returns +a `Secret` resource, which is an opaque type—it doesn’t directly expose its data like + +Licensed to Ibrahim Mohammed Mohammed + +**36** Chapter 3 **_Enhancing portability and security with Wasm components_** + +Figure 3.2 The interaction between two isolated Wasm components starting from a function call to +communicating with an adapter component, doing required memory copies, and returning a resource that can be +accessed via a resource table + +strings or integers, and you interact with it through specific functions. That said, it +can’t be copied by value, so `my_secret_store` returns a _resource handle_ instead. This handle is just an integer that points to the resource in a resource table—a separate memory space alongside linear memory. + +Now, with that handle, `my_app` can call functions on the `Secret` type, like, say, +some method to actually get the `Secret` ’s data. If the idea of resource tables still feels +a bit unclear, don’t worry—we’ll dig deeper into this with a hands-on example in +chapter 4. + +This process closely resembles interprocess communication, which might seem +complicated but overall helps us understand the inner workings of Wasm components. + +Licensed to Ibrahim Mohammed Mohammed + +**_Benefits of Wasm components_** **37** + +This complexity is hidden from the programmer consuming the interface—from the +perspective of the programmer writing `my_app.wasm`, `secret-store.get` is just a simple +function call. + +This is a big shift in how we understand Wasm. That is, initially, we learn of it as being +simply a compilation target—often compared to x86_64, arm64, RISC-V, etc. But Wasm +components, while retaining that function, add a lot of structure around component-­ +to-component or component-to-Wasm runtime communication, which much more +closely resembles the idea of microservices. So now we can think of components as +something that can be developed and deployed independently and interact with one +another as part of a larger application. + +_3.1.3_ _Only-what-you-need security and improved vulnerability mitigation response_ + +Another security benefit highlighted by Wasm components is that each component +only ever has access to its specified imports. There are no unexpected or unauthorized +dependencies that the component could access. + +The best way to illustrate why this is important is by looking at Sysdig’s 2023 Cloud-­ +[Native Security & Usage Report (https://mng.bz/4nVQ). That states that 87% of con-](https://mng.bz/4nVQ) +tainer images running in production have a critical or high severity vulnerability, but +only 15% of high or critical severity vulnerabilities with an available fix are in use at run +time. This means that most app code and, more importantly, most vulnerable code is +not actually our app logic but rather some hidden dependency. + +Now, of course, just because our components only have access to specific imports, it +doesn’t mean we are now vulnerability free. The few capabilities we have access to could +still have bugs in them, but even in that scenario, Wasm components offer a benefit in +terms of mitigation response, as shown in figure 3.3. + +|link|ed| +|---|---| +||| + +Figure 3.3 An example of improved mitigation response with Wasm components + +Licensed to Ibrahim Mohammed Mohammed + +**38** Chapter 3 **_Enhancing portability and security with Wasm components_** + +Why only relink the vulnerable component? Because Wasm components are isolated +and communicate through explicit interfaces, you can swap out just the vulnerable +component with a patched version—no rebuild of your business logic needed. The +vulnerable component and the other component are completely separate components +that connect via imports/exports. Your business logic ( `my_business_logic.wasm` ) never +changes, so there’s nothing to recompile. You simply replace the vulnerable component and reestablish the connection. + +This is different from traditional approaches where dependencies are often baked +into your compiled binary. Even with dynamic libraries (like `.dll` or `.so` files), while you +can replace them without rebuilding, they have full access to your application’s memory and global variables. Wasm components use a shared-nothing isolation model— +each component only accesses its specified imports and cannot share memory with +other components. This means the effect or “blast radius” of a vulnerable component is +contained. Figure 3.4 illustrates this difference in mitigation approaches. + +Traditional Wasm + +**Components are completely** +**isolated, so you don’t need** +**to touch your own business** +**logicto update a vulnerable** +**third-party component.** + +Figure 3.4 Illustrated difference between traditional architectures and one using Wasm components in +terms of vulnerability mitigation + +Let’s consider a real-world scenario: imagine you’re running an e-commerce platform +and discover a critical vulnerability in your PDF-generation library: + +¡ _Traditional approach_ —You update the library version, rebuild your entire applica +tion (which includes your checkout logic, inventory management, user authentication, etc.), run your full test suite, and deploy a new release. Even with dynamic +libraries, that PDF library has access to your entire application’s memory—­ +meaning a vulnerability could potentially expose customer data or credentials. + +¡ _Wasm components approach_ —You swap out just the PDF component with a patched + +version and relink it. Your checkout logic, inventory system, and authentication +never get touched or recompiled. And because the PDF component was already + +Licensed to Ibrahim Mohammed Mohammed + +**_Building a componentized “Hello, World” guest application with JavaScript_** **39** + +isolated to only its specified imports (like file-writing capabilities), even before +patching, it couldn’t accidentally leak customer data or access your database. + +The isolation and composability of Wasm components turns vulnerability mitigation +from an application-wide event into a surgical replacement. + +_3.2_ _Building a componentized “Hello, World” guest application_ +_with JavaScript_ + +Now that we understand the benefits of Wasm components, let’s pick up where we left +off in chapter 2 and build a componentized “Hello, World” application. And because +we already used Rust in the previous chapter, let’s use JavaScript to create our Wasm +component. + +_3.2.1_ _Setting up the project and installing the toolchain_ + +We left off having crafted the WIT for our “Hello, World” application as displayed in +the following listing. + +Listing 3.1 The WIT file for our “Hello, World” application ( **`greet.wit`** ) + +``` + package component:hello-world-guest-wit; + + world example { + export greet: func(name: string) -> string; + } + +``` + +To use this WIT file to create a component with JavaScript, we will need to set up the +JavaScript Wasm toolchain. First, make sure you have Node and NPM installed. The +Node.js documentation recommends using Node Version Manager (nvm) to handle +this. + +If you’re on Linux, macOS, or Windows Subsystem for Linux, you can install nvm by +running + +``` + curl -o- https://raw.githubusercontent.com +``` + +➥ `/nvm-sh/nvm/v0.40.2/install.sh | bash` + +Once that’s done, restart your terminal and install Node version 22 (along with npm) +by running + +``` + nvm install 22 + +``` + +If you’re on Windows, you can grab the nvm installer from [https://github.com/](https://github.com/coreybutler/nvm-windows) +[coreybutler/­nvm-windows and follow the same steps.](https://github.com/coreybutler/nvm-windows) + +After Node and npm are set up, install the JavaScript component toolchain (jco) +globally: + +``` + npm install -g @bytecodealliance/jco@1.10.2 + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**40** Chapter 3 **_Enhancing portability and security with Wasm components_** + +You’ll also need the componentization tooling that jco uses: + +``` + npm install -g @bytecodealliance/componentize-js@0.17.0 + +``` + +With that done, from inside our `chapter03/` folder, let’s create a folder for our project +and call it `hello_world_component/` . In it, place the WIT file ( `greet.wit` ) from listing +3.1 and an empty JavaScript file called `greet.js` . + +_3.2.2_ _Implementing the component logic_ + +As explicitly stated in the WIT file, our component will have a single function called + +`greet` that takes a `name` ( `string` ) and returns another `string` (i.e., the name prepended +with `"Hello, "` ). Let’s implement this function in our JavaScript file as per the following listing. + +Listing 3.2 The JavaScript file for our “Hello, World” component ( **`greet.js`** ) + +``` + export function greet(name) { + return `Hello, ${name}!`; + } + +``` + +Note that, unlike before, we are no longer walking on eggshells with memory by managing pointers and offsets. Instead, we are working directly with high-level types like +strings. This is because the Wasm component wrapping handles the memory layout for +us. We only need to focus on the logic of our component. + +_3.2.3_ _Compiling the Wasm component_ + +Finally, let’s componentize. To do this, we will use the JavaScript Wasm toolchain we +set up earlier. Run the following command in the terminal inside your `hello_world_` + +`component` folder: + +``` + jco componentize greet.js --wit greet.wit --world-name +``` + +➥ `example --out greet.wasm --disable stdio random clocks http` + +NOTE Remember the security benefits we talked about in chapter 1 where +Wasm only has access to the native OS via specific tightly controlled capabilities? You can see a bit of that with this command where we explicitly disable +access to `stdio`, `random`, `clocks`, and `http` . + +This command will bundle our JavaScript file into a Wasm component, utilizing the +WIT file as a reference for imports and exports, creating a file called `greet.wasm` . Note, +we need to provide the WIT file together with the JavaScript code, because our guest +code does not provide a full picture of what needs to be linked for this component— +for example, say we specified in our WIT that we want to import a capability to print +to `stdout` ; `jco` would only know that from our WIT. Next, let’s move on to creating a +custom Wasm runtime where we will execute our component. + +Licensed to Ibrahim Mohammed Mohammed + +**_Building a Python host for a JavaScript-bundled Wasm component_** **41** + +_3.3_ _Building a Python host for a JavaScript-bundled Wasm component_ + +To run our component, we will need a host. And we will not use the HelloWorldHost +from chapter 2 because that host meddled with memory directly, which goes against +the whole point of Wasm components. So let’s create a new host ourselves using +Python, which will also serve to demonstrate how straightforward it is to call our Java­ +Script function cross-language from Python. + +_3.3.1_ _Setting up the project and installing the toolchain_ + +Before we start building our Python app, we need to set up the Python environment. +[If you don’t have Python installed, you can find installation instructions at https://wiki](https://wiki.python.org/moin/BeginnersGuide/Download) +[.python.org/moin/BeginnersGuide/Download. At the time of writing, I installed and](https://wiki.python.org/moin/BeginnersGuide/Download) +am using Python version 3.12.3. + +NOTE Python projects typically use a virtual environment to manage dependencies and avoid interfering with your global Python setup. Before starting +any Python project in this book, it’s recommended to create a dedicated virtual +environment for that project. You can do this by running `python` `-m` `venv` `venv` +in the root directory of the specific Python project you’re working on. Once +that’s done, you can activate the environment from the same directory. On +Windows, use `.\venv\Scripts\activate` and on Linux or macOS, use `source` +`venv/bin/activate` . When you’re finished and want to return to the global +Python environment, simply run `deactivate` . + +To start, inside our `chapter03/` folder, create a new folder called `hello_world_python_` + +`host` at the same level as our `hello_world_component` folder and place an empty Python +file called `main.py` in it. + +Next, create a `requirements.txt` file in the `hello_world_python_host` folder and add +the following line to it: + +``` + wasmtime==30.0.0 + +``` + +This will allow us to use the Wasmtime runtime in our Python host. We can then install +the required package by running the following command in the terminal: + +``` + python -m pip install -r requirements.txt + +``` + +NOTE Depending on your Python installation, you may need to use `python3` +or `py` instead of `python` to run commands. On Linux, you might also need to +install pip using `apt-get install python3-pip` . + +_3.3.2_ _Implementing the host logic_ + +Now let’s implement the logic for our Python host. This step, as illustrated in figure +3.5, involves embedding the Wasmtime runtime to load our Wasm component and +directly call the `greet` function. + +Licensed to Ibrahim Mohammed Mohammed + +**42** Chapter 3 **_Enhancing portability and security with Wasm components_** + +To start, let’s import some necessary modules. The +first is the runtime itself, and the second is a loader +module, which will help us load our Wasm component. In `main.py`, write + +``` +import wasmtime, wasmtime.loader + +``` + +Next, copy the `greet.wasm` file from our `hello_world_` + +`component` folder to our `hello_world_python_host` +folder. + +With that done, we can directly load our `greet` + +`.wasm` file directly from disk, like so: + +``` +import greet + +``` + +Next, we set up a store—a Wasmtime construct for +storing data related to a component or module—and +attach or “root” it to the greeter component we bundled earlier from JavaScript: + +``` +store = wasmtime.Store() +component = greet.Root(store) + +``` + +Figure 3.5 The host is a thin +wrapper around Wasmtime, which +we will use to load our guest +component and execute the +functions it implements. + +Now we have everything we need to call the `greet` function from the component we +wrote earlier. Let’s do that: + +``` + print(component.greet(store, "World")) + +``` + +Finally, the following listing shows the full Python file for our “Hello, World” host. + +Listing 3.3 The Python file for our “Hello, World” host ( **`main.py`** ) + +``` + import wasmtime, wasmtime.loader + import greet + + store = wasmtime.Store() + component = greet.Root(store) + print(component.greet(store, "World")) + +``` + +_3.3.3_ _Running the Python host_ + +The Python host can then be run like any other Python file: + +``` + python main.py + +``` + +If everything went well, you should see the output `"Hello,` `World!"` . Let’s take a moment +to reflect on what happened here with figure 3.6. + +Licensed to Ibrahim Mohammed Mohammed + +**_Expanding the example project with Wasm components_** **43** + +JS guest + +WIT file + +implemented + +in + +|Col1|Col2| +|---|---| +|bundl|ed into| + +Wasm component Python host + +executed + +with + +Figure 3.6 The flow of our +“Hello, World” application +with a Wasm component + +Take a moment to pat yourself on the back. You have come a long way! Starting only +with a WIT file that defined a `greet` function, we implemented the functionality in Java­ +Script, bundled it into a Wasm component, and loaded that in a self-made Python host +that embedded Wasmtime to call a function cross-language. It was a lot of work, and, +in fact, maybe too much work. Constantly creating WIT files and custom hosts can be +cumbersome and is much more effort than a conventional project. As app developers, +we should only be concerned with our JavaScript Wasm component and not with crafting WITs or dealing with Wasmtime. So, in subsequent chapters, we will see how to do +just that when we discuss some standardized WIT interfaces and even prebuilt generic +Wasm runtimes that simplify the process of making an app. But, for now, let’s dig even +deeper into Wasm components and establish some solid foundational knowledge. + +_3.4_ _Expanding the example project with Wasm components_ + +In chapter 2, we introduced the smart content management system (CMS) for children’s stories that we’ll build throughout this book. We started by crafting a simple, +initial WIT file for interacting with the key-value store that will hold those stories. Now, +before expanding on that, let’s talk about where we’re putting that key-value store +logic. In this case, we’re going to implement the functionality in the host so that the +guest can import and use it. + +This might feel different from our “Hello, World” example, where the host mostly +acted as an entry point and the component contained all the logic. But here, we’re +treating the host as the provider of the environment and supporting functions the guest +needs during execution. By placing this logic in the host, guests can stay focused on +using that functionality rather than defining it, which also makes it easier to swap in + +Licensed to Ibrahim Mohammed Mohammed + +**44** Chapter 3 **_Enhancing portability and security with Wasm components_** + +generic hosts later on. So let’s see how we can implement our key-value store in the host +and have the guest service call into it. + +_3.4.1_ _Improving the WIT file for the key-value store_ + +In chapter 2, we created the WIT file in listing 3.4 for our key-value store. To begin +using the interface, copy the contents of listing 3.4 into a new file named `smart_cms` + +`.wit` . Save this file inside a folder called `smart_cms/`, which should be located within +the `chapter03/` directory. + +Listing 3.4 The preliminary WIT file for our key-value store ( **`smart_cms.wit`** ) + +``` + package component:smartcms-kvstore; + + world kvstore { + export get: func(key: string) -> string; + export set: func(key: string, value: string); + } + +``` + +Now that we have a better mental model for how the application will work, let’s move +the `get` and `set` methods to a unique `kvstore` interface to be called like so: + +``` + // ... + interface kvstore { + get: func(key: string) -> string; + set: func(key: string, value: string); + } + // ... + +``` + +We are doing this because we will modify our existing `world` . We’ll rename it to `app` +and have it import the `kvstore` interface we just created. This is particularly important because it indicates that a host is responsible for implementing the key-value store +functionality, which is different from before, where we `export` ed functions that were to +be implemented by the guest: + +``` + // ... + world app { + import kvstore; + } + // ... + +``` + +We will also add a new `export` for a `run` function that is meant to be implemented by a +guest. This function will be the entry point for the guest’s execution, and it will return +a `string` so that we can see some output from calling into the guest: + +``` + // ... + world app { + import kvstore; + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Expanding the example project with Wasm components_** **45** + +``` +export run: func() -> string; +} +// ... + +``` + +Lastly, let’s update the package name of our WIT file to be `component:smartcms` : + +``` +package component:smartcms; +// ... + +``` + +Overall, our updated WIT file should look like the following listing. + +Listing 3.5 The updated WIT file for our key-value store ( **`smart_cms.wit`** ) + +``` +package component:smartcms; + +``` + +``` +interface kvstore { +get: func(key: string) -> string; +set: func(key: string, value: string); +} + +``` + +**kvstore was moved to its own interface.** + +``` + world app { + import kvstore; + + } + +``` + +_3.4.2_ _Creating the SmartCMS host_ + +**world app imports kvstore,** +**meaning a host must implement it.** + +**world app exports run, meaning a guest** +**(i.e., a Wasm component) must implement it.** + +Now let’s try our hand at using Rust to create the host for our SmartCMS. To reiterate, +we are doing this for two main reasons: + +¡ We don’t currently have an environment for our guest to run in, so we have to + +handcraft one. + +¡ We need to implement the host functionality for the key-value store that our + +guest is expecting to be capable of importing. + +By the end of this, we will have a full-fledged host to load our component in and execute the guest’s `run` function from. + +Inside our `smart_cms/` folder, we can set up our new project by running the following +command in the terminal: + +``` +cargo new smartcms_test_host + +``` + +Next, let’s move our newly created `smart_cms.wit` file to the `smartcms_test_host` +folder. + +Then open the project with your IDE of choice, and let’s edit the `Cargo.toml` file to +add the dependency we need to utilize Wasmtime. This is all very similar to how we built +our Python host just prior: + +Licensed to Ibrahim Mohammed Mohammed + +**46** Chapter 3 **_Enhancing portability and security with Wasm components_** + +``` + # ... + + [dependencies] + wasmtime = "30" + +``` + +Next, let’s look at the autogenerated `main.rs` file and delete all its contents. Then we +can start implementing our host by making a call to `wasmtime::component::bindgen!` at +the top of our file, which will generate a bunch of glue code for us to be able to implement the key-value store functionality: + +``` + wasmtime::component::bindgen!({ + path: "./smart_cms.wit", + world: "app", + }); + +``` + +This call will give us access to a `Host` trait that we can implement for our own type. If +you’re not familiar with traits, think of them as interfaces in Go and Java or protocols +in Python and Swift. Our own type will be a `struct` called `KeyValue` that will serve as the +implementor for our `kvstore` interface. To keep things simple, we will use a `HashMap` as +the backing to store our key-value pairs, like so: + +``` + // ... + struct KeyValue { + mem: std::collections::HashMap, + } + +``` + +Next, let’s implement the host interface. We can do so like this: + +``` + impl component::smartcms::kvstore::Host for KeyValue { + // ... + } + +``` + +Inside the `impl` block, we need to implement the `get` and `set` functions. Let’s start +with `get` : + +``` + impl component::smartcms::kvstore::Host for KeyValue { + fn get(&mut self, key: String) -> String { + self.mem.get(&key).cloned().unwrap() + } + } + +``` + +In here, we will access the `mem` field of our `KeyValue` instance, which, of course, is a + +`HashMap`, so we can call its very own `get` method. Next, we clone that value and `unwrap` it. +This is because `get` from `HashMap` returns an `Option`, which is Rust’s way of handling `null` values (i.e., for when the key isn’t present). In our usage, `unwrap` ping means +we are assuming that the key will always be present. If it isn’t, our code will `panic` / +crash, which isn’t particularly graceful error handling. Let’s get rid of this assumption + +Licensed to Ibrahim Mohammed Mohammed + +**_Expanding the example project with Wasm components_** **47** + +by slightly modifying our WIT file to have our `get` function match the `HashMap` ’s `get` +function: + +``` +// ... +interface kvstore { +get: func(key: string) -> option; +set: func(key: string, value: string); +} +// ... + +``` + +Then we can modify our get implementation and remove that `unwrap` call: + +``` +// ... +fn get(&mut self, key: String) -> Option { +self.mem.get(&key).cloned() +} +// ... + +``` + +Next, let’s implement the `set` function. It is very similar to the `get` function except for +the `&mut` `self` parameter (i.e., Rust’s way to indicate that the function will be able to +modify the `KeyValue` instance it is called on) and the fact that, instead of returning a +value, it will set a value, like so: + +``` +// ... +fn set(&mut self, key: String, value: String) { +self.mem.insert(key, value); +} +// ... + +``` + +Now that we are done implementing the `Host` interface, let’s implement the `main` function. The `main` function will be the entry point of our host. So, there, we will be configuring Wasmtime to execute our components. We can start on that by first creating the +configuration object for Wasmtime: + +``` +fn main() { +let mut config = wasmtime::Config::default(); +config.wasm_component_model(true); +} + +``` + +Note that we are utilizing default configuration for the runtime, but we are also setting +the `wasm_component_model` feature to `true` . This is because we are using Wasm components, and we need to enable this to use them. + +Next, we will create the `Engine` object to that configuration, which is essentially +Wasmtime itself, where our component will be executed: + +``` +fn main() { +// ... +let engine = wasmtime::Engine::new(&config).unwrap(); +} + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**48** Chapter 3 **_Enhancing portability and security with Wasm components_** + +One of the last items we need to create is the `Store` object, which, like in our Python +host, is where we will store the data related to our component or module. Before, we +didn’t really have any data to store, but now we have all the data inside our key-value +component. So the store configuration is a little different. For starters, let’s create a +new `struct` to collect our runtime state and call it `State` . Right now, this `struct` is a bit +redundant because the only possible state we can have is related to our key-value store, +so we could make do with just `KeyValue` . But, by creating `State`, we are being futureproof, so we can build upon our host in the future. `State` will look like this: + +``` + struct State { + key_value: KeyValue, + } + +``` + +Now, back in `main`, we can initialize our store with such state: + +``` + // ... + fn main() { + // ... + let mut store = wasmtime::Store::new(&engine, State { + key_value: KeyValue { + mem: std::collections::HashMap::new(), + } + }); + } + +``` + +And, finally, we can load the component that this host will run: + +``` + // ... + fn main() { + // ... + let component = wasmtime::component::Component::from_file +``` + +➥ `(&engine, "guest.wasm").unwrap();` +``` + } + +``` + +`guest.wasm` will be the file name of the Wasm guest component we will be building in +the next section, which will implement our `run` function. + +Finally, the last thing that we need to do is start our `Linker` object. The linker is in +charge of connecting our host and guest: it is what will allow our guest to access the +functions we implemented in our host: + +``` + // ... + fn main() { + // ... + let mut linker = wasmtime::component::Linker::new(&engine); + component::smartcms::kvstore::add_to_linker +``` + +➥ `(&mut linker, |state: &mut State| &mut state.key_value).unwrap();` +``` + } + +``` + +The second parameter to the `add_to_linker` function is a closure/lambda/anonymous +function that will return a mutable reference to our `KeyValue` instance, which is how we + +Licensed to Ibrahim Mohammed Mohammed + +**_Expanding the example project with Wasm components_** **49** + +are able to give our guest access to the host’s key-value store implementation. Next, we +can simply instantiate the guest and call the `run` function, which we will be wrapping in +a `println` so we can see the output `string` the guest will return: + +``` +// ... +fn main() { +// ... +let app = App::instantiate(&mut store, &component, &linker).unwrap(); +println!("{:?}", app.call_run(&mut store).unwrap()); +} + +``` + +And that’s it! We have our host ready to go. In the following listing, you can see the full + +`main.rs` file. + +Listing 3.6 The full **`main.rs`** file for our SmartCMS host + +``` +wasmtime::component::bindgen!({ +path: "./smart_cms.wit", +world: "app", +}); + +``` + +**We use macros to create glue code to** +**facilitate our host implementation.** + +``` +struct KeyValue { +mem: std::collections::HashMap, +} + +fn get(&mut self, key: String) -> +``` + +➥ `Option {` +``` +self.mem.get(&key).cloned() +} + +``` + +**The implementation of** +**our kvstore world, using** +**a HashMap to store** +**key-value pairs** + +**The get function now returns an** +**Option for better error handling.** + +``` +fn set(&mut self, key: String, value: String) { +self.mem.insert(key, value); +} +} + +``` + +``` +struct State { +key_value: KeyValue, +} + +``` + +**The runtime state containing** +**our KeyValue struct** + +``` +fn main() { +let mut config = wasmtime::Config::default(); +config.wasm_component_model(true); + +``` + +**Enables the component** +**model feature to indicate** +**that we are using components** +**instead of modules** + +``` +let engine = wasmtime::Engine::new(&config).unwrap(); + +``` + +``` +let mut store = wasmtime::Store::new + +``` + +➥ `std::collections::HashMap::new() } });` + +**Sets our runtime state** +**in the Store object** + +``` +let component = wasmtime::component:: +``` + +➥ `Component::from_file(&engine, "guest.wasm").unwrap();` + +Licensed to Ibrahim Mohammed Mohammed + +**50** Chapter 3 **_Enhancing portability and security with Wasm components_** + +``` +let mut linker = wasmtime::component::Linker::new(&engine); +component::smartcms::kvstore:: +``` + +➥ `add_to_linker(&mut linker, |state: &mut State| &mut` +➥ `state.key_value).unwrap();` + +``` +let app = App::instantiate(&mut store, +``` + +➥ `&component, &linker).unwrap();` + +➥ `.unwrap());` +``` +} + +``` + +**Links our kvstore host** +**implementation together** +**with its respective state** + +**The App symbol isn’t defined by us,** +**but rather it is implemented by** +**wasmtime::component::bindgen!** + +**Calls the run function that will be** +**implemented in the guest** + +Now let’s move onto creating the guest component. + +_3.4.3_ _Implementing and running a guest Wasm component inside our SmartCMS host_ + +To store our guest code, let’s create a new folder called `smartcms_kvstore_guest` . It +should be a sibling to `smartcms_test_host` . Inside of it, we will make a new file called + +`guest.js` where we will be creating another JavaScript guest component. This guest +code won’t be incredibly meaningful and, for now, will just demonstrate we can do a +full roundtrip from the host to the guest and back—all while using the key-value store +we implemented in the host, which, in particular, uses types that are not present in +JavaScript (like Rust’s `Option` type). + +First, let’s bring in the imports from the host. We can do so like this: + +``` + import { get, set } from "component:smartcms/kvstore"; + +``` + +Next, let’s implement the `run` function. It will simply set a key-value pair, then get it, +and return that value: + +``` + export function run() { + set("guest-hello", "Hello from the Guest!"); + return get("guest-hello"); + } + +``` + +The full file can be seen in the following listing. + +Listing 3.7 The full **`guest.js`** file for our SmartCMS guest + +``` +import { get, set } from "component: +``` + +➥ `smartcms/kvstore";` + +**The guest only has access to what is provided** +**to it (i.e., the functions from kvstore).** + +``` +export function run() { +set("guest-hello", "Hello from the Guest!"); +return get("guest-hello"); +} + +``` + +**The guest communicates** + +**does not meddle with** + +Licensed to Ibrahim Mohammed Mohammed + +**_Expanding the example project with Wasm components_** **51** + +But remember: our `run` function returns a `string`, so how can this work with our `get` +function returning an `option` ? Let’s try componentizing it. To componentize, +run the following command from inside the `smartcms_kvstore_guest` folder: + +``` +jco componentize guest.js --wit ../smartcms_test_host/smart_cms.wit -``` + +➥ `world-name app -o ../smartcms_test_host/guest.wasm --` +➥ `disable stdio random clocks http` + +And—it all works! To figure out what is going on here, let’s run `jco` `transpile` . This will +allow us to provide our `.wasm` component back to `jco` so it can spit out the generated +JavaScript glue code. This way, we can see how the types are being handled: + +``` +jco transpile ../smartcms_test_host/guest.wasm --out-dir transpile + +``` + +Now, inside `transpile/interfaces`, we can see our WIT interface defined as a TypeScript declaration file at `transpile/interfaces/component-smartcms-kvstore.d.ts` . +There, we will see what is displayed in the following listing. + +Listing 3.8 The TypeScript declaration file for our key-value store interface + +``` +/** @module Interface component:smartcms/kvstore **/ +export function get(key: string): string | undefined; +export function set(key: string, value: string): void; + +``` + +Aha! This is what allows our JavaScript guest to still work. The `Option` type has been +translated to `string | undefined`, with `undefined` aligning to `Option` ’s `None` variant. So, +essentially, the signature of `run` is still fulfilled and the code works. + +NOTE The `run` function signature is technically fulfilled—but only at run time, +not statically. Since our key-value store has a value for `guest-hello`, everything +works fine. But if it didn’t, and `undefined` were returned, the app would crash. + +To finish off, let’s run this component. Back in our host folder, execute the following +in the terminal: + +``` +cargo run –-release + +``` + +If everything went well, you should see the output: `"Hello` `from` `the` `Guest!"` . + +Again, that was quite a bit of work, but it highlighted several important pieces of the +puzzle coming together. Primarily, we saw how we can now utilize high-level types, in +high-level languages, across different languages. But we also saw some notable security +principles in practice. For example, we saw only-what-you-need security being displayed +with our guest utilizing nothing but the `kvstore` interface—that is, our guest, did not +even have access to standard output, which is why I deferred displaying the result of `get` +to our host. And we also saw the shared-nothing principle in action, where our guest + +Licensed to Ibrahim Mohammed Mohammed + +**52** Chapter 3 **_Enhancing portability and security with Wasm components_** + +and host did not share memory but instead communicated through the `kvstore` interface that manipulated the runtime state. + +Throughout this chapter, we put on a lot of different hats. While designing interfaces, we acted as architects. While implementing the host, we acted as system engineers. And, while implementing the guest, we acted as application developers. This, of +course, is not how developing a server-side Wasm application is supposed to be. Most +of the time, we will be using standardized interfaces and prebuilt generic hosts, so we +can dedicate ourselves to simply caring about the business logic. But, hopefully, by +going through this process, we now have a much better understanding of the system as a +whole, which will be invaluable when things don’t go exactly as planned. + +_Summary_ + +¡ Wasm components are a wrapper around Wasm modules that allow us to use + +higher-level types. + +¡ Wasm components add a new level of portability to Wasm by allowing easy + +cross-language communication. + +¡ Wasm components enforce a shared-nothing principle between components or + +within the host. They communicate exclusively through imports and exports and +do not share memory. + +¡ Wasm components improve security by only allowing access to specific imports + +and exports, and they contain no hidden dependencies. + +¡ Wasm components improve mitigation response by allowing us to update com +ponents simply by relinking the patched component without rebuilding the +entire project. + +¡ There are language-specific toolchains for creating Wasm components that take + +care of generating the glue code needed to interact across languages. + +¡ Developers generally use standardized interfaces and prebuilt generic hosts to + +run Wasm components. + +Licensed to Ibrahim Mohammed Mohammed + +# ~~_4_~~ + +##### _Interfacing Wasm with_ + +##### _the underlying system_ + +_This chapter covers_ + +¡ Implementing APIs with WASI + +¡ The new capabilities of WASI 0.2 + +¡ Converting a Wasm module to a Wasm + +component + +¡ Using WASI-Virt to restrict or allow component + +capabilities + +¡ Composing Wasm components together + +At this point in our exploration of Wasm for server-side development, we have a +somewhat clear picture of what Wasm modules and Wasm components are, together +with the differences between the two, as illustrated in figure 4.1. + +Wasm modules are core Wasm. They are what we got from the base implementa[tion of the Wasm spec (https://webassembly.github.io/spec/core/): a binary format](https://webassembly.github.io/spec/core/) +with four basic types and a linear memory block that sets the stage for very quick +(near-native) and safe (sandboxed) execution of code—be it in a JavaScript engine +(on the browser) or in a Wasm runtime (on the server). + +**53** + +Licensed to Ibrahim Mohammed Mohammed + +**54** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Wasm components, on the other +hand, build on top of Wasm modules +to expand their versatility and make +certain interactions much smoother. +While Wasm modules are intentionally low-level—giving developers the +freedom to build exactly what they +need—components take that foundation and add a clear, standardized +way for different Wasm programs +(or a Wasm program and its host) to +talk to each other. + +Figure 4.1 Comparing Wasm modules and Wasm +components + +Without components, these interactions required a custom contract, defining how +to use a module’s linear memory block to represent anything beyond the four basic +Wasm types. For instance, in chapter 2, we showed how to handle strings this way. Components simplify all that by introducing the Canonical ABI: a ready-made agreement +for passing data around in Wasm binary form. The Canonical ABI defines how to lift +those four basic Wasm types into richer, more complex data structures and lower them +back down again. This shift—from the manual handling we used in chapter 2 to the +streamlined process enabled by the Canonical ABI—is shown in figure 4.2. + +**The host assumes that output from** +**the Wasm module will be written to** +**a particular offset of linear memory.** +**If we modify the guest to write at** +**any other offset, the code breaks!** + +**WIT provides our Wasm component** +**with well-defined high-level types,** +**and their lifting and lowering is** +**standardized and used behind the** +**scenes via the Canonical ABI.** + +**functionality to our guest, using the** +**Canonical ABI allows us to potentially** +**use generic hosts that understand** +**this standardized contract!** + +Figure 4.2 Comparing how data flows in chapter 2’s Wasm module example versus using Wasm components. The +top diagram shows the manual setup, with a custom contract between the module and a custom host. The bottom +diagram shows how components rely on WebAssembly Interface Types (WIT) and the Canonical ABI, making it +possible to interact with a potentially generic host without custom wiring. + +Licensed to Ibrahim Mohammed Mohammed + +**_The WebAssembly System Interface_** **55** + +But components go beyond just data handling. Once you factor in how they let you +interface with the underlying system itself, the differences between Wasm modules and +components really start to stand out. Let’s dig into that next. + +_4.1_ _The WebAssembly System Interface_ + +Wasm, as we’ve established, is platform agnostic, meaning it makes no assumptions on +what platform it will be running, and that’s one of its main superpowers. This is what +makes it possible for it to run anywhere, from the browser to an on premises server, +cloud, or edge devices. But if Wasm is platform agnostic, what happens when you actually want to interact with the platform your Wasm is running on? How can a Wasm program ever open a file or print to `stdout` ? + +If you’ve ever used Wasm in a browser, you might remember that browsers provide +a Wasm JavaScript API. With this API, you can write functions in JavaScript and make +them available for your Wasm module to use, under whatever names you choose. +In other words, when your Wasm code calls these functions, it’s really calling Java­ +Script functions behind the scenes! You set this up by instantiating the Wasm module +with an object that maps Wasm function names to JavaScript functions, as shown in +figure 4.3. + +**This text format is called** +**WebAssembly Text Format** +**as it is a human-readable** +**representation of a** +**Wasm binary.** + +Figure 4.3 JavaScript API providing access to host capabilities to a Wasm guest + +With this mechanism, it is very easy to expose something like the `console.log` function +and have it work in Wasm. This is great in the browser, because Wasm’s purpose there + +Licensed to Ibrahim Mohammed Mohammed + +**56** Chapter 4 **_Interfacing Wasm with the underlying system_** + +is to be embedded within a larger application for complex processing that is too slow +to do in JavaScript—for example, calculations for rendering a large 3D game. + +On the server side, to some extent, Wasmtime (and any other Wasm runtime) serves +the purpose of the browser. It is where we embed our Wasm, and it is the engine where +it runs. So, equally, it should be in charge of providing the necessary APIs for Wasm to +interact with its environment. This is where the WebAssembly System Interface (WASI) +comes in. WASI establishes the set of APIs that Wasm runtimes implement to provide +Wasm programs with the necessary system calls to interact with the setting in which they +are placed, as illustrated in figure 4.4. + +Figure 4.4 Wasmtime/WASI providing access to host capabilities to a Wasm guest + +Let’s take a look at how we can make use of WASI to interact with our system’s standard +output. + +Previously, when crafting our componentized “Hello, World” application, we created +the following WIT file: + +``` + package component:hello-world-guest-wit; + + world example { + export greet: func(name: string) -> string; + } + +``` + +Then, our implementation got the name parameter and prepended `"Hello, "` to it: + +``` + export function greet(name) { + return `Hello, ${name}!`; + } + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_The WebAssembly System Interface_** **57** + +Finally, we created a Python host to instantiate and call our `greet` component and +function. + +With WASI, our WIT definition no longer has to return a string. Instead, in `greet`, we +can directly print to the standard output. This is possible because WASI provides access +to system capabilities—things like printing to standard output, reading environment +variables, or working with files—that a plain Wasm component wouldn’t have on its +own. + +To get started, copy the `greet.js` and `greet.wit` files we created in the previous chapter to a new directory (say, `chapter04/hello_world_wasi/hello_world_wasi_guest` ). +Then modify the `greet.wit` file to look like the following listing. And, for organization’s +sake, let’s put our `greet.wit` file inside a directory at `hello_world_wasi_guest/wit/` . + +Listing 4.1 Modified WIT file for guest component ( **`greet.wit`** ) + +``` +package component:hello-world-guest-wit; + +``` + +``` +world example { +export greet: func(name: string); +} + +``` + +**greet no longer** +**returns a string.** + +Then modify the `greet.js` file to look like the following listing. + +Listing 4.2 Modified implementation of guest component ( **`greet.js`** ) + +``` +export function greet(name) { +console.log(`Hello, ${name}!`); +} + +``` + +**We can utilize console.log to** +**print to the standard output.** + +Now let’s try compiling our component using `jco` like we did in section 3.2.3. From the + +`hello_world_wasi_guest/` folder: + +``` +jco componentize greet.js --wit wit/greet.wit +``` + +➥ `--world-name example --out greet.wasm --disable http` + +But note this time we are only disabling the `http` capability. Disabling `http` makes sense +here because our simple greeting application doesn’t need network access. We are not +disabling `stdio`, though—with WASI, we can now interact with the system’s standard +output, and we want to make use of that. + +Next, because `wasmtime-py` (i.e., the Python bindings for Wasmtime) does not currently fully support WIT, we will not be able to use the Python host we worked on prior +to call the `greet` function and, instead, we’ll have to create a Rust host. + +Let’s start by creating a new Rust project at the same directory level from the `hello_` + +`world_wasi/` folder: + +``` +cargo new hello_world_wasi_host + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**58** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Then let’s add the `wasmtime` and `wasmtime-wasi` dependencies to our `Cargo.toml` file: + +``` + [dependencies] + wasmtime = "30" + wasmtime-wasi = "30" + +``` + +We can then delete all the contents of the `src/main.rs` file and start our host implementation by using `wasmtime` ’s `bindgen` macros, like so: + +``` + wasmtime::component::bindgen!({ + path: "../hello_world_wasi_guest/wit", + world: "example", + }); + +``` + +Next, we can create our `State` struct. Just like in our SmartCMS test host, we’ll use +this struct to keep the state associated with our application, but, unlike the SmartCMS +test host state, which only held our key-value’s store state, this host’s state will hold +our WASI context and the resource table associated with WASI and component usage. +Remember the resource tables we talked about when going over figure 3.2 in the last +chapter? This is where they come into play: + +``` + // ... + struct State { + wasi: wasmtime_wasi::WasiCtx, + table: wasmtime_wasi::ResourceTable, + } + +``` + +Next, we need to update our `State` struct to implement two traits: `WasiView` and `IoView` . + +`WasiView` will give access to the WASI context, and `IoView` will provide access to the +resource table: + +``` + // ... + impl wasmtime_wasi::WasiView for State { + fn ctx(&mut self) -> &mut wasmtime_wasi::WasiCtx { + &mut self.wasi + } + } + + impl wasmtime_wasi::IoView for State { + fn table(&mut self) -> &mut wasmtime_wasi::ResourceTable { + &mut self.table + } + + } + +``` + +Then we can finally implement the entry point of our host: the `main` function. This +function will be responsible for loading our component, creating an instance of it, and +calling the `greet` function. + +Licensed to Ibrahim Mohammed Mohammed + +**_The WebAssembly System Interface_** **59** + +To start off, just like we did with the SmartCMS test host, we’ll create a `Config` and an + +`Engine` object. The `Config` object will hold the configuration for our Wasmtime engine +(e.g., a flag specifying we are dealing with components), and the `Engine` object will be +the engine itself, where we will load and run our component: + +``` +// ... +fn main() { +let mut config = wasmtime::Config::default(); +config.wasm_component_model(true); + +let engine = wasmtime::Engine::new(&config).unwrap(); +} + +``` + +Next, always inside the `main` function, we can create a `Linker` object associating our + +`State` with the engine and linking `wasmtime_wasi` to our setup: + +``` +fn main() { +// ... +let mut linker = wasmtime::component::Linker::::new(&engine); +wasmtime_wasi::add_to_linker_sync(&mut linker).unwrap(); +} + +``` + +Then, we can create our `WasiCtx` and, in it, specify we want to tie the context’s standard +output stream (i.e., whatever we get from components running within our engine) to +the host’s standard output: + +``` +fn main() { +// ... +let wasi = wasmtime_wasi::WasiCtxBuilder::new() +.inherit_stdout() +.build(); +} + +``` + +With that done, we are finally ready to create our `Store` object and load our component from a file: + +``` +fn main( ) { +// ... +let mut store = wasmtime::Store::new +``` + +➥ `(&engine, State { wasi, table:` +➥ `wasmtime_wasi::ResourceTable::new() });` +``` +let component = wasmtime::component +``` + +➥ `::Component::from_file(&engine,` +➥ `"../hello_world_wasi_guest/greet.wasm").unwrap();` +``` +} + +``` + +Now all that’s left is to instantiate our component and call the `greet` function: + +``` +fn main() { +// ... + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**60** Chapter 4 **_Interfacing Wasm with the underlying system_** + +``` + let app = Example::instantiate +``` + +➥ `(&mut store, &component, &linker).unwrap();` + +``` + app.call_greet(&mut store, "World").unwrap(); + } + +``` + +And that’s it! As seen in the following listing, we now have a Wasm component that +interacts with the system’s standard output. + +Listing 4.3 Rust host for guest component ( **`main.rs`** ) + +``` + wasmtime::component::bindgen!({ + path: "../hello_world_wasi_guest/wit", + world: "example", + }); + +``` + +``` +struct State { +wasi: wasmtime_wasi::WasiCtx, +table: wasmtime_wasi::ResourceTable, +} + +``` + +**Our State struct now contains a** +**WASI context and a resource table.** + +``` +impl wasmtime_wasi::WasiView for State { +fn ctx(&mut self) -> &mut wasmtime_wasi::WasiCtx { +&mut self.wasi +} +} + +impl wasmtime_wasi::IoView for State { +fn table(&mut self) -> &mut wasmtime_wasi::ResourceTable { +&mut self.table +} +} + +fn main() { +let mut config = wasmtime::Config::default(); +config.wasm_component_model(true); +let engine = wasmtime::Engine::new(&config).unwrap(); +let mut linker = wasmtime::component::Linker::::new(&engine); +wasmtime_wasi::add_to_linker_sync + +``` + +``` +let wasi = wasmtime_wasi::WasiCtxBuilder::new() +.inherit_stdout() +.build(); + +``` + +**We inherit standard output** +**from the host environment.** + +``` +let mut store = wasmtime::Store::new(&engine, +``` + +➥ `State { wasi, table:` +➥ `wasmtime_wasi::ResourceTable::new() });` +``` +let component = wasmtime:: +``` + +➥ `component::Component::from_file(&engine,` +➥ `"../hello_world_wasi_guest/greet.wasm").unwrap();` + +``` +let app = Example::instantiate + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_What can you build with WASI?_** **61** + +➥ `(&mut store, &component, &linker).unwrap();` + +``` + app.call_greet(&mut store, "World").unwrap(); + } + +``` + +Then, from the `hello_world_wasi_host/`, we can run this with + +``` + cargo run --release + +``` + +We should see the output `Hello,` `World!` printed to the standard output, showing that +we successfully provided our Wasm component with the ability to interact with its +environment. + +_4.2_ _What can you build with WASI?_ + +Upon its release in 2019, WASI or, more specifically, WASI 0.1/WASI Preview 1 (i.e., +what is used by Wasm modules), was essentially a super portable subset of the Portable Operating System Interface (POSIX), which is a standard maintained to ensure +compatibility and interoperability between UNIX-like operating systems (e.g., Linux, +macOS, etc.). POSIX defines a set of APIs that a conforming operating system must +provide to be POSIX compliant. Users can run various commands and programs on +any POSIX-compliant system without worrying about compatibility problems or receiving different results. + +This means that, with WASI 0.1, you could develop applications you are used to +because WASI would essentially provide you access to the same APIs or system calls +you’d normally expect to have. But, as WASI grew, its focus shifted from mirroring +expectations to trying to go beyond them and create something fundamentally lighter, +faster to start, and more secure. + +Enter WASI 0.2, also known as WASI Preview 2. This new version introduced Wasm +components, making it possible for Wasm to use a much broader set of interfaces—or, +more specifically, worlds defined via WIT—than the limited POSIX-style subset offered +by WASI 0.1. WASI 0.1’s interfaces were just a foundational base layer. In contrast, +WASI 0.2’s interfaces are aimed at the kinds of applications developers actually want to +build. These differences are highlighted in figure 4.5. + +Figure 4.5 Comparison of +WASI 0.1 (a more POSIX-like +interface) to WASI 0.2 that +defines higher-level interfaces + +|Col1|Col2| +|---|---| +|WASI 0.1
-POSIX-like|WASI 0.1
-POSIX-like| + +|Col1|Col2| +|---|---| +|WASI 0.2
-Beyond POSIX
-wasi:cli/command
-wasi:http/proxy|WASI 0.2
-Beyond POSIX
-wasi:cli/command
-wasi:http/proxy| + +Licensed to Ibrahim Mohammed Mohammed + +**62** Chapter 4 **_Interfacing Wasm with the underlying system_** + +As Oscar Spencer (coauthor of the Grain programming language) put it, WASI has +evolved from being just a WebAssembly System Interface to more of a _WebAssembly Stan-_ +_dard Interfaces_ collection. Now it bundles together standardized ways of doing things +like running on the cloud with SQL database access or even performing machine +learning inference. + +That said, WASI 0.2, by itself, defined two worlds: + +¡ _Command world_ (i.e., `wasi:cli/command` ), which resembles a traditional POSIX + +command-line application with access to the filesystem, sockets, and the terminal + +¡ _HTTP proxy world_ (i.e., `wasi:http/proxy` ), which can send and receive HTTP + +requests and responses + +But these two worlds are just the beginning. There are already several proposals in +progress for WASI 0.3 and beyond, some of which we’ll discuss in chapter 5. + +To start, let’s see how much easier it is to create a “Hello, World!” application when +we utilize WASI 0.2. + +_4.2.1_ _Creating a CLI application_ + +Let’s create a new component using Rust’s toolchain. We’ll use the `cargo` `component` +tool, which is Rust’s official tooling for building and working with Wasm components. +If you don’t have it installed yet, you can install it with this: + +``` + cargo install cargo-component@0.20.0 –locked + +``` + +NOTE `cargo` `component` is being deprecated in favor of the native Rust toolchain, but it’s not yet archived and still offers convenient functionality that +isn’t available out of the box with the native approach. For now, this book uses +`cargo` `component` for its ease of use. To see how the native toolchain works, +[check out the example at https://mng.bz/26do. Future editions of this book](https://mng.bz/26do) +may switch to the native toolchain once its usage is more established. + +Then, from our `chapter04/` folder, create a new directory called `wasi02/` and, inside it, +run + +``` + cargo component new --bin hello_world_wasi02 + +``` + +In this project, by inspecting its `Cargo.toml`, you will see that, while we have a dependency for `wit-bindgen-rt` in our `Cargo.toml` for code generation of WIT files, we don’t +have any visible WIT. This is because `cargo` `component` abstracts away a lot of the setup +work that we’d have to do to provide a more streamlined developer experience. Next, +from inside `hello_world_wasi02/`, let’s run + +``` + cargo component build --release + +``` + +Now you should see `hello_world_wasi02.wasm` at `target/wasm32-wasip1/release` . But +wait a minute! `wasm32-wasip1` (i.e., Wasm 32–WASI Preview 1)? Doesn’t that mean we + +Licensed to Ibrahim Mohammed Mohammed + +**_What can you build with WASI?_** **63** + +are using WASI 0.1? At first glance, you’re right—it does! This is because, as of writing +this book, the `wasm32-wasip2` target (i.e., the target for WASI 0.2) is still experimental. +However, this doesn’t necessarily mean we are using WASI 0.1. In fact, the way `cargo` + +`component` works right now is that, while it does compile to `wasm32-wasip1`, it also utilizes an adapter module to convert to WASI 0.2 during the compilation process. So, in +reality, we are using WASI 0.2. To confirm this, we can use `wasm-tools` —a command-­ +line interface that provides a set of tools useful for working with Wasm. You can install +it with the following: + +``` +cargo install wasm-tools@1.227.1 --locked + +``` + +Then run + +``` +wasm-tools component wit /hello_world_wasi02.wasm + +``` + +NOTE If you’re using a Cargo workspace and have added the path to `hello_` + +`world_wasi02/` to your workspace’s `Cargo.toml`, the `` from inside + +`hello_world_wasi02/` should be `../../../target/wasm32-wasip1/release/` + +`hello_world_wasi02.wasm` . + +At the start of the output, you should see the contents of the following listing. + +Listing 4.4 Output of **`wasm-tools component wit`** + +``` +package root:component { +world root { +import wasi:cli/environment@0.2.0; +import wasi:cli/exit@0.2.0; +import wasi:io/error@0.2.0; +import wasi:io/streams@0.2.0; +import wasi:cli/stdin@0.2.0; +import wasi:cli/stdout@0.2.0; +import wasi:cli/stderr@0.2.0; +import wasi:clocks/wall-clock@0.2.0; +import wasi:filesystem/types@0.2.0; +import wasi:filesystem/preopens@0.2.0; + +export wasi:cli/run@0.2.0; +} +} + +``` + +This is the meat and potatoes of the WIT output: the `root` `world` . It specifies all our +application has access to (i.e., the `import` s) and what we are implementing ourselves +(i.e., the `export` ). The rest of the WIT is just the definition of the `import` / `export` interfaces themselves, which you could still use as a reference for the APIs you can use. +In addition, you can see all `import` s/ `export` s are versioned at `0.2.0`, which is a pretty +strong indication we are using WASI 0.2 APIs. + +Licensed to Ibrahim Mohammed Mohammed + +**64** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Next, we can see we are `export` ing the `wasi:cli/run` function. This function is the +entry point of our application, and it is tied to the `main` function in `main.rs` . Now, how +can we run the code to verify that? Do we have to create our own host yet again? No! In +fact, Wasmtime provides a way to directly run applications that `export` the `wasi:cli/run` +function. To try it out, first install Wasmtime like this: + +``` + cargo install wasmtime-cli@30.0.0 --locked + +``` + +And then run + +``` + wasmtime /hello_world_wasi02.wasm + +``` + +That’s it! We now have a “Hello, World” application that utilizes WASI 0.2. + +_4.2.2_ _Creating libraries_ + +What if you’d like to create an application that has access to all WASI 0.2 capabilities we +saw earlier but does not necessarily export the `wasi:cli/run` function? With Rust, you +can do so very easily. From the `wasi02` directory, run + +``` + cargo component new --lib wasi02_lib + +``` + +This will create a new library project. In it, you will be provided with a WIT file, so +you can define your own exports to implement. By default, `cargo` `component` provides +you with a `hello_world` function specified in WIT and implemented in `src/lib.rs` . As +usual, from inside `wasi02_lib/`, you can build it with + +``` + cargo component build --release + +``` + +Let’s use `wasm-tools` again to extract the WIT and compare the differences between a + +`bin` and a `lib` project. Run + +``` + wasm-tools component wit /wasi02_lib.wasm + +``` + +At the end of the output, you should see the contents of the following listing. + +Listing 4.5 Output of **`wasm-tools component wit`** + +``` + package root:component { + world root { + import wasi:cli/environment@0.2.0; + import wasi:cli/exit@0.2.0; + import wasi:io/error@0.2.0; + import wasi:io/streams@0.2.0; + import wasi:cli/stdin@0.2.0; + import wasi:cli/stdout@0.2.0; + import wasi:cli/stderr@0.2.0; + import wasi:clocks/wall-clock@0.2.0; + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Converting a Wasm module to a Wasm component_** **65** + +``` + import wasi:filesystem/types@0.2.0; + import wasi:filesystem/preopens@0.2.0; + + export hello-world: func() -> string; + } + } + +``` + +The only difference here is that we are `export` ing the `hello-world` function instead of +the `wasi:cli/run` function. This is because, in a library project, you are not expected +to have an entry point function. Instead, you are expected to provide a set of functions +that can be utilized by other applications. + +_4.2.3_ _Creating HTTP proxies_ + +Another world that became a standard in WASI 0.2 was the `wasi:http/proxy` world. +With it also came a new command to Wasmtime: `wasmtime` `serve` . This command spins +up an HTTP server that directs requests directly to our component. Now, while we +do have the convenience of `wasmtime` `serve`, there is no easy way of creating an HTTP +proxy application that doesn’t require a lot of manual setup with `cargo` `component` . So +we will leave creating HTTP Wasm applications for when we introduce other generic +hosts that are tailored particularly for improved developer experience. + +NOTE For an example application using the `wasi:http/proxy` world, see +[https://github.com/sunfishcode/hello-wasi-http.git.](https://github.com/sunfishcode/hello-wasi-http.git) + +_4.3_ _Converting a Wasm module to a Wasm component_ + +Now we have a better idea of the differences between Wasm modules and Wasm components, not just in theory, but also in practice. Up until this moment, every Wasm +we’ve created, except the very first one, has been a Wasm component. But say you’ve +been working with Wasm modules for a while and now you want to make use of the +benefits of Wasm components, like WASI 0.2. How can you convert a Wasm module +to a Wasm component? Surely, it should be simple. After all, Wasm components were +designed to be wrappers around modules. And, indeed, they are! + +For this example, let’s take our very first “Hello, World” Wasm module: `hello_world_` + +`guest.wasm` . This is the module we created that dealt with integers to extract a string +added to linear memory by the host. + +We can use `wasm-tools` to check what type of Wasm it is, like so: + +``` + wasm-tools metadata show /hello_world_guest.wasm + +``` + +The output of this command should look like figure 4.6. + +Under `kind`, you see `module` . To convert it to a component, do the following: + +``` + wasm-tools component new +``` + +➥ `/hello_world_guest.wasm -o` +➥ `/hello_world_guest_component.wasm` + +Licensed to Ibrahim Mohammed Mohammed + +**66** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Figure 4.6 The output from inspecting the metadata of our Wasm binary with **`wasm-tools`**, showing +details like the module name, size, language, and the Rust compiler version used to build it + +Now let’s run `wasm-tools metadata show` on the newly created `hello_world_guest_` + +`component.wasm` : + +``` + wasm-tools metadata show /hello_world_guest_component.wasm + +``` + +The output of this command should look like figure 4.7. + +As you can see, it shows we now have a component acting as a wrapper over a “child” +module! If you try to run this component with the custom host we used in chapter 2, you +will get + +``` + Error: failed to parse WebAssembly module + + Caused by: + Unsupported feature: component model + +``` + +So that’s it! You’ve successfully converted a Wasm module to a Wasm component, +which means we now have access to modern component model features and tooling. + +It’s important to note that this conversion didn’t give us access to Preview 2 APIs; +we’ve simply wrapped the module as a component. Because our module was compiled +to the `wasm32-unknown-unknown` target, it didn’t originally have access to any WASI APIs. +You can verify this by running the following: + +``` + wasm-tools component wit hello_world_guest_component.wasm + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Converting a Wasm module to a Wasm component_** **67** + +Figure 4.7 The output from inspecting the metadata of our Wasm binary with **`wasm-tools`**, now +showing that the Wasm binary includes a component kind created by **`wit-component`** + +The output should show an empty world: + +``` +package root:component; + +world root { +} + +``` + +If we want to convert and adapt to Preview 2 APIs, we could go back to our chapter 2 +example (at `chapter02/hello_world/hello_world_guest/` ) and recompile it as a module with WASI access: + +``` +cargo build --target wasm32-wasip1 --release + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**68** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Then we can create a component using an _adapter component,_ which translates the module’s Preview 1 WASI imports into Preview 2 component imports. You can download +the adapter component directly from Wasmtime releases: + +``` + curl -L -O https://github.com/bytecodealliance/wasmtime/ +``` + +➥ `releases/download/v30.0.0/` +➥ `wasi_snapshot_preview1.reactor.wasm` + +NOTE If your module was created as a binary project (i.e., with a main func[tion), you’d want to use this link instead: https://mng.bz/15dQ.](https://mng.bz/15dQ) + +Next, run the same conversion command we used before, but provide both the adapter +and your newly compiled Wasm module (let’s call it `p1-hello_world_guest` ): + +``` + wasm-tools component new / +``` + +➥ `p1-hello_world_guest.wasm --adapt` +➥ `wasi_snapshot_preview1=wasi_snapshot_preview1` +➥ `.reactor.wasm -o p1-hello_world_guest_component.wasm` + +You can verify the adapted component now has Preview 2 API access by running + +``` + wasm-tools component wit p1-hello_world_guest_component.wasm + +``` + +This time, you should see Preview 2 imports like `wasi:cli/environment`, `wasi:` + +`filesystem/types`, and others. + +And there we go! We’ve now converted our previous `wasm32-unknown-unknown` Wasm +module to, essentially, a `wasm32-wasip2` Wasm component. + +Now, if you think about it, this whole process may seem a bit roundabout. After +all, just like we went back and recompiled to a `wasm32-wasip1` target, we could have +recompiled directly to `wasm32-wasip2` and avoided the conversion and adaptation steps +entirely. This would be the ideal approach when you have access to source code and +your language has good Wasm support like Rust does. + +However, this conversion process becomes valuable in situations where + +¡ You’re working with legacy code or third-party modules without source access. + +¡ Your language doesn’t yet support `wasip2` directly. + +¡ You need to gradually migrate a large codebase to components. + +In these cases, you can use WASI Preview 1 support, Wasm tooling, and Wasmtime-­ +provided adapters to bridge the gap and get a working component with Preview 2 APIs! + +_4.4_ _Managing component capabilities_ + +As we have seen in listing 4.4, the WIT output for our “Hello, World” WASI 0.2 application specified all the capabilities our application has access to. But what if we want to +restrict some of these capabilities? For example, what if we want to restrict our application from actually accessing the standard output? To do so, aside from creating our + +Licensed to Ibrahim Mohammed Mohammed + +**_Managing component capabilities_** **69** + +own custom worlds, we can utilize a tool called WASI-Virt. WASI-Virt is a tool that allows +you to encapsulate a Wasm component and virtualize its capabilities to either restrict +or allow them. + +Let’s see it in practice. To install WASI Virt, run + +``` +cargo install wasi-virt --git https: +``` + +➥ `//github.com/bytecodealliance/WASI-Virt` +➥ `--rev b662e419 --locked` + +Then copy the component we created from the “Hello, World” WASI 0.2 application +(i.e., the `hello_world_wasi02.wasm` file from `target/wasm32-wasip1/release/`, where +the `target/` directory is located at the same level as your chapter folders when using +the workspace setup) to a new directory (say, `wasi_virt/` inside `wasi_02/` ). Then, from +that directory, run + +``` +wasi-virt ./hello_world_wasi02.wasm -o +``` + +➥ `./hello_world_wasi02_fully_restricted.wasm` + +If you try running this `hello_world_wasi02_fully_restricted.wasm` with `wasmtime`, you +will get a panic (the program will crash). This is because, by default, virtualization will +deny all capabilities and will cause a panic on an attempt to use any of them—e.g., standard output usage from `println!("Hello,` `World!")` . + +Let’s try seeing what the WIT output of this new Wasm component is. Run + +``` +wasm-tools component wit ./hello_world_wasi02_fully_restricted.wasm + +``` + +At the end of the output, you should see the contents of the following listing. + +Listing 4.6 Output of **`wasm-tools component wit`** + +``` +package root:component; + +world root { +export wasi:cli/run@0.2.0; +} +package wasi:cli@0.2.0 { +interface run { +run: func() -> result; +} +} + +``` + +It shows that, after virtualization, we restricted all capabilities, and their imports are +gone. Now, to only allow the `stdout` capability, run + +``` +wasi-virt ./hello_world_wasi02.wasm --stdout=allow -o +``` + +➥ `./hello_world_wasi02_stdout_allowed.wasm` + +Then running this new Wasm component with `wasmtime` should work as expected. + +Licensed to Ibrahim Mohammed Mohammed + +**70** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Once again, checking the WIT output of this new Wasm component, at the end, you +should see the contents of the following listing. + +Listing 4.7 Output of **`wasm-tools component wit`** + +``` + package root:component { + world root { + import wasi:io/error@0.2.0; + import wasi:io/poll@0.2.0; + import wasi:io/streams@0.2.0; + import wasi:cli/stdin@0.2.0; + import wasi:cli/stdout@0.2.0; + import wasi:cli/stderr@0.2.0; + import wasi:cli/terminal-input@0.2.0; + import wasi:cli/terminal-output@0.2.0; + import wasi:cli/terminal-stdin@0.2.0; + import wasi:cli/terminal-stdout@0.2.0; + import wasi:cli/terminal-stderr@0.2.0; + + export wasi:cli/run@0.2.0; + } + } + +``` + +This shows we are only bringing the capabilities necessary to use `stdout` . And that’s it! +You’ve successfully restricted and allowed capabilities for a Wasm component. + +But this only scratches the surface of what you can do with WASI-Virt. Under the +hood, WASI-Virt works through composition—the same component model composition we’ll explore in the next section. Your original component still imports WASI +interfaces internally, but WASI-Virt composes it with an adapter component that provides implementations of those interfaces. When you restrict an interface, the adapter +provides a deny-all implementation. When you allow it, the adapter either passes calls +through to the host or provides a virtualized implementation—for example, you could +mount a read-only filesystem directly into the component. The resulting composed +component only imports what the adapter itself requires from the host. This is why our +fully restricted component had no imports at all—the adapter satisfied all of the original component’s imports internally, so the final composition doesn’t need anything +from the host. You can also use it to set environment variables, mount virtual filesystem +directories, and more. Figure 4.8 illustrates the capabilities of WASI-Virt. + +Further uses for WASI-Virt + +While we didn’t explore much of the `wasi:http/proxy` `world`, WASI-Virt could actually +be very helpful with it. In particular, due to the fact that `wasi:http/proxy` was made +to be able to run in many different environments, it doesn’t import the filesystem API. +But, if you wanted to serve some files, you could utilize WASI-Virt to bundle a filesystem with the component and use it as such. + +Licensed to Ibrahim Mohammed Mohammed + +**_Composing components together_** **71** + +**app.wasm has imports to** +**filesystem and stdio and** +**needs a host to provide** +**them.** + +**After using WASI-Virt,** +**app.wasm now has** +**virtualized** +**implementations of** +**filesystem and stdio that** +**panic by default.** + +Figure 4.8 Utilizing WASI-Virt to restrict capabilities of a Wasm component + +_4.5_ _Composing components together_ + +You have heard me say quite a lot that one of the main benefits of Wasm components is +that they allow for easy interaction between two Wasms or a Wasm and a host. But, up +until now, we didn’t actually get to directly compose any components together. So, in +this section, let’s do just that. + +To display composability, we’ll compose a binary component together with a library +component. And, for convenience, we will reuse the components we created earlier. +Figure 4.9 illustrates the architecture we want to create. + +Figure 4.9 Composing a library component with a binary component and displaying +matching imports and exports + +Licensed to Ibrahim Mohammed Mohammed + +**72** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Inside `chapter04/`, create a new directory called `composability/` . Then copy the `hello_` + +`world_wasi02/` directory to inside `composability/` and rename it from `hello_world_` + +`wasi02/` to `composability_bin/` . Equally, we’ll reflect the name changes in the `Cargo` + +`.toml` file by changing the `name` field to `composability_bin` : + +``` + name = "composability_bin" + +``` + +In addition, we will modify the `package` field under the `[package.metadata.component]` +section to + +``` + package = "composability:bin" + +``` + +Binaries created with `cargo component` by default hide their WIT file from the user. +But, in this instance, we need access to the WIT file because we will be modifying it +to import some library function. So, at the same level as `composability_bin/`, create a +folder called `wit/` and, inside this new folder, create a WIT file called `bin.wit` with the +contents of the following listing. + +Listing 4.8 WIT file for our binary component ( **`bin.wit`** ) + +``` +package composability:bin; + +``` + +**The package name we defined** +**earlier in the Cargo.toml file** + +``` +world bin { + +} + +``` + +**Imports some hello-world function,** +**supposedly coming from a library** + +Note that we don’t need to specify all the WASI dependencies (e.g., `stdio`, etc.) +because `cargo` `component` will take care of all of that for us during the build process. So, +conveniently, we just need to specify the extra interfaces or functions our component +will be using. + +Then, inside our `Cargo.toml` file, we need to specify the path to this WIT file we’ve +just created. Open the `Cargo.toml` file and add the following: + +``` +[package.metadata.component.target] +path = "../wit/bin.wit" +world = "bin" + +``` + +Finally, our `Cargo.toml` file should look like the following listing. + +Listing 4.9 **`Cargo.toml`** file for our binary component + +``` +[package] +name = "composability_bin" +version = "0.1.0" +edition = "2021" + +[dependencies] + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Composing components together_** **73** + +``` +wit-bindgen-rt = { version = "0.41.0", features = ["bitflags"] } + +[profile.release] +codegen-units = 1 +opt-level = "s" +debug = false +strip = true +lto = true + +[package.metadata.component] +package = "composability:bin" + +[package.metadata.component.dependencies] + +[package.metadata.component.target] +path = "../wit/bin.wit" +world = "bin" + +``` + +Now let’s try building the binary component with `cargo` `component` `build --release` +run from inside `composability_bin/` . With everything running smoothly, we can then +make use of our new `import` inside our `main.rs`, as in the following listing. + +Listing 4.10 **`main.rs`** file of our binary component + +``` +#[allow(warnings)] +mod bindings; + +use bindings::hello_world; + +fn main() { +println!("{}", hello_world()); +} + +``` + +Then, with these changes, build again. + +**Brings the hello_world function** +**from our generated bindings** + +**Uses it and prints the result** +**to the standard output** + +NOTE What is `mod bindings` ? It’s autogenerated Rust code that refers to a sibling file to our `main.rs` called `bindings.rs` . This `bindings.rs` file contains all +the glue code needed for our Rust program to interact with the functions and +types defined in our WIT files. In other words, it connects the `world` we defined +in WIT to the actual Rust code we write. + +Now, for convenience, let’s reuse the `wasi02_lib` project we created earlier—this will +be the provider of that `hello-world` function. Copy that entire directory to inside + +`composability/` . Then move the `world.wit` file from the `wasi02_lib/wit/` folder to the + +`composability/wit/` folder. With that done, rename the `world.wit` file to `lib.wit` . Then +open the `Cargo.toml` file from `composability/wasi02_lib/` and add the following: + +``` +[package.metadata.component.target] +path = "../wit/lib.wit" +world = "lib" + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**74** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Note that we modified the `world` name. So let’s reflect that in our WIT. Overall, the + +`lib.wit` file should look like the following listing. + +Listing 4.11 WIT file for our library component ( **`lib.wit`** ) + +``` +package composability:lib; + +``` + +**We changed the package name.** + +``` +world lib { +export hello-world: func() -> string; +} + +``` + +**We changed the world name.** + +You can then delete the `wit/` directory from `composability/wasi02_lib.` + +Now we need to distinguish between the two `wasi02_lib` projects. Let’s rename the +one in `composability/` to `composability_lib/` . And, just like we did for `composability_` + +`bin`, in the `Cargo.toml` file, change the `name` field to `composability_lib` : + +``` +name = "composability_lib" + +``` + +Change the `package` field under the `[package.metadata.component]` section to + +``` +package = "composability:lib" + +``` + +Finally, our `Cargo.toml` file should look like the following listing. + +Listing 4.12 **`Cargo.toml`** file for our library component + +``` +[package] +name = "composability_lib" +version = "0.1.0" +edition = "2021" + +[dependencies] +wit-bindgen-rt = { version = "0.41.0", features = ["bitflags"] } + +[lib] +crate-type = ["cdylib"] + +[profile.release] +codegen-units = 1 +opt-level = "s" +debug = false +strip = true +lto = true + +[package.metadata.component] +package = "composability:lib" + +[package.metadata.component.dependencies] + +[package.metadata.component.target] +path = "../wit/lib.wit" +world = "lib" + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Composing components together_** **75** + +Now let’s also modify the `lib.rs` file to look like the following listing. + +Listing 4.13 **`lib.rs`** file of our binary component + +``` +#[allow(warnings)] +mod bindings; + +use bindings::Guest; + +struct Component; + +``` + +``` +impl Guest for Component { +fn hello_world() -> String { +"Hello from Lib!".to_string() +} +} + +``` + +**We modified the hello_world** +**function to return a different string.** + +``` +bindings::export!(Component with_types_in bindings); + +``` + +This string modification, of course, is an optional change, but it can serve as validation +that we are composing the two components together. + +From inside `compatibility_lib/`, you can then also build the library component with + +``` +cargo component build --release + +``` + +Now we are finally ready to compose the two components together. To do so, we can +use another Wasm tool called `wac` (pronounced “whack”) that stands for Wasm composition. `wac` is a powerful tool that even provides its own composition language for +complex linkage scenarios (e.g., matching specific component imports and exports). +You can install it with + +``` +cargo install --locked wac-cli@0.6.1 + +``` + +Now, for our simple use case, we can simply plug the two compiled components +together. So, at the same level as `composability/`, run + +``` +wac plug /composability_bin. +``` + +➥ `wasm --plug /` +➥ `composability_lib.wasm -o composability.wasm` + +`wac` metaphorically refers to “plugs” that get plugged into “sockets”—in other words, +Wasm exports that can be used as Wasm imports. In this example, the library exports a +plug that gets put into the appropriate socket in our binary. + +Then, because the two components are bundled together, we don’t need to provide +any custom host functionality. Instead, we can just run the two components with + +`wasmtime` from `composability/` like so: + +``` +wasmtime composability.wasm + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**76** Chapter 4 **_Interfacing Wasm with the underlying system_** + +And you should get + +``` + Hello from Lib! + +``` + +That’s it! You’ve successfully composed two components together. + +When we ran `wac plug`, the tool created a new component that embeds both of our +original components inside itself. The binary component’s import for `hello-world` gets +wired to the library component’s export of that same function internally. The resulting + +`composability.wasm` is a standalone component that no longer imports `hello-world` +from the outside world—all the dependencies are resolved within the composed component itself. + +This is why we could run it with just `wasmtime` `composability.wasm` without any custom host setup. The component model’s Canonical ABI ensures that data flowing +between the two components works seamlessly, even though they were compiled separately. This build-time composition approach creates tightly coupled components that +are deployed as a single unit. + +_4.6_ _Expanding our example project with WASI and composability_ + +In this section, we will expand upon our SmartCMS project. In particular, our main +goal will be to create a fake machine learning component together with its usage and +composition with our existing architecture. Figure 4.10 illustrates the architecture we +want to create. + +This machine learning component will utilize WASI to access the host’s filesystem, +load a text file containing one-liner stories, and randomly select one story to return to +our `smartcms_kvstore_guest`, which will then add that story to the host’s key-value store +and return the result of `get` for that story’s key back to the host. Plus, we will be composing this fake machine learning component and the `smartcms_kvstore_guest` component together, which will be putting into practice all we learned about composability. +In the future, we’ll replace this fake component with a real machine learning model to +generate children’s stories. + +We will be working on our existing SmartCMS implementation. So feel free to work +from the existing project or copy your work from chapter 3 onto a new directory (i.e., + +`chapter04/` ) if you want to keep each chapter’s work separate. + +NOTE If you’re copying over your work from the SmartCMS in chapter 3 and +have both the chapter 3 and chapter 4 SmartCMS projects in your workspace, +it’s a good idea to suffix each host’s name (in their respective `Cargo.toml` ) with +the chapter number. This helps avoid any naming clashes. For example, you +could name it `smartcms_test_host_chapter04` . + +With that done, let’s start by creating a new library component for our machine +learning component. At the same directory level as `smartcms_kvstore_guest/` and + +`smartcms_test_host`, run + +``` + cargo component new --lib smartcms_ml_guest + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Expanding our example project with WASI and composability_** **77** + +Figure 4.10 The architecture of our SmartCMS project with the addition of a machine learning +component + +Then modify its WIT file at `smartcms_ml_guest/wit/world.wit` to look like the following listing. + +Listing 4.14 WIT file for our ML guest component ( **`world.wit`** ) + +``` +package component:smartcms-ml-guest; +world example { +export storygen: func() -> string; +} + +``` + +`storygen` will be the function doing all the heavy lifting. Next, for the random number +generation, we will need to randomly select a story; we will be using the `rand` crate, +which is possible because Rust’s `rand` crate supports compiling to Wasm (meaning it +can compile to targets like `wasm32-unknown-unknown` ). Under `[dependencies]` in the + +`Cargo.toml` file, add + +``` +rand = "0.8.5" + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**78** Chapter 4 **_Interfacing Wasm with the underlying system_** + +Then modify the `lib.rs` file. First, rename the `hello_world` function to `storygen` . The +function signature should look like this: + +``` + // ... + fn storygen() -> String { + // ... + } + +``` + +Inside it, we will be reading a file called `stories.txt` from the host’s filesystem, like so: + +``` + // ... + fn storygen() -> String { + let stories = std::fs::read_to_string("stories.txt").unwrap(); + } + // ... + +``` + +Inside `smartcms_test_host/` you can create a file called `stories.txt` . Inside it, add 10 +or so lines of one-liner stories—feel free to be creative or, if not, just add the numbers +from 1 to 10. + +NOTE Another option is to grab the stories I made from the book’s repository: +[https://mng.bz/Pwq8.](https://mng.bz/Pwq8) + +Then, back in `lib.rs`, under the creation of the `stories` variable, we can randomly +select a story and return it like so: + +``` + // ... + fn storygen() -> String { + let stories = std::fs::read_to_string("stories.txt").unwrap(); + let lines: Vec<&str> = stories.lines().collect(); + let random = rand::random::() % lines.len(); + lines[random].to_string() + } + // ... + +``` + +Overall, the `lib.rs` file should look like the following listing. + +Listing 4.15 Implementation of our ML guest component ( **`lib.rs`** ) + +``` + #[allow(warnings)] + mod bindings; + + use bindings::Guest; + + struct Component; + +``` + +``` +impl Guest for Component { +fn storygen() -> String { +let stories = std::fs:: +``` + +➥ `read_to_string("stories.txt").unwrap();` +``` +let lines: Vec<&str> = + +``` + +**Reads the stories.txt file** + +Licensed to Ibrahim Mohammed Mohammed + +**_Expanding our example project with WASI and composability_** **79** + +➥ `stories.lines().collect();` +``` +let random = rand::random:: +``` + +➥ `() % lines.len();` +``` +lines[random].to_string() +} +} + +``` + +**Collects the lines into a vector** + +**Generates a random** +**number from 0 to the** +**number of lines in the** +**file to select a story** + +``` +bindings::export!(Component with_types_in bindings); + +``` + +With that done, from inside `smartcms_ml_guest/`, you can build the library component +with + +``` +cargo component build --release + +``` + +Next, let’s make use of that in the `smartcms_kvstore_guest` component. Before, + +`smartcms_kvstore_guest` used the same WIT as `smartcms_test_host`, but because we +are now bringing in an `import` from another component, we will need to split them up. +So copy the `smart_cms.wit` file at `smartcms_test_host/` to `smartcms_kvstore_guest/`, +and rename it to `kvstore_guest.wit` . Then modify the `kvstore_guest.wit` file to look +like the following listing. + +Listing 4.16 WIT for key-value store guest ( **`kvstore_guest.wit`** ) + +``` +package component:smartcms; + +interface kvstore { +get: func(key: string) -> option; +set: func(key: string, value: string); +} + +``` + +``` +world app { +import kvstore; +import storygen: func() -> string; +export run: func() -> string; +} + +``` + +**Imports the storygen function from** +**the smartcms_ml_guest component** + +Note that if we had added this `import` to the same WIT utilized by the host, the host +would think it should be the one providing the `storygen` function and would fail at the +linking stage. Modify `guest.js` to look like the following listing. + +Listing 4.17 Guest component for the SmartCMS ( **`guest.js`** ) + +``` +import { get, set } from "component:smartcms/kvstore"; +import storygen from 'storygen'; + +``` + +``` +export function run() { +set('guest-hello', storygen()); +return get('guest-hello'); +} + +``` + +**Imports the storygen function** + +**Sets the story generated by the** +**storygen function to the key guest-hello** + +Licensed to Ibrahim Mohammed Mohammed + +**80** Chapter 4 **_Interfacing Wasm with the underlying system_** + +You can then build the `smartcms_kvstore_guest` component (from within `smartcms_` + +`kvstore_guest/` ) with + +``` + jco componentize guest.js --wit kvstore_guest.wit --world-name app -o +``` + +➥ `../smartcms_test_host/guest.wasm --disable stdio random clocks http` + +Now we can compose the two components together. From inside `smartcms_ml_guest/`, run + +``` + wac plug ../smartcms_test_host/guest.wasm --plug /smartcms_ml_guest.wasm -o ../smartcms_test_host/guest_with_ml.wasm` + +Finally, all we are missing are some modifications to our `smartcms_test_host` to have it +utilize WASI. First, let’s add the following dependencies to the `Cargo.toml` file: + +``` + // ... + wasmtime-wasi = "30" + +``` + +Then, in `main.rs`, let’s modify our `State` variable to also contain WASI state as a tuple, +like so: + +``` +// ... +struct State { +key_value: KeyValue, +wasi: (wasmtime_wasi::WasiCtx, +``` + +➥ `wasmtime_wasi::ResourceTable)` +``` +} +// ... + +``` + +**Adds a tuple containing the WASI** +**context and resource table** + +Because we are now using WASI, just like we did for `hello_world_wasi02`, we need to +implement the `WasiView` and `IoView` traits for our `State` struct. Add the following to +the `impl` blocks for `State` —note; we are accessing the tuple elements to get the specific +struct properties: + +``` +impl wasmtime_wasi::WasiView for State { +fn ctx(&mut self) -> &mut wasmtime_wasi::WasiCtx { +&mut self.wasi.0 + +impl wasmtime_wasi::IoView for State { +fn table(&mut self) -> &mut wasmtime_wasi::ResourceTable { +&mut self.wasi.1 + +``` + +Then let’s modify the `main` function to utilize WASI. After creating the `engine`, modify +the `store` creation to create a new WASI resource, pre-open the current directory in +the host and map it to the guest with `READ` access, build the WASI context, and, finally, +create a tuple containing the WASI context and resource table for `State` : + +Licensed to Ibrahim Mohammed Mohammed + +**_Expanding our example project with WASI and composability_** **81** + +``` +fn main() { + +let state = State { +key_value: KeyValue { mem: std::collections::HashMap::new() }, +wasi: (wasi_ctx, wasi_table) + +// ... + +``` + +**Creates a new WASI** + +**directory (i.e., ".") in** +**the host and maps it to** +**"." in the guest with** +**READ permissions for** +**files and directories** + +**Builds the WASI context** + +**Creates a tuple** +**containing the WASI** +**context and resource** +**table for State** + +You’ll be familiar with most of what we did in this bit from `hello_world_wasi02` . But, +in particular, I want to highlight how WASI is giving the guest access to the host filesystem. WASI “pre-opens” the host directory (i.e., in this case `"."` ) and maps it onto the +guest’s `"."` directory. This way, the guest only has access to a very limited view of the +host’s filesystem, and that view can be controlled with the permissions provided (i.e., + +`READ` for files and directories). + +Then let’s modify the `component` variable to load the `guest with guest_with_ml.wasm` +instead of `guest.wasm` : + +``` +fn main() { +// ... + +``` + +➥ `Component::from_file(&engine,` +➥ `"guest_with_ml.wasm").unwrap();` +``` +// ... +} + +``` + +**Loads the guest_with_ml.wasm file** +**instead of the guest.wasm file** + +Finally, we can modify our linker setup to include WASI by adding this just after declaring the linker: + +``` +fn main() { +// ... +let mut linker = wasmtime::component::Linker::new(&engine); +wasmtime_wasi::add_to_linker_sync(&mut linker).unwrap(); +// ... +} + +``` + +Overall, the code should look like the following listing. + +Listing 4.18 Final **`main.rs`** file for our SmartCMS test host + +``` +wasmtime::component::bindgen!({ +path: "./smart_cms.wit", + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**82** Chapter 4 **_Interfacing Wasm with the underlying system_** + +``` + world: "app", + }); + + struct KeyValue { + mem: std::collections::HashMap, + } + + impl crate::component::smartcms::kvstore::Host for KeyValue { + fn get(&mut self, key: String) -> Option { + self.mem.get(&key).cloned() + } + + fn set(&mut self, key: String, value: String) { + self.mem.insert(key, value); + } + } + +``` + +``` +struct State { +key_value: KeyValue, +wasi: (wasmtime_wasi::WasiCtx, + +} + +``` + +**Adds a tuple containing the WASI** +**context and resource table** + +``` +impl wasmtime_wasi::WasiView for State { +fn ctx(&mut self) -> &mut wasmtime_wasi::WasiCtx { +&mut self.wasi.0 + +``` + +``` +impl wasmtime_wasi::IoView for State { +fn table(&mut self) -> &mut wasmtime_wasi::ResourceTable { +&mut self.wasi.1 + +fn main() { +let mut config = wasmtime::Config::default(); +config.wasm_component_model(true); + +let engine = wasmtime::Engine::new(&config).unwrap(); + +``` + +**directory (i.e., ".") in** +**the host and maps it to** +**"." in the guest with** +**READ permissions for** +**files and directories** + +``` +.build(); + +key_value: KeyValue { mem: std::collections::HashMap::new() }, +wasi: (wasi_ctx, wasi_table) + +``` + +**Builds the WASI context** + +``` +let component = wasmtime::component:: + +``` + +**Creates a tuple** +**containing the WASI** +**context and resource** +**table for State** + +Licensed to Ibrahim Mohammed Mohammed + +**_Summary_** **83** + +➥ `Component::from_file(&engine,` +➥ `"guest_with_ml.wasm").unwrap();` + +``` +let mut linker = wasmtime::component::Linker::new(&engine); +wasmtime_wasi::add_to_linker_sync(&mut linker).unwrap(); +component::smartcms::kvstore::add_to_linker +``` + +**Loads the guest_with_ml.wasm** + +➥ `state.key_value).unwrap();` + +**file instead of the guest.wasm file** +``` +let app = App::instantiate(&mut store, &component, &linker).unwrap(); + +println!("{:?}", app.call_run(&mut store).unwrap()); +} + +``` + +And that’s it! You can run the `smartcms_test_host` with `cargo` `run` `--release`, and you +should see a story added to the key-value store and then retrieved from it (which will +get printed to the standard output). + +_Summary_ + +¡ Wasm components have an established Canonical ABI that allows for easy inter +action between two Wasms or a Wasm and a host. + +¡ Wasm hosts implement WASI to provide a set of APIs for Wasm to interact with + +the host. + +¡ WASI 0.1 defined a low-level POSIX-like set of APIs for Wasm to interact with the + +host. WASI 0.2 interfaces, on the other hand, provide higher-level interfaces for +common application tasks, like databases and HTTP requests. + +¡ WASI 0.2 defined two worlds: `wasi:cli/command` and `wasi:http/proxy` . + +¡ Wasm’s command components created with `cargo` `component` `new` export the + +`wasi:cli/run` function and can be run directly with `wasmtime` . + +¡ Extracting a component’s WIT with `wasm-tools component` `wit` `` + +can be helpful for debugging. + +¡ You can examine a component or module by seeing its metadata with `wasm-tools` + +`metadata` `show` `` . + +¡ Wasm modules can be converted to Wasm components with `wasm-tools` + +`component` `new` `` to make use of WASI 0.2, higher-level types, and +better composability. + +¡ You can restrict the capabilities your Wasm component has access to with + +WASI-Virt. + +¡ Aside from restricting capabilities, WASI-Virt can be used to set environment vari +ables and mount virtual filesystem directories. + +¡ You can use `wac` `plug` `` `--plug` `` `-o` `` to compose two components together and match their imports/ +exports, allowing easy interaction between two distinct Wasms. + +Licensed to Ibrahim Mohammed Mohammed + +##### _From machine_ +# _5_ + +##### _From machine_ _learning to databases:_ + +##### _Applications of Wasm_ + +_This chapter covers_ + +¡ WASI as a collection of standardized interfaces + +¡ Machine learning inference in Wasm + +¡ Applying the ONNX format + +¡ Running Wasm within databases + +Up until now, we have undertaken much of the heavy lifting ourselves. We began +the book without even using Wasm components, implementing our own lifting and +lowering for the string type. Even after transitioning to Wasm components, we continued to create our own interfaces and hosts to serve as the backbone of our applications. This approach is not ideal for server-side development with Wasm. Instead, +we should use out-of-the-box hosts and prewritten interfaces—unless, of course, we +are developing a very specific application. + +In this chapter, we will start utilizing standardized interfaces. Specifically, we will +explore how to use the `wasi:nn` (for neural networks) interface to perform machine +learning (ML) with Wasm, and we will discuss a separate use case for Wasm running +inside a database to power user-defined functions. + +**84** + +Licensed to Ibrahim Mohammed Mohammed + +**_WebAssembly Standard Interfaces_** **85** + +_5.1_ _WebAssembly Standard Interfaces_ + +In chapter 4, you were introduced to WASI—the WebAssembly System Interface. Upon +its release in 2019, WASI served as a subset of POSIX to enable Wasm to operate outside of the browser. Fast-forwarding to today, a more appropriate interpretation of +WASI is perhaps “WebAssembly Standard Interfaces,” as it encompasses a wide range +of interfaces beyond POSIX and offers higher-level capabilities. As shown in figure +5.1, WASI serves as an umbrella for numerous interfaces, including WebGPU, threads, +SQL, messaging, logging, distributed lock service, crypto, blob store, runtime config, +ML, key-value store, HTTP, sockets, filesystem, I/O, and more. + +Figure 5.1 Some of WASI’s interfaces + +However, as of this writing, none of these interfaces are fully standardized. Instead, they +are currently undergoing various phases of development. Specifically, for an interface +to be considered fully standardized, among other criteria, it must have demonstrated +general interest by the community, have precise and complete overview documents, +possess a test suite, and have two or more implementers for the feature. You can verify +[the status of WASI interfaces here: https://mng.bz/rZ8x.](https://mng.bz/rZ8x) + +As we explored in chapter 4, when WASI 0.2 was released, it defined two worlds: +the CLI command world (i.e., `wasi:cli/command` ) and the HTTP proxy world (i.e., + +`wasi:http/proxy` ). With each release of WASI, more of these worlds will be defined +and become easily accessible. An example of a world that is not yet standardized but +is in the works is `wasi:cloud-core`, which groups interfaces you’d likely need to create + +Licensed to Ibrahim Mohammed Mohammed + +**86** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +cloud applications. It includes interfaces such as `wasi:keyvalue`, `wasi:blobstore`, + +`wasi:messaging`, `wasi:sql`, `wasi:config`, and `wasi:http` . Aside from `wasi:cloud-core`, +another interesting interface is `wasi:nn`, which is used for using ML. Let’s try making +use of it! + +_5.2_ _Machine learning with Wasm_ + +If you are in a function-as-a-service system and using Wasm, `wasi:nn` is the go-to way of +getting native performance for your ML workloads. In addition to native performance, +you gain access to an ML SDK out of the box for your language of choice (provided +it can be compiled or bundled into Wasm) and enjoy all of the security benefits of +Wasm’s sandboxed execution. + +Alternatives to `wasi:nn` worth mentioning are some ML +libraries (e.g., TVM and `tract` ), which compile to Wasm modules. This allows ML workloads to run inside Wasm directly without relying on a host’s implementation of `wasi:nn` . However, +to conform to WASI 0.1’s POSIX subset, these libraries make +performance sacrifices, limiting their efficiency. On the other +hand, `wasi:nn` uses the optimizations already implemented by +existing ML frameworks, such as larger vector widths, specialized hardware instructions, threading, and accelerators like +GPUs and neural processing units (NPUs). This results in significantly better performance, making `wasi:nn` the preferred +choice for users who require high-performance ML workloads +within a Wasm-based function-as-a-service system. + +What’s more, `wasi:nn` can be used directly with Wasmtime, +as there is experimental support built into the runtime. All we +have to be concerned with is creating our component, so let’s +do just that! + +_5.2.1_ _Understanding ML inference_ + +For this example, we will be performing ML inference. Inference is the process of using ML to make predictions or decisions based on new, unseen data. In our use case, we’ll apply +inference to an image to classify what that image represents. +Before we get started with the example, I want to mention that, +while we can use ML as an example for server-side Wasm, ML +is a vast field in itself, and I am by no means an ML expert. At +best, I am a practitioner who has learned a great deal about it +for the purpose of writing this section. If you find yourself in +the same boat, see the following sidebar; it describes key concepts for the example we will be creating. Figure 5.2 shows a +schematic for the application we will develop. + +Figure 5.2 **`wasi_nn_`** +**`example`** application +schematic + +Licensed to Ibrahim Mohammed Mohammed + +**_Machine learning with Wasm_** **87** + +ML 101: Essential concepts for server-side developers + +Before we delve into the code example, it’s important to understand some basic ML +concepts that are relevant to the application. This sidebar provides a straightforward +introduction to the key ideas you need to grasp to follow along. + +ML models +An ML model is a program that has been trained to recognize patterns in data. In our +case, we’re using a model that can classify images—it can identify what objects are +present in a picture. Also, we’re using a pretrained model, which means that it has +already been trained on large datasets and can be used out of the box for tasks like +image classification, without the need to train it ourselves. + +ONNX format +Open Neural Network Exchange (ONNX) is an open format for representing machine +learning models. It allows models to be transferred between different frameworks +and tools, making them more versatile. + +Tensors +A tensor is essentially a multidimensional array used to store data. In the context of +image processing, an image can be represented as a tensor with dimensions corresponding to width, height, and color channels (e.g., red, green, blue). Tensors are +then used to feed data into the model and to receive outputs from the model. + +Labels in classification +In classification tasks, labels are the categories that the model can predict. For image +classification, labels might be objects the model can recognize in images, e.g., “cat,” +“dog,” “car,” etc. After the model processes an image, it outputs predictions associated with these labels. + +Softmax function +The softmax function is a mathematical function that converts the raw output of the +model (which can be any range of numbers) into probabilities that sum up to 1. It makes +it easy to interpret the model’s output by providing a probability for each possible label. +Then the label with the highest probability is typically chosen as the model’s prediction. + +Putting it all together +In our example, we perform these steps: + +1 _Load the model_ —We load a pretrained image classification model in ONNX format. + +2 _Prepare the input_ —We process an input image and convert it into a tensor that +the model can understand. + +3 _Run inference_ —We feed the tensor into the model to get predictions. + +4 _Interpret results_ —We apply the softmax function to the model’s output to get +probabilities for each label and identify the top predictions. + +_5.2.2_ _Using wasi-nn_ + +To get started, inside our folder for chapter 5 projects, let’s create a new project with + +`cargo` `component` : + +``` + cargo component new wasi_nn_example + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**88** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +As a reminder, this creates a command component, meaning it will export the `wasi:cli` + +`run` function. In theory, this allows us to run the component directly with Wasmtime. +However, it’s important to remember that we are also planning to use a specific interface (i.e., `wasi:nn` ). If Wasmtime doesn’t provide an implementation for this interface, +we would be stuck having to create our own host implementation again. Fortunately, +as we mentioned earlier, Wasmtime does provide experimental implementations for + +`wasi:nn` that can be enabled via command-line flags. At the time of writing, Wasmtime +supports three different `wasi:nn` backends: OpenVINO (an open source toolkit that +optimizes and deploys deep learning models for Intel hardware), ONNX (the format +mentioned in the ML 101 sidebar), and WinML (a Windows-specific machine learning +platform optimized for running ML models on Windows devices). All we have to do is +create this component. + +Next, we will need to add three dependencies inside our `Cargo.toml` file, as shown +in listing 5.1. First, delete any dependencies that are already there. Then, we will add a +dependency for `wit-bindgen` to allow us to generate code for the `wasi:nn` interface, the + +`image` crate, which is useful for processing our image to be inferred on, and `ndarray`, a +crate that provides efficient numerical operations and matrix manipulation capabilities. + +Listing 5.1 **`Cargo.toml`** file for the **`wasi_nn_example`** + +``` + [package] + name = "wasi_nn_example" + version = "0.1.0" + edition = "2021" + +``` + +``` +[dependencies] +wit-bindgen = { version = "0.33.0", +``` + +➥ `default-features = false, features =` +➥ `["macros"] }` +``` +image = "0.25.2" +ndarray = "0.16.1" + +[profile.release] +codegen-units = 1 +opt-level = "s" +debug = false +strip = true +lto = true + +[package.metadata.component] +package = "component:wasi-nn-example" + +[package.metadata.component.dependencies] + +``` + +**The three added** +**dependencies** + +Finally, to finish off our setup, we will need to download the `wasi:nn` interface. This step +is currently a bit cumbersome, but it is being actively worked on upstream in the Wasm +community. In particular, people are exploring ways to package Wasm components as +Open Container Initiative (OCI) artifacts, but we’ll cover that in more detail in later + +Licensed to Ibrahim Mohammed Mohammed + +**_Machine learning with Wasm_** **89** + +chapters. Currently, because `wasi:nn` isn’t available as an OCI artifact, we have to handle it manually. For this particular demo, I have made `wasi:nn@0.2.0-rc-2024-08-19` +available in this book’s GitHub repository. You can access it here: [https://mng.bz/](https://mng.bz/V9WN) +[V9WN. Copy the entire interface and paste it into a file called](https://mng.bz/V9WN) `wasi-nn.wit` inside the + +`wit/` folder at the same level as our component’s `src/` folder. + +Creating our component +Now that we are all set up, we are ready to start creating our component. First, delete +all the autogenerated contents of our current `main.rs` file. Then, we can start by adding all dependencies that we need at the top of our file: + +``` +use image::{ImageReader, imageops::Triangle}; +use ndarray::Array; +use std::{env, fs}; +use std::io::BufRead; + +``` + +Next, we will make a call to `wit_bindgen::generate!` . This is similar to `wasmtime::` + +`component::bindgen!`, which we’ve used in the past to generate interface glue code for +the host side. However, in this case, `wit_bindgen::generate!` generates the glue code +for the guest side: + +``` +wit_bindgen::generate!({ +path: "wit/wasi-nn.wit", +world: "ml", +}); + +``` + +In particular, we are saying that we want to generate glue code for the `wit/wasi-nn` + +`.wit` interface and then use its `ml` world. This is an alternative to using Web­Assembly +Interface Types (WIT) files and `Cargo.toml` sections directly like we did in chapter 4 +with our composability example. There, our setup involved writing a WIT file that contained our imports and using the `[package.metadata.component.target]` section to +point to it. A similar approach is possible here by adding `wasi:nn` as a dependency in + +`[package.metadata.component.target.dependencies]`, but we are choosing to use `wit_` + +`bindgen` directly to highlight a different way of doing things and also because, for now, +I have found it works a little bit better with code completion. + +Next, we can add imports to structures and functions we will use from the generated +code, like so: + +``` +use self::wasi::nn::{ +graph::{Graph, load, ExecutionTarget, GraphEncoding}, +tensor::{Tensor, TensorType}, +}; + +``` + +With code generation done and the imports all in place, we are ready to start working on our `main` function. To start, we need to access the image path provided as a +command-­line argument to our application: + +Licensed to Ibrahim Mohammed Mohammed + +**90** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +``` + fn main() { + let image_path = env::args().nth(1).expect("Usage: "); + } + +``` + +Then, continuing inside our `main` function, we can read our pretrained ONNX ML +model from the filesystem and load it into a graph representation, specifying that we +want to use the CPU as the execution target. After that, we initialize an execution context for the graph, which prepares it to run inference operations. Of course, at this +point you don’t have the model in your own filesystem, but we’ll get more details on +that in a later section: + +``` + let model_data = fs::read("fixture/models/squeezenet1.1``` + +➥ `7.onnx").unwrap();` +``` + let graph = load(&[model_data], GraphEncoding::Onnx, +``` + +➥ `ExecutionTarget::Cpu).unwrap();` +``` + let exec_context = Graph::init_execution_context(&graph).unwrap(); + +``` + +Then, equally, we can also read in our labels data and convert it into a vector of strings, +where each string represents a label that corresponds to the classification categories +our model can predict: + +``` + let labels_data = fs::read("fixture/labels/squeezenet1.1``` + +➥ `7.txt").unwrap();` +``` + let class_labels: Vec = labels_data.lines().map(|line| +``` + +➥ `line.unwrap()).collect();` + +We then convert our input image into a tensor format that the model can process +using the `image_to_tensor` function, which we will code later. If you want a test image +[to use for the example, you can download the one I used from this link: https://mng](https://mng.bz/xZ17) +[.bz/xZ17. Next, we create a tensor with its dimensions (224 pixels, for both height and](https://mng.bz/xZ17) +width), specifying the data type as 32-bit floating-point ( `Fp32` ). Finally, we set this tensor as the input to our execution context, preparing the model to perform inference +with the provided image data: + +``` + let data = image_to_tensor(&image_path, 224, 224); + let dimensions = vec![1, 3, 224, 224]; + let tensor = Tensor::new(&dimensions, TensorType::Fp32, &data); + exec_context.set_input("data", tensor).unwrap(); + +``` + +We are now finally ready to execute our inference. This is where the magic happens— +our model processes the input data and generates predictions based on what it has +learned during training. With just a single call, we trigger the computation that runs +through the model’s layers, performing the mathematical operations needed to interpret the image data. Here’s how we do it: + +``` + exec_context.compute().unwrap(); + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Machine learning with Wasm_** **91** + +This command initiates the inference process, transforming the input tensor into a set +of predictions that we can analyze and interpret in the next steps. + +Next, we retrieve the output from the model, which contains the raw prediction data +in bytes. We then convert this byte data into a more usable format using the `bytes_to_` + +`f32_vec` function, which we’ll code later. This function transforms the byte chunks into +32-bit floating-point numbers. Finally, we wrap this data into an array structure for easier manipulation and analysis in the subsequent steps: + +``` +let output_data = exec_context.get_output +``` + +➥ `("squeezenet0_flatten0_reshape0").unwrap().data();` +``` +let output_f32 = bytes_to_f32_vec(&output_data); +let output_array = Array::from_vec(output_f32); + +``` + +Next, we apply a transformation to the model’s output to make it easier to interpret +as probabilities. We begin by taking the exponential of each value in the array, which +amplifies the differences between the predicted scores. This is a common step in ML +known as applying the softmax function. We then calculate the sum of all these exponential values. Finally, we divide each exponential value by this sum, normalizing the +scores so that they represent probabilities that add up to 1. Here’s the code: + +``` +let exp_output = output_array.mapv(f32::exp); +let sum_exp = exp_output.sum(); +let softmax_output = exp_output / sum_exp; + +``` + +This softmax operation converts the raw output into a probability distribution, where +each value represents the likelihood of the input image belonging to a specific class. + +Finally, we take the processed output probabilities and rank them to identify the +most likely predictions. We start by pairing each probability with its corresponding class +index and collecting these pairs into a list. Then, we sort this list in descending order to +place the highest probabilities at the top. To finish, we display the top three predictions +along with their associated class labels and probabilities, indicating the model’s best +guesses for the input image: + +``` +let mut probabilities: Vec<_> = +``` + +➥ `softmax_output.iter().enumerate().collect();` +``` +probabilities.sort_by(|a, b| b.1.partial_cmp(a.1).unwrap()); + +for &(index, &probability) in probabilities.iter().take(3) { +println!("Class: {} - Probability: {}", class_labels[index], +``` + +➥ `probability);` +``` +} + +``` + +This step reveals the classes that the model considers the most likely matches for the +input image, along with the confidence level for each prediction, helping us interpret +the results of the inference. + +Licensed to Ibrahim Mohammed Mohammed + +**92** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +This wraps up our `main` function! The final tasks remaining are to code the `bytes_to_` + +`f32_vec` function and the `image_to_tensor` function, which will help us process the +data for our model’s inference. + +Let’s start with the `bytes_to_f32_vec` function. This function is responsible for converting a slice of byte data into a vector of 32-bit floating-point numbers ( `f32` ). It does +this by iterating over the byte data in chunks of `4` (the size of an `f32` ), converting each +chunk into its corresponding floating-point value, and collecting these values into a +vector: + +``` + pub fn bytes_to_f32_vec(data: &[u8]) -> Vec { + data.chunks_exact(4) + .map(|c| f32::from_le_bytes(c.try_into().unwrap())) + .collect() + } + +``` + +This function is crucial because it transforms the raw byte output from the model into +a format we can use for numerical analysis, allowing us to perform operations like softmax to interpret the model’s predictions. + +Let’s now tackle the `image_to_tensor` function to finish our example. The function +starts by opening the image from the specified path, decoding it, and resizing it to +the given dimensions. The `resize_exact` method ensures that the image is precisely +adjusted to the required width and height. We’re using the `Triangle` filter for resizing, +which provides a good balance between speed and image quality. The resulting image +data is then converted into a raw pixel array: + +``` + fn image_to_tensor(path: &str, height: u32, width: u32) -> Vec { + let img = ImageReader::open(path).unwrap().decode().unwrap(); + let resized_img = img.resize_exact(width, height, Triangle).to_rgb8(); + let raw_pixels = resized_img.into_raw(); + +``` + +Next, we normalize the pixel values using predefined mean and standard deviation +arrays. This normalization process is a common practice in ML to ensure that the input +data is using the same scale the model expects. We iterate over each pixel, normalize its +red, green, and blue values, and store the results in the `data_f32` vector. The normalization helps the model process the input more effectively by standardizing the color +values: + +``` + let mean = [0.485, 0.456, 0.406]; + let std = [0.229, 0.224, 0.225]; + + let num_pixels = (height * width) as usize; + let mut data_f32 = vec![0f32; num_pixels * 3]; + + for i in 0..num_pixels { + let idx = i * 3; + let r = raw_pixels[idx] as f32 / 255.0; + let g = raw_pixels[idx + 1] as f32 / 255.0; + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Machine learning with Wasm_** **93** + +``` +let b = raw_pixels[idx + 2] as f32 / 255.0; + +data_f32[i] = (r - mean[0]) / std[0]; +data_f32[i + num_pixels] = (g - mean[1]) / std[1]; +data_f32[i + 2 * num_pixels] = (b - mean[2]) / std[2]; +} + +``` + +Finally, we convert the normalized floating-point values into bytes, which are required +for the tensor representation used by the model. Each floating-point value is transformed into its corresponding byte representation using the `to_ne_bytes` method, and +these bytes are then stored in the `data_u8` vector: + +``` +let mut data_u8 = Vec::with_capacity(data_f32.len() * 4); +for &v in &data_f32 { +data_u8.extend_from_slice(&v.to_ne_bytes()); +} + +data_u8 + +``` + +Overall, the code should come together as shown in the following listing. + +Listing 5.2 **`main.rs`** file of our **`wasi_nn_example`** component + +``` +use image::{ImageReader, imageops::Triangle}; +use ndarray::Array; +use std::{env, fs}; +use std::io::BufRead; + +wit_bindgen::generate!({ +path: "wit/wasi-nn.wit", +world: "ml", +}); + +use self::wasi::nn::{ +graph::{Graph, load, ExecutionTarget, GraphEncoding}, +tensor::{Tensor, TensorType}, +}; + +fn main() { +let image_path = env::args().nth(1).expect("Usage: "); + +``` + +``` +let model_data = fs::read("fixture/models +``` + +➥ `/squeezenet1.1-7.onnx").unwrap();` +``` +let graph = load(&[model_data], GraphEncoding::Onnx, +``` + +➥ `ExecutionTarget::Cpu).unwrap();` +``` +let exec_context = Graph::init_execution_ +``` + +➥ `context(&graph).unwrap();` + +``` +let labels_data = fs::read("fixture/labels/ +``` + +➥ `squeezenet1.1-7.txt").unwrap();` +``` +let class_labels: Vec = labels_data. +``` + +➥ `lines().map(|line| line.unwrap()).collect();` + +**Loads the ONNX model** + +**Loads labels** + +Licensed to Ibrahim Mohammed Mohammed + +**94** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +``` +let data = image_to_tensor +``` + +➥ `(&image_path, 224, 224);` +``` +let dimensions = vec![1, 3, 224, 224]; +let tensor = Tensor::new +``` + +➥ `(&dimensions, TensorType::Fp32, &data);` +``` +exec_context.set_input +``` + +➥ `("data", tensor).unwrap();` + +**Prepares input tensor** + +``` +exec_context.compute().unwrap(); + +``` + +**Executes inference** + +``` +let output_data = +``` + +➥ `exec_context.get_output("squeezenet0_flatten0_` +➥ `reshape0").unwrap().data();` +``` +let output_f32 = bytes_to_ +``` + +➥ `f32_vec(&output_data);` +``` +let output_array = Array::from_vec(output_f32); + +let exp_output = output_array.mapv(f32::exp); +let sum_exp = exp_output.sum(); +let softmax_output = exp_output / sum_exp; + +let mut probabilities: Vec<_> = softmax_output. +``` + +➥ `iter().enumerate().collect();` +``` +probabilities.sort_by(|a, b| b.1. +``` + +➥ `partial_cmp(a.1).unwrap());` + +**Gets and processes** +**output** + +**Computes softmax** + +**Gets top three** +**probabilities** + +``` +for &(index, &probability) in probabilities.iter().take(3) { +println!("Class: {} - Probability: {}", class_labels[index], +``` + +➥ `probability);` +``` +} +} + +pub fn bytes_to_f32_vec(data: &[u8]) -> Vec { +data.chunks_exact(4) +.map(|c| f32::from_le_bytes(c.try_into().unwrap())) +.collect() +} + +fn image_to_tensor(path: &str, height: u32, width: u32) -> Vec { +let img = ImageReader::open(path).unwrap().decode().unwrap(); +let resized_img = img.resize_exact(width, height, Triangle).to_rgb8(); +let raw_pixels = resized_img.into_raw(); + +let mean = [0.485, 0.456, 0.406]; +let std = [0.229, 0.224, 0.225]; + +let num_pixels = (height * width) as usize; +let mut data_f32 = vec![0f32; num_pixels * 3]; + +for i in 0..num_pixels { +let idx = i * 3; +let r = raw_pixels[idx] as f32 / 255.0; +let g = raw_pixels[idx + 1] as f32 / 255.0; +let b = raw_pixels[idx + 2] as f32 / 255.0; + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Machine learning with Wasm_** **95** + +``` +data_f32[i] = (r - mean[0]) / std[0]; +data_f32[i + num_pixels] = (g - mean[1]) / std[1]; +data_f32[i + 2 * num_pixels] = (b - mean[2]) / std[2]; +} + +let mut data_u8 = Vec::with_capacity(data_f32.len() * 4); +for &v in &data_f32 { +data_u8.extend_from_slice(&v.to_ne_bytes()); +} + +data_u8 +} + +``` + +Running the component +Now all that is left is to run the component! Like previously mentioned, we can do so +directly with Wasmtime, as our component implements `wasi:cli` `run` thanks to `cargo` + +`component` ’s behind-the-scenes scaffolding and because Wasmtime provides an implementation for `wasi:nn` . However, there’s a small caveat: Wasmtime does not include its +ONNX backend by default, which is what we rely on for this example. + +To avoid having you build Wasmtime from source to include the ONNX backend, +I’ve done so myself and packaged it as a Docker image that you can run. This image +also contains our ONNX SqueezeNet model and labels baked in, which means we don’t +need them in our local filesystem. Just like we did in chapter 2, we’ll be passing our +Wasm component (and the image to run inference on) to the container by mounting a +local directory containing our `.wasm` and `.jpg` files. + +NOTE To see how this image was built, check out its Dockerfile in the book’s +[GitHub repo: https://mng.bz/AGme.](https://mng.bz/AGme) + +So, from inside `wasi_nn_example/`, run + +``` +cargo component build --release + +``` + +Then, at the same level as the `src/` folder of `wasi_nn_example/`, create a folder called + +`data/` and copy the `.wasm` file there. If you haven’t already, place the image you want +to run inference on together with the `.wasm` file in this `data/` folder. Overall, the tree +structure for our `wasi_nn_example` project should look somewhat like this: + +``` +wasi_nn_example/ +``` + +├── data/ +│ ├── cat.jpg +│ ├── wasi_nn_example.wasm +├── src/ +│ ├── main.rs +│ ├── bindings.rs +├── wit/ +│ ├── wasi-nn.wit +├── Cargo.toml + +Licensed to Ibrahim Mohammed Mohammed + +**96** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +Finally, from inside our `wasi_nn_example/` folder, we can run our component with the +following command: + +``` + docker run --rm -v ./data:/data ghcr.io/danbugs/serverside-wasm-book``` + +➥ `code/wasmtime-onnx:latest run -Snn --dir /fixture::fixture --dir` +➥ `/data::data /data/wasi_nn_example.wasm /data/cat.jpg` + +I’ve set up this Docker image to provide an entry point to Wasmtime directly. This way, +all commands we pass to Docker after the image name `ghcr.io/danbugs/serverside` + +`-wasm-book-code/wasmtime-onnx:latest` are actually Wasmtime’s CLI arguments. Let’s +walk through it: + +¡ `run` —Wasmtime’s command to run a Wasm module or component. + +¡ `-Snn` —Enables Wasmtime’s experimental support for `wasi:nn` . + +¡ `--dir /fixture::fixture --dir /data::data` —Grants the Wasm guest access to + +the host’s `fixture/` and `data/` directories, where our model, labels, Wasm component, and JPG image are located. This allows the guest to access these directories just as we do in the code. + +Finally, we pass in our Wasm component and an argument specifying the path to our +JPG image. + +If you ran this example with the same cat image I used in figure 5.3, you should have +gotten output from the Docker command similar to the following: + +``` + Class: n02123394 Persian cat - Probability: 0.9488756 + Class: n02086079 Pekinese, Pekingese, Peke - Probability: 0.017302925 + Class: n02112018 Pomeranian - Probability: 0.01678654 + +``` + +As a result, our inference correctly classified our image as a Persian cat! + +But perhaps even more importantly, +we’ve successfully used a standardized WASI +interface for the first time, demonstrating +the immense potential of using WASI to +bring powerful capabilities to Wasm. We +accomplished all of this from within the +safety of Wasm’s secure, sandboxed environment. This showcases not only the versatility of Wasm but also its ability to handle +complex operations like ML inference +without compromising on security or portability. By tapping into these standardized +interfaces, we’ve taken a significant step +toward building robust, server-side applications powered by Wasm. + +Figure 5.3 Image of a Persian cat + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm inside databases_** **97** + +ONNX vs. OpenVINO + +Another option for running this example is to use another backend that Wasmtime +provides for `wasi:nn` —OpenVINO. By making slight adjustments to our example and +using a model suitable for OpenVINO, we could run this example directly with Wasmtime from the CLI. This is possible because the OpenVINO backend relies on runtime +linking to the OpenVINO system library, meaning it does not require vendoring like the +ONNX backend does. + +However, for this example, we chose the ONNX backend to avoid the need to install +additional dependencies on our system, prioritizing convenience. + +Next, let’s take a step back and see a different application of Wasm on the server side. + +_5.3_ _Running Wasm inside databases_ + +Next up, we will talk about another application of Wasm—running it inside of a +database. This has been a point of interest because it allows computations to be executed directly where the data resides, reducing latency and improving performance. +By bringing the code close to the data, we minimize the amount of data we need to +send between our database client and server, which is often a bottleneck in traditional +architectures. Additionally, running Wasm inside a database uses Wasm’s sandboxed +execution environment, ensuring that the computations are performed securely and +efficiently. This approach opens up possibilities for extending database functionalities +with custom logic, enabling more complex operations and real-time data processing +without compromising speed or security. + +But how can we embed Wasm inside of a database? We achieve this by using _user-­_ +_defined functions_ (UDFs), which are custom functions that extend the capabilities of the +database engine. UDFs allow developers to execute bespoke logic directly within the +database, enabling more sophisticated data processing and operations without the overhead of transferring data between the database and external applications. Traditionally, +UDFs are limited to specific programming languages like C or SQL, restricting flexibility and requiring developers to learn unfamiliar languages. However, with Wasm, we +can write UDFs in any language that compiles to Wasm, offering greater language flexibility and enabling developers to use the tools they are most comfortable with. + +We’ll demonstrate this using libSQL, a popular SQLite fork with built-in Wasm support. It lets you embed Wasm functions directly into SQL queries, blending SQLite’s +lightweight reliability with Wasm’s flexibility and performance. + +_5.3.1_ _Making Wasm UDFs_ + +Wasm UDFs are an example of server-side Wasm applications where we currently use +Wasm modules instead of Wasm components. There is ongoing work in the community +around SQL embedding for Wasm components, notably the `wasi:sql-embed` proposal +[(https://github.com/WebAssembly/wasi-sql-embed), but this effort is still too nascent](https://github.com/WebAssembly/wasi-sql-embed) + +Licensed to Ibrahim Mohammed Mohammed + +**98** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +as of now. Therefore, because we’ll be creating a Wasm module instead of a Wasm component, we’ll create our project directly with `cargo` instead of `cargo` `component` . We’ll +use Rust because it provides excellent convenience for producing Wasm UDFs. In particular, the `libsql_bindgen` crate simplifies the process by generating bindings between +Rust and libSQL, streamlining the creation of custom Wasm-based functions within +the database. To start off, in your directory for chapter 5 projects, run + +``` + cargo new --lib wasm_udf_example + +``` + +In this library, we will create two functions: `encrypt` and `decrypt` . The idea here is +that we can implement custom encryption and decryption logic directly within the +database, ensuring that sensitive data is securely handled without the need to transfer it outside the database environment. This minimizes the number of systems that +can read the unencrypted data, reducing the chances that a bug in some other system +could accidentally or maliciously leak private data. By using Wasm UDFs, we can perform these operations efficiently while maintaining data privacy, all within the database’s sandboxed execution context. See figure 5.4 for an architecture diagram that +illustrates how these functions integrate with the database system. + +Figure 5.4 Architecture diagram displaying how an application relies on our database with Wasm UDFs + +When developing UDFs in Rust for this example, we must navigate both Wasm’s security restrictions and libSQL’s type compatibility requirements. Wasm’s sandboxed execution environment ensures that our functions cannot access system resources such +as the network, filesystem, or system time/date, maintaining a secure and isolated +runtime. Beyond these isolation rules, we need to limit our function parameters and +return types to those compatible with both Wasm and libSQL. Specifically, for our Rust +UDFs, we will use any primitive integer type for libSQL’s `INTEGER` type, `f32` or `f64` for +libSQL’s `REAL` type, `String` or `&str` for libSQL’s `TEXT` type, and `Vec` for libSQL’s `BLOB` +type. The `libsql_bindgen` crate plays a crucial role in this setup by enforcing these + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm inside databases_** **99** + +type constraints and automatically generating the necessary bindings between Rust +and libSQL. This ensures that our custom Wasm-based functions are both type-safe +and seamlessly integrated within the libSQL framework, simplifying development and +reducing the potential for runtime errors. + +With that said, let’s now modify our `Cargo.toml` file, as shown in the following listing, +to properly compile to Wasm (similar to how we did in chapter 2) and add the dependencies we need for the `encrypt` and `decrypt` functions, including `libsql_bindgen` . + +Listing 5.3 **`Cargo.toml`** file for our **`wasm_udf_example`** project + +``` +[package] +name = "wasm_udf_example" +version = "0.1.0" +edition = "2021" + +``` + +``` +[dependencies] +libsql_bindgen = "0.3.1" +magic-crypt = "3.1" + +[lib] +crate-type = ["cdylib"] + +``` + +**Provides encryption and** +**decryption functionality for** +**user-defined functions** + +With that done, we can move to our `lib.rs` file. Let’s delete everything in the file and +then bring in the imports we need: + +``` +use libsql_bindgen::*; +use magic_crypt::{new_magic_crypt, MagicCryptTrait}; + +``` + +We then move on to our first Wasm UDF: `encrypt` . We begin by applying the `libsql_` + +`bindgen` attribute to indicate that this function should be exposed as a user-defined +function within libSQL. This attribute facilitates the generation of the necessary bindings, allowing our Rust function to be invoked directly from SQL queries. Notice that +we are using standard Rust types— `String` —for both the input parameters and the +return type, ensuring compatibility with libSQL’s type system. + +Inside the function, we utilize the `new_magic_crypt!` macro from the `magic_crypt` +crate to create a new encryption instance with the provided key and a 256-bit encryption strength. This macro simplifies the setup process, enabling us to focus on implementing the core encryption logic. We then call the `encrypt_str_to_base64` method +on our encryption instance, passing in the data we wish to encrypt. This method processes the input string and returns its encrypted form encoded in Base64, which is then +returned by the function: + +``` +#[libsql_bindgen::libsql_bindgen] +pub fn encrypt(data: String, key: String) -> String { +let mc = new_magic_crypt!(key, 256); +mc.encrypt_str_to_base64(data) +} + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**100** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +How do encryption and decryption work? + +Encryption is the process of converting readable data, known as _plaintext_, into an +unreadable format called _ciphertext_ using a specific key. This ensures that sensitive information remains secure and cannot be accessed by unauthorized parties. +Decryption is the reverse process, where the ciphertext is transformed back into +its original plaintext form using the same key. In our example, we use symmetric +encryption, which means the same key is used for both encrypting and decrypting +the data. This approach allows us to securely store and retrieve information directly +within the database, ensuring that only those with the correct key can access the +original data. + +We then move on to our second Wasm UDF: `decrypt` . Similar to the `encrypt` function, +we apply the `libsql_bindgen` attribute to designate this function as a user-defined function within libSQL. The `decrypt` function takes in the encrypted data and the encryption key as `String` parameters. Inside the function, we initialize a new encryption +instance with the provided key using the `new_magic_crypt!` macro, maintaining the +same 256-bit encryption strength. We then call the `decrypt_base64_to_string` method +to convert the encrypted Base64 string back to its original plaintext form. If decryption +fails—perhaps due to an incorrect key—we handle the error gracefully by returning +a default message, such as `"[ACCESS` `DENIED]"` . This setup ensures that sensitive data +can be securely retrieved and managed directly within the database environment using +Wasm’s secure execution model and libSQL’s extensibility: + +``` + #[libsql_bindgen::libsql_bindgen] + pub fn decrypt(data: String, key: String) -> String { + let mc = new_magic_crypt!(key, 256); + mc.decrypt_base64_to_string(data) + .unwrap_or("[ACCESS DENIED]".to_owned()) + } + +``` + +We have now finished writing all the code needed for our Wasm UDFs. For the full +code, see the following listing. + +Listing 5.4 **`lib.rs`** file of our **`wasm_udf_example`** project + +``` + use libsql_bindgen::*; + use magic_crypt::{new_magic_crypt, MagicCryptTrait}; + + #[libsql_bindgen::libsql_bindgen] + pub fn encrypt(data: String, key: String) -> String { + let mc = new_magic_crypt!(key, 256); + mc.encrypt_str_to_base64(data) + } + + #[libsql_bindgen::libsql_bindgen] + pub fn decrypt(data: String, key: String) -> String { + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm inside databases_** **101** + +``` + let mc = new_magic_crypt!(key, 256); + mc.decrypt_base64_to_string(data) + .unwrap_or("[ACCESS DENIED]".to_owned()) + } + +``` + +_5.3.2_ _Getting Wasm into SQL_ + +Now we are ready to compile our Rust code as a Wasm module. It’s important to note +that this is possible because the `magic_crypt` crate supports compilation to Wasm. To +compile our project, from inside `wasm_udf_example/` run the following command: + +``` + cargo build --release --target wasm32-unknown-unknown + +``` + +Then grab our compiled `wasm_udf_example.wasm` file and place it in the `wasm_udf_` + +`example/­data/` directory. Our next step is to convert this Wasm module into a format +that libSQL understands. There are two acceptable formats: + +¡ _Literal blob_ —In SQLite, a blob (binary large object) can be represented using a + +hexadecimal string prefixed with X. For example, `X'474F4F444341544348'` allows +us to embed binary data directly within SQL statements. This format is suitable for +storing the binary Wasm module as part of SQL queries or function definitions. + +¡ _WebAssembly Text Format (WAT)_ —WAT is a human-readable text representation + +of Wasm binary code. It enables easier inspection and editing of Wasm modules, +which can be beneficial for debugging or manual adjustments. Converting our +Wasm module to WAT allows us to define user-defined functions in a more transparent and editable format within libSQL. + +For our purposes, we will use a literal blob. To create our blob from a Wasm binary, +we will use a tool called `wasm_hex_dump`, which converts the binary data into a format +that can be used directly in SQL statements. To install it, run `cargo` `install` `wasm_hex_` + +`dump@0.1.0 --locked` . + +But before generating the hex dump for our Wasm binary, we need to create a `.sql` +file—let’s call it `create_wasm_udfs.sql` —inside the same `data/` folder within the `wasm_` + +`udf_example/` directory. This file will contain the necessary SQL statements to define +our UDFs. As a preliminary step, add the following lines: + +``` + .init_wasm_func_table + DROP FUNCTION IF EXISTS encrypt; + DROP FUNCTION IF EXISTS decrypt; + +``` + +This initializes the Wasm function table that libSQL uses to register Wasm-based UDFs. +By dropping any existing `encrypt` and `decrypt` functions (if they exist), we ensure that +we have a clean slate for adding these functions after their Wasm binaries have been +converted and embedded as literal blobs. This avoids conflicts and ensures that the +UDFs are defined properly when we recreate them. + +Licensed to Ibrahim Mohammed Mohammed + +**102** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +Next, we are ready to get the hex dump for our Wasm binary. You can do this by running from the `data/` folder: + +``` + wasm_hex_dump -i wasm_udf_example.wasm + +``` + +By default, `wasm_hex_dump` will output an `output.txt` file containing the hex dump for +our input. We will use this hex dump to define our UDFs. Copy the generated hex and +then, back in our SQL file, add the following: + +``` + CREATE FUNCTION encrypt LANGUAGE wasm AS X''; + +``` + +Next, using the same hex dump, add + +``` + CREATE FUNCTION decrypt LANGUAGE wasm AS X''; + +``` + +With the `CREATE` `FUNCTION` commands, we are defining our encrypt and decrypt UDFs +within the database. Each function is associated with the Wasm binary we converted to +hex and embedded as a literal blob. By specifying `LANGUAGE` `wasm`, we indicate that these +functions are implemented in Wasm, allowing libSQL to run them in a sandboxed +environment. The hex dump following the `AS` `X'...'` clause represents the Wasm +binary code in hexadecimal format, which the database uses to execute the respective +encryption and decryption logic. + +Overall, your file should look like the following listing. + +Listing 5.5 **`data/create_wasm_udfs.sql`** file + +``` + .init_wasm_func_table + DROP FUNCTION IF EXISTS encrypt; + DROP FUNCTION IF EXISTS decrypt; + CREATE FUNCTION encrypt LANGUAGE wasm AS X'0061736d01...'; + CREATE FUNCTION decrypt LANGUAGE wasm AS X'0061736d01...'; + +``` + +Do pay special attention to ensure that both `CREATE` `FUNCTION` statements are properly +closed with a closing single quote ( `'` ) and semicolon ( `;` ). This ensures that the functions are correctly defined within the SQL file and ready to be executed when importing the UDFs. + +Optimizing Wasm UDFs with wasm-opt + +When embedding Wasm binaries as UDFs in a database, it’s worth considering optimization tools like `wasm-opt` . This tool is specifically designed to optimize Wasm +binaries by reducing their size and improving performance. It applies a variety of optimizations, such as removing unused code, simplifying control flow, and making memory usage more efficient. For Wasm UDFs, running `wasm-opt` before embedding the +binary as a literal blob in the database can lead to smaller file sizes and faster execution times, ensuring your UDF performs efficiently within the database environment. + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm inside databases_** **103** + +_5.3.3_ _Running Wasm UDFs in libSQL_ + +Now we are ready to run our Wasm UDFs. For convenience, we will once again use a +Docker image for this example. This is a Docker image I have prepared that comes with +a prebuilt version of libSQL and provides an entry point to it. We will run the container +while mounting the path to our `data/` folder where our SQL file and Wasm module +reside. You can do so with the following command from the `wasm_udf_example/` folder: + +``` + docker run --rm -v ./data:/data -it ghcr.io/danbugs/ +``` + +➥ `serverside-wasm-book-code/libsql-wasmudf:latest` + +This command ensures that libSQL can access the `data/` directory, allowing it to locate +the necessary SQL file and Wasm module to execute the UDFs defined within your +project. + +Next, you should be presented with a prompt like + +``` + libsql> + +``` + +This indicates that you are inside the interactive libSQL shell, ready to execute SQL +commands. Now run the following command to load and execute the SQL file that +defines your Wasm UDFs: + +``` + .read /data/create_wasm_udfs.sql + +``` + +This command reads and executes the SQL statements from the file, including the +ones that initialize the Wasm function table and create your `encrypt` and `decrypt` functions. At this point, your functions should be fully defined and ready to use within the +libSQL environment. + +Now let’s test our Wasm UDFs! Execute the following in your libSQL shell: + +``` + SELECT encrypt('Hello, I am a Wasm UDF!', 'dan'); + +``` + +In this command, we are encrypting the string `"Hello,` `I` `am` `a` `Wasm` `UDF!"` using the key + +`'dan` ’. This should output the following: + +``` + YEBtLTvktUr2941iAOpOoY+Q6CO+ZfM8T1F+eqhzlKI= + +``` + +There we go! We just successfully used the `encrypt` function we defined as a Wasm +UDF. To conclude, we can then copy this encrypted result and provide it as the input to +our `decrypt` function, like so: + +``` + SELECT decrypt('YEBtLTvktUr2941iAOpOoY+Q6CO+ZfM8T1F+eqhzlKI=', 'dan'); + +``` + +And we should get back + +``` + 'Hello, I am a Wasm UDF!' + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**104** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +This confirms that both the encryption and decryption functions work as expected, +with the data being securely processed within the database using our Wasm UDFs! + +Using WAT to define Wasm UDFs + +We can also use WAT to define UDFs directly in libSQL. Like previously mentioned, +WAT is a human-readable format for Wasm that allows us to write and edit Wasm modules more easily. Here’s an example of a simple `my_add` function written in WAT that +adds two integers: + +``` + CREATE FUNCTION my_add LANGUAGE wasm AS ' + (module + (func $my_add (param i64 i64) (result i64) + local.get 0 + local.get 1 + i64.add) + (memory 16) + (export "my_add" (func $my_add)) + (export "memory" (memory 0)) + )'; + +``` + +This defines a Wasm function named `my_add` that takes two 64-bit integers as parameters and returns their sum. The memory export is necessary for managing memory, +though in this case, it’s not actively used. + +Once you’ve defined the function, you can test it just like we did with the previous +example. In your libSQL shell, run + +``` + SELECT my_add(1, 41); + +``` + +This should output + +``` + 42 + +``` + +With WAT, you can write and test simple Wasm functions like this, directly defining +UDFs in a clear, readable format. It’s a great alternative for small or custom logic without needing to precompile Wasm binaries. + +With that, we’ve successfully created and executed Wasm UDFs within libSQL! Starting from writing the encryption and decryption logic in Rust, we compiled our code +to Wasm, embedded it as a literal blob into SQL, and then tested our UDFs directly +within the database. Along the way, we also explored how tools like `wasm_hex_dump` +and `libsql_bindgen` help bridge the gap between Wasm and SQL, allowing us to +extend the capabilities of the database with custom functions in a secure, sandboxed +environment. + +TIP To exit the libSQL shell, you can use the `.exit` command. + +Licensed to Ibrahim Mohammed Mohammed + +**_Improving our example project with wasi-nn_** **105** + +By embedding Wasm UDFs, we not only keep computations close to the data, minimizing latency and data movement, but we also take advantage of Wasm’s language-­ +agnostic nature and security features. Whether you need to encrypt data, perform +complex calculations, or even run ML models directly in the database, Wasm UDFs +provide a powerful, efficient way to extend database functionality with custom logic— +all while ensuring performance, portability, and security and letting you use whatever +programming language you already know. + +_5.4_ _Improving our example project with wasi-nn_ + +In chapter 4, we introduced an ML component to our SmartCMS application. However, +it wasn’t truly an ML component at the time. Instead, it acted as a placeholder, simply +querying a file of stories and selecting a line at random—a rudimentary stand-in for the +real thing. But now that we’ve familiarized ourselves with `wasi:nn`, we can take things a +step further. In this section, as shown in figure 5.5, we’ll upgrade our `smartcms_ml_guest` + +**Replaced with a component** + +Key-value store guest + +import kvstore; +import storygen:func()->string; + +export run:func()->string; + +Machine learning library + +export storygen:func()->string; + +|Col1|Col2| +|---|---| +|com|SmartCMS test host

pose| + +implements + +runs + +~~on~~ + +provides + +host’s + +Figure 5.5 The architecture of our SmartCMS project with the addition of a real ML component + +Licensed to Ibrahim Mohammed Mohammed + +**106** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +to use `wasi:nn` for actual ML functionality. With that change in place, we’ll also need to +modify our host implementation to incorporate Wasmtime’s host support for `wasi:nn`, +ensuring that the necessary backends (such as ONNX) are properly configured. + +_5.4.1_ _Setting up our project_ + +We’ll be continuing to work on our existing SmartCMS implementation from chapter +4. Feel free to work from the existing project, or copy your work from chapter 4 onto a +new directory (i.e., `chapter05/` ) if you want to keep each chapter’s work separate. + +TIP If you’re copying over your work from chapter 4 and have both the chapter 4 and chapter 5 SmartCMS projects in your workspace, it’s a good idea to +suffix each package’s name (in their respective `Cargo.toml` files) with the chapter number. This helps avoid any naming clashes. + +Inside the `smart_cms/` folder we worked on before, you should have three subfolders: + +`smartcms_kvstore_guest/`, `smartcms_ml_guest/`, and `smartcms_test_host/` . These represent the key components of our SmartCMS system: + +¡ `smartcms_kvstore_guest/` —This folder contains the code for our key-value store + +guest component, which handles storage and retrieval of content for the CMS. + +¡ `smartcms_ml_guest/` —This folder holds the logic for the ML guest component, + +which we’ll be upgrading to use real ML with `wasi:nn` . + +¡ `smartcms_test_host/` —This folder contains the host code that runs and manages + +the execution of the guest components. + +Inside `smartcms_test_host/`, you should see two Wasm files from our previous work: + +`guest.wasm` and `guest_with_ml.wasm` . The `guest.wasm` file is the compiled Wasm +version of our `smartcms_kvstore_guest` component, while `guest_with_ml.wasm` is +the composition of `guest.wasm` along with the compiled version of our makeshift + +`smartcms_­ml_guest` component. Feel free to delete the `guest_with_ml.wasm` file, as +we’ll be rebuilding the `smartcms_ml_guest` project and recomposing the two Wasm +components with real ML support using `wasi:nn` . Still inside the `smartcms_test_host/` +folder, create a new folder called `fixture/` . Inside this folder, we’ll place the ONNX +ML model that we’ll be using to generate stories. You can download the model from +[this link: https://mng.bz/Z9DN.](https://mng.bz/Z9DN) + +In that same `fixture/` folder, you will also need to add the tokenizer file from this +[link: https://mng.bz/Rw0R. The tokenizer is a tool that converts text into tokens, which](https://mng.bz/Rw0R) +are smaller pieces of text that the ML model can understand and process. In our case, +we will use the tokenizer to preprocess the input text and postprocess the output from +the model, enabling the ML model to generate coherent stories based on the tokens it +receives. To finish off with our configuration changes to `smartcms_test_host`, let’s add +a dependency to our `Cargo.toml` file. In particular, we are adding `wasmtime-wasi-nn`, +which is Wasmtime’s host implementation for the `wasi:nn` interface. This is similar to +how we previously wrote our own implementation for the key-value store, except it’s + +Licensed to Ibrahim Mohammed Mohammed + +**_Improving our example project with wasi-nn_** **107** + +provided by Wasmtime. With the added dependency, the full `Cargo.toml` file should +look like the following listing. + +Listing 5.6 **`Cargo.toml`** file for the **`smartcms_test_host project`** + +``` +[package] +name = "smartcms_test_host" +version = "0.1.0" +edition = "2021" + +``` + +``` +[dependencies] +wasmtime = "30" +wasmtime-wasi = "30" +wasmtime-wasi-nn = { version = "30", +``` + +➥ `features = ["onnx"] }` +``` +ort = { version = "=2.0.0-rc.6", default-features +``` + +➥ `= false, features = ["copy-dylibs",` +➥ `"download-binaries"] }` +``` +ort-sys = { version = "=2.0.0-rc.6", +``` + +➥ `default-features = false, features =` +➥ `["copy-dylibs", "download-binaries"] }` + +**Adds the wasmtime-wasi-nn** +**dependency with the ONNX feature** + +**Adds the ort and ort-sys** +**dependencies (i.e., an ML** +**inference in Rust library) at** +**the specific version required** +**by wasmtime-wasi-nn** + +Note that we are adding the dependency with the ONNX feature, which isn’t enabled +by default. This means we are opting to vendor (i.e., include and manage the ONNX +backend directly within our project) the ONNX backend together with our host implementation, allowing Wasmtime to support ONNX-based ML models. + +Now let’s move our attention to the `smartcms_ml_guest` project. We’ll be adding +some dependencies to its `Cargo.toml` file—two in particular: `ndarray` (which we’ve seen +before in the `wasm_nn_example` project) and `tokenizers`, a library used for tokenization +(as introduced earlier). To use `tokenizers` with Wasm, we need to disable the default +features and enable its `unstable_wasm` feature. Additionally, since we’ll be including +an extra WIT file (i.e., `wasi-nn.wit`, just like in `wasm_nn_example` ), we need to add a + +`[package.metadata.component.target]` section to the `Cargo.toml` file. This section +specifies our target world, which tells the build system what component we’re targeting. +We also need to specify the dependency on our target (i.e., `wasi:nn` ) in the `[package` + +`.metadata.component.target.dependencies]` section. Overall, the updated `Cargo.toml` +file looks like the following listing. + +Listing 5.7 **`Cargo.toml`** file for the **`smartcms_ml_guest`** project + +``` +[package] +name = "smartcms_ml_guest" +version = "0.1.0" +edition = "2021" + +[dependencies] +wit-bindgen-rt = { version = "0.41.0", features = ["bitflags"] } +rand = "0.8.5" +tokenizers = { version = "0.20.1", + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**108** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +➥ `default-features = false, features =` +➥ `["unstable_wasm"] }` +``` +ndarray = "0.16.1" + +``` + +**tokenizers is added with the** +**Wasm-compatible unstable_wasm** +**feature enabled.** + +``` +[lib] +crate-type = ["cdylib"] + +[profile.release] +codegen-units = 1 +opt-level = "s" +debug = false +strip = true +lto = true + +``` + +**ndarray is added to handle** +**multidimensional arrays, a key tool for** +**managing tensor data in ML applications.** + +``` +[package.metadata.component] +package = "component:smartcms-ml-guest" + +[package.metadata.component.target] +path = "wit/world.wit" +world = "example" + +``` + +**In the [package.metadata.component** +**.target] section, we specify the path to** +**the WIT file and the world name.** + +**The [package.metadata.component** +**.target.dependencies] section** +**includes the path to wasi-nn.wit.** + +``` +[package.metadata.component.target.dependencies] +"wasi:nn" = { path = "wit/wasi-nn.wit" } + +``` + +For our `[package.metadata.component.target]` section, we specify the path to our WIT +file and the world name, both of which are unaltered from chapter 4. This ensures that +our guest component is properly connected to the correct world definition, as defined +in `world.wit` . + +In the `[package.metadata.component.target.dependencies]` section, we add the +path to our `wasi-nn.wit`, which defines the ML interface that we will use. You can copy +this `wasi-nn.wit` file from the `wit/` folder in the `wasi_nn_example/` project and place it +in the `wit/` folder of your `smartcms_ml_guest/` directory. This step ensures that our component will have access to the `wasi:nn` functionality for performing ML tasks. + +Finally, with everything in place, we can now modify our `smartcms_ml_guest/wit/` + +`world.wit` file to include the `wasi-nn.wit` dependency that we’ll be utilizing. Note that +this method of using `wasi:nn` is different from how we did it in the `wasi_nn_example` +project, where we manually generated bindings via the `wit_bindgen` dependency. Here +we’re directly referencing the WIT dependency in the `world.wit` file. The updated + +`world.wit` file can be seen in the following listing. + +Listing 5.8 Updated **`world.wit`** file for **`smartcms_ml_guest`** + +``` +package component:smartcms-ml-guest; + +``` + +``` +world example { +include wasi:nn/ml@0.2.0-rc-2024-08-19; +export storygen: func() -> string; +} + +``` + +**We include the wasi:nn/ml** +**interface at the specified version.** + +Now we are ready to start working on our `smartcms_ml_guest lib.rs` file. + +Licensed to Ibrahim Mohammed Mohammed + +**_Improving our example project with wasi-nn_** **109** + +_5.4.2_ _Writing the ML Wasm component_ + +Let’s begin by importing the necessary dependencies and setting up the foundation +for our implementation: + +``` +#[allow(warnings)] +mod bindings; + +use rand::prelude::*; +use ndarray::Array; +use std::fs; + +``` + +**Uses autogenerated glue code rather than** +**wit_bindgen directly, unaltered from before** + +**Aside from bindings::Guest, also** +**brings into scope needed wasi:nn** +**structures and functions** + +``` +use bindings::{Guest, wasi::nn::{ +graph::{Graph, load, ExecutionTarget, GraphEncoding}, +tensor::{Tensor, TensorType}, +}}; + +``` + +Now, luckily, we can breeze through much of this code since it’s quite similar to what +we worked on in the `wasi_nn_project` . However, one key difference is that here we’re +building a library rather than a binary. This means we’ll be writing our business logic +inside the `storygen` function that our component is expected to export. + +To keep things simple, remove any existing code inside the `storygen` function so we +can start fresh. After bringing in the necessary dependencies, your `lib.rs` should look +like this: + +``` +struct Component; + +impl Guest for Component { +fn storygen() -> String { +// ... +} +} + +bindings::export!(Component with_types_in bindings); + +``` + +Now every subsequent piece of code in this section should be placed inside the + +`storygen` function, starting with loading the ONNX model: + +``` +let model_data = fs::read("fixture/modified_model.onnx").unwrap(); +let graph = load(&[model_data], GraphEncoding::Onnx, +``` + +➥ `ExecutionTarget::Cpu).unwrap();` +``` +let exec_context = Graph::init_execution_context(&graph).unwrap(); + +``` + +We read the ONNX model from the filesystem, load it into a graph, and initialize an +execution context to perform ML inference. + +Next, we’ll initialize the tokenizer, which we previously introduced. The tokenizer +helps convert the input text into tokens that the model can process: + +``` +let tokenizer = Tokenizer::from_file("fixture/tokenizer.json").unwrap(); + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**110** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +Here we’ll load the tokenizer from the `tokenizer.json` file, preparing it to handle the +text input for story generation. The tokenizer will break down the input into manageable pieces, which are then passed to the model for inference. + +Next, we’ll define the prompt and tokenize it, converting the text into a format the +model can work with: + +``` + let prompt = "Once upon a time"; + let encoding = tokenizer.encode(prompt, true).unwrap(); + let mut input_ids = encoding.get_ids().to_vec(); + +``` + +We start by setting a simple text prompt, `"Once` `upon` `a` `time"`, which serves as the initial +input for the story generation. The tokenizer then encodes this prompt, turning it into +tokens. The encoded tokens are extracted as `input_ids`, which are numerical representations of the text that the model will use during inference. + +Then we set up a random number generator (RNG) to introduce variability in the +generated stories and define the end-of-sequence (EOS) token, which marks the conclusion of a generated story: + +``` + let mut rng = thread_rng(); + let eos_token_id = tokenizer.token_to_id("").unwrap_or(0); + +``` + +The `rng` ensures that each story generated will be unique by adding randomness during +inference. The `eos_token_id` retrieves the token ID for the `EOS` marker, which signals +when the model should stop generating tokens, defaulting to 0 if the token isn’t found. +This fallback assumes that token 0 has a safe or meaningful interpretation (such as +padding or an undefined token) and prevents runtime errors. However, if token 0 +does not represent a valid stopping point, the generated output might not terminate +as intended. To mitigate this, the code includes additional stopping criteria, such as +checking for paragraph breaks. + +Now we start a `for` loop that runs from 0 to 100, an arbitrary stopping point, to iteratively generate tokens for our story. Until noted otherwise, all subsequent code will be +inside this `for` loop: + +``` + for _ in 0..100 { + // … + } + +``` + +This loop serves to generate new tokens, which will eventually form the story. The number 100 acts as a safeguard to prevent the model from generating too many tokens, +though the process may stop earlier if the end-of-sequence token is reached. + +Inside the loop, we first prepare the input data by converting our token IDs into the +format the model expects: + +``` + let sequence_length = input_ids.len(); + let dimensions = vec![1, sequence_length as u32]; + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Improving our example project with wasi-nn_** **111** + +``` +let input_f32: Vec = input_ids.iter().map(|&id| id as f32).collect(); +let mut input_data = Vec::with_capacity(input_f32.len() * 4); +for &val in &input_f32 { +input_data.extend_from_slice(&val.to_ne_bytes()); +} +let tensor = Tensor::new(&dimensions, TensorType::Fp32, &input_data); +exec_context.set_input("input_ids", tensor).unwrap(); + +``` + +We start by calculating the length of the token sequence, then define the tensor dimensions based on this length. The token IDs are converted into 32-bit floating-point values ( `f32` ) since the model requires this format. Next, we create the byte representation +of these values and store them in `input_data` . Finally, we wrap the data in a tensor and +set it as the input for the model, using the key `"input_ids"` to ensure the model processes the correct data. + +[TIP You can use Netron (https://netron.app/) to inspect your ML models](https://netron.app/) +and get a better understanding of their structure. It allows you to see details +like tensor shapes, layer connections, and crucial information such as the tensor keys (e.g., `"input_ids"` ). + +Finally, we execute the model inference, just as we did before: + +``` +exec_context.compute().unwrap(); + +``` + +This runs the model on the provided input, processing the token sequence to generate +the next output. + +Next, we retrieve the model’s output and convert it into a usable format: + +``` +let output_data = exec_context.get_output("logits").unwrap().data(); +let output_f32 = bytes_to_f32_vec(&output_data); + +``` + +We access the output tensor, which is stored under the key `"logits"` (i.e., the raw, +unnormalized scores output by the neural network for each possible token in the +vocabulary) and use the `bytes_to_f32_vec` function to convert the raw bytes into `f32` +values that can be processed. This `bytes_to_f32_vec` function is identical to the one +from the `wasi_nn_example` project, so you can copy it from there and paste it at the end +of this file. + +Next, we process the model’s output to isolate the logits for the last generated token: + +``` +let vocab_size = output_f32.len() / sequence_length; +let start = (sequence_length - 1) * vocab_size; +let end = sequence_length * vocab_size; +let last_token_logits = &output_f32[start..end]; + +``` + +Here we calculate the size of the vocabulary (i.e., the full set of tokens, such as words or +subwords, that the model recognizes and can generate as output) by dividing the total +length of the output by the `sequence_length` . We then determine the start and end + +Licensed to Ibrahim Mohammed Mohammed + +**112** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +positions for the logits of the last token in the sequence. The `last_token_logits` slice +represents the predicted probabilities for each possible next token based on the input +sequence, which will be used to generate the next token in our story. + +Next, we apply the softmax function to the logits to convert them into probabilities: + +``` + let probabilities = softmax(last_token_logits); + +``` + +This step is essentially the same as what we did in the `wasi_nn_example` project, but +now the softmax operation has been split into its own function for reusability. You can +define the softmax function at the end of the file, together with `bytes_to_f32_vec` : + +``` + fn softmax(logits: &[f32]) -> Vec { + let logits_array = Array::from_vec(logits.to_vec()); + let exp_logits = logits_array.mapv(f32::exp); + let sum_exp = exp_logits.sum(); + (exp_logits / sum_exp).to_vec() + } + +``` + +By applying softmax, we convert the logits into a probability distribution over the +possible next tokens. This will help us sample the next token based on the model’s +predictions. + +Next, we sample the next token based on the probability distribution generated by +the softmax function: + +``` + let dist = rand::distributions::WeightedIndex::new(&probabilities).unwrap(); + let next_token = dist.sample(&mut rng); + input_ids.push(next_token as u32); + let generated_text = tokenizer.decode(&input_ids, true).unwrap(); + +``` + +Here we use a weighted distribution ( `WeightedIndex` ) to sample the next token. The + +`probabilities` vector is passed to the distribution, and a token is sampled based on +those probabilities using the random number generator ( `rng` ). This ensures that the +model picks the next token based on the predicted likelihood while still introducing +some randomness to the output. The sampled token is then appended to the `input_` + +`ids`, representing the sequence generated so far. Finally, the `tokenizer.decode` function +is used to convert the sequence of token IDs in `input_ids` back into a human-readable +string ( `generated_text` ), updating the generated text output with the newly sampled +token. + +Next, we add checks to determine when to terminate the story generation loop. +Specifically, we break the loop if the generated text ends with two newline characters +( `"\n\n"` ) or if the next token matches the `EOS` token. I decided to include the `"\n\n"` +check in addition to the `EOS` token because, empirically, it helped our code run quicker +and reduced the likelihood of stories being cut short by the 100-token limit: + +``` + if generated_text.ends_with("\n\n") { + break; + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Improving our example project with wasi-nn_** **113** + +``` +} + +if next_token as u32 == eos_token_id { +break; +} + +``` + +This ensures that story generation stops appropriately, either when a natural ending is +detected or when the predefined token limit is reached. + +Finally, outside the loop, we decode the generated tokens back into a human-­ +readable story and return the result: + +``` +tokenizer.decode(&input_ids, true).unwrap().trim().to_string() + +``` + +Here the `tokenizer.decode` function takes the `input_ids` (which now contain the generated tokens) and converts them back into a readable string. We use `trim()` to remove +any extra whitespace and then convert it to a `String`, which will be returned as the final +generated story. Overall, the full code can be seen in the following listing. + +Listing 5.9 **`lib.rs`** file from **`smartcms_ml_guest`** + +``` +#[allow(warnings)] +mod bindings; + +use tokenizers::Tokenizer; +use rand::prelude::*; +use ndarray::Array; +use std::fs; + +use bindings::{Guest, wasi::nn::{ +graph::{Graph, load, ExecutionTarget, GraphEncoding}, +tensor::{Tensor, TensorType}, +}}; + +struct Component; + +``` + +``` +impl Guest for Component { +fn storygen() -> String { +let model_data = fs::read("fixture +``` + +➥ `/modified_model.onnx").unwrap();` +``` +let graph = load(&[model_data], +``` + +➥ `GraphEncoding::Onnx, ExecutionTarget` +➥ `::Cpu).unwrap();` +``` +let exec_context = Graph::init_execution_ +``` + +➥ `context(&graph).unwrap();` + +**Loads the ONNX model** +**and sets up the graph** +**and execution context** + +``` +let tokenizer = + +``` + +➥ `tokenizer.json").unwrap();` + +``` +let prompt = "Once upon a time"; +let encoding = tokenizer. + +``` + +**Loads the tokenizer** +**from the provided file** + +**Defines the prompt and** +**encodes it into token IDs** + +Licensed to Ibrahim Mohammed Mohammed + +**114** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +➥ `encode(prompt, true).unwrap();` +``` +let mut input_ids = +``` + +➥ `encoding.get_ids().to_vec();` + +``` +let mut rng = thread_rng(); +let eos_token_id = tokenizer.token_ +``` + +➥ `to_id("").unwrap_or(0);` + +**Defines the prompt and** +**encodes it into token IDs** + +**Initializes the random number** +**generator and sets the EOS token** + +**Loops up to 100 iterations to generate tokens** + +``` +for _ in 0..100 { +let sequence_length = input_ids.len(); +let dimensions = vec! +``` + +➥ `[1, sequence_length as u32];` +``` +let input_f32: Vec = input_ +``` + +➥ `ids.iter().map(|&id| id as f32).collect();` +``` +let mut input_data = Vec:: +``` + +➥ `with_capacity(input_f32.len() * 4);` +``` +for &val in &input_f32 { +input_data.extend_from_slice +``` + +➥ `(&val.to_ne_bytes());` +``` +} +let tensor = Tensor::new +``` + +➥ `(&dimensions, TensorType::Fp32, &input_data);` +``` +exec_context.set_input +``` + +➥ `("input_ids", tensor).unwrap();` + +**Prepares and sets** +**the input tensor** + +``` +exec_context.compute().unwrap(); + +let output_data = exec_context. +``` + +➥ `get_output("logits").unwrap().data();` +``` +let output_f32 = bytes_to_f32_vec +``` + +➥ `(&output_data);` + +``` +let vocab_size = output_f32. +``` + +➥ `len() / sequence_length;` +``` +let start = (sequence_length - 1) +``` + +➥ `* vocab_size;` +``` +let end = sequence_length * vocab_size; +let last_token_logits = +``` + +➥ `&output_f32[start..end];` + +**Runs the model inference** + +**Retrieves and converts** +**the model’s output** + +**Extracts the logits for the** +**last token in the sequence** + +``` +let probabilities = softmax +``` + +➥ `(last_token_logits);` + +**Applies softmax to calculate** +**token probabilities** + +``` +let dist = rand::distributions:: +``` + +➥ `WeightedIndex::new(&probabilities).unwrap();` +``` +let next_token = dist.sample(&mut rng); +input_ids.push(next_token as u32); + +let generated_text = +``` + +➥ `tokenizer.decode(&input_ids, true).unwrap();` + +**Samples the next token** +**based on probabilities** +**and appends it to the** +**sequence** + +**Decodes the** +**generated text** + +``` +if generated_text.ends_with("\n\n") { +break; +} + +``` + +**Breaks the loop if a natural** +**end to the story is detected** + +Licensed to Ibrahim Mohammed Mohammed + +**_Improving our example project with wasi-nn_** **115** + +``` +if next_token as u32 == eos_token_id { +break; +} +} + +``` + +**Breaks the loop if a natural** +**end to the story is detected** + +``` +tokenizer.decode(&input_ids, true) +``` + +➥ `.unwrap().trim().to_string()` +``` +} +} + +``` + +**Returns the final generated story** + +``` +pub fn bytes_to_f32_vec(data: &[u8]) -> Vec { +data.chunks_exact(4) +.map(|c| f32::from_le_bytes(c.try_into().unwrap())) +.collect() +} + +``` + +**The function to convert** +**bytes to f32 values** + +**The softmax function to compute** +**the probability distribution** + +``` +fn softmax(logits: &[f32]) -> Vec { +let logits_array = Array::from_vec(logits.to_vec()); +let exp_logits = logits_array.mapv(f32::exp); +let sum_exp = exp_logits.sum(); +(exp_logits / sum_exp).to_vec() +} + +``` + +``` + bindings::export!(Component with_types_in bindings); + +``` + +Finally, inside the `smartcms_ml_guest/` folder, we can compile our component by +running + +``` + cargo component build --release + +``` + +With this done, from the `smartcms_ml_guest/` folder, we can then once again compose +it with the `guest.wasm` (our key-value store component) like we did prior: + +``` + wac plug ../smartcms_test_host/guest.wasm --plug /smartcms_ml_guest.wasm -o ../smartcms_test_host/guest_with_ml.wasm` + +This step creates the `guest_with_ml.wasm` file, which is the composition of the two components: the SmartCMS key-value store and the newly generated ML component. + +_5.4.3_ _Updating our host to use ONNX for wasi:nn_ + +Now we are ready to update our host at `smartcms_test_host/` . Our changes are straightforward because we don’t have to implement `wasi:nn` manually—there’s already an +implementation provided by Wasmtime, which we included via the `wasmtime-wasi-nn` +dependency. + +First, we need to modify our `State` structure to include the `WasiNnCtx` . This is similar +to when we added WASI support to our host by including the `WasiCtx` in the store. In + +`smartcms_test_host/src/main.rs`, add + +``` + struct State { + key_value: KeyValue, + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**116** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +``` + wasi: (wasmtime_wasi::WasiCtx, wasmtime_wasi::ResourceTable), + wasi_nn: wasmtime_wasi_nn::wit::WasiNnCtx + } + +``` + +This setup prepares our host to manage both key-value storage and ML functionality, +the latter using Wasmtime’s `wasi:nn` implementation. Then we can act on the updated + +`State` struct in our `main` function by preloading the ML backends, initializing the + +`WasiNnCtx`, and setting up the store: + +``` + let (backends, registry) = wasmtime_wasi_nn::preload(&[]).unwrap(); + let wasi_nn_ctx = wasmtime_wasi_nn::wit::WasiNnCtx::new(backends, registry); + + let state = State { + key_value: KeyValue { mem: std::collections::HashMap::new() }, + wasi: (wasi_ctx, wasi_table), + wasi_nn: wasi_nn_ctx, + }; + + let mut store = wasmtime::Store::new(&engine, state); + +``` + +Here the `preload` function loads the available ML backends and creates a registry (i.e., +a container for graphs). We then create a new `WasiNnCtx` using these values. Finally, we +initialize the `Store` with the engine and updated `State`, which now includes both the + +`WasiCtx` for general WASI functionality and the `WasiNnCtx` for ML support. + +Finally, we can complete the setup by adding the `wasi:nn` context to the linker so +that it can be accessed during the component’s execution, alongside the key-value store +functionality: + +``` + wasmtime_wasi_nn::wit::add_to_linker(&mut linker, |state: &mut State| { + wasmtime_wasi_nn::wit::WasiNnView::new(&mut state.wasi.1, &mut +``` + +➥ `state.wasi_nn)` +``` + }).unwrap(); + component::smartcms::kvstore::add_to_linker(&mut linker, |state: &mut +``` + +➥ `State| &mut state.key_value).unwrap();` +``` + let app = App::instantiate(&mut store, &component, &linker).unwrap(); + +``` + +Here we add the `wasi:nn` context to the linker with `add_to_linker` to ensure that +the neural network functionality can be invoked by the Wasm component. We also +link the key-value store as before. Finally, the component is instantiated using the + +`App::instantiate` method, with the fully set-up `store` and `linker` . This allows our host +to handle both key-value store operations and ML inference using `wasi:nn` . Overall, +the code should look like the following listing. + +Listing 5.10 **`main.rs`** file for the **`smartcms_test_host`** project + +``` + wasmtime::component::bindgen!({ + path: "./smart_cms.wit", + world: "app", + }); + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Improving our example project with wasi-nn_** **117** + +``` +struct KeyValue { +mem: std::collections::HashMap, +} + +impl component::smartcms::kvstore::Host for KeyValue { +fn get(&mut self, key: String) -> Option { +self.mem.get(&key).cloned() +} + +fn set(&mut self, key: String, value: String) { +self.mem.insert(key, value); +} +} + +``` + +``` +struct State { +key_value: KeyValue, +wasi: (wasmtime_wasi::WasiCtx, wasmtime_wasi::ResourceTable), +wasi_nn: wasmtime_wasi_nn::wit::WasiNnCtx +} + +``` + +``` +impl wasmtime_wasi::WasiView for State { +fn ctx(&mut self) -> &mut wasmtime_wasi::WasiCtx { +&mut self.wasi.0 +} +} + +``` + +**Adds the wasi_nn** +**context to the state** +**for handling ML tasks** + +``` +impl wasmtime_wasi::IoView for State { +fn table(&mut self) -> &mut wasmtime_wasi::ResourceTable { +&mut self.wasi.1 +} +} + +fn main() { +let mut config = wasmtime::Config::default(); +config.wasm_component_model(true); + +let engine = wasmtime::Engine::new(&config).unwrap(); + +let wasi_table = wasmtime_wasi::ResourceTable::new(); +let wasi_ctx = wasmtime_wasi::WasiCtxBuilder::new() +.preopened_dir(".", ".", wasmtime_wasi::DirPerms::READ, +``` + +➥ `wasmtime_wasi::FilePerms::READ).unwrap()` +``` +.build(); + +``` + +``` +let (backends, registry) = wasmtime_wasi_nn +``` + +➥ `::preload(&[]).unwrap(); #B` +``` +let wasi_nn_ctx = wasmtime_wasi_nn:: +``` + +➥ `wit::WasiNnCtx::new(backends, registry);` + +**Preloads ML backends and** +**creates a new WasiNnCtx to** +**handle the wasi:nn interface** +**for neural network execution** + +``` +let state = State { +key_value: KeyValue { mem: std::collections::HashMap::new() }, +wasi: (wasi_ctx, wasi_table), + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**118** Chapter 5 **_From machine learning to databases: Applications of Wasm_** + +``` + let mut store = wasmtime::Store::new(&engine, state); + + let component = wasmtime::component::Component::from_file(&engine, +``` + +➥ `"guest_with_ml.wasm").unwrap();` + +``` + let mut linker = wasmtime::component::Linker::new(&engine); + wasmtime_wasi::add_to_linker_sync(&mut linker).unwrap(); + wasmtime_wasi_nn::wit::add_to_linker +``` + +**Links the wasi_nn functionality to the** + +➥ `(&mut linker, |state: &mut State| {` + +**component by adding wasi:nn to the linker,** +``` + wasmtime_wasi_nn::wit::WasiNnView::new +``` +**enabling the SmartCMS component to use** + +➥ `(&mut state.wasi.1, &mut state.wasi_nn)` **Wasmtime’s ML capabilities** +``` + }).unwrap(); + component::smartcms::kvstore::add_to_linker +``` + +➥ `(&mut linker, |state: &mut State| &mut state.key_value).unwrap();` + +``` + let app = App::instantiate(&mut store, &component, &linker).unwrap(); + + println!("{:?}", app.call_run(&mut store).unwrap()); + } + +``` + +Finally, we are ready to run our host over the updated component. From the `smartcms_` + +`test_host/` folder, run + +``` + cargo run --release + +``` + +This should generate and output a story. For example, when I ran it, I got the following: + +``` + "Once upon a time, there was a little girl named Lily. She loved playing +``` + +➥ `with her toys and running around outside. One day, Lily's aunt came to` +➥ `visit. She brought a big present for Lily. Lily was very excited to see` +➥ `what was inside."` + +At this point, our setup is complete, and our ML component is now fully integrated +with the SmartCMS host using `wasi:nn` ! + +_Summary_ + +¡ WASI has expanded beyond POSIX and now serves as a collection of standard +ized interfaces offering higher-level capabilities, including WebGPU, ML, and +SQL interfaces. + +¡ The `wasi:nn` interface allows native performance for ML workloads within Wasm, + +using optimizations from existing ML frameworks like GPUs and NPUs. Alternatives like TVM and `tract` can run ML within Wasm but often sacrifice performance to conform to WASI 0.1’s POSIX subset. + +¡ Running Wasm within databases is achieved by using UDFs, which allow secure, + +efficient data processing within a sandboxed environment. + +¡ By integrating Wasm into databases, UDFs can be written in any language that + +compiles to Wasm, enabling flexible and secure database extensions. + +Licensed to Ibrahim Mohammed Mohammed + +**_Summary_** **119** + +¡ The libSQL database can embed Wasm UDFs, providing a reliable environment + +for running secure, sandboxed Wasm functions directly in SQL queries. + +¡ The WAT provides a human-readable representation of Wasm binaries, enabling + +easier inspection, debugging, and manual adjustments. + +¡ Optimizing Wasm UDFs with tools like `wasm-opt` ensures performance and + +reduces the size of Wasm binaries embedded in SQL databases. + +Licensed to Ibrahim Mohammed Mohammed + +Licensed to Ibrahim Mohammed Mohammed + +##### _Part 2_ _WebAssembly for developers_ + +### Aready to put that knowledge into practice. This part of the book shifts from the- rmed with a deep understanding of WebAssembly’s architecture, you’re + +ory to implementation, guiding you through building production-ready Web­ +Assembly applications using modern tools and best practices. You’ll move beyond +handcrafting hosts and interfaces to using the standardized tooling and prebuilt +environments that make WebAssembly development practical and efficient. + +The transition from architect to developer mirrors the evolution of WebAssembly itself—from low-level primitives to high-level productivity. Whereas part +1 taught you to understand the machinery, part 2 teaches you to operate it effectively in real-world scenarios. You’ll discover how familiar tools like Docker and +Kubernetes have evolved to support WebAssembly workloads, making deployment as straightforward as traditional containers while delivering WebAssembly’s +unique benefits. + +Chapter 6 demonstrates creating production-grade applications using standardized interfaces and generic hosts, freeing you from the complexity of custom implementations. Chapter 7 introduces WebAssembly containers, showing +how to package and deploy WebAssembly workloads using the containerization +workflows you already know. Chapter 8 explores Kubernetes integration, teaching +you to orchestrate and scale WebAssembly applications in cloud environments +with the same tools used for traditional workloads. Finally, chapter 9 examines the +future of WebAssembly, from emerging proposals to its potential effects on cloud +computing paradigms. + +Licensed to Ibrahim Mohammed Mohammed + +Licensed to Ibrahim Mohammed Mohammed + +##### _Creating_ _production-grade_ _Wasm applications_ +# _6_ + +_This chapter covers_ + +¡ wasmCloud as an off-the-shelf host + +¡ Integrating persistent storage + +¡ Scaling Wasm applications + +¡ Distributing applications using NATS + +¡ Embedding machine learning capabilities in + +wasmCloud + +We’ve been steadily expanding our toolbox. We started with Wasm modules, where +all we had were four basic types, linear memory, and a dream. Then we introduced +WebAssembly Interface Types (WIT) and Wasm components, which brought higher-­ +level types and a canonical ABI for lifting and lowering constructs like `String` values, +enabling composability between components across languages. Next, we explored +interacting with the underlying system via WebAssembly System Interface (WASI), +applying it to scenarios like executing machine learning workloads with wasi-nn­ +(using WASI 0.2) and running Wasm user-defined functions in a database (using +WASI 0.1). + +**123** + +Licensed to Ibrahim Mohammed Mohammed + +**124** Chapter 6 **_Creating production-grade Wasm applications_** + +Yet, in nearly all our examples, we’ve written custom hosts to run our applications. +For instance, our SmartCMS host is currently bootstrapped to set up WASI and wasi-nn, +which diverts us from focusing on business logic and is not ideal for server-side Wasm +development. In this chapter, I’ll introduce you to wasmCloud and Spin, two off-the­shelf hosts designed to streamline the development of Wasm microservices. + +These off-the-shelf hosts function similarly to the `HelloWorldHost` from chapter 2 or +the SmartCMS host we’ve been building in recent chapters, as they also embed Wasmtime to run Wasm. However, unlike our custom hosts, off-the-shelf ones are designed +to be generic and versatile for any application. Beyond that, they also provide software +development kits (SDKs) and developer toolchains to simplify using Wasm. Let’s dive +deeper by creating a “Hello, World” application, beginning with wasmCloud. + +_6.1_ _Creating a wasmCloud “Hello, World” application_ + +Begin by installing wasmCloud’s developer tooling: + +``` + cargo install --locked wash-cli --version "^0.37" + +``` + +Specifically, you are installing `wash`, the wasmCloud Shell—a comprehensive command-­ +line interface that facilitates various development tasks within the wasmCloud ecosystem. With `wash`, you can generate new projects, manage cryptographic keys, interact +with Open Container Initiative (OCI)–compliant registries, and more. + +WARNING wasmCloud’s support for Windows is not at the same tier as its support for Linux and macOS. If you are using Windows, I recommend that you +run the examples in this chapter within Windows Subsystem for Linux. + +Once installed, inside your `chapter06/` folder, run + +``` + wash new component hello-world-wasmcloud --template-name hello-world-rust + +``` + +This command initializes a new wasmCloud component named `hello-world-wasmcloud` +using the `hello-world-rust` template. This template provides a foundational Rust +project configured for wasmCloud, including necessary files and directory structures. + +TIP While this example utilizes the `hello-world-rust` template, wasmCloud +[offers a variety of templates in different languages. Visit https://mng.bz/Jwqz](https://mng.bz/Jwqz) +for more templates in languages such as C, Go, Python, and TypeScript. + +To use this with Rust, you’ll need to install a new target for Wasm compilation using the +following command: + +``` + rustup target add wasm32-wasip2 + +``` + +With that done, inside this template, you’ll notice a `wit/` folder, which contains the +following structure: + +Licensed to Ibrahim Mohammed Mohammed + +**_Creating a wasmCloud “Hello, World” application_** **125** + +``` +. +``` + +├── deps +│ ├── wasi-cli-0.2.2 +│ ├── wasi-clocks-0.2.2 +│ ├── wasi-http-0.2.2 +│ ├── wasi-io-0.2.2 +│ └── wasi-random-0.2.2 +└── world.wit + +Our application’s world definition (i.e., `world.wit` ), as shown in the following listing, +exports an incoming handler—a mechanism for handling HTTP requests routed to +this application. + +Listing 6.1 **`hello-world-wasmcloud`** ’s **`wit/world.wit`** + +``` +package wasmcloud:hello; + +world hello { +export wasi:http/incoming-handler@0.2.2; +} + +``` + +This incoming handler relies on several other WASI interfaces, which are listed as +dependencies in the `wit/deps` folder. + +We also have a `wasmcloud.toml` file, shown in the following listing, which serves as the +manifest for configuring the wasmCloud component. + +Listing 6.2 **`hello-world-wasmcloud`** ’s **`wasmcloud.toml`** file + +``` +name = "http-hello-world" +version = "0.1.0" +language = "rust" +type = "component" + +[component] +wit_world = "hello" +wasm_target = "wasm32-wasip2" + +``` + +This manifest specifies key details about the application, such as its name, version, language, and type. Under the `[component]` section, it links the `wit_world` to the `hello` +world definition and defines the Wasm compilation target as `wasm32-wasip2`, which +corresponds to WASI 0.2. This setup ensures compatibility with the interfaces and features provided by the WASI 0.2 standard. + +Let’s now take a look at the implementation of the incoming handler in `src/lib.rs` . + +Listing 6.3 **`src/lib.rs`** **`incoming-handler`** implementation + +``` +use wasmcloud_component::http; + +struct Component; + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**126** Chapter 6 **_Creating production-grade Wasm applications_** + +``` + http::export!(Component); + + impl http::Server for Component { + fn handle( + _request: http::IncomingRequest, + ) -> http::Result> { + Ok(http::Response::new("Hello from Rust!\n")) + } + } + +``` + +This incoming handler is designed to work alongside an HTTP server, routing requests +to the root route (i.e., `/` ) to this Wasm component. When invoked, it responds to +HTTP requests with a plain text message: `"Hello` `from` `Rust!"` . This routing functionality is defined in another file included with our template— `wadm.yaml` —partially shown +in the following listing. + +Listing 6.4 **`wadm.yaml`** file (template comments/annotations hidden) + +``` + apiVersion: core.oam.dev/v1beta1 + kind: Application + metadata: + name: rust-hello-world + # ... + spec: + components: + - name: http-component + type: component + properties: + image: file://./build/http_hello_world_s.wasm + traits: + - type: spreadscaler +``` + +**The spreadscaler trait configures the number of** + +`properties:` **instances to deploy, in this case, setting it to 1.** +``` + instances: 1 + +``` + +``` +- name: httpserver +type: capability +properties: +image: ghcr.io/wasmcloud/http-server:0.23.2 +traits: +- type: link +properties: +target: http-component +namespace: wasi +package: http +interfaces: [incoming-handler] +source_config: +- name: default-http +properties: +address: 127.0.0.1:8000 + +``` + +**Capabilities define reusable** +**components for specific** +**functions, such as the HTTP** +**server in this example.** + +**Links HTTP server capability** +**to our defined incoming** +**handler implementation** + +This _wasmCloud Application Deployment Manager_ ( `wadm` ) file outlines the deployment +configuration for our application. It specifies the application’s components, including + +Licensed to Ibrahim Mohammed Mohammed + +**_Creating a wasmCloud “Hello, World” application_** **127** + +the `http-component` and the HTTP server capability, as well as the link between the two +via the `incoming-handler` implementation. Additionally, it uses the `spreadscaler` trait +to configure the number of instances to deploy, allowing you to scale the application +by adjusting this value—in this example, it’s set to a single instance. + +Now, we are ready to run our application. Start by executing + +``` +wash up -d + +``` + +You will see output similar to the following: + +``` +>>> Starting NATS ... +NATS server successfully started, using config @ [\nats.conf] +NATS server logs written to [\nats.log] +>>> Starting wadm ... +Found wadm version on the disk: wadm-cli 0.18.0 +Using wadm version [v0.18.0] + +wash up completed successfully +NATS is running in the background at 127.0.0.1:4222 +Logs for the host are being written to \wasmcloud.log + +To stop wasmCloud, run "wash down" + +``` + +TIP Make a note of the path where logs are being written to (it’s useful when +things go wrong!) and remember to execute the `wash` `down` command to stop +the wasmCloud platform once we are done with this exercise. + +This command bootstraps the essential processes for a wasmCloud application: + +¡ _wasmCloud Host_ —Embeds the Wasmtime runtime to execute Wasm components + +and provides capabilities such as the HTTP server. Like in previous chapters, +Wasmtime runs locally on your machine. + +¡ _NATS lattice_ [—Sets up a NATS (https://nats.io/) server to facilitate commu-](https://nats.io/) + +nication between application components within the lattice, which is a distributed network that allows multiple wasmCloud hosts to communicate and +coordinate seamlessly, even if they are running on different physical machines +or environments—­similar to how a Kubernetes cluster orchestrates containers +across nodes. + +¡ `wadm` _monitor_ —Oversees the lattice and manages the state of deployments, ensur +ing that components are correctly deployed and linked. + +What is NATS? + +NATS is a lightweight, high-performance messaging system designed for cloud-native +applications. It acts as a message broker that enables communication between distributed components in a system. + +Licensed to Ibrahim Mohammed Mohammed + +**128** Chapter 6 **_Creating production-grade Wasm applications_** + +TIP You can inspect the wasmCloud host we just started by running `wash` `get` +`hosts` . + +In chapter 4, when we learned about composability, we looked at examples where two +Wasm components were combined into a single Wasm binary at build time, before running our application. While you can still use composed Wasm binaries in wasmCloud, +it goes a step further by enabling runtime linking through `wadm` and NATS. + +In wasmCloud, components can either run on the same host or be distributed across +multiple hosts within the NATS lattice. By defining links between components in the + +`wadm` manifest, wasmCloud ensures these connections are dynamically established and +managed during run time. This approach allows components to communicate over +NATS, regardless of their physical location, using the component-native remote procedure call (wRPC) protocol facilitated by the wasmCloud host. Overall, the architecture +of our application looks like figure 6.1. + +What is RPC? + +Remote procedure call (RPC) is a communication protocol that allows programs to +execute functions or procedures on another system, making it easier to build distributed systems by enabling interaction between components running on different +machines or environments. + +**machine than the HTTP server** + +Figure 6.1 The architecture of our “Hello, World” wasmCloud application + +Now that we better understand what happens when we execute the `wash` `up` `-d` command and how wasmCloud applications operate, let’s build our Wasm component. +From the `hello-world-wasmcloud/` folder, you can do so with the following: + +``` + wash build + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Creating a wasmCloud “Hello, World” application_** **129** + +This command will build our Wasm component inside of the `build/` directory. Upon +inspecting this directory, you’ll see + +``` +. +``` + +├── http_hello_world.wasm +└── http_hello_world_s.wasm + +NOTE Even though we’re building Rust applications, I didn’t add any of the +wasmCloud apps from this chapter to our Cargo workspace. As you can see +in listing 6.4, our `wadm.yaml` file directly points to the path of our built Wasm. +Since adding the project to the workspace would change that, I decided it’s +better not to bother. + +Why do we have two build artifacts? wasmCloud produces two files because it not only +builds the Wasm component ( `http_hello_world.wasm` ) but also signs it, creating a +signed artifact ( `http_hello_world_s.wasm` ) for secure deployment. + +What is cryptographic signing? + +Cryptographic signing is the process of generating a unique digital signature for a file +or message using a private key. This signature can be verified with the corresponding +public key, ensuring the file’s integrity (it hasn’t been tampered with) and authenticity +(it comes from a trusted source). Signing often includes metadata, such as the +signer’s identity and claims about the file’s purpose or permissions. + +Inspecting the `wadm` manifest from listing 6.4 once again, you’ll notice it refers to the +signed component. Now, with the wasmCloud host up and the component built, we +can run our application by executing + +``` +wash app deploy ./wadm.yaml + +``` + +This command deploys the application to the lattice, registering its components and +linking them as specified in the manifest. It ensures that all dependencies, such as +capabilities and routes, are properly configured for the application to function. + +We can verify that the deployment succeeded by running + +``` +wash app list + +``` + +This command displays information about your deployment, similar to what is shown +in table 6.1. + +Table 6.1 Currently deployed applications + +|Name|Deployed version|Status| +|---|---|---| +|`rust-hello-world`|01JDR8HJYJWCV1AJM559R72KE8|Deployed| + +Licensed to Ibrahim Mohammed Mohammed + +**130** Chapter 6 **_Creating production-grade Wasm applications_** + +From the status column, you can see our application is marked as deployed. + +TIP You can also verify we properly linked the HTTP server capability with +our HTTP component incoming handler by running `wash` `get` `links` . + +The app’s deployed HTTP server is running and should be accessible at the address +specified in our `wadm` manifest (i.e., `http://127.0.0.1:8000` ). To confirm, you can test +it by running + +``` + curl http://127.0.0.1:8000 + +``` + +This should produce the following output: + +``` + Hello from Rust! + +``` + +Awesome! We set up a wasmCloud host, built and signed a Wasm component, explored +the `wadm` manifest, and deployed our application to the lattice—all with just a few commands and without having to write any nonbusiness logic, such as the HTTP server +capability itself. Next up, let’s explore how we can provide even more capabilities to +our wasmCloud apps. + +_6.2_ _Adding persistent storage to our Wasm app_ + +So far, in our SmartCMS application, we’ve been using a custom WIT interface for +a key-value store. While this approach has been effective and allowed us to explore +WIT, we’ll now shift to using a standardized interface. For persistent storage, one such +choice is the `wasi:keyvalue` interface, which provides a consistent way to handle keyvalue operations across various providers. + +The `wasi:keyvalue` interface provides basic operations like `get` and `set`, similar to our +custom interface. However, it also introduces additional interface capabilities, such as + +¡ _Atomics_ for operations like increment and compare-and-swap, enabling safe con +current updates + +¡ _Batch_ for eventually consistent batch operations, reducing the number of net +work roundtrips and improving performance in distributed systems + +By adopting `wasi:keyvalue`, we align with best practices and ensure greater interoperability. Beyond its additional capabilities, `wasi:keyvalue` is designed as a portable interface that works across various key-value store providers, such as Redis, Memcached, +Etcd, AWS DynamoDB, Azure CosmosDB, Google Cloud Firestore, and so on. It serves +as the standard for providing persistent storage capabilities in hosts like Wasmtime and +wasmCloud. So let’s see how we can use `wasi:keyvalue` in wasmCloud. + +_6.2.1_ _Modifying our WIT and wadm manifest_ + +Let’s start by removing our current application from the lattice. You can do so with + +``` + wash app delete rust-hello-world + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Adding persistent storage to our Wasm app_** **131** + +With that done, we can begin working on the new version of our application, which +will include persistent storage. First, we need to add an import for the `wasi:keyvalue` +interfaces in our WIT file. + +Listing 6.5 **`hello-world-wasmcloud/wit/world.wit`** + +``` +package wasmcloud:hello; + +``` + +``` +world hello { +import wasi:keyvalue/atomics@0.2.0-draft; +import wasi:keyvalue/store@0.2.0-draft; +export wasi:http/incoming-handler@0.2.2; +} + +``` + +**Adds wasi:keyvalue interfaces** +**for atomics and store** + +By importing these interfaces, we grant our Wasm component permission to access the + +`wasi:keyvalue` capability (recall the only-what-you-need security principle from chapter 3). Specifically, we’re importing the `store` and `atomics` interfaces: + +¡ The _store_ interface provides access to a bucket resource—a collection of key-value + +pairs. Using the `open` function, we can connect to a bucket by passing a string +identifier for the resource provided by the host. + +¡ The _atomics_ interface is included because, for this example, we’ll increment a + +counter from multiple requests concurrently. + +Next, let’s modify our `wadm.yaml` file to support persistent storage. First, we’ll add +a new capability for the key-value provider. Then, we’ll link this capability to the + +`wasi:keyvalue` interfaces we imported in our WIT. + +Listing 6.6 **`wadm.yaml`** (template comments/annotations hidden) + +``` +apiVersion: core.oam.dev/v1beta1 +kind: Application +metadata: +name: rust-hello-world +# ... +spec: +components: +- name: http-component +type: component +properties: +image: file://./build/http_hello_world_s.wasm +traits: +- type: spreadscaler +properties: +instances: 1 +- type: link + +package: keyvalue + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**132** Chapter 6 **_Creating production-grade Wasm applications_** + +``` +interfaces: [store, atomics] +target: +name: kvnats +config: +- name: wasi-keyvalue-config +properties: +bucket: default +enable_bucket_auto_create: +``` + +➥ `'true'` + +**Links the wasi:keyvalue interfaces (store** +**and atomics) to the kvnats capability** + +**Configuration to use a default** +**bucket and enable automatic** +**bucket creation if it doesn’t exist** + +``` +- name: httpserver +type: capability +properties: +image: ghcr.io/wasmcloud/http-server:0.23.2 +traits: +- type: link +properties: +target: http-component +namespace: wasi +package: http +interfaces: [incoming-handler] +source_config: +- name: default-http +properties: +address: 127.0.0.1:8000 + +- name: kvnats +type: capability +properties: +image: ghcr.io/wasmcloud/keyvalue-nats:0.3.1 + +``` + +**Defines the key-value store** +**provider, named kvnats** + +In this updated manifest, we added a NATS-based key-value provider ( `kvnats` ). While +NATS is primarily known as a high-performance messaging system, it also offers keyvalue storage as part of its feature set. This allows our application to use NATS for +storing and managing key-value pairs. Additionally, wasmCloud officially supports +other key-value providers, such as Redis and HashiCorp Vault. Since these providers +all implement the `wasi:keyvalue` interface, you can switch between them with minor +configuration changes and no code modifications. + +Finally, we link the `kvnats` provider to the specific `wasi:keyvalue` interfaces ( `store` +and `atomics` ) we gave our component access to and provide some basic configuration. +This includes setting the default bucket name and enabling the automatic creation +of buckets as needed. With these changes, our updated architecture is illustrated in +figure 6.2. + +_6.2.2_ _Modifying our application and redeploying it_ + +Now it’s time to update our application code to utilize persistent storage. The changes, +as shown in listing 6.7, are straightforward. + +Licensed to Ibrahim Mohammed Mohammed + +**_Adding persistent storage to our Wasm app_** **133** + +Figure 6.2 Updated architecture diagram for “Hello, World” wasmCloud app with persistent storage + +Listing 6.7 Updated **`hello-world-wasmcloud/src/lib.rs`** + +``` +use wasmcloud_component::http; +use wasmcloud_component::wasi::keyvalue::*; + +struct Component; + +http::export!(Component); + +``` + +**Imports the wasi:keyvalue** +**bindings for storage operations** + +``` +impl http::Server for Component { +fn handle( +_request: http::IncomingRequest, +) -> http::Result> { +let bucket = store::open +``` + +**Opens the default bucket, automatically** + +➥ `("default").unwrap();` + +**creating it if it doesn’t exist** +``` +let count = atomics::increment +``` + +➥ `(&bucket, "counter", 1).unwrap();` + +**Opens the default bucket, automatically** +**creating it if it doesn’t exist** + +``` +Ok(http::Response::new(format! + +} +} + +``` + +**Increments the counter** +**key in the bucket by 1** + +**Returns a response indicating** +**how many times the endpoint** +**has been accessed** + +Here we updated the application to interact with the `wasi:keyvalue` interfaces. The +key steps involve opening a persistent bucket (named `default` ), accessing a key called +“counter,” incrementing the value stored within it, and returning its value in the HTTP +response. + +Licensed to Ibrahim Mohammed Mohammed + +**134** Chapter 6 **_Creating production-grade Wasm applications_** + +This marks a significant departure from the work we did in prior chapters. When +we implemented our own key-value store interface, the process required not only the +heavy lifting of building the interface itself but also substantial operational setup to run +the Wasm component. From configuring and creating a raw Wasm engine to setting up +the `Store` with WASI and custom contexts and programmatically linking interfaces, the +effort quickly added up. + +Now, with standardized interfaces like `wasi:keyvalue` and a robust off-the-shelf host +like wasmCloud, we can focus entirely on writing business logic—like incrementing a +counter! + +With that done, we can redeploy our application. Since we never terminated our +wasmCloud host (e.g., by running `wash` `down` ), it should still be running. To confirm, run + +``` + wash get hosts + +``` + +This will produce output similar to table 6.2. + +Table 6.2 Currently active wasmCloud hosts + +|Host ID|Friendly name|Uptime (seconds)| +|---|---|---| +|NCJXH5…|`silent-dew-9876`|7461| + +If no hosts are listed or you encounter any problems, you may need to restart the host +with + +``` + wash up -d + +``` + +TIP If you prefer a graphical interface for managing wasmCloud, you can +open a separate terminal window and run `wash ui` . This UI provides information on hosts, components, capabilities, and links. + +Next, let’s ensure no old versions of our component are running. You can check this +by running + +``` + wash app list + +``` + +If the table isn’t empty and shows the old component, you can remove it by running + +``` + wash app delete rust-hello-world + +``` + +Now, with a running wasmCloud host and no old versions of our component deployed, +we can rebuild and redeploy the new version of our application: + +``` + wash build + wash app deploy ./wadm.yaml + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Scaling our wasmCloud application_** **135** + +After deployment, you can verify the new component is running by checking + +``` + wash app list + +``` + +You should see your newly deployed component listed. To confirm all links are working as expected, you can run + +``` + wash get links + +``` + +That should show you something similar to table 6.3. + +Table 6.3 Currently active links between components and capabilities + +|Source ID|Target|Interface(s)|…| +|---|---|---|---| +|`…-http_component`
`…-httpserver`|`…-kvnats`
`…-http_component `|`wasi:keyvalue/store,atomics`
`wasi:http/incoming-handler`|…
…| + +Finally, test the application by making a few calls to the endpoint: + +``` + curl http://127.0.0.1:8000 + +``` + +You should see the counter increment with each call. To test the persistence of the +application, shut down the host with + +``` + wash down + +``` + +Then restart it with + +``` + wash up -d + +``` + +Rerun + +``` + curl http://127.0.0.1:8000 + +``` + +The counter should continue from where it left off, rather than resetting to 1. + +Awesome! We’ve successfully created a wasmCloud application that uses the standard + +`wasi:keyvalue` interface and relies on the capability provided by the wasmCloud host +to implement persistent storage. Up next: exploring the scalability of our application. + +_6.3_ _Scaling our wasmCloud application_ + +When we first created our application, recall that in our `wadm` manifest, we defined a + +`spreadscaler` trait with just one instance: + +``` + - type: spreadscaler + properties: + instances: 1 + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**136** Chapter 6 **_Creating production-grade Wasm applications_** + +This configuration limits our application to handling only a single request at a time. +For short-lived applications like our current implementation that quickly increments +a counter and returns a response, this limitation might not seem significant. However, for longer-running operations, it means that concurrent requests must wait for +the previous ones to finish, potentially leading to poor average response times under +load. + +Let’s test this limitation by updating our application to simulate a longer-running +operation by sleeping for 2 seconds after incrementing the counter. The updated code +is shown in the following listing. + +Listing 6.8 Updated **`hello-world-wasmcloud/src/lib.rs`** + +``` + use wasmcloud_component::http; + use wasmcloud_component::wasi::keyvalue::*; + + struct Component; + + http::export!(Component); + + impl http::Server for Component { + fn handle( + _request: http::IncomingRequest, + ) -> http::Result> { + let bucket = store::open("default").unwrap(); + let count = atomics::increment(&bucket, "counter", 1).unwrap(); + std::thread::sleep(std::time:: +``` + +**Simulates a long-running operation** + +➥ `Duration::from_secs(2));` + +**by sleeping for 2 seconds** +``` + Ok(http::Response::new(format!("Hello! I was called {count} +``` + +➥ `times\n")))` +``` + } + } + +``` + +With this change, rebuild and redeploy the application: + +``` + wash build + wash app delete rust-hello-world + wash app deploy ./wadm.yaml + +``` + +Now let’s benchmark the application. We can use `oha`, a lightweight HTTP benchmarking tool written in Rust. + +TIP If you’d like to try this yourself, you can install `oha` with: `cargo` `install` + +`oha@1.4.7 --locked` . + +To benchmark, run + +``` + oha -n 10 -c 5 http://127.0.0.1:8000 + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Scaling our wasmCloud application_** **137** + +This command sends 10 requests, 5 of them concurrently. Given our single-instance +setup, we expect to see poor response times due to the bottleneck. The results are +shown in the following listing. + +Listing 6.9 Benchmarks from wasmCloud application with one replica + +``` +Summary: +Success rate: 100.00% +Total: 20.0729 secs +Slowest: 10.0551 secs +Fastest: 2.0055 secs +Average: 8.0334 secs +Requests/sec: 0.4982 +# … + +Response time histogram: +``` + +2.006 [1] |■■■■■ +``` +2.810 [0] | +3.615 [0] | +``` + +4.420 [1] |■■■■■ +``` +5.225 [0] | +``` + +6.030 [1] |■■■■■ +``` +6.835 [0] | +7.640 [0] | +``` + +8.445 [1] |■■■■■ +``` +9.250 [0] | +``` + +10.055 [6] |■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ +``` +# … + +``` + +As you can see, the first response takes 2 seconds, the second takes 4 seconds, the third +takes 6 seconds, and so on. This sequential processing results in an average response +time of 8 seconds, which is far from ideal. + +To address this, we can update our `spreadscaler` configuration to deploy 10 replicas +of our `http-component`, allowing our application to handle requests in parallel: + +``` +- type: spreadscaler +properties: +instances: 10 + +``` + +Redeploy the application with the updated configuration: + +``` +wash app delete rust-hello-world +wash app deploy ./wadm.yaml + +``` + +Finally, rerun the benchmark: + +``` +oha -n 10 -c 5 http://127.0.0.1:8000 + +``` + +The results, shown in the following listing, demonstrate significant improvement. + +Licensed to Ibrahim Mohammed Mohammed + +**138** Chapter 6 **_Creating production-grade Wasm applications_** + +Listing 6.10 Benchmarks from wasmCloud app with 10 replicas + +``` + Summary: + Success rate: 100.00% + Total: 4.0910 secs + Slowest: 2.0471 secs + Fastest: 2.0055 secs + Average: 2.0315 secs + Requests/sec: 2.4444 + # … + + Response time histogram: +``` + +2.005 [1] |■■■■■ +2.010 [1] |■■■■■ +2.014 [1] |■■■■■ +``` + 2.018 [0] | + 2.022 [0] | +``` + +2.026 [1] |■■■■■ +``` + 2.030 [0] | + 2.035 [0] | + 2.039 [0] | + 2.043 [0] | +``` + +2.047 [6] |■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ +``` + # … + +``` + +With 10 replicas, each request is handled in parallel, resulting in an average response +time of just over 2 seconds—exactly what we expect given the simulated application +processing. The wasmCloud host manages these 10 replicas as separate component +instances within the host process, with the NATS lattice distributing incoming requests +across them. As we saw in chapter 1, components are internally single threaded, which +is why this external load balancing is necessary—it allows multiple requests to be processed concurrently by running multiple instances of the component. The updated +architecture reflecting this scaled deployment is shown in figure 6.3. + +By scaling our application to multiple instances, we’ve effectively improved its ability +to handle concurrent requests, significantly reducing response times and increasing +throughput. Up next: exploring how to distribute our application. + +_6.4_ _Distributing our wasmCloud application_ + +We’ve learned how to increase the number of replicas of our Wasm components, but +we can take it a step further. These replicas can be distributed across different hosts, +which, in principle, could run on separate machines or data centers, or even across +different geographic regions. This architecture is displayed in figure 6.4. + +This image illustrates how requests are routed through the NATS lattice to replicas +spread across multiple wasmCloud hosts. The `spreadscaler` load balances incoming +requests across all available replicas, regardless of their physical or geographic location. +Each replica links to the `kvnats` key-value store provider, ensuring consistent access to +shared, persistent storage. + +Licensed to Ibrahim Mohammed Mohammed + +**_Distributing our wasmCloud application_** **139** + +**has multiple replicas.** + +Figure 6.3 Updated architecture diagram for “Hello, World” wasmCloud app with 10 replicas of the +``` +http-component + +``` + +**Multiple hosts with** +**replicas split across them** + +Figure 6.4 Updated architecture diagram for “Hello, World” wasmCloud app with 10 replicas of the +**`http-component`** split across different hosts and regions (i.e., us-east-1 and us-west-1) + +Licensed to Ibrahim Mohammed Mohammed + +**140** Chapter 6 **_Creating production-grade Wasm applications_** + +To demonstrate this, we can modify our `spreadscaler` configuration to distribute our +10 replicas across two different hosts—simulating East and West Coasts: + +``` + - type: spreadscaler + properties: + instances: 10 + spread: + - name: eastcoast + weight: 80 + requirements: + zone: us-east-1 + - name: westcoast + weight: 20 + requirements: + zone: us-west-1 + +``` + +This configuration assigns 80% of the instances to the East Coast ( `us-east-1` ) and 20% +to the West Coast ( `us-west-1` ). By distributing components in this way, we achieve not +only better fault tolerance but also reduced latency for users in different regions, as +requests can be routed to the closest available host. + +Before we can deploy the updated manifest, we need to run multiple hosts on our +machine to simulate this multiregion setup. Start by shutting down any currently running host: + +``` + wash down + +``` + +Then spin up two separate hosts with distinct zone labels using the `--multi-local` +flag: + +``` + wash up --multi-local --label zone=us-east-1 -d + wash up --multi-local --label zone=us-west-1 -d + +``` + +The `--multi-local` flag is what enables starting independent hosts on the same +machine. Then zone labels ( `zone=us-east-1` and `zone=us-west-1` ) are assigned to each +host, allowing the `spreadscaler` to distribute replicas based on geographic zones. + +Now that the multihost setup is ready, redeploy the application with the updated +configuration: + +``` + wash app delete rust-hello-world + wash app deploy ./wadm.yaml + +``` + +Finally, we can confirm the `spreadscaler` is working as expected by running + +``` + wash get inventory + +``` + +This command provides detailed information about each host, as shown in the following listing. + +Licensed to Ibrahim Mohammed Mohammed + +**_Distributing our wasmCloud application_** **141** + +Listing 6.11 Partial output of **`wash get inventory`** + +``` +Host ID Friendly name +NAKHN5... divine-emoji-6927 + +Host labels +hostcore.arch x86_64 +hostcore.os linux +hostcore.osfamily unix +zone us-west-1 + +``` + +**Two replicas (20%** +**weight) were assigned** +**to the us-west-1 host.** + +**weight) were assigned** +**to the us-west-1 host.** + +**weight) were assigned** + +`Provider ID Name` **to the us-west-1 host.** +``` +rust_hello_world-kvnats keyvalue-nats-provider + +Host ID Friendly name +NBIYLP... solitary-wildflower-2472 + +Host labels +hostcore.arch x86_64 +hostcore.os linux +hostcore.osfamily unix +zone us-east-1 +``` + +**Eight replicas (80%** +**weight) were assigned** +**to the us-east-1 host.** +``` +...-http_component http-hello-world 8 +``` + +**Eight replicas (80% weight) were** +**assigned to the us-east-1 host.** +``` +Provider ID Name +...-httpserver http-server-provider + +``` + +**Two replicas (20%** +**weight) were assigned** +**to the us-west-1 host.** + +**Eight replicas (80%** +**weight) were assigned** +**to the us-east-1 host.** + +**Eight replicas (80% weight) were** +**assigned to the us-east-1 host.** + +This demonstrates that the `spreadscaler` successfully distributed our application across +multiple hosts based on the defined weights and zone labels. + +Now let’s simulate an outage by shutting down one of our hosts. Select one of the two +host IDs from the inventory and run + +``` +wash down --host-id + +``` + +Next, try calling the endpoint again: + +``` +curl http://127.0.0.1:8000 + +``` + +You should still receive a “ `Hello! …` ” message, demonstrating that the remaining host +continues handling requests. Verify the inventory again with + +``` +wash get inventory + +``` + +It will now show only one active host. + +Licensed to Ibrahim Mohammed Mohammed + +**142** Chapter 6 **_Creating production-grade Wasm applications_** + +Awesome! Not only did we learn how to create multiple instances of our Wasm components to handle concurrent requests better, but we also demonstrated how to + +¡ Simulate a globally distributed deployment with lower latency by spreading com +ponents across multiple hosts in different regions. + +¡ Achieve fault tolerance, where application availability is maintained even when + +one of the hosts goes offline. + +With that, we’ve concluded our “Hello, World” wasmCloud application that incorporates persistent storage using the standardized `wasi:keyvalue` interface, scales effortlessly with `spreadscaler`, and supports global distribution for enhanced performance +and reliability. + +TIP To clean up your environment, remember to run `wash` `app` `delete` `rust` +`-hello-world` (or `wash` `app` `delete` `./wadm.yaml` ) and `wash` `down --all` . + +If you’ve worked with Kubernetes in the past, you may notice that much of what we’ve +done here mirrors concepts like scaling workloads, managing deployments, and ensuring high availability. However, it’s important to note that wasmCloud is not a replacement for Kubernetes. Instead, it complements Kubernetes, making it easier to run +Wasm workloads in production while using existing infrastructure. We’ll explore these +integrations in upcoming chapters. + +Upcoming changes to wasmCloud + +wasmCloud is actively evolving. This chapter reflects the architecture and tooling +available as of wasmCloud version 1.4.2. The wasmCloud team has announced +[significant architectural changes for Q3 2025 and beyond (detailed at https://mng](https://mng.bz/qRvN) +[.bz/qRvN), including simplifying the host model, transitioning capability providers to](https://mng.bz/qRvN) +“wRPC servers,” and moving away from the application-centric deployment model. + +While the core concepts covered here—such as component scaling, distributed networking via the NATS lattice, and the separation of business logic from capabilities— +will remain foundational to wasmCloud, specific implementation details may change +substantially. In particular, `wadm` manifests, the Open Application Model (OAM)–based +application model, and how capability providers are configured and deployed are all +[subject to revision. For the latest updates, visit https://wasmcloud.com/blog/.](https://wasmcloud.com/blog/) + +_6.5_ _Another off-the-shelf host: Spin_ + +wasmCloud isn’t the only off-the-shelf host available—another popular option is Spin. +To demonstrate the differences between the two, let’s create a simple “Hello, World” +application with Spin. + +_6.5.1_ _Creating a Spin “Hello, World” application_ + +First things first: you’ll need to install Spin. You can do so following the instructions +[from https://developer.fermyon.com/spin/v3/install.](https://developer.fermyon.com/spin/v3/install) + +Licensed to Ibrahim Mohammed Mohammed + +**_Another off-the-shelf host: Spin_** **143** + +NOTE For the Spin examples in this book, I’ll be using `spin` `3.2.0` `(c9be6d8` +`2025-03-24)` . To keep things consistent, it’s best to install the same version +when following along. + +IMPORTANT Spin requires Rust 1.85.0 or later because some of its dependencies use Rust edition 2024. If you’ve been following along with Rust 1.84.0 (as +recommended earlier in the book), you’ll need to upgrade for this section. +Install Rust 1.85.0 and its required components with `rustup` `install` `1.85.0`, + +`rustup` `default` `1.85.0`, and `rustup` `target` `add` `wasm32-wasip1` . You can verify +the change with `rustc --version` . + +With that done, in your `chapter06/` folder, run + +``` +spin new + +``` + +This will give you an option to choose from a few Spin templates: + +``` +> http-c (HTTP request handler using C and the Zig toolchain) +http-empty (HTTP application with no components) +http-go (HTTP request handler using (Tiny)Go) +http-grain (HTTP request handler using Grain) +http-js (HTTP request handler using JavaScript) +http-php (HTTP request handler using PHP) +http-py (HTTP request handler using Python) +http-rust (HTTP request handler using Rust) +http-ts (HTTP request handler using TypeScript) +http-zig (HTTP request handler using Zig) +redirect (Redirects a HTTP route) +redis-go (Redis message handler using (Tiny)Go) +redis-js (Redis message handler using JavaScript) +redis-rust (Redis message handler using Rust) +redis-ts (Redis message handler using TypeScript) +static-fileserver (Serves static files from an asset directory) + +``` + +NOTE Spin’s SDK is also available in various languages, including JavaScript/ +TypeScript, Python, .NET, Rust, Go, Java, and more—feel free to choose the +language you are most comfortable with! + +For this example, I’ll select the `http-rust` template, which generates a Rust-based +HTTP request handler with the necessary setup. + +After selecting the template, you’ll be prompted to name your application—let’s +call it `hello-world-spin` . Next, it will ask for a description and an HTTP path; you can +accept the defaults by pressing Enter. Once completed, your project will be created in +the `hello-world-spin` directory. Inside, you’ll find the following: + +``` +. +``` + +├── Cargo.toml +├── spin.toml +└── src + +Licensed to Ibrahim Mohammed Mohammed + +**144** Chapter 6 **_Creating production-grade Wasm applications_** + +This structure resembles a typical Rust project, with the addition of `spin.toml` . The + +`spin.toml` file serves as the application manifest, defining the configuration for your +Spin application. It specifies details such as the application name, version, components, and their respective triggers. In Spin, a trigger maps an event—like an HTTP +request or a Redis message—to a component that handles that event. For instance, +an HTTP trigger routes incoming HTTP requests to the appropriate Wasm component based on the specified route. This setup enables Spin to efficiently manage eventdriven applications by directing events to their corresponding handlers as defined in +the manifest. You can see the `spin.toml` for our `hello-world-spin` application in the +following listing. + +Listing 6.12 **`spin.toml`** file for the **`hello-world-spin`** project + +``` + spin_manifest_version = 2 + +``` + +``` +[application] +name = "hello-world-spin" +version = "0.1.0" +authors = ["your-name-here "] +description = "" + +``` + +``` +[[trigger.http]] +route = "/..." +component = "hello-world-spin" + +``` + +**All incoming HTTP requests, regardless** +**of their path, are directed to the** +**hello-world-spin component.** + +``` +[component.hello-world-spin] +source = "target/wasm32-wasip1/release/hello_world_spin.wasm" +allowed_outbound_hosts = [] +[component.hello-world-spin.build] +``` + +**Defines the command to compile the** + +`command = "cargo build --target` **component’s source code into a Wasm module** +➥ `wasm32-wasip1 --release"` +``` +watch = ["src/**/*.rs", "Cargo.toml"] + +``` + +Spin’s goal is to make developing Wasm microservices straightforward. Part of that, as +shown in figure 6.5, is achieved through its trigger-based, event-driven setup. + +**multiple Wasm instances** +**to handle heavy loads.** + +Figure 6.5 Spin’s event-driven setup for Wasm microservices + +Licensed to Ibrahim Mohammed Mohammed + +**_Another off-the-shelf host: Spin_** **145** + +Inside `src/lib.rs` (shown in listing 6.13), you’ll find the default application generated +by Spin’s `http-rust` template. This code, compiled to Wasm, serves as the event handler and executes whenever the defined trigger—in our case, an HTTP request—is +activated. + +Listing 6.13 **`src/lib.rs`** file for the **`hello-world-spin`** project + +``` +use spin_sdk::http::{IntoResponse, Request, Response}; +use spin_sdk::http_component; + +/// A simple Spin HTTP component. +#[http_component] +fn handle_hello_world_spin(req: Request) -> anyhow::Result {` +``` +println!("Handling request to {:?}", req.header("spin-full-url")); +Ok(Response::builder() +.status(200) +.header("content-type", "text/plain") +.body("Hello World!") +.build()) +} + +``` + +This code uses Spin’s SDK to define a handler for incoming HTTP requests. The handler logs the request URL and responds with `"Hello, World!"` alongside an HTTP +status of 200. + +Next, from `hello-world-spin/`, compile the Spin application with + +``` +spin build + +``` + +And run it using + +``` +spin up + +``` + +Upon running the command, you’ll see output similar to this: + +``` +Logging component stdio to ".spin/logs/" + +Serving http://127.0.0.1:3000 +Available Routes: +hello-world-spin: http://127.0.0.1:3000 (wildcard) + +``` + +From another terminal, test the application by running + +``` +curl http://127.0.0.1:3000 + +``` + +You should see the response: `"Hello World!"` . Awesome! With that, we’ve successfully +created our simple `"Hello, World!"` application using Spin! + +Licensed to Ibrahim Mohammed Mohammed + +**146** Chapter 6 **_Creating production-grade Wasm applications_** + +IMPORTANT Now that you’ve completed the Spin example, you should revert +to Rust 1.84.0 to maintain consistency with the rest of the book. Run `rustup` +`default 1.84.0` . You can verify the change with `rustc --version` . + +_6.5.2_ _Comparing wasmCloud and Spin_ + +While we didn’t dive as deeply with Spin as we did with wasmCloud (e.g., scaling the +application or simulating global distribution), it’s clear that Spin and wasmCloud have +fundamentally different designs. The following is a breakdown of the key distinctions +and considerations: + +¡ _Scaling and concurrency_ + + - wasmCloud exposes scalability details, such as the `spreadscaler`, allowing + +users to configure instances and scaling behavior directly in the manifest. + + - Spin handles concurrency by default, creating new instances to handle + +requests out of the gate without requiring explicit configuration. For example, you can test this by benchmarking the Spin app with `oha` `-n` `10` `-c` `5` + +`http://127.0.0.1:3000` . However, scaling Spin applications beyond a single +host typically requires an orchestrator like Kubernetes (via SpinKube). + +¡ _Language support_ + + - Spin offers templates in a broader range of languages, including C#, Grain, + +Swift, and Zig, in addition to Rust. + + - wasmCloud has more limited language support but focuses on extensibility + +and native integration with its ecosystem. + +¡ _Extensibility and capabilities_ + + - wasmCloud shines in extensibility. It allows users to easily integrate third-party + +capabilities by pointing to OCI artifacts in the manifest. For instance, while +neither wasmCloud nor Spin officially supports `wasi:nn`, wasmCloud lets you +integrate a third-party implementation. + + - Spin, on the other hand, relies solely on the capabilities provided by the host + +or external microservices accessed via HTTP. + +No host is inherently “better” than the other—it all comes down to your specific use +case. Each host shines in different scenarios, and understanding their strengths can +help you make an informed decision: + +¡ If you value language support and prefer a simple setup, Spin may be the better + +choice. For instance, if your team uses a mix of languages like Go, Python, or +TypeScript, Spin’s extensive template and SDK support make it a versatile option. +The ability to get started with minimal configuration means it’s ideal for teams +looking to quickly prototype event-driven applications without delving deeply +into custom configurations. + +¡ If extensibility and fine-grained configurability are critical for your application, + +wasmCloud offers significant advantages. For example, if your application needs + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **147** + +to integrate with a specific machine learning capability or a proprietary system +that doesn’t conform to standardized WASI interfaces, wasmCloud’s support for +third-party providers—using WIT interfaces and custom capability providers— +lets you extend its functionality to fit your needs. + +For our SmartCMS host application, I chose wasmCloud due to its extensibility and the +ease of integrating third-party capabilities. + +_6.6_ _Implementing the example project as a wasmCloud app_ + +For this iteration of improvements to our SmartCMS, rather than migrating our existing code, we’ll start fresh with a new wasmCloud application. Since we’ll be retiring our +custom host, this approach simplifies the transition. To create the application, use the +following command in the `chapter06/` folder: + +``` + wash new component smart_cms --template-name hello-world-rust + +``` + +Then we need to update the name of the application to reflect our SmartCMS project. +This requires changes in two locations: `Cargo.toml` and `wasmcloud.toml` . The updated + +`Cargo.toml` file is shown in the following listings. + +Listing 6.14 Updated application name in **`Cargo.toml`** + +``` + [package] + name = "smart-cms" + edition = "2021" + version = "0.1.0" + + [workspace] + + [lib] + crate-type = ["cdylib"] + + [dependencies] + wasmcloud-component = "0.2.0" + +``` + +Next, we modify our `wasmcloud.toml` . + +Listing 6.15 Updated application name in **`wasmcloud.toml`** + +``` +name = "smart-cms" +version = "0.1.0" +language = "rust" +type = "component" + +[component] +wit_world = "backend" +wasm_target = "wasm32-wasip2" + +``` + +**Also updated the wit_world** +**name, which we’ll align with our** +**WIT file later in the process** + +Licensed to Ibrahim Mohammed Mohammed + +**148** Chapter 6 **_Creating production-grade Wasm applications_** + +With the template set up, let’s consider the architecture of our application, as illustrated in figure 6.6. + +We want it to include the following functionalities: + +¡ A `wasi:keyvalue` -backed key-value store linked to a NATS key-value store for ease + +of setup + +¡ A machine learning component to generate children’s stories + +User + +NATS lattice + +|routes to server spread|dscaler| +|---|---| +|80% of the load goes
|20% of the load goes
| +|to the East Coast.
to the West Coast.|to the East Coast.
to the West Coast.| + +Figure 6.6 The architecture of our SmartCMS application + +For simplicity, these functionalities can be bundled into a single `http-component` . This +approach enables the component to inspect incoming requests and handle them +appropriately. Specifically, the application will expose three routes: + +¡ `/api/create` —Accepts a story name and content in the request body and persists + +them to the key-value store. + +¡ `/api/generate` —Triggers the machine learning backend to generate a story, + +persists the story (e.g., using names like generate-x to maintain a counter), and +returns it to the client. + +¡ `/api/retrieve` —Accepts a story name in the request body and fetches the cor +responding story from the key-value store. If the story doesn’t exist, it returns an +appropriate error message. + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **149** + +Now let’s make some small configuration changes to our `wadm.yaml` . Specifically, we’ll +update the application name from `rust-hello-world` to `smart-cms` and clean up our app’s +annotations. Additionally, we’ll modify the `spreadscaler` trait to deploy 100 instances of +our component, distributed across two hosts with an 80:20 weight ratio between East +Coast and West Coast zones. You can see the updated `wadm.yaml` in the following listing. + +Listing 6.16 Updated **`wadm.yaml`** file for the SmartCMS + +``` +apiVersion: core.oam.dev/v1beta1 +kind: Application + +annotations: +description: "A smart CMS!" +spec: +components: +- name: http-component +type: component +properties: +image: file://./build/smart_cms_s.wasm +traits: +- type: spreadscaler +properties: +instances: 100 +spread: +- name: eastcoast +weight: 80 +requirements: +zone: us-east-1 +- name: westcoast +weight: 20 +requirements: +zone: us-west-1 + +``` + +**Changes the application** +**name to smart-cms** + +**Cleans up application** +**annotations** + +**Changes Wasm artifact** +**name to smart_cms_s.wasm** + +**Updates the spreadscaler to** +**deploy 100 instances, distributing** +**them between East Coast (80%)** +**and West Coast (20%)** + +``` +- name: httpserver +type: capability +properties: +image: ghcr.io/wasmcloud/http-server:0.23.2 +traits: +- type: link +properties: +target: http-component +namespace: wasi +package: http +interfaces: [incoming-handler] +source_config: +- name: default-http +properties: +address: 127.0.0.1:8000 + +``` + +We’re now ready to spin up our hosts. Use the following commands to set up two local +wasmCloud hosts, each labeled with its respective zone: + +Licensed to Ibrahim Mohammed Mohammed + +**150** Chapter 6 **_Creating production-grade Wasm applications_** + +``` + wash up --multi-local --label zone=us-east-1 -d + wash up --multi-local --label zone=us-west-1 -d + +``` + +With that set, let’s get started by adding persistent storage to our application and creating the first two routes: `/api/create` and `/api/retrieve` . + +_6.6.1_ _Adding persistent storage to the SmartCMS_ + +Like we did for our “Hello, World” application, the first step is to update our WIT file +to import `wasi:keyvalue` . While we’re at it, we’ll also update the namespace, package, +and world name. The updated WIT is shown in the following listing. + +Listing 6.17 Updated SmartCMS **`world.wit`** file + +``` +package smartcms:store-and-ai-story-generator; + +world backend { +import wasi:keyvalue/atomics@0.2.0-draft; +import wasi:keyvalue/store@0.2.0-draft; +export wasi:http/incoming-handler@0.2.2; +} + +``` + +**Updates namespace** +**and package name** + +**Updates world name to backend** + +**Imports wasi:keyvalue interfaces** +**for atomic and store operations** + +With the WIT updated, the next step is to add the capability provider and create a link +to it in our `wadm.yaml` file. The updated configuration is shown in the following listing. + +Listing 6.18 Updated **`wadm.yaml`** file with key-value provider and link + +``` +apiVersion: core.oam.dev/v1beta1 +kind: Application +metadata: +name: smart-cms +annotations: +description: "A smart CMS!" +spec: +components: +- name: http-component +type: component +properties: +image: file://./build/smart_cms_s.wasm +traits: +- type: spreadscaler +properties: +instances: 100 +spread: +- name: eastcoast +weight: 80 +requirements: +zone: us-east-1 +- name: westcoast +weight: 20 + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **151** + +``` +requirements: +zone: us-west-1 +- type: link +properties: +namespace: wasi +package: keyvalue +interfaces: [store, atomics] +target: +name: kvnats +config: +- name: wasi-keyvalue-config +properties: +bucket: default +enable_bucket_auto_create: +``` + +➥ `'true'` + +``` +- name: httpserver +type: capability +properties: +image: ghcr.io/wasmcloud/http-server:0.23.2 +traits: +- type: link +properties: +target: http-component +namespace: wasi +package: http +interfaces: [incoming-handler] +source_config: +- name: default-http +properties: +address: 127.0.0.1:8000 + +``` + +**Adds the key-value** +**capability link to the** +**wasi:keyvalue interface** + +``` +- name: kvnats +type: capability +properties: +image: ghcr.io/wasmcloud/keyvalue-nats:0.3.1 + +``` + +**Adds the key-value** +**provider from OCI image** + +With the updated `wadm.yaml` in place, we can now move to the application code in `src/` + +`lib.rs` . First, update the imports at the top of your file to include + +``` +use std::io::Read; +use wasmcloud_component::http; +use wasmcloud_component::wasi::keyvalue::*; + +``` + +These imports bring in two new dependencies. The `std::io::Read` one will allow us to +process the body of incoming HTTP requests, while the `wasi::keyvalue::*` one provides the interfaces to interact with our persistent storage. + +Next, delete the body of the existing `handle` function and replace it with the following skeleton: + +``` +fn handle( +request: http::IncomingRequest, + +``` + +**Renames the variable to request** +**as we are now actively using it** + +Licensed to Ibrahim Mohammed Mohammed + +**152** Chapter 6 **_Creating production-grade Wasm applications_** + +``` + ) -> http::Result> { + let (parts, mut body) = request.into_parts(); + + match parts.uri.path() { + "/api/create" => { + // ... + } + "/api/retrieve" => { + // ... + } + _ => { + Ok(http::Response::new("Invalid route!\n".to_string())) + } + } + } + +``` + +In this updated function, the request is split into `parts` and `body` . The `parts` allow us to +determine which route is being accessed, while the `body` gives us the data needed for +specific routes. Using a `match` statement, we handle requests for the `/api/create` and ­ + +`/api/retrieve` routes, while returning an error message for invalid paths. + +NOTE We’re only matching against the URI (e.g., `"/api/create"`, `"/api/` +`retrieve"`, etc.) of the request. This means we’re not considering the request’s +method (e.g., `GET`, `POST`, etc.). If you want to check the method, you can inspect +`parts.method` . + +With this skeleton in place, we can now implement the logic for each route. + +Creating the /api/create route +Inside the `/api/create` `match` arm, add the following code: + +``` + let bucket = store::open("default").unwrap(); + + let mut buf = Vec::new(); + body.read_to_end(&mut buf).unwrap(); + let body = String::from_utf8(buf).unwrap(); + +``` + +This code opens the default key-value bucket and reads the body of the incoming +HTTP request into a buffer. The buffer is then converted to a UTF-8 string for processing. Note that this code includes minimal error handling—many of the unwrap +statements could be replaced with more robust error-handling strategies to ensure +resilience in production. + +Next, add the following: + +``` + let mut lines = body.lines(); + let story_name = lines.next().unwrap_or("Unnamed Story").trim(); + let story_content = lines.collect::>().join("\n"); + +``` + +Here we define how the backend expects information to be passed in the request body. +The first line of the body is treated as the story name, defaulting to “Unnamed Story” + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **153** + +if no name is provided. The remaining lines are joined to form the story content. In +a more robust implementation, the request body would likely be formatted in a structured way, such as JSON, and parsed using serialization/deserialization techniques for +better clarity and error handling. + +Finally, add the logic to persist the data: + +``` +bucket.set(story_name, story_content.as_bytes()).unwrap(); +Ok(http::Response::new(format!("Stored {}\n", story_name))) + +``` + +This code stores the story in the key-value bucket using the name as the key and the +content as the value. The HTTP response confirms the operation by returning the +story name. + +Next, let’s implement the `/api/retrieve` route. + +Creating the /api/retrieve route +Just like with the `/api/create` route, the first step for the `/api/retrieve` route is to +open the default key-value bucket and extract the request body. Here’s how: + +``` +let bucket = store::open("default").unwrap(); + +let mut buf = Vec::new(); +body.read_to_end(&mut buf).unwrap(); +let story_name = String::from_utf8(buf).unwrap().trim().to_string(); + +``` + +This code opens the key-value store bucket and processes the incoming HTTP request +body. The body is read into a buffer, converted to a UTF-8 string, and trimmed to +extract the story name. + +Next, we attempt to retrieve the story: + +``` +match bucket.get(&story_name).unwrap() { +Some(content) => { +let story_content = String::from_utf8(content).unwrap(); +Ok(http::Response::new(format!("{story_content}\n"))) +} +None => { +Ok(http::Response::new("Story not found\n".to_string())) +} +} + +``` + +Here the code checks if the requested story exists in the key-value store. If found, the +content is retrieved, converted to a UTF-8 string, and returned as the HTTP response. +Otherwise, the response indicates that the story was not found. Overall, the updated + +`lib.rs` file for our application should look like the following listing. + +Listing 6.19 Updated **`smart-cms/src/lib.rs`** + +``` +use std::io::Read; +use wasmcloud_component::http; + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**154** Chapter 6 **_Creating production-grade Wasm applications_** + +``` + use wasmcloud_component::wasi::keyvalue::*; + + struct Component; + + http::export!(Component); + + impl http::Server for Component { + fn handle( + request: http::IncomingRequest, + ) -> http::Result> { + let (parts, mut body) = request.into_parts(); + +``` + +``` +match parts.uri.path() { +"/api/create" => { + +``` + +➥ `("default").unwrap();` + +**Opens the default bucket of our** +**NATS key-value persistent storage** + +**Matches on endpoints** +**to route requests** + +``` +let mut buf = Vec::new(); +body.read_to_end(&mut buf).unwrap(); +let body = String::from_utf8(buf).unwrap(); + +``` + +``` +let mut lines = body.lines(); +let story_name = lines.next() +``` + +➥ `.unwrap_or("Unnamed Story").trim();` +``` +let story_content = +``` + +➥ `lines.collect::>().join("\n");` + +``` +bucket.set(story_name, +``` + +➥ `story_content.as_bytes()).unwrap();` + +``` +Ok(http::Response::new +``` + +➥ `(format!("Stored {}\n", story_name)))` +``` +} +"/api/retrieve" => { + +``` + +**Reads HTTP request** +**body lines, where the** +**first line is the story** +**title and the rest is the** +**story content** + +**Stores the story** +**in the bucket** + +**Returns a success** +**response for** +**storing the story** + +``` +let mut buf = Vec::new(); +body.read_to_end(&mut buf).unwrap(); +let story_name = +``` + +➥ `String::from_utf8(buf).unwrap().trim().to_string();` + +``` +Some(content) => { +let story_content = +``` + +➥ `String::from_utf8(content).unwrap();` + +➥ `Ok(http::Response::new(format!("{story_content}\n")))` +``` +} +None => { +Ok(http:: +``` + +➥ `Response::new("Story not found\n".to_string()))` +``` +} +} +} + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **155** + +``` +_ => { +Ok(http::Response::new +``` + +➥ `("Invalid route!\n".to_string()))` +``` +} +} +} +} + +``` + +Now let’s test our work so far. + +**Handles other routes by** +**returning an “Invalid** +**route!” message** + +Testing our application +At the beginning of section 6.6, we started our hosts. By now, you should be working in a clean environment with no applications deployed. If you’re unsure, verify using `wash` `app` `list` and delete any outdated applications with `wash` `app` `delete` + +`` . + +Once your environment is ready, build and deploy the application: + +``` +wash build +wash app deploy ./wadm.yaml + +``` + +After deployment, check the application status to confirm it was successfully deployed: + +``` +wash app list + +``` + +Once you see a Deployed status, you’re ready to test your API. + +To test the `/api/create` route, run + +``` +curl -X POST -H "Content-Type: text/plain" +``` + +➥ `-d $'MyStory\nOnce upon a time, in a land far, far away...'` +➥ `http://127.0.0.1:8000/api/create` + +This command sends a POST request to the `/api/create` route with the `Content-Type` +set to `text/plain` . The body of the request contains a new story, where the first line +specifies the story’s title ( `MyStory` ) and the remaining lines provide the story content +( `Once` `upon` `a` `time,` `in` `a` `land` `far,` `far` `away...` ). + +NOTE The key-value provider does not support spaces in keys, so use `MyStory` +instead of `My` `Story` . + +The response should confirm: + +``` +Stored MyStory + +``` + +Next, test the `/api/retrieve` route with + +``` +curl -X POST -H "Content-Type: text/plain" +``` + +➥ `-d 'MyStory'` +➥ `http://127.0.0.1:8000/api/retrieve` + +Licensed to Ibrahim Mohammed Mohammed + +**156** Chapter 6 **_Creating production-grade Wasm applications_** + +This retrieves the content of the story `MyStory`, returning + +``` + Once upon a time, in a land far, far away... + +``` + +Awesome! You’ve successfully created and retrieved a story using your SmartCMS +application! + +_6.6.2_ _Adding machine learning to the SmartCMS_ + +wasmCloud does not officially support the `wasi:nn` interface. This means there isn’t +an out-of-the-box solution, such as a prebuilt OCI artifact for a provider or a simple dependency to integrate machine learning capabilities. However, wasmCloud’s +extensible architecture allows us to use third-party providers to achieve similar +functionality. + +To demonstrate this, we’ll integrate a third-party provider for the Ollama interface. +Unlike `wasi:nn`, the Ollama interface is not part of an ongoing standardization process. +This makes it a valuable example to show how custom WIT interfaces can be embedded +into your wasmCloud applications. + +What is Ollama? + +Ollama is a lightweight machine learning runtime that allows you to locally run large +language models. It simplifies inference by offering an easy-to-use CLI for downloading and interacting with models, enabling developers to run large language models +without needing specialized server infrastructure. + +To proceed with this integration, you’ll need to install Ollama on your local machine. +[You can find installation instructions at https://ollama.com/.](https://ollama.com/) + +Once installed, pull the model we’ll use for this example with the following +command: + +``` + ollama pull gurubot/tinystories-656k-q8 + +``` + +This model, similar to the one used in chapter 5, generates stories based on a +given prompt containing the beginning of a story. While the generated stories +may not always be perfectly coherent, this lightweight model (less than 1 MB) is +ideal for demonstrating the integration due to its simplicity and minimal resource +requirements. + +With Ollama installed and the model ready, let’s move on to embedding the custom +interface into our SmartCMS. + +Building the custom provider +While we could create our own custom provider embedding ONNX for `wasi:nn`, we’ll +simplify things by utilizing a third-party provider. + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **157** + +To include the provider in our project, we’ll add it as a Git submodule. A Git submodule allows us to track an external repository within our project’s directory. From inside +the `smart-cms/` directory, add the submodule with the following command: + +``` +git submodule add https://github.com/danbugs/ollama-provider.git + +``` + +Inside the `ollama-provider/` directory, two key components are relevant for our +application: + +¡ The source code for the Ollama provider + +¡ The WIT interface for the custom Ollama interface, located in the `wit/` directory + +The Ollama WIT interface is shown in the following listing. + +Listing 6.20 **`ollama.wit`** interface (comments/documentation omitted) + +``` +interface generate { +record request { +prompt: string, +images: option> +} + +record response { +model: string, +created-at: string, +response: string, +done: bool, +context: option>, +total-duration: option, +load-duration: option, +prompt-eval-count: option, +prompt-eval-duration: option, +eval-count: option, +eval-duration: option, +} + +generate: func(req: request) -> result; +} + +``` + +This interface defines + +¡ _Request record_ —Contains input parameters for the Ollama API, such as a `prompt` + +or `images` (e.g., PNG or JPEG files) + +¡ _Response record_ —The output of the model, which includes properties like + +`response` (the generated text), `done` (indicating process completion), and additional metadata for evaluation and performance + +¡ _Generate function—_ The primary function that takes a `request` and returns either a + +`response` or an error message + +Additionally, the provider includes another WIT file, `provider.wit`, as shown in the +following listing. + +Licensed to Ibrahim Mohammed Mohammed + +**158** Chapter 6 **_Creating production-grade Wasm applications_** + +Listing 6.21 **`provider.wit`** file for the **`ollama-provider`** + +``` + package thomastaylor312:ollama; + + world provider-ollama { + export generate; + } + +``` + +This WIT defines the `provider-ollama` world, where the provider implements the + +`generate` function. The provider interacts with the Ollama API, which is accessible +locally by default at `http://127.0.0.1:11434` . + +Ensure the Ollama API is running by either starting the Ollama application or +executing `ollama` `serve` . At the root of the `ollama-provider/` directory, you’ll find a + +`wasmcloud.toml` file. + +Listing 6.22 **`wasmcloud.toml`** file for the **`ollama-provider`** + +``` + name = "Ollama" + language = "rust" + type = "provider" + + [provider] + vendor = "thomastaylor312" + +``` + +This file declares the provider type with the line `type = "provider"` . Similar to building +Wasm components, you can use `wash` to build providers. In the `ollama-provider/` directory, build the Ollama provider with + +``` + wash build + +``` + +This will generate `ollama-provider.par.gz` in the `build/` directory, which is a provider +archive. + +TIP wasmCloud offers tools to interact with provider archives using `wash` `par` . +To inspect the archive, run `wash` `par` `inspect` `` . + +With the provider built, add it to the `wadm.yaml` file, placing it below the `kvnats` +provider: + +``` + # ... + + - name: ollama + type: capability + properties: + image: file://./ollama-provider/build/ollama-provider.par.gz + +``` + +Before linking it to our application, we need to add the WIT dependency. Let’s handle +that next. + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **159** + +Using custom WIT interfaces with wasmCloud +To integrate custom WIT interfaces in wasmCloud, the first step is to define the namespace and package in the application’s `wasmcloud.toml` file. This is done by adding an +override. See the following listing for the updated `smart-cms/wasmcloud.toml` file with +the necessary changes. + +Listing 6.23 Updated **`wasmcloud.toml`** file with custom interface override + +``` +name = "smart-cms" +version = "0.1.0" +language = "rust" +type = "component" + +[component] +wit_world = "backend" +wasm_target = "wasm32-wasip2" + +``` + +``` +[overrides] +"thomastaylor312:ollama" = +``` + +➥ `{ path = "./ollama-provider/wit/" }` + +**Adds override to link the** +**thomastaylor312:ollama interface** + +Here the override links the `thomastaylor312:ollama` namespace and package combination to the local path of the Ollama provider’s WIT directory. This ensures that the +interface is resolvable within our application. + +With the override in place, we can now import the Ollama interface in our `smart-cms` +WIT file. The updated `smart-cms/wit/world.wit` file is shown in the following listing. + +Listing 6.24 Updated **`world.wit`** file with import to custom ollama interface + +``` +package smartcms:store-and-ai-story-generator; + +``` + +``` +world backend { +import wasi:keyvalue/atomics@0.2.0-draft; +import wasi:keyvalue/store@0.2.0-draft; +import thomastaylor312:ollama/generate; +export wasi:http/incoming-handler@0.2.2; +} + +``` + +**Imports thomastaylor312:ollama/** +**generate interface** + +This import makes the `generate` interface from the Ollama package accessible to our +application, allowing us to use its machine learning functionality. + +The next step is to link the Ollama capability provider to the WIT interface in the + +`wadm.yaml` file. The following listing shows the updated `wadm.yaml` manifest with this +addition. + +Listing 6.25 Updated **`wadm.yaml`** file + +``` +apiVersion: core.oam.dev/v1beta1 +kind: Application +metadata: + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**160** Chapter 6 **_Creating production-grade Wasm applications_** + +``` +name: smart-cms +annotations: +description: "A smart CMS!" +spec: +components: +- name: http-component +type: component +properties: +image: file://./build/smart_cms_s.wasm +traits: +- type: spreadscaler +properties: +instances: 100 +spread: +- name: eastcoast +weight: 80 +requirements: +zone: us-east-1 +- name: westcoast +weight: 20 +requirements: +zone: us-west-1 +- type: link +properties: +namespace: wasi +package: keyvalue +interfaces: [store, atomics] +target: +name: kvnats +config: +- name: wasi-keyvalue-config +properties: +bucket: default +enable_bucket_auto_create: 'true' +- type: link +properties: +target: ollama +namespace: thomastaylor312 +package: ollama +interfaces: [generate] +target_config: +- name: ollama-conf +properties: +model_name: gurubot/ +``` + +➥ `tinystories-656k-q8` + +``` +- name: httpserver +type: capability +properties: +image: ghcr.io/wasmcloud/http-server:0.23.2 +traits: +- type: link +properties: +target: http-component +namespace: wasi + +``` + +**Links capability provider** +**(ollama) to WIT interface** +**(thomastaylor312:ollama/** +**generate)** + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **161** + +``` +package: http +interfaces: [incoming-handler] +source_config: +- name: default-http +properties: +address: 127.0.0.1:8000 + +- name: kvnats +type: capability +properties: +image: ghcr.io/wasmcloud/keyvalue-nats:0.3.1 + +- name: ollama +type: capability +properties: +image: file://./ollama-provider/build/ollama-provider.par.gz + +``` + +In this configuration, + +¡ The `ollama` provider is linked to the `generate` interface. + +¡ The `target_config` specifies the model name `gurubot/tinystories-656k-q8`, + +which we downloaded earlier. + +Finally, since the Ollama interface isn’t officially supported by wasmCloud, we need to +use `wit-bindgen` to generate bindings for the WIT interface. Add the following dependency to your `Cargo.toml` file: + +``` +wit-bindgen = "0.36.0" + +``` + +With that, we’ve prepared everything needed to integrate the Ollama functionality. +Next, we’ll add the `/api/generate` route to our SmartCMS application. + +Creating the /api/generate route +To enable interaction with our custom WIT interface in the SmartCMS application, +we use `wit-bindgen` to generate the necessary glue code. At the top of your `smart-cms/` + +`src/lib.rs` file, add + +``` +wit_bindgen::generate!({ generate_all }); + +``` + +This generates all required glue code for our custom interface. With that, next, we can +bring in the specific bindings for the `generate` function and its `Request` struct: + +``` +use thomastaylor312::ollama::generate::{generate, Request}; + +``` + +With the setup complete, add a `/api/generate` route to the `match` statement in your +component’s `handle` function: + +``` +match parts.uri.path() { +"/api/create" => { +// ... + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**162** Chapter 6 **_Creating production-grade Wasm applications_** + +``` + } + "/api/retrieve" => { + // ... + } + "/api/generate" => { +``` + +`// ...` **Adds the /api/generate route** +``` + } + _ => { + Ok(http::Response::new("Invalid route!\n".to_string())) + } + } + +``` + +All the new logic will be inside the `/api/generate` match arm. Start by generating a +story: + +``` + let prompt = "Once upon a time".to_string(); + let generated_story = generate(&Request { + prompt: prompt.clone(), + images: None, + }).unwrap(); + let story = format!("{}{}", prompt, generated_story.response); + +``` + +Here a static prompt, `"Once` `upon` `a` `time"`, is passed to the `generate` function. Then the +response is concatenated with the prompt to create the final story. + +Next, store the generated story in the key-value bucket: + +``` + let bucket = store::open("default").unwrap(); + let count = atomics::increment(&bucket, "counter", 1).unwrap(); + let story_name = format!("generated{}", count); + bucket.set(&story_name, story.clone().as_bytes()).unwrap(); + +``` + +Here again, the bucket is opened using the default namespace. Then a counter is +incremented to create a unique story title, `generatedX`, and the story is stored in the +key-value bucket. + +Finally, structure the HTTP response: + +``` + Ok(http::Response::new(format!("{}\n{}\n", story_name, story))) + +``` + +The response follows the format established in the `/api/create` route, where the first +line is the title, and the subsequent lines are the content of the story. + +See the following listing for the overall updated `smart-cms/src/lib.rs` file. + +Listing 6.26 Final **`smart-cms/src/lib.rs`** file + +``` + use std::io::Read; + use wasmcloud_component::http; + use wasmcloud_component::wasi::keyvalue::*; + + wit_bindgen::generate!({ generate_all }); + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Implementing the example project as a wasmCloud app_** **163** + +``` +use thomastaylor312::ollama::generate::{generate, Request}; + +struct Component; + +``` + +``` +http::export!(Component); + +``` + +**Implements http::Server to expose** +**the component via HTTP** + +``` +fn handle( +request: http::IncomingRequest, +) -> http::Result> { +let (parts, mut body) = request.into_parts(); + +``` + +**/api/create handles story creation and** +``` +"/api/create" => { +let bucket = store::open("default").unwrap(); + +let mut buf = Vec::new(); +body.read_to_end(&mut buf).unwrap(); +let body = String::from_utf8(buf).unwrap(); + +let mut lines = body.lines(); +let story_name = lines.next().unwrap_or("Unnamed +``` + +➥ `Story").trim();` +``` +let story_content = lines.collect::>().join("\n"); + +bucket.set(story_name, story_content.as_bytes()).unwrap(); + +``` + +``` +Ok(http::Response::new(format!("Stored {}\n", story_name))) +} + +``` + +**/api/retrieve** +**retrieves stories** +**from the bucket** +**based on a** +**provided key.** + +``` +let mut buf = Vec::new(); +body.read_to_end(&mut buf).unwrap(); +let story_name = +``` + +➥ `String::from_utf8(buf).unwrap().trim().to_string();` + +``` +match bucket.get(&story_name).unwrap() { +Some(content) => { +let story_content = +``` + +➥ `String::from_utf8(content).unwrap();` + +➥ `Ok(http::Response::new(format!("{story_content}\n")))` +``` +} +None => { +Ok(http::Response::new +``` + +➥ `("Story not found\n".to_string()))` +``` +} + +"/api/generate" => { +let prompt = "Once upon a time".to_string(); +let generated_story = generate(&Request { +prompt: prompt.clone(), +images: None, + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**164** Chapter 6 **_Creating production-grade Wasm applications_** + +``` + }).unwrap(); + let story = format!("{}{}", prompt, +``` + +➥ `generated_story.response);` + +``` + let bucket = store::open("default").unwrap(); + let count = atomics::increment(&bucket, "counter", +``` + +➥ `1).unwrap();` +``` + let story_name = format!("generated{}", count); + bucket.set(&story_name, story.clone().as_bytes()).unwrap(); + + Ok(http::Response::new(format!("{}\n{}\n", story_name, +``` + +➥ `story)))` +``` + } + _ => { + Ok(http::Response::new("Invalid route!\n".to_string())) + } + } + } + } + +``` + +Finally, in the `smart-cms/` folder, build and deploy your application: + +``` + wash build + wash app deploy ./wadm.yaml + +``` + +Verify the deployment status with `wash app list` . If everything is configured correctly, +the app will show a Deployed status. Up next: testing the functionality of our `/api/` + +`generate` route. + +Testing our application +With our application running via an HTTP endpoint, we can now connect it to a front­ +end interface. You’re welcome to build your own, or you can use the one I’ve created. +[You can find it at https://mng.bz/7QqV.](https://mng.bz/7QqV) + +To use this frontend: + +1 Copy the raw file content from the link. + +2 Save it locally as `index.html` in the `smart-cms/` directory. + +3 Open the file in a browser. + +NOTE If you want, instead of opening in your browser directly, you can use a +tool like `http-server` (installable with `cargo install http-server` ). From the +`smart-cms/` folder, run `http-server ./index.html`, then navigate to the appropriate localhost URL (shown in the terminal output) in your browser. + +The interface should look like figure 6.7. + +Using this frontend, you can + +¡ Create new stories. + +¡ Retrieve existing stories by name. + +¡ Generate AI stories. + +Licensed to Ibrahim Mohammed Mohammed + +**_Summary_** **165** + +Figure 6.7 Interface of +our SmartCMS frontend + +When you click the “Generate Story” button, the application will call the `/api/generate` +route, and you should see a response pop up. + +Awesome! We’ve successfully rebuilt our SmartCMS application using wasmCloud. +This time, we avoided writing any host-related code while gaining the benefits of a +robust hosting environment. wasmCloud handles + +¡ Load balancing across instances of our component + +¡ Spreading those instances across multiple hosts, which could be on entirely dif +ferent physical machines + +We only had to focus on implementing the business logic for our application. Additionally, we’ve enhanced the SmartCMS by making it accessible via an HTTP interface, +ensuring smooth integration with frontend tools like the one demonstrated here. + +_Summary_ + +¡ wasmCloud and Spin provide robust platforms for running Wasm microservices, + +significantly reducing the need for custom host development. + +¡ Standardized interfaces, like `wasi:keyvalue`, streamline the process of inte +grating capabilities such as persistent storage into applications while ensuring +interoperability with various providers. + +Licensed to Ibrahim Mohammed Mohammed + +**166** Chapter 6 **_Creating production-grade Wasm applications_** + +¡ Applications in wasmCloud can be scaled dynamically with the `spreadscaler` + +trait, allowing efficient handling of concurrent requests by deploying multiple +component instances. + +¡ The NATS lattice enables seamless communication between components distrib +uted across multiple hosts, ensuring resilience and fault tolerance. + +¡ Machine learning functionalities can be added to wasmCloud applications using + +custom WIT interfaces and third-party providers, showcasing wasmCloud’s +extensibility. + +¡ wasmCloud’s architecture eliminates the need for developers to write nonbusi +ness logic, focusing solely on application features and functionality. + +¡ By using off-the-shelf Wasm hosts, developers can achieve faster development + +cycles while maintaining robust and scalable architectures. + +Licensed to Ibrahim Mohammed Mohammed + +##### _Introducing_ _Wasm containers_ +# ~~_7_~~ + +_This chapter covers_ + +¡ The differences between containers and Wasm + +¡ Wasm containers + +¡ Distributing Wasm as Open Container Initiative + +images + +¡ Containerizing wasmCloud applications + +¡ Converting containers to Wasm + +We’ve come a long way from manually handling `String` objects by storing their +UTF-8 encoded characters in a Wasm module’s linear memory and passing them +across the host-to-module boundary using integer parameters for the `String` ’s +length and memory location. In the previous chapter, we explored wasmCloud—a +high-level Wasm runtime—and saw how it enables the use of standard and nonstandard WebAssembly Interface Types (WIT) interfaces to bring higher-level capabilities such as key-value stores and machine learning models to our Wasm applications. + +However, one important aspect we’ve yet to explore—aside from briefly mentioning it in chapter 1—is Wasm’s distribution story. Traditionally, developers distribute + +**167** + +Licensed to Ibrahim Mohammed Mohammed + +**168** Chapter 7 **_Introducing Wasm containers_** + +and deploy their applications using containers, which package code and its dependencies into a single unit, allowing applications to run consistently across environments, +from local machines to cloud platforms. At first glance, Wasm, as a hardware abstraction layer, may sound similar to containers—and if you think so, you’re not wrong! + +In this chapter, we’ll dive deeper into how containers work and highlight the parallels between containers and Wasm. We’ll also examine Wasm containers—the concept +we teased in chapter 1. Finally, we’ll explore how to convert traditional containers to +Wasm, bridging the gap between these two powerful paradigms for application deployment and distribution. + +_7.1_ _Containers vs. Wasm_ + +If you’ve ever developed an application, chances are you’ve reached a point where you +wanted to share it with others. But what if your application requires other software +(e.g., Ubuntu 22.04 and Python 3.8) that other people don’t have installed? You’d like +your app to behave consistently, even if its users are running it in a very different environment, like Windows using Python 3.10. Today, the most common way to do that is +by using containers, which package your application so it runs consistently across any +two computers with the same CPU architecture. + +This consistency is achieved by using operating system mechanisms such as control +groups (cgroups) and namespaces (or their equivalents on platforms like Windows). +These mechanisms provide the isolation and resource +control needed for containers to function. As illustrated + +with everything it needs to run—such as a userspace envi +ries, and other dependencies—on top of the container + +ers share the host’s kernel rather than including their + +By packaging your application this way, you ensure +that it behaves identically on two different computers +running different operating systems. This consistency is +guaranteed because the application always runs in the +same userspace environment and with the same dependencies, regardless of the underlying host. For example, +your application might require Ubuntu userspace utilities and libraries, but it could be deployed to a machine +running either Ubuntu or a different Linux, or even +Windows. + +Wasm, as we know, is also a method for packaging +applications, and its execution model shares many similarities with Docker’s approach. As shown in figure 7.2, + +Figure 7.1 The typical +architecture of a containerized +system, illustrating how +applications are packaged +with their dependencies +and userspace environment, +all managed by the Docker +engine on top of the host +operating system + +Licensed to Ibrahim Mohammed Mohammed + +**_Containers vs. Wasm_** **169** + +Figure 7.2 A side-by-side comparison of the execution models for containers and Wasm, showing how +both systems provide an abstraction layer between applications and the host operating system + +the Wasm runtime, when combined with WebAssembly System Interface (WASI), +occupies an architectural layer similar to the Docker engine, providing the interface +between your application binaries and the host operating system. + +However, to understand the differences between containers and Wasm, we need to +zoom in and examine what exactly is being distributed. When you distribute your app +via containers, you often package a userspace environment with system libraries and +utilities. But when you distribute via Wasm, you only package your application logic. +This distinction brings two key benefits: + +¡ _Smaller binaries_ —Wasm binaries are typically much smaller than container + +images, making them easier to distribute and faster to download. For comparison, even minimal “distroless” container images are typically a few megabytes, +while Wasm binaries are often measured in kilobytes. + +¡ _Faster startup times_ —Wasm binaries can start almost instantaneously, whereas con +tainers often have higher overhead due to initializing their userspace environment and its resources. + +These advantages are achieved without sacrificing portability, because Wasm doesn’t +make assumptions about its hosting environment. Instead, it relies on WASI—our standardized, portable system interface—to communicate with any host operating system. + +However, it’s worth noting that WASI itself must be implemented by Wasm runtimes +(e.g., Wasmtime). This means the responsibility of ensuring portability shifts from +application developers to runtime developers, simplifying the process for those building Wasm applications while maintaining flexibility and cross-platform compatibility. + +Wasm is actually even more portable than traditional containers. As mentioned earlier, Docker ensures an application runs the same on two computers, provided they +share the _same CPU architecture_ . This means that a container image (i.e., what you build +containers from) created for, say, AMD will not work on ARM without creating a multiarchitecture image. Wasm, on the other hand, eliminates this requirement. Wasm is +entirely hardware agnostic, meaning a Wasm binary built on AMD will run the same on +an ARM machine. + +Licensed to Ibrahim Mohammed Mohammed + +**170** Chapter 7 **_Introducing Wasm containers_** + +_7.2_ _Wasm containers_ + +What do people mean when they talk about Wasm containers? Is it Wasm inside a traditional Linux container? Is it just a rebranding of Wasm itself as a means of distribution? +Or is it something entirely new? To answer that, let’s first explore how traditional containers work. + +We’ll start by taking a closer look at the Docker engine. When you run a container, a +series of processes and mechanisms work together to bring it to life. At its core, the Docker +engine relies on _runC_ —a low-level container runtime—to spawn the container process. +Once the container is up and running, runC hands off control to the _containerd-shim_ . + +Think of a shim like a physical shim: it fits between two things and fills the gap so they +can work together smoothly. In this case, the containerd-shim sits between the runtime +and the container, acting as a kind of spacer and helper. It allows the container to continue running even after the parent process exits, redirects the container’s logs, and +manages lifecycle events like pause and restart. + +Sitting above these shims is the _containerd process_, which acts as the central manager +for all containers on a host system. It interfaces with the Docker engine and orchestrates interactions between the containerd-shims, the underlying host system, and any +external resources required by the containers. Both containerd and containerd-shim +are integral components of the Cloud Native Computing Foundation (CNCF) ecosystem. This layered architecture is illustrated in figure 7.3. + +Figure 7.3 A detailed +view inside the Docker +engine, showcasing the key +components and mechanisms +(e.g., containerd, runC, and +containerd-shim) responsible +for managing and starting +container processes + +Licensed to Ibrahim Mohammed Mohammed + +**_Wasm containers_** **171** + +Back in 2022, Docker introduced the _Docker+Wasm Technical Preview_, enabling developers to use their familiar Docker tooling for Wasm workloads. While this feature +has moved from technical preview to beta, support remains experimental and can be +unstable across different environments and Docker versions. For learning purposes, +we’ll focus on running Wasm containers directly with containerd—the underlying container runtime that Docker itself uses. + +What do the inner workings of the Docker engine look like when running Wasm? +Let’s take a look at figure 7.4 and compare it to figure 7.3. + +Figure 7.4 A closer +look inside the Docker +engine when executing +a Wasm module, +showing the key +components, including +containerd-shim-wasm +and the Wasm runtime + +As you can see, there are very few differences. First, instead of having a container +process, we now have the Wasm module. The Wasm module is executed by a runtime we’re already familiar with (e.g., Wasmtime) and is managed by a new type +of shim—the _containerd-shim-wasm_, which, again, handles tasks such as redirecting +logs, etc. + +TIP If you want to check out which other runtimes are supported aside from +Wasmtime, visit the `runwasi` project: [https://mng.bz/64qR.](https://mng.bz/64qR) `runwasi` is the +umbrella name for container runtime extensions for containerd that support +running Wasm workloads. + +Licensed to Ibrahim Mohammed Mohammed + +**172** Chapter 7 **_Introducing Wasm containers_** + +Running Wasm inside traditional containers + +Using containerd-shims isn’t the only way to run Wasm modules or components within +containers. Another option is to embed a Wasm runtime inside a traditional Docker +container, effectively allowing you to run Wasm on top of it. + +This approach is particularly useful for projects that require more than just a standalone Wasm binary—such as those that rely on additional infrastructure components. +For example, wasmCloud projects often depend on an ecosystem that includes a +NATS server. We’ll explore this approach in more detail later in the chapter. + +_7.3_ _Running a Wasm container_ + +Let’s walk through running a simple Wasm container. In your `chapter07/` directory, +create a standard Rust application with the following command: + +``` + cargo new hello-wasm-container + +``` + +Feel free to update the default `println!("Hello,` `World!")` message in the `main.rs` file +to something like + +``` + println!("Hello, Wasm containers!"); + +``` + +Once that’s done, from the `hello-wasm-container/` folder, compile the application +into a Wasm module by running + +``` + cargo build --target wasm32-wasip1 --release + +``` + +After the build completes, locate the generated Wasm module. For convenience, move +it to a new folder called `bin/` within `chapter07/hello-wasm-container/` . + +With the Wasm module ready, we can now use it to build our very first Wasm container. To do this, we’ll use a Dockerfile. Dockerfiles are configuration files that define +how a container image is built, specifying the base image, copying application files, and +setting the execution command. + +Next, we should create a file called `Dockerfile` at the root of `hello-wasm-container/` +and populate it as shown in the following listing. + +Listing 7.1 The Dockerfile for our first Wasm container + +``` + FROM scratch + COPY /bin/hello-wasm-container.wasm /hello-wasm-container.wasm + + ENTRYPOINT ["hello-wasm-container.wasm"] + +``` + +In this listing, we are doing the following: + +Licensed to Ibrahim Mohammed Mohammed + +**_Running a Wasm container_** **173** + +¡ `FROM scratch` —Specifies an empty base image, as Wasm binaries don’t require an + +operating system + +¡ `COPY` —Copies our compiled Wasm module ( `hello-wasm-container.wasm` ) into + +the container at the specified path ( `/hello-wasm-container.wasm` ) + +¡ `ENTRYPOINT` —Defines the Wasm module as the main executable to run when the + +container starts + +_7.3.1_ _Setting up containerd and Wasm support_ + +Before building our Wasm container, we need to install containerd, `ctr` (i.e., a +command-­line client for containerd), and the Wasmtime shim. The setup script we’ll +use is designed for Linux systems. If you’re on Windows, Windows Subsystem for Linux +(WSL) is recommended. Mac users should note that `ctr` doesn’t currently run natively +[on macOS (see https://mng.bz/oZoD), so a Linux VM is recommended.](https://mng.bz/oZoD) + +Run the following command to download and execute the installation script: + +``` + curl -sSL https://raw.githubusercontent.com/danbugs/serverside-wasm-book``` + +➥ `code/refs/heads/main/chapter07/hello-wasm-container/install-containerd-` +➥ `wasm.sh | bash` + +The script will + +¡ Download and install containerd + +¡ Configure containerd to run as a systemd service + +¡ Download and install the containerd-shim-wasmtime binary + +¡ Verify the installation + +After the script completes, verify that containerd is running: + +``` + sudo systemctl status containerd + +``` + +You should see output indicating that containerd is active and running. + +Verify the Wasmtime shim is installed: + +``` + which containerd-shim-wasmtime-v1 + +``` + +This should output `/usr/local/bin/containerd-shim-wasmtime-v1` . + +_7.3.2_ _Building and running the Wasm container_ + +Now that we have containerd and the Wasmtime shim installed, we can build our Wasm +container image. We’ll use Docker’s `buildx` tool to create the image, then run it with +containerd’s `ctr` command. + +First, create a builder that supports the Wasm platform: + +``` + docker buildx create --name wasm-builder --driver docker-container --use + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**174** Chapter 7 **_Introducing Wasm containers_** + +The `buildx` plugin extends Docker with advanced build capabilities powered by BuildKit. We name it `wasm-builder` and set it as the active builder with the `--use` flag. This +builder will allow us to build images for the `wasi/wasm32` platform. Now, from inside + +`hello-wasm-container/`, build the Wasm container image and export it as an OCI archive: + +``` + docker buildx build --platform wasi/wasm32 -t +``` + +➥ `hello-wasm-container --output type=oci,dest=hello-wasm-container.oci .` + +In this command, + +¡ The `--platform wasi/wasm32` flag specifies that we are building a Wasm container + +targeting the `wasi/wasm32` platform. This ensures the image metadata correctly +identifies the binary as Wasm. + +¡ The `--output type=oci,dest=hello-wasm-container.oci` flag exports the image + +as an OCI archive instead of loading it into Docker’s image store. We’ll dive +deeper into OCI and its role in Wasm distribution in the next section. + +¡ The `-t` flag (short for “tag”) assigns a name and optional tag to the container + +image (e.g., `hello-wasm-container` ). + +¡ The final `.` indicates the current directory, where the Dockerfile and build con +text are located. + +Import the OCI image into containerd: + +``` + sudo ctr -n default images import --platform=wasi/wasm32 hello-wasm``` + +➥ `container.oci` + +The `-n` `default` flag specifies the namespace ( `default` is the standard namespace for +containerd), and the `--platform` flag tells containerd which platform variant to import. + +Verify the image was imported successfully: + +``` + sudo ctr -n default images ls | grep hello-wasm + +``` + +Now run the Wasm container: + +``` + sudo ctr run --rm --runtime io.containerd.wasmtime.v1 --platform +``` + +➥ `wasi/wasm32 docker.io/library/hello-wasm-container:latest hello-wasm-` +➥ `instance` + +The `--runtime` `io.containerd.wasmtime.v1` flag specifies we want to use the Wasmtime +runtime via its containerd-shim, and `hello-wasm-instance` is a unique identifier we +assign to this running container instance. This should output + +``` + Hello, Wasm containers! + +``` + +Congratulations! You just ran your very first Wasm container! This is significant +because, as we discussed in chapter 1, Wasm containers offer several advantages over +traditional Docker containers. + +Licensed to Ibrahim Mohammed Mohammed + +**_Wasm and the OCI_** **175** + +One of the biggest benefits is faster cold starts. Unlike Docker containers, which +require initializing their userspace environment, Wasm containers only load the application itself. This significantly reduces startup times—often making Wasm containers +up to 160% faster to boot compared to their Docker counterparts. + +Another key advantage is smaller binary sizes. Traditional Docker container images +include not just the application but often also a userspace environment with system +libraries and utilities, resulting in large image sizes that need to be downloaded and +deployed. In contrast, Wasm binaries contain only the application logic and any necessary dependencies, often making them 80% smaller than equivalent Docker container +images. This reduction in size speeds up distribution and deployment while minimizing +storage and bandwidth requirements. + +These benefits make Wasm containers an excellent choice for resource-constrained +environments, such as IoT devices or edge-computing platforms, where traditional containers may struggle due to their size and initialization overhead. + +_7.4_ _Wasm and the OCI_ + +The OCI is an open governance structure maintained by the Linux Foundation, +focused on defining industry standards for container runtimes, images, and distribution. It ensures interoperability between container technologies, allowing developers +to build and run applications across different platforms without being locked into +vendor-­specific solutions. + +[In early 2024, OCI released version 1.1 of its specification (https://mng.bz/4n7Q).](https://mng.bz/4n7Q) +One of the most significant additions in this update was support for OCI artifacts, which +allows metadata and noncontainer objects to be stored and distributed using OCI registries. This extends OCI’s scope beyond just containers, enabling it to package and +manage arbitrary content, including Wasm components and WIT definitions. + +[With this expanded capability, the Wasm artifact format (https://mng.bz/Qw54)](https://mng.bz/Qw54) +was introduced, defining a standardized way to package Wasm components and their +required WIT dependencies as OCI artifacts. This means that both Wasm components and WIT definitions can now be stored, distributed, and deployed using OCI +registries—­just like traditional container images. As a result, Wasm workloads can integrate with existing cloud and container infrastructure, making it easier to deploy and +manage Wasm applications across major cloud providers. Let’s see an example of packaging and publishing a Wasm component as an OCI image! + +_7.4.1_ _Packaging and publishing Wasm components as OCI images_ + +We’ll be publishing our image to our personal GitHub accounts using the GitHub +Container Registry (GHCR). Before we begin, if you’d like to follow along, you’ll first +need to log in to GHCR. + +To do so, you’ll need a personal access token (PAT) for your GitHub account. You +[can generate one by following these instructions: https://mng.bz/X7XE. Make sure to](https://mng.bz/X7XE) +grant the token the following permissions: + +Licensed to Ibrahim Mohammed Mohammed + +**176** Chapter 7 **_Introducing Wasm containers_** + +¡ `write:packages` allows pushing images to GHCR + +¡ `read:packages` allows pulling images from GHCR + +¡ `delete:packages` (optional) allows deleting images from GHCR + +Once you have your token, you’re ready to log in. Run the following command in your +terminal: + +``` + echo "" | docker login ghcr.io -u --password``` + +➥ `stdin` + +NOTE While testing this example, I encountered mixed results on Windows. +If you’re using Windows, it’s recommended to run this inside WSL for better +compatibility. + +Now that we’re all set up, we need a Wasm component to publish. To keep our focus +on OCI packaging, let’s reuse the component we built in chapter 4, section 4.2.1. If you +don’t have that component handy, you can create a new one using + +``` + cargo component new --bin + cd + cargo component build --release + +``` + +Once you have a component, place it in a new directory inside `chapter07/` —for example, `chapter07/wasm-oci/.wasm` . + +Now, in your terminal, run + +``` + wkg oci push ghcr.io//:latest ` + +NOTE `wkg` is a tool for managing Wasm OCI artifacts, making it easy to package +and distribute Wasm components. You can install it locally by running `cargo` + +`install wkg@0.10.0 --locked` . + +If everything went as expected, you should now see your package listed under the Packages tab of your GitHub profile. However, keep in mind that, by default, packages published to GHCR are private. If you want to share your Wasm component with others, +you’ll need to update its visibility settings. + +Awesome! We’ve just packaged a Wasm component as an OCI image! Now, what can +we do with it? + +_7.4.2_ _Inspecting Wasm component OCI images_ + +Let’s take a moment to understand what’s happening under the hood. To do that, we’ll +inspect our published image using `regctl`, a command-line tool for interacting with +OCI registries. + +Licensed to Ibrahim Mohammed Mohammed + +**_Wasm and the OCI_** **177** + +NOTE If you’d like to follow along, you can install `regctl` on Ubuntu using +snap with `sudo` `snap` `install` `regclient` . At the time of writing, this installs + +`regctl` version 0.8.0. For other operating systems, follow [https://regclient](https://regclient.org/install) +[.org/install.](https://regclient.org/install) + +To start, let’s take a look at our application’s manifest by running + +``` +regctl manifest get ghcr.io//:latest + +``` + +NOTE If your package is not public, you’ll need to log in to the registry first +using your GH PAT: `echo " " | regctl registry login ghcr.io -u` + +`"" --pass-stdin` . + +You should get output like the following listing. + +Listing 7.2 Partial manifest for a Wasm OCI image + +``` +... +Config: +Digest: sha256:2ef427... + MediaType: application/vnd.wasm.config.v0+json +Size: 520B +Layers: +Digest: sha256:0223cb... + MediaType: application/wasm +Size: 82583B + +``` + +Here we can see that `Config.MediaType` and `Layers.MediaType` indicate that this is a +Wasm OCI image. These fields follow the Wasm artifact format we mentioned earlier, +ensuring compatibility across different tools and platforms. By adhering to these standards, the ecosystem can build tooling around Wasm OCI images just like it does for +traditional container images, enabling distribution and integration across registries, +cloud environments, and runtimes. + +Next up, we can further inspect our `Config.MediaType` by referencing its digest—a +unique identifier derived from the content of a file or object, ensuring integrity and +preventing accidental modifications. We do this using the following command: + +``` +regctl blob get ghcr.io// +``` + +➥ `:latest sha256:2ef427... | jq` + +Here we are fetching the config blob associated with our OCI image using `regctl` `blob` + +`get` and then piping ( `|` ) the output to `jq`, a command-line JSON processor that helps +format and analyze JSON data. + +NOTE To install `jq` [, visit https://jqlang.org/download/.](https://jqlang.org/download/) + +This command produces output similar to the following listing. + +Licensed to Ibrahim Mohammed Mohammed + +**178** Chapter 7 **_Introducing Wasm containers_** + +Listing 7.3 Result from inspecting a Wasm **`Config.MediaType`** + +``` + { + "created": "", + "author": null, + " architecture " : " wasm ", + " os " : " wasip2 ", + "layerDigests": [ + "sha256:0223cb..." + ], + "component": { + " exports " : [ + " wasi:cli/run@0.2.0 " + ], + " imports " : [ + " wasi:cli/environment@0.2.0 ", + " wasi:cli/exit@0.2.0 ", + " wasi:io/error@0.2.0 ", + " wasi:io/streams@0.2.0 ", + " wasi:cli/stdin@0.2.0 ", + " wasi:cli/stdout@0.2.0 ", + " wasi:cli/stderr@0.2.0 ", + " wasi:clocks/wall-clock@0.2.0 ", + " wasi:filesystem/types@0.2.0 ", + " wasi:filesystem/preopens@0.2.0 " + ], + "target": null + } + } + +``` + +In this output, we can see that our Wasm OCI image is explicitly marked as targeting +the `wasm` architecture with `wasip2` as the operating system. Additionally, we get a full +breakdown of the component’s WIT imports and exports, showing exactly what capabilities our Wasm module expects from the host and what it provides. + +This standardized approach to packaging Wasm components within OCI artifacts +establishes a foundation for distributing and discovering Wasm components at scale. +Instead of manually copying WIT interfaces as we did in previous chapters, we can now +pull them directly as OCI artifacts using familiar tooling. + +To see some examples of standard WIT interfaces being distributed as OCI artifacts, +check out the Bytecode Alliance’s published packages: [https://github.com/orgs/](https://github.com/orgs/bytecodealliance/packages) +[bytecodealliance/packages.](https://github.com/orgs/bytecodealliance/packages) + +_7.4.3_ _Pulling and running Wasm OCI images_ + +Now that we’ve successfully published our Wasm component as an OCI image, let’s +pull it back down and run it to complete the cycle. + +From inside the `wasm-oci/` folder, run the following command: + +``` + wkg oci pull ghcr.io// +``` + +➥ `:latest -o ./pulled.wasm` + +Licensed to Ibrahim Mohammed Mohammed + +**_wasmCloud and Wasm containers_** **179** + +This command fetches the Wasm OCI image from GHCR and outputs it as a standalone Wasm module, ready to be executed by a Wasm runtime. + +To run it using Wasmtime, do + +``` + wasmtime run ./pulled.wasm + +``` + +If everything works correctly, you should see the following output: + +``` + Hello, World! + +``` + +And that’s it! We’ve pushed, inspected, pulled, and executed a Wasm OCI image. + +Overall, Wasm OCI images provide a standardized way to distribute Wasm components, integrating seamlessly into existing OCI-based tooling. This enables + +¡ _Discoverability_ —Developers can store and retrieve Wasm components from regis +tries just like container images. + +¡ _Integration with cloud-native workflows_ —By using OCI, Wasm components can be + +managed alongside traditional containers, making them easier to adopt in existing infrastructure. + +_7.5_ _wasmCloud and Wasm containers_ + +Earlier, when running our Wasm container, we used the following command: + +``` + sudo ctr run --rm --runtime io.containerd.wasmtime.v1 --platform +``` + +➥ `wasi/wasm32 docker.io/library/hello-wasm-container:latest hello-wasm-` +➥ `instance` + +This let us run a Wasm module inside a container using Wasmtime as the execution +runtime. But, as we’ve seen earlier, there are other options—different `--runtime` values +or shims, like `io.containerd.spin.v2`, let you run Spin applications in a similar way. + +So does wasmCloud work the same way? Can we just swap in a wasmCloud shim and +pass a different `--runtime` flag to containerd? + +The answer is no. Unlike the containerd-shim-based approach we’ve used so far, +wasmCloud doesn’t just wrap a single Wasm binary in a container—it packages the entire +wasmCloud ecosystem. This ensures that wasmCloud retains its full feature set, including `wadm` and the NATS lattice, even when running in a containerized environment. + +Rather than using a lightweight shim to start individual Wasm binaries, wasmCloud +provides a complete, self-contained runtime that runs inside a container. Figure 7.5 +illustrates how this setup works. + +On the left, we see the approach we’ve explored throughout this chapter—the native +method of running Wasm in containers. This involves using containerd with a containerd-­ +shim that provides a Wasm runtime, such as Wasmtime or a higher-level runtime like +Spin. On the right, we have wasmCloud’s approach, which takes a different route. +Instead of relying on a shim, wasmCloud packages its entire host environment inside + +Licensed to Ibrahim Mohammed Mohammed + +**180** Chapter 7 **_Introducing Wasm containers_** + +**Option 1: Wasm as** +**a container** + +**Option 2: Wasm in a** +**container (with tooling)** + +Figure 7.5 A comparison of two approaches to running Wasm in containers. On the left, Wasm runs +directly inside a container using a lightweight shim. On the right, Wasm is packaged within a container +alongside supporting infrastructure, as seen in wasmCloud’s approach, which includes components like +the NATS server, a local OCI registry, and metrics/tracing tools. + +the container, running it as a self-contained unit. This approach is more opinionated. +It packages more tools, resulting in larger containers but ensuring that developers +can still take full advantage of wasmCloud’s ecosystem, including features like linking +Wasm components to specific implementations that satisfy their imports and so on. + +Let’s take a look at an example. To demonstrate, we’ll use _Docker Compose_, which +allows us to define and manage multicontainer applications using a single configuration file. One of its key benefits is that it sets up a virtual network, enabling communication between the containers it orchestrates. + +In figure 7.5, we illustrated a Docker Compose setup with multiple components +[(available here: https://mng.bz/Mwq8). This setup includes not only the core wasm­](https://mng.bz/Mwq8) +Cloud infrastructure—such as the required NATS server, `wadm` server, and wasmCloud +host—but also additional services like a local OCI registry (for storing Wasm artifacts) +and metrics/tracing tooling to monitor performance. For our example, we’ll be using a +streamlined version of that setup, shown in the following listing. + +Listing 7.4 **`docker-compose.yaml`** for wasmCloud ecosystem + +``` +version: "3" +services: +nats: +image: nats:2.10-alpine + +``` + +**Defines the NATS service, pulling the nats:2.10-alpine image** +**and exposing port 4222. The -js flag enables JetStream.** + +Licensed to Ibrahim Mohammed Mohammed + +**_wasmCloud and Wasm containers_** **181** + +``` +ports: +``` + +**Defines the NATS service, pulling the nats:2.10-alpine image** +``` +- "4222:4222" +``` +**and exposing port 4222. The -js flag enables JetStream.** +``` +command: ["-js"] +wasmcloud: +depends_on: +- "nats" +image: wasmcloud/wasmcloud:latest +environment: +RUST_LOG: debug,hyper=info,async_nats=info,oci_client= +``` + +➥ `info,cranelift_codegen=warn` +``` +WASMCLOUD_LOG_LEVEL: debug +WASMCLOUD_RPC_HOST: nats +WASMCLOUD_CTL_HOST: nats +ports: +- "8000:8000" +wadm: +``` + +`depends_on:` **Defines the wadm server** +``` +- "nats" +image: ghcr.io/wasmcloud/wadm:latest +``` + +`environment:` **wasmcloud/wadm image** +``` +- WADM_NATS_SERVER=nats + +``` + +**Defines the NATS service, pulling the nats:2.10-alpine image** +**and exposing port 4222. The -js flag enables JetStream.** + +**Defines the** +**wasmCloud host** +**service, which** +**depends on NATS. It** +**runs the wasmcloud/** +**wasmcloud image** +**and sets environment** +**variables for logging** +**and RPC** +**communication.** + +**Defines the wadm server** +**service, which also depends** +**on NATS and runs the ghcr.io/** +**wasmcloud/wadm image** + +This Docker Compose file sets up a minimal wasmCloud ecosystem, ensuring that the +necessary services—NATS, the wasmCloud host, and the `wadm` server—are correctly +orchestrated within separate containers. Now place this `docker-compose.yaml` file +inside a new directory under your `chapter07/` projects folder, naming the subdirectory + +`hello-wasmcloud-container/` . + +With this done, let’s quickly put together a `wadm.yaml` file for our application. This +file defines how our wasmCloud components will be deployed and connected within +our Docker Compose setup. Overall, it should look like the following listing. + +Listing 7.5 **`wadm.yaml`** file for our Docker Compose example + +``` +apiVersion: core.oam.dev/v1beta1 +kind: Application +metadata: +name: hello-wasmcloud-container +annotations: +description: '' +spec: +components: +- name: http-component +type: component +properties: +image: ghcr.io/wasmcloud/ +``` + +➥ `components/http-hello-world-rust:0.1.0` +``` +traits: +- type: spreadscaler +properties: +instances: 1 + +- name: httpserver + +``` + +**Defines the http-component,** +**which is sourced from a** +**prebuilt Wasm OCI image** +**stored in GHCR.** + +Licensed to Ibrahim Mohammed Mohammed + +**182** Chapter 7 **_Introducing Wasm containers_** + +``` + type: capability + properties: + image: ghcr.io/wasmcloud/http-server:0.25.0 + traits: + - type: link + properties: + target: + name: http-component + namespace: wasi + package: http + interfaces: [incoming-handler] + source: + config: + - name: default-http + properties: + address: 0.0.0.0:8000 + +``` + +This `wadm.yaml` file specifies how our Wasm components will be orchestrated inside +the wasmCloud runtime. Unlike previous examples where we referenced a locally built +Wasm component, here, for ease of setup, we are using a prepackaged `http-component` +stored in GHCR. With this in place, we’re ready to run the example! + +On one terminal, from inside our `hello-wasmcloud-container/` directory, run + +``` + docker compose up + +``` + +This command will start all the services defined in our `docker-compose.yaml` file, bringing up the NATS server, wasmCloud host, and `wadm` server as separate containers. Note +that since Docker Compose creates a dedicated network for these containers, they can +communicate without additional configuration. + +Once the services are up and running, in another terminal, we can verify that our +wasmCloud host is active by running + +``` + wash get hosts + +``` + +If everything is set up correctly, the host should appear in the output, confirming that +wasmCloud is successfully running inside the container. + +With our host verified, we can proceed with deploying our `wadm.yaml` application +definition. Now run + +``` + wash app deploy ./wadm.yaml + +``` + +After deploying, we can check that the application is running by listing active applications with + +``` + wash app list + +``` + +If everything is working correctly, we should see our `hello-wasmcloud-container` application in the output with confirmation that it has been deployed successfully. + +Licensed to Ibrahim Mohammed Mohammed + +**_Converting Linux containers to Wasm_** **183** + +To further verify that our deployed component is handling requests, we can send a +request to the exposed HTTP server by running + +``` + curl http://127.0.0.1:8000/ + +``` + +If the application is running properly, this should return + +``` + Hello from Rust! + +``` + +Awesome—with that, we just deployed our Wasm component onto the host running +inside the `wasmcloud` Docker Compose service! To stop Docker Compose, press Ctrl-C, +and then run `docker` `compose` `down` to clean up our resources. + +Now, while this setup demonstrates how to run a wasmCloud application inside a +container, it is by no means production ready. Docker Compose is primarily designed +for local development and lacks key features required for production-grade orchestration in a real-world deployment. In the next chapter, we’ll take this a step further +by learning how to use Kubernetes to orchestrate and manage our wasmCloud-based +applications in a production-ready manner. + +_7.6_ _Converting Linux containers to Wasm_ + +Unlike previous chapters, we won’t be modifying our SmartCMS in this one—we’re +saving that for the next chapter, where we’ll explore Wasm in Kubernetes. Instead, +we’ll focus on a different hands-on demo: converting a Linux container to Wasm. Let’s +get started! + +_7.6.1_ _Introducing container2wasm_ + +[If you are not familiar with QEMU (https://www.qemu.org/), it is an open source](https://www.qemu.org/) +machine emulator and virtualizer that enables running programs compiled for one +architecture on another (e.g., running an ARM binary on an x86 machine). It is commonly used for cross-platform development, testing, and virtualization. + +Much like QEMU, container2wasm provides CPU emulation, allowing Linux-based +container workloads to be converted into Wasm binaries. This is particularly valuable +because many Linux applications require substantial modifications—or even complete +rewrites—to run as Wasm due to WASI’s limited POSIX support. + +Rather than requiring developers to manually port applications, container2wasm +works by compiling CPU emulators—including QEMU itself—into Wasm. This enables +existing Linux applications to be executed inside a Wasm runtime with minimal modifications. Figure 7.6 illustrates this process. + +The higher degree of portability does come with a cost, though. In particular, you’ll +find that container2wasm applications, compared to their equivalent raw Wasm counterparts, have + +¡ Lower performance due to the virtualization layer introduced by the CPU + +emulator. + +Licensed to Ibrahim Mohammed Mohammed + +**184** Chapter 7 **_Introducing Wasm containers_** + +¡ Larger binary sizes because of + +additional dependencies, such +as the embedded CPU emulator +and minimal Linux runtime. + +Still, wasmifying a container allows you + +previously possible. Beyond simply + +**x86_64,** + +run an entire container inside the +browser, creating in-browser develop +Figure 7.6 container2wasm encapsulates a CPU + +ment and testing environments where emulator to run Linux and Linux applications inside +users can interact with a full Linux- Wasm, enabling execution within either a WASI +based application without needing a runtime or a browser environment. +local container runtime. It also makes +offline-first computing much more practical. Applications that previously required +a cloud backend—like collaborative document editors, CRM dashboards, or project +management tools—can now run entirely in the browser and sync data back to the +cloud when a connection is available. + +**x86_64,** +**RISC-V,** +**AArch64,** +**etc.** + +Figure 7.6 container2wasm encapsulates a CPU +emulator to run Linux and Linux applications inside +Wasm, enabling execution within either a WASI +runtime or a browser environment. + +Additionally, this technique enables secure, ephemeral execution environments. +Think of things like running a confidential form-filling app, an online coding interview +tool, or even short-lived data analysis tasks. These containers run in a fully isolated way, +and once closed, they leave no trace on the user’s device. + +Now let’s try wasmifying our first container! + +_7.6.2_ _Installing container2wasm_ + +To start off, let’s install container2wasm. At the time of writing, prebuilt releases are +only available for Linux amd64 and arm64. If you’re using Windows, you can run it +through WSL. On macOS, since there’s no native support, again, you’ll likely need to +provision a Linux environment to run it. + +As of this writing, container2wasm’s latest version is 0.8.3, so we’ll be using that. Run +the following command to begin the installation: + +``` + wget https://github.com/container2wasm/container2wasm/releases +``` + +➥ `/download/v0.8.3/container2wasm-v0.8.3-linux-amd64.tar.gz` + +NOTE If you’re on an arm64 Linux machine instead of amd64, make sure to +download the arm64 release by replacing amd64 in the link with arm64. You +[can find all available versions at https://mng.bz/gmWG.](https://mng.bz/gmWG) + +After downloading the release, extract the archive with + +``` + tar -xvf container2wasm-v0.8.3-linux-amd64.tar.gz + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Converting Linux containers to Wasm_** **185** + +Next, move the extracted binaries to a directory in your `PATH` so they can be used system wide: + +``` + sudo mv c2w c2w-net /usr/local/bin/ + +``` + +Ensure the binaries are executable by running + +``` + chmod +x /usr/local/bin/c2w /usr/local/bin/c2w-net + +``` + +Now verify that the installation was successful by checking the version: + +``` + c2w --version + +``` + +You should see output similar to + +``` + c2w version v0.8.3 + +``` + +Finally, clean up by removing the downloaded archive: + +``` + rm container2wasm-v0.8.3-linux-amd64.tar.gz + +``` + +_7.6.3_ _Using container2wasm_ + +Now, we’re all set up to start using `container2wasm` . In your `chapter07/` directory, create +a new folder for this project—let’s call it `hello-container2wasm/` . + +For this section, we’ll set up a simple Python web server using Flask, a widely used +web framework for building lightweight web applications and APIs. + +Start by creating an `app.py` file inside the `hello-container2wasm/` folder. Its contents +should match the following listing. + +Listing 7.6 **`app.py`** file for a simple Python web server + +``` + from flask import Flask + + app = Flask(__name__) + + @app.route('/') + def hello(): + return "hello from inside a container running on Wasm" + + if __name__ == '__main__': + app.run(host='0.0.0.0', port=8000) + +``` + +In this file, we define a minimal Flask web server. The `Flask` object initializes the application, and we define a single route, `/`, which returns a simple text response when +accessed. The final block ensures the server runs on all available network interfaces at +port 8000, making it accessible from outside the container once deployed. + +Licensed to Ibrahim Mohammed Mohammed + +**186** Chapter 7 **_Introducing Wasm containers_** + +With this done, in the same folder, let’s now create a `Dockerfile` to containerize our +application. This file will define the environment and dependencies needed to run our +Python web server inside a container. Your `Dockerfile` should look like the following +listing. + +Listing 7.7 Dockerfile for a simple Python web server + +``` + FROM python:3.9-slim + + WORKDIR /app + + RUN pip install flask + + COPY app.py . + + EXPOSE 8000 + + CMD ["python", "app.py"] + +``` + +In this file, we define a lightweight containerized environment for our Flask application. We start from the `python:3.9-slim` base image, which provides a minimal Python +runtime. We set the working directory to `/app`, install Flask using `pip`, and copy our + +`app.py` file into the container. The `EXPOSE` `8000` instruction indicates that the application listens on port 8000, and the final `CMD` instruction runs the application when the +container starts. + +With the `Dockerfile` in place, you can now build your container image as you normally would by running + +``` + docker build -t python-wasm-webserver . + +``` + +This will compile your image and store it locally. Next, we can use `container2wasm` to +convert our newly built container image into a Wasm binary by running + +``` + c2w python-wasm-webserver python-wasm-webserver.wasm + +``` + +NOTE This step can take a while. + +Now that we have our Wasm binary, how do we run it? Theoretically, we could use +Wasmtime directly, but there’s a limitation: Wasmtime doesn’t provide a built-in way +to forward ports to the local host. This means that while we can execute the binary, we +won’t be able to interact with our web server. + +[Other runtimes, such as wazero (https://github.com/tetratelabs/wazero), offer this](https://github.com/tetratelabs/wazero) +functionality out of the box. However, for convenience—since we already installed it— +we’ll use `c2w-net` . `c2w-net` is a wrapper around Wasmtime that enables networked applications to function properly by acting as a proxy. + +Do this by running the following command: + +Licensed to Ibrahim Mohammed Mohammed + +**_Converting Linux containers to Wasm_** **187** + +``` +c2w-net --invoke -p localhost:8000:8000 ./python-wasm-webserver.wasm -``` + +➥ `net=socket` + +You might see some “failed” logs, but these can be ignored. After a short moment, you +should see output similar to the following: + +``` +* Serving Flask app 'app' +* Debug mode: off +WARNING: This is a development server. Do not use it in a production +``` + +➥ `deployment. Use a production WSGI server instead.` +``` +* Running on all addresses (0.0.0.0) +* Running on http://127.0.0.1:8000 +* Running on http://192.168.127.3:8000 +Press CTRL+C to quit + +``` + +Now, in another terminal, run the following command: + +``` +curl http://127.0.0.1:8000/ + +``` + +This should return + +``` +hello from inside a container running on Wasm + +``` + +And there it is! We just ran a Python Flask web server that was containerized and converted to Wasm, providing it with enhanced portability and security. + +If you’re interested in exploring more examples—especially its use in browsers— +[check out https://ktock.github.io/container2wasm-demo/. There you’ll find demon-](https://ktock.github.io/container2wasm-demo/) +strations such as a full Debian image being wasmified and executed. Or, if you’d like to +try it yourself, you can run + +``` +c2w ubuntu:22.04 ubuntu.wasm + +``` + +Then, using Wasmtime, you can start a Wasm-encapsulated Ubuntu environment with + +``` +wasmtime ubuntu.wasm + +``` + +This should give you a shell prompt, like + +``` +root@localhost:/# + +``` + +With that, we’ve seen how container2wasm allows us to take existing Linux containers and convert them into Wasm binaries, making them more portable and secure. +While this approach introduces some tradeoffs, such as increased binary size and performance overhead, it opens up new possibilities for running containerized workloads +in constrained environments—including the browser. + +Licensed to Ibrahim Mohammed Mohammed + +**188** Chapter 7 **_Introducing Wasm containers_** + +_Summary_ + +¡ Containers and Wasm share similarities in providing portability and isolation, + +but Wasm eliminates the need for a guest OS, resulting in smaller binaries and +faster startup times. + +¡ Wasm containers use containerization principles to package and distribute + +Wasm binaries, integrating them into cloud-native ecosystems while maintaining +Wasm’s lightweight nature. + +¡ The OCI now supports Wasm artifacts, enabling standardized distribution of + +Wasm components via OCI registries like GHCR. + +¡ Unlike traditional container-based Wasm execution, wasmCloud packages an + +entire host environment inside a container, facilitating managed orchestration +of Wasm components. + +¡ container2wasm enables running Linux applications as Wasm binaries by encap +sulating a CPU emulator, expanding Wasm’s use cases to legacy applications and +in-browser execution. + +Licensed to Ibrahim Mohammed Mohammed + +##### _with Kubernetes_ +# ~~_8_~~ + +##### _Scalability for Wasm_ + +##### _with Kubernetes_ + +_This chapter covers_ + +¡ Exploring the Custom Resource Definition and + +operator patterns in Kubernetes + +¡ Extending Kubernetes to support Wasm workloads + +¡ Using SpinKube to deploy Spin Wasm apps to + +Kubernetes + +¡ Scaling Spin apps with Horizontal Pod Autoscalers + +¡ Deploying wasmCloud applications to Kubernetes + +Wasm has some clear advantages over containers. It’s smaller, which makes it +quicker to transfer over the network; it starts up faster; and it’s built with a stronger +security model. But despite all that, Wasm is still playing catch-up when it comes to +the mature tooling and ecosystem that containers enjoy. + +In the previous chapter, we looked at how to package Wasm applications as container images. Using the Open Container Initiative (OCI) image format, we tap into +the rich tooling built for containers—most notably, Kubernetes. + +**189** + +Licensed to Ibrahim Mohammed Mohammed + +**190** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +Kubernetes is the standard way to manage containers in production. It takes care of +scaling, load balancing, service discovery, and more. In this chapter, we’ll look at how +Wasm can plug into that same system and how we can take advantage of the Kubernetes +feature set to run and manage Wasm workloads. + +_8.1_ _Kubernetes and Wasm_ + +Imagine you’re running an online game server that needs more instances during peak +hours. You don’t want to manually spin up new servers every evening and shut them +down at night. Kubernetes can handle that for you. It watches how busy things are and +scales up or down as needed. That’s just one example of the kind of operational heavy +lifting Kubernetes is built for. + +If you’ve used Kubernetes before, you’ve probably used it to deploy containers— +maybe a web app, an API, or even that same game server. That’s what it’s best known +for. But Kubernetes isn’t just a container orchestrator. At its core, it’s an extensible API +server that manages workloads through a well-defined architecture. As shown in figure +8.1, a Kubernetes cluster consists of two main parts: the control plane and the data + +Figure 8.1 Kubernetes architecture showing the control plane (API server, scheduler, controller manager) and +data plane (nodes with pods). The control plane manages cluster state and makes scheduling decisions, while +the data plane runs the actual workloads. Users interact with the cluster through the API server, typically using +**`kubectl`** commands or YAML manifests. + +Licensed to Ibrahim Mohammed Mohammed + +**_Kubernetes and Wasm_** **191** + +plane. The control plane makes all the decisions—where to place workloads, when to +scale them, and how to respond to failures. The data plane is where your applications +actually run, distributed across nodes that can host both traditional containers and, as +we’ll see, WebAssembly workloads. + +Since version 1.7, when Custom Resource Definitions (CRDs) were introduced, +Kubernetes has been capable of managing more than just containers—it can orchestrate pretty much anything. But there’s no built-in concept of an “application” in Kubernetes. Instead, we compose apps from primitives like deployments, services, and config +maps. CRDs let us extend that model by defining our own top-level resources—things +that look and behave more like actual applications. We can then tweak their configuration the same way we would for native Kubernetes resources. + +There are plenty of real-world examples of this pattern. Crossplane uses CRDs to +model things like databases or storage buckets. KubeVirt does it for virtual machines. +And we can do it for Wasm too. + +Once a CRD is applied to our Kubernetes cluster, we can deploy our custom +resources (CRs) just like we would any other Kubernetes object. But CRDs aren’t the +full story. Having a CRD and applying a CR is only useful if there’s an operator—a +controller running in the cluster that knows how to handle that CR. The operator +interprets what the resource represents and acts accordingly. That might mean spinning up a deployment, wiring up networking, or handling day 2 concerns like updates +and restarts. + +As shown in figure 8.2, this is exactly how Wasm fits into Kubernetes today. Projects +like Spin and wasmCloud have done the heavy lifting of defining CRDs and building +operators, so we can simply apply them to our clusters and let Kubernetes take care of +orchestrating our Wasm apps like any other workload. + +**Wasm CRD enables** +**Wasm to be used like** +**any other Kubernetes** +**resource.** + +**Wasm operator uses the** +**CRD to create Wasm** +**deployments/services.** + +Figure 8.2 How a Wasm operator fits into Kubernetes: the user deploys a Wasm app by submitting a CR to the +Kubernetes API server. This resource is defined by a Wasm-specific CRD, which is handled behind the scenes by a +Wasm operator. + +Licensed to Ibrahim Mohammed Mohammed + +**192** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +Kubernetes concepts for Wasm developers + +While we’ll introduce Kubernetes terms as they appear in context, a few concepts are +worth understanding up front: + +¡ _YAML manifests_ are the format Kubernetes uses for its declarative configura +tions. You describe the desired state (e.g., “I want 3 replicas of my app running”), and Kubernetes figures out how to make it happen. +¡ _Deployments_ are higher-level abstractions that manage groups of identical + +pods. Think of them as templates for your application instances that handle +scaling, updates, and recovery automatically. +¡ _Services_ provide stable networking for your pods. Since pods can come and go, + +services give them a consistent address and handle load balancing. +¡ _Namespaces_ are logical partitions within a cluster. They’re like folders in a + +filesystem—useful for organizing resources and keeping different projects or +teams separated. + +_8.2_ _Running Wasm in Kubernetes with SpinKube_ + +Now let’s put what we’ve just learned into practice—starting with SpinKube. SpinKube +is the Kubernetes integration for Spin, the Wasm framework we explored back in chapter 6. It brings Spin’s developer-friendly tooling into the Kubernetes world using CRDs +and operators under the hood. + +Before we begin, it’s worth noting that you’ll need Spin installed on your system—we +covered that in chapter 6. In this chapter, we’ll extend Spin’s capabilities by installing +an official plugin called `kube`, which streamlines the process of deploying Spin applications to Kubernetes. + +Other than that, to follow along with the rest of this chapter, you’ll need a few more +tools installed locally: `k3d` to spin up a local Kubernetes cluster, `kubectl` to interact with +it, and _Helm_ to simplify deploying resources to it. Once you’ve got those in place, we’ll +be ready to start working with SpinKube. + +First, we’ll install `k3d` to set up the Kubernetes cluster itself. `k3d` is a tool that makes it +easy to run a local Kubernetes cluster by using Docker containers. It’s fast, lightweight, +and a great option for development and testing. At the time of writing, I’ve installed the +[latest available version: v5.8.3. You can find installation instructions here: https://k3d](https://k3d.io/v5.8.3/?h=installation#releases) +[.io/v5.8.3/?h=installation#releases.](https://k3d.io/v5.8.3/?h=installation#releases) + +Next up is `kubectl`, the command-line tool used to interact with a Kubernetes cluster. +You’ll use it to apply configs, inspect resources, and run most Kubernetes-related com[mands throughout this chapter. Installation instructions are here: https://kubernetes](https://kubernetes.io/docs/tasks/tools/) +[.io/docs/tasks/tools/. Locally, I’m using](https://kubernetes.io/docs/tasks/tools/) + +``` + kubectl version --client + Client Version: v1.33.0 + Kustomize Version: v5.6.0 + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with SpinKube_** **193** + +Then, we’ll need Helm. Helm is a templating tool that helps you generate the YAML +needed to interact with the Kubernetes API. It’s built for managing complex deployments without needing to handwrite pages of YAML. You can think of it as a way to +bundle Kubernetes manifests together with configuration values, making deployment +simpler and more repeatable. + +NOTE In some ways, Helm and CRDs solve similar problems—they both +abstract complex Kubernetes deployments that would otherwise require writing large, complicated manifests. But while CRDs extend the Kubernetes API +from the inside, Helm works on the client side. It prepares and sends the full +set of objects—deployments, services, and so on—to the cluster in one go. + +[You can install Helm by following https://helm.sh/docs/intro/install/. I’m using the](https://helm.sh/docs/intro/install/) +following version: + +``` + helm version + version.BuildInfo{Version:"v3.17.3", +``` + +➥ `GitCommit:"e4da49785aa6e6ee2b86efd5dd9e43400318262b",` +➥ `GitTreeState:"clean", GoVersion:"go1.23.7"}` + +Finally, we’ll install the Spin plugin that makes it easier to deploy Spin applications to +Kubernetes. Assuming you already have Spin installed, run + +``` + spin plugins update + spin plugins install kube + spin plugins list --installed + +``` + +You should see output similar to this (versions may vary): + +``` + cloud 0.11.0 [installed] + kube 0.4.0 [installed] + +``` + +If you want to upgrade your plugin version to match this book, use the command `spin` + +`plugins upgrade` and choose the right version using their interactive picker. With that, +we’re ready to prepare our cluster for Wasm. + +_8.2.1_ _Preparing the cluster for SpinKube_ + +A _cluster_ in Kubernetes is the whole system: a control plane that manages the cluster state +and a set of nodes that are running your workloads. When we say “deploy to Kubernetes,” we mean deploying to a cluster. So, before we can deploy our Wasm application, we +first need to set up a local Kubernetes cluster and prepare it with a few key components: + +¡ Support tools like `cert-manager` to enable secure webhooks + +¡ A `RuntimeClass` so Kubernetes knows how to launch Wasm workloads + +¡ The CRDs and operator from SpinKube, which handle the actual orchestration + +Let’s go through those steps one by one. + +Licensed to Ibrahim Mohammed Mohammed + +**194** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +Creating the cluster +First, we’ll create the Kubernetes cluster itself: + +``` + k3d cluster create wasm-cluster --image ghcr.io/spinframework/containerd``` + +➥ `shim-spin/k3d:v0.19.0 --port "8081:80@loadbalancer" --agents 2` + +This command creates a cluster named `wasm-cluster` using a custom `k3d` image that +includes the `containerd-shim-spin` runtime we looked at in chapter 7. That’s what lets +Kubernetes run Spin-based Wasm workloads. It also exposes port 80 on the cluster’s +load balancer as port 8081 on your local machine and sets up two agent nodes—the +physical or virtual machines where Kubernetes will actually run our workloads. + +When we deploy our Wasm applications later, Kubernetes will package them into +pods (the smallest deployable units in Kubernetes, each typically running a single container or Wasm module) and schedule these pods across our two nodes based on available resources. + +Installing support tools +Next, we need to install `cert-manager` —a Kubernetes add-on that automates the +issuance and renewal of _TLS certificates_ . SpinKube relies on it to secure its _admission_ +_webhooks_ . These webhooks play a critical role: when you submit a CR like a Spin app, +Kubernetes may call out to the operator’s webhook to validate the configuration or +apply defaults. That call must be made securely, and that’s where TLS comes in. + +You can install `cert-manager` with + +``` + kubectl apply -f https://github.com/cert-manager/cert``` + +➥ `manager/releases/download/v1.14.3/cert-manager.yaml` +``` + kubectl wait --for=condition=available --timeout=300s deployment/cert``` + +➥ `manager-webhook -n cert-manager` + +This sets up `cert-manager` and ensures its own webhook server is available. Later, the +SpinKube operator will request a TLS certificate from `cert-manager` for its webhook +service, so that the Kubernetes API server can communicate with it over HTTPS. + +NOTE TLS (Transport Layer Security) enables encrypted and authenticated +communication between services—it’s what puts the “S” in HTTPS. In Kubernetes, TLS is used not just for external traffic but also for securing internal +components like webhooks. + +Defining Spin’s runtime with RuntimeClass +With that in place, the next piece we need is a `RuntimeClass` . Most of the time in Kubernetes, you don’t need to think about container runtimes at all. When you deploy a pod, +it just runs—usually using the default container runtime configured on the node, like +containerd or CRI-O. But in our case, we’re not running regular containers. We’re +deploying Wasm binaries, and those require adding a new, specialized runtime that +knows how to execute them. + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with SpinKube_** **195** + +That’s where `RuntimeClass` comes in. It’s a standard Kubernetes resource that lets +you explicitly tell Kubernetes which runtime to use for a particular workload. In our +case, as shown in the following listing, we want to direct Kubernetes to use the `spin` runtime, which is provided by the containerd shim we set up earlier. + +Listing 8.1 Spin **`RuntimeClass`** + +``` +apiVersion: node.k8s.io/v1 +kind: RuntimeClass +metadata: +name: wasmtime-spin-v2 +handler: spin + +``` + +This defines a new `RuntimeClass` called `wasmtime-spin-v2` . Any pod that specifies this + +`RuntimeClass` will be scheduled to use the `spin` handler, which runs Wasm binaries +instead of standard containers. + +You can install it with + +``` +kubectl apply -f https://github.com/spinframework/spin``` + +➥ `operator/releases/download/v0.5.0/spin-operator.runtime-class.yaml` + +That’s all we need to get things working in our `k3d` setup. That’s because, when we +created the cluster, we used a custom image that includes the Spin-compatible +containerd-­shim on every node. With all nodes already equipped to run Wasm workloads, Kubernetes can freely schedule pods using the `wasmtime-spin-v2` `RuntimeClass` +without any extra configuration. + +In a production environment—where maybe not every node will have the Spin runtime installed—you’d likely want to be more selective. You’d typically label the compatible nodes and use a `nodeSelector` to ensure that only those nodes run Wasm workloads. +That setup gives you tighter control over where these specialized workloads land, but +for local development, we can keep things simple. + +Adding Spin’s CRDs and operator +Now that Kubernetes knows how to run Wasm workloads through our `RuntimeClass`, +we need to introduce the resources that actually define what those workloads are. This +is where SpinKube’s CRDs come in. + +Install them in your cluster with + +``` +kubectl apply -f https://github.com/spinframework/spin``` + +➥ `operator/releases/download/v0.5.0/spin-operator.crds.yaml` + +This manifest defines two key resource types that the operator will manage: + +¡ `SpinAppExecutor` tells the Spin operator which executor to use when running a + +`SpinApp` . In practice, it links your workloads to the right runtime—in our case, +the one specified by the `wasmtime-spin-v2` `RuntimeClass` . It also lets you control + +Licensed to Ibrahim Mohammed Mohammed + +**196** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +whether the operator should create a deployment, include default CA certificates, and so on. + +¡ `SpinApp` is the main workload definition. It represents a Wasm application and + +exposes a set of options tailored to running it in Kubernetes. At the simplest +level, you give it a container image, and the operator handles the deployment, +pod, and service setup for you. But it also supports deeper runtime configuration, including + + - Image pull secrets for private registries + + - Liveness and readiness probes for health checking + + - Resource requests and limits + + - Spin variables for runtime configuration + + - Volume mounts to attach persistent storage + + - Autoscaling based on CPU or memory usage + +Together, these CRDs give us the knobs and switches we need to configure and manage +Wasm apps in a way that feels native to Kubernetes. + +With our CRDs registered, we now need to bring in the component that will actually +act on them. Remember, CRDs declare what you want in your cluster, but operators +actually alter your cluster, adding or removing services so that the desired state in the +CRD is reached. Defining a `SpinApp` or `SpinAppExecutor` doesn’t do much on its own— +the real logic lives inside the operator. + +We’ll install the Spin operator into the cluster using Helm: + +``` + helm install spin-operator --namespace spin-operator --create-namespace -``` + +➥ `version 0.5.0 --wait oci://ghcr.io/spinframework/charts/spin-operator` + +This deploys the operator into its own namespace and wires it up to watch for `SpinApp` +and `SpinAppExecutor` resources. From this point forward, whenever one of those +resources is created or updated, the operator will respond accordingly—scheduling +pods, provisioning services, and managing the lifecycle of our Wasm workloads. + +To see what that looks like in practice, let’s take a look at a `SpinAppExecutor` +manifest. + +Listing 8.2 **`SpinAppExecutor`** resource + +``` + apiVersion: core.spinkube.dev/v1alpha1 + kind: SpinAppExecutor + metadata: + name: containerd-shim-spin + spec: + createDeployment: true + deploymentConfig: + runtimeClassName: wasmtime-spin-v2 + installDefaultCACerts: true + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with SpinKube_** **197** + +This resource tells the operator how Spin apps should be executed. In this case, we’re +asking it to use the `wasmtime-spin-v2` `RuntimeClass` we defined earlier, to automatically +create a deployment, and to include a default set of CA certificates in the workload +environment. + +You can apply it with + +``` + kubectl apply -f https://github.com/spinframework/spin``` + +➥ `operator/releases/download/v0.5.0/spin-operator.shim-executor.yaml` + +With this in place, the operator is fully equipped to handle incoming `SpinApp` +resources—and to turn them into running Wasm applications inside our cluster. + +_8.2.2_ _Running a Wasm app in Kubernetes with SpinKube_ + +With our cluster fully configured and the Spin operator ready to go, it’s time to actually +run a Spin application on Kubernetes. + +To do that, we’ll follow a simple pipeline: + +1 Package our Spin app. + +2 Push it to a container registry. + +3 Scaffold the necessary Kubernetes resources using the Spin `kube` plugin. + +4 Apply the generated manifest to our cluster. + +If you followed along in chapter 6, you’ll remember our `hello-world-spin` app—a +minimal Spin application that responds to HTTP requests to any route with a plaintext message. We’ll reuse that same app here to keep things simple. + +With that, copy your `hello-world-spin/` directory into your `chapter08/` folder. Also, +just to make things more obvious during testing, let’s update the response message +slightly. + +Listing 8.3 **`hello-world-spin/src/lib.rs`** + +``` + use spin_sdk::http::{IntoResponse, Request, Response}; + use spin_sdk::http_component; + #[http_component] + fn handle_hello_world_spin(req: Request) -> anyhow::Result {` +``` + println!("Handling request to {:?}", req.header("spin-full-url")); + Ok(Response::builder() + + .build()) + } + +``` + +Now, to apply our changes, from the root of the `chapter08/hello-world-spin/` folder, +go ahead and rebuild the app: + +``` + spin build + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**198** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +IMPORTANT Spin requires Rust 1.85.0 or later because some of its dependencies use Rust edition 2024. If you’ve been following along with Rust 1.84.0 (as +recommended earlier in the book), you’ll need to upgrade for this section. +Install Rust 1.85.0 and its required components with `rustup` `install` `1.85.0`, + +`rustup` `default` `1.85.0` . You can verify the change with `rustc --version` . + +With our app built, the next step is to publish it to a registry so that Kubernetes can +pull and run it. + +TIP To push to a registry, you need to be authenticated. If you followed +chapter 7, you should already be logged in to the GitHub Container Registry +(GHCR). + +Spin provides a simple command to push applications to a registry: + +``` + spin registry push ghcr.io//serverside-wasm-book``` + +➥ `code/hello-world-spin:latest` + +This command uploads the app as an OCI image, similar to what we did with `wkg` +before. Once the image is published, we’re ready to generate the Kubernetes manifest +for it. + +IMPORTANT Pushed packages are private by default. You’ll need to change the +package’s visibility settings on GitHub to public for the following steps to work. + +That’s where the `kube` plugin comes in. We can use it to scaffold a `SpinApp` manifest that +references the image we just pushed: + +``` + spin kube scaffold --from ghcr.io// +``` + +➥ `serverside-wasm-book-code/hello-world-spin:latest` + +NOTE If you’d rather skip building and pushing your own image, you can use +[mine instead: https://mng.bz/eB9q.](https://mng.bz/eB9q) + +This generates a manifest like the one shown in the following listing, which defines a + +`SpinApp` resource pointing to the image we just pushed and tells the operator to use the + +`containerd-shim-spin` executor and run two replicas. + +Listing 8.4 SpinApp manifest (scaffolded output) + +``` + apiVersion: core.spinkube.dev/v1alpha1 + kind: SpinApp + metadata: + name: hello-world-spin + spec: + image: "ghcr.io/danbugs/serverside-wasm-book-code/hello-world-spin:latest" + executor: containerd-shim-spin + replicas: 2 + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with SpinKube_** **199** + +Now that we have the manifest, we can deploy it to our cluster by piping the scaffolded +output directly into `kubectl` : + +``` + spin kube scaffold --from ghcr.io// +``` + +➥ `serverside-wasm-book-code/hello-world-spin:latest | kubectl create -f -` + +And that’s it—you’ve just deployed your first Spin app to Kubernetes! + +To check that it worked, run + +``` + kubectl get spinapps + +``` + +You should see something like + +``` + NAME READY DESIRED EXECUTOR + hello-world-spin 2 2 containerd-shim-spin + +``` + +This output confirms that both requested replicas are up and running. + +Now let’s access the app. Since we didn’t expose it through a load balancer or ingress +controller, we’ll use `kubectl` `port-forward` to connect locally: + +``` + kubectl port-forward svc/hello-world-spin 8083:80 + +``` + +Then, in another terminal, send a request to the app: + +``` + curl localhost:8083/ + +``` + +You should see + +``` + Hello World from Spin in Kubernetes! + +``` + +That response confirms that our Spin app is up, running, and reachable from outside +the cluster. We’ve gone from source code to live service, all through Kubernetes and +Spin. + +We’re just getting started. Next, we’ll take a closer look at how to scale these apps and +explore some of the other runtime options SpinKube gives us. + +_8.2.3_ _Scaling Wasm in Kubernetes with SpinKube_ + +So far, we’ve focused on getting a basic Spin application up and running in Kubernetes. But one of Kubernetes’ core strengths is its ability to scale applications automatically based on demand—just like that game server we talked about at the start of the +chapter. In this section, we’ll see how to bring that same dynamic scaling to a Spin app. + +Kubernetes supports a feature called the Horizontal Pod Autoscaler (HPA). The +HPA watches metrics like CPU and memory usage and adjusts the number of replicas of +your app to match the load. If your app is under pressure and needs more resources, it + +Licensed to Ibrahim Mohammed Mohammed + +**200** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +scales up. When things quiet down, it scales back down—saving resources and improving responsiveness. + +To try this out, we’ll build a new Spin app that can simulate load. That way, we can see +the autoscaler in action. + +Creating a new app +To get started, navigate to your `chapter08/` folder and create a new Spin app: + +``` + spin new + +``` + +Select the `http-js` template for this demo—or use a different language if you’d like. + +NOTE If you need a refresher on creating new Spin applications, refer to section 6.5.1. + +Name your app `scaling-spin` . If you chose the same template, your project folder +should contain a file at `src/index.js` with the contents shown in the following listing. + +Listing 8.5 src/index.js ( **`http-js`** template) + +``` + import { AutoRouter } from 'itty-router'; + + let router = AutoRouter(); + + router + .get("/", () => new Response("hello universe")) + .get("/hello/:name", ({ name }) => `Hello, ${name}!`); + + addEventListener("fetch", (event) => { + event.respondWith(router.fetch(event.request)); + }); + +``` + +This app already has a couple of basic routes, but we’ll add one more to simulate some +CPU activity and trigger scaling behavior. + +Generating load with JavaScript +To generate CPU load, let’s add a simple recursive Fibonacci calculator. It’s intentionally inefficient—which is exactly what we want. Each request to this endpoint will +put the CPU to work, creating the kind of sustained pressure that the autoscaler is +designed to react to. + +Add the following route after the `/hello/:name` handler: + +``` + .get("/load", () => { + function fib(n) { + if (n < 2) return n; + return fib(n - 1) + fib(n - 2); + } + const result = fib(40); // Adjust this value to control CPU intensity + return new Response(`fib(40) = ${result}`); + }) + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with SpinKube_** **201** + +Every time we hit `/load`, this route burns a noticeable amount of CPU, which gives +the autoscaler something to monitor and act on. Once you’ve added it, your full file +should look like the following listing. + +Listing 8.6 src/index.js (with load generator) + +``` +import { AutoRouter } from 'itty-router'; + +let router = AutoRouter(); + +``` + +``` +router +.get("/", () => new Response("hello universe")) +.get('/hello/:name', ({ name }) => `Hello, ${name}!`) +.get("/load", () => { +function fib(n) { +if (n < 2) return n; +return fib(n - 1) + fib(n - 2); +} +const result = fib(40); +return new Response(`fib(40) = ${result}`); +}) + +addEventListener('fetch', (event) => { +event.respondWith(router.fetch(event.request)); +}); + +``` + +**This route calculates** +**the 40th Fibonacci** +**number using a** +**deliberately inefficient** +**recursive algorithm.** + +Publishing to a registry +Once that’s in place, in the `scaling-spin/` folder, build your app and push it to a container registry just like we did earlier: + +``` +spin build +spin registry push ghcr.io// +``` + +➥ `serverside-wasm-book-code/scaling-spin:latest` + +NOTE If you prefer, you can use my image for all the commands that follow: +[https://mng.bz/eB9q.](https://mng.bz/eB9q) + +Setting up ingress +Before we scaffold the app, we need a way to send traffic into the cluster. Rather than +using port forwarding like before, we’ll set up an Ingress controller. This gives us a stable external endpoint to hit with requests. + +Apply the provided Ingress configuration: + +``` +kubectl apply -f https://raw.githubusercontent.com/danbugs/ +``` + +➥ `serverside-wasm-book-code/refs/heads/main/chapter08/scaling-` +➥ `spin/ingress.yaml` + +Licensed to Ibrahim Mohammed Mohammed + +**202** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +NOTE An Ingress lets you route HTTP traffic to your services based on path +or hostname. Unlike port forwarding, which is great for local development, +Ingress is closer to how things would be set up in a real deployment. + +The Ingress manifest we’re applying is shown in the following listing. + +Listing 8.7 Ingress resource + +``` + apiVersion: networking.k8s.io/v1 + kind: Ingress + metadata: + name: nginx + annotations: + ingress.kubernetes.io/ssl-redirect: "false" + spec: + rules: + - http: + paths: + - path: / + pathType: Prefix + backend: + service: + name: scaling-spin + port: + number: 80 + +``` + +This configuration tells Kubernetes to forward all HTTP traffic coming to `/` to the + +`scaling-spin` service on port 80. We’re using a simple path prefix here, so any route— +like `/load` —will be routed correctly. + +Scaffolding the app with autoscaling +Now we’re ready to scaffold our app using the `kube` plugin, just like before—but this +time, we’ll enable autoscaling with the `--autoscaler` flag and add resource constraints +so the HPA has something to work with. + +Here’s the command we’ll use: + +``` + spin kube scaffold +``` + +➥ `--from ghcr.io//serverside-wasm-book-code/scaling-` +➥ `spin:latest` +➥ `--autoscaler hpa` +➥ `--cpu-limit 100m` +➥ `--memory-limit 128Mi` +➥ `--cpu-request 50m` +➥ `--memory-request 64Mi` +➥ `--replicas 1` +➥ `--max-replicas 10` + +This tells SpinKube to + +¡ Enable the HPA ( `--autoscaler hpa` ) + +¡ Start with 1 replica, scaling up to 10 if needed + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with SpinKube_** **203** + +¡ Set CPU/memory requests and limits to give the scheduler and HPA room to + +make decisions + +NOTE You can also use `--autoscaler` `keda` instead of HPA. KEDA is another +autoscaling option that supports more event-driven triggers, like queue length +or Kafka lag—not just CPU or memory usage. It’s a good fit when your workloads respond to external systems or asynchronous traffic patterns. + +This command will generate a manifest like the one shown in listing 8.8, which defines +both a `SpinApp` and a `HorizontalPodAutoscaler` resource. The autoscaler watches CPU +usage and scales the app between 1 and 10 replicas based on a target utilization threshold (defaulting to 60% unless otherwise specified). + +Listing 8.8 **`scaling-spin.yaml`** + +``` +apiVersion: core.spinkube.dev/v1alpha1 +kind: SpinApp +metadata: +name: scaling-spin +spec: +image: "ghcr.io/danbugs/serverside-wasm-book-code/scaling-spin:latest" +executor: containerd-shim-spin +enableAutoscaling: true +resources: +limits: +cpu: 100m +memory: 128Mi +requests: +cpu: 50m +memory: 64Mi +--apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: +name: scaling-spin-autoscaler +spec: +scaleTargetRef: +apiVersion: apps/v1 +kind: Deployment +name: scaling-spin +minReplicas: 1 +maxReplicas: 10 +metrics: +- type: Resource +resource: +name: cpu +target: +type: Utilization +averageUtilization: 60 +- type: Resource +resource: +name: memory + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**204** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +``` + target: + type: Utilization + averageUtilization: 60 + +``` + +Deploying the autoscaled app +Let’s apply the manifest from listing 8.8 to the cluster: + +``` + spin kube scaffold --from ghcr.io//serverside-wasm-book``` + +➥ `code/scaling-spin:latest --autoscaler hpa --cpu-limit 100m --memory-` +➥ `limit 128Mi --cpu-request 50m --memory-request 64Mi --replicas 1 --max-` +➥ `replicas 10 | kubectl create -f -` + +Once the manifest is applied, we can confirm that the HPA was created and is tracking +our deployment by running + +``` + kubectl get hpa + +``` + +You should see something like this: + +``` + NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE + scaling-spin- Deployment/ 4%/60%, 33%/60% 1 10 1 114s + autoscaler scaling-spin + +``` + +This output shows that the autoscaler is watching the `scaling-spin` deployment. It’s +currently running 1 replica, but will scale up to 10 if CPU or memory usage crosses the +configured thresholds. + +Generating traffic +To test our scaling setup, we’ll hit the `/load` route repeatedly to put pressure on the +app. In chapter 6 (see section 6.3), we installed `oha`, a lightweight HTTP benchmarking tool. Let’s use it here: + +``` + oha -c 40 -z 3m -t 5s http://localhost:8081/load + +``` + +This command runs 40 concurrent connections for 3 minutes. Any individual request +that takes longer than 5 seconds will time out. It’s a good way to simulate sustained traffic and give the autoscaler something to respond to. + +Verifying autoscaling behavior +After you’ve run `oha` and generated some load, Kubernetes should have responded +by scaling up the deployment. To confirm that scaling took place, you can inspect the +deployment history with + +``` + kubectl describe deploy scaling-spin + +``` + +Even after the traffic stops, Kubernetes keeps a record of the scaling events. Here’s an +example of what that might look like: + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with wasmCloud_** **205** + +``` + Type Status Reason + ---- ------ ----- Progressing True NewReplicaSetAvailable + Available True MinimumReplicasAvailable + OldReplicaSets: + NewReplicaSet: scaling-spin-797fbdb76 (10/10 replicas created) + Events: + Type Reason Age From Message + ---- ------ ---- ---- ------ Normal ScalingReplicaSet 27m deployment Scaled up replica set scaling +``` + +`-controller` ➥ `-spin-797fbdb76 to 1` +``` + Normal ScalingReplicaSet 18m deployment Scaled up replica set scaling +``` + +`-controller` ➥ `-spin-797fbdb76 to 2 from 1` +``` + Normal ScalingReplicaSet 17m deployment Scaled up replica set scaling +``` + +`-controller` ➥ `-spin-797fbdb76 to 4 from 2` +``` + Normal ScalingReplicaSet 17m deployment Scaled up replica set scaling +``` + +`-controller` ➥ `-spin-797fbdb76 to 8 from 4` +``` + Normal ScalingReplicaSet 17m deployment Scaled up replica set scaling +``` + +`-controller` ➥ `-spin-797fbdb76 to 10 from 8` + +This output confirms that HPA responded to the increased CPU load by scaling the +number of replicas in stages—eventually reaching the maximum of 10. + +And there we have it: a fully deployed, autoscaling Spin app on Kubernetes! We simulated traffic, watched the system respond, and saw the benefits of combining Wasm’s +efficiency with Kubernetes’ operational tooling. + +IMPORTANT Now that you’ve completed the Spin example, you should revert +to Rust 1.84.0 to maintain consistency with the rest of the book. Run `rustup` + +`default` `1.84.0` . You can verify the change with `rustc` `--version` . + +Next, we’ll look at another approach to Wasm on Kubernetes with wasmCloud. + +_8.3_ _Running Wasm in Kubernetes with wasmCloud_ + +Just like SpinKube, wasmCloud can run WebAssembly workloads in Kubernetes. And +at first glance, the setup looks pretty familiar: install a few CRDs, apply some manifests, +and let the operator manage your workloads. But under the hood, wasmCloud’s model +is a bit different. + +The biggest difference, as we briefly saw in section 7.5, is that wasmCloud doesn’t rely +on a containerd-shim or, in this case, on a `RuntimeClass` . There’s no equivalent to the +containerd-shim-spin we used with Spin. Instead, wasmCloud deploys its full runtime +environment inside the cluster. This includes the core wasmCloud host, a NATS message broker, and a component called `wadm` (i.e., wasmCloud application deployment +manager). + +Figure 8.3 illustrates this architectural difference. While SpinKube extends the +container runtime layer to run Wasm directly in pods, wasmCloud takes a platform +approach—deploying its complete application runtime as services within Kubernetes, +then managing Wasm workloads through that platform layer. + +Licensed to Ibrahim Mohammed Mohammed + +**206** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +**binaries directly** + +**integration** + +**platform as pods** + +**approach** + +Figure 8.3 Architectural comparison of SpinKube and wasmCloud in Kubernetes. SpinKube (left) extends the +container runtime with a Wasm shim to run Spin applications directly as pods. wasmCloud (right) deploys its +platform components (NATS, **`wadm`**, and hosts) as regular Kubernetes pods, then orchestrates Wasm workloads +within those hosts. Both approaches use CRDs and operators but integrate at different layers of the stack. + +Let’s see how that model plays out in practice. + +_8.3.1_ _Preparing the cluster for wasmCloud_ + +To keep things simple, we’ll reuse the same Kubernetes cluster we used earlier for +Spin. There’s no need to tear anything down—wasmCloud can be layered on top without conflict. + +We’ll install the wasmCloud platform using its official Helm chart, which includes +all required services and configuration. It bundles together NATS, `wadm`, and the wasm­ +Cloud operator. + +``` + helm upgrade --install +``` + +➥ `wasmcloud-platform` +➥ `--values https://raw.githubusercontent.com/wasmCloud` +➥ `wasmcloud/main/charts/wasmcloud-platform/values.yaml` +➥ `oci://ghcr.io/wasmcloud/charts/wasmcloud-platform:0.1.2` +➥ `--dependency-update` + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with wasmCloud_** **207** + +To ensure everything’s up and running before we continue, let’s wait for the installed +items to become available: + +``` + kubectl rollout status deploy,sts -l app.kubernetes.io/name=nats + kubectl wait --for=condition=available --timeout=600s deploy -l +``` + +➥ `app.kubernetes.io/name=wadm` +``` + kubectl wait --for=condition=available --timeout=600s deploy -l +``` + +➥ `app.kubernetes.io/name=wasmcloud-operator` + +Once ready, we can start using the wasmCloud operator to manage two key CRDs: + +¡ `WasmCloudHostConfig` defines a host—a running instance of the wasmCloud run +time, just like the ones we’d create with `wash up` . Think of it like a replacement +for Spin’s `SpinAppExecutor` : it’s what actually runs your app. + +¡ `Application` represents a wasmCloud app. Remember those `wadm.yaml` files + +we’ve written in previous chapters? They are application CRs! + +So, aside from creating hosts with `WasmCloudHostConfig`, deploying wasmCloud apps to +Kubernetes is as simple as reusing the application specs we already built! + +_8.3.2_ _Running a Wasm app in Kubernetes with wasmCloud_ + +Let’s put it all together by deploying a simple wasmCloud app into our Kubernetes +cluster. + +Start by creating a new directory at `chapter08/hello-world-wasmcloud/` . Inside that +folder, create a file named `wasmcloud-host.yaml` and add the contents from the following listing. + +Listing 8.9 **`WasmCloudHostConfig`** CR + +``` + apiVersion: k8s.wasmcloud.dev/v1alpha1 + kind: WasmCloudHostConfig + metadata: + name: wasmcloud-host + spec: + lattice: default + version: "1.4.1" + +``` + +This sets up a host named `wasmcloud-host` . This will be the actual process running your +Wasm components—similar to how Spin’s executor managed application pods. + +Apply the host resource to your cluster from the `hello-world-wasmcloud/` folder: + +``` + kubectl apply -f wasmcloud-host.yaml + +``` + +You can verify that it’s active using + +``` + kubectl get wasmcloudhostconfig + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**208** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +You should see + +``` + NAME APP COUNT + wasmcloud-host 0 + +``` + +At this point, your host is running, but it’s not doing anything yet. To interact with it +more directly, let’s port forward the cluster’s NATS port: + +``` + kubectl port-forward nats-0 4222 + +``` + +With that open, from another terminal, you can now use the full suite of `wash` +commands—­for example, with + +``` + wash get inventory + +``` + +you’ll get output listing the running host and its information: + +``` + Host ID Friendly name + NBH2MFW... cool-glade-4654 + + Host labels + hostcore.arch x86_64 + hostcore.os linux + hostcore.osfamily unix + kubernetes true + ... + +``` + +NOTE For wash installation instructions, see chapter 6, section 6.1. + +Now that we have our host ready, let’s deploy the actual application. + +We’ll reuse the same app we created in chapter 6. From inside `chapter06/hello` + +`-world-wasmcloud`, assuming you’ve previously run `wash` `build` there, push your Wasm +component to a registry: + +``` + wash push ghcr.io//serverside-wasm-book-code/ +``` + +➥ `hello-world-wasmcloud:v1 ./build/http_hello_world_s.wasm` + +This is similar to how we pushed Spin applications—just using the `wash` tool instead of + +`spin registry` . + +NOTE Don’t want to push your own image? Feel free to use mine: ghcr.io/ +danbugs/serverside-wasm-book-code/hello-world-wasmcloud:v1. If you decide +to use your own registry, note that pushed packages are private by default. +You’ll need to change the package’s visibility settings on GitHub to public for +the following steps to work. + +Next, copy the `wadm.yaml` file from your chapter 6 example into the `chapter08/hello` + +`-world-wasmcloud/` directory. There, we’ll make a few changes: + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with wasmCloud_** **209** + +1 Update the image path for the HTTP component to point to your registry. + +2 Remove the custom `spreadscaler` setup. We originally used that to simulate multiple hosts for fault tolerance. Now that we have real Kubernetes replicas, we can +rely on Kubernetes for scheduling and resiliency. + +3 Change the HTTP server’s address to `0.0.0.0:8080` . When running inside Kubernetes, using `127.0.0.1` can cause network errors because it won’t bind to the container’s external interface. + +NOTE Want to see how Kubernetes handles fault tolerance? After deploying +the app, run `wash` `get` `inventory`, then shut down one of the hosts with `wash` + +`down --host-id ` . You’ll see all components migrate to the remaining host, +with zero downtime—and Kubernetes will restart the failed pod automatically. + +The final application file should look like the following listing. + +Listing 8.10 wasmCloud application spec + +``` +apiVersion: core.oam.dev/v1beta1 +kind: Application +metadata: +name: rust-hello-world +annotations: +description: 'HTTP hello world demo in Rust, using the WebAssembly +``` + +➥ `Component Model and WebAssembly Interfaces Types (WIT)'` +``` +spec: +components: +- name: http-component +type: component + +traits: +- type: spreadscaler +``` + +**Removes custom** +``` +properties: +``` +**spreadscaler setup** +``` +instances: 10 +- type: link +properties: +namespace: wasi +package: keyvalue +interfaces: [store, atomics] +target: +name: kvnats +config: +- name: wasi-keyvalue-config +properties: +bucket: default +enable_bucket_auto_create: 'true' + +``` + +**Updates to use** +**our image from** +**the registry** + +**Removes custom** +**spreadscaler setup** + +``` +- name: httpserver +type: capability +properties: + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**210** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +``` + image: ghcr.io/wasmcloud/http-server:0.23.2 + traits: + - type: link + properties: + target: http-component + namespace: wasi + package: http + interfaces: [incoming-handler] + source_config: + - name: default-http + + - name: kvnats + type: capability + properties: + image: ghcr.io/wasmcloud/keyvalue-nats:0.3.1 + +``` + +NOTE While editing the file, feel free to clean up unused annotations. + +Back in the `chapter08/hello-world-wasmcloud/` folder, apply the app: + +``` + kubectl apply -f wadm.yaml + +``` + +You can verify deployment with + +``` + kubectl get app + +``` + +NOTE The `app` resource type isn’t a built-in Kubernetes resource—it’s specific +to wasmCloud. We can use `kubectl` `get` `app` here because we applied wasm­ +Cloud’s application CRD earlier. This CRD defines what a wasmCloud application looks like to Kubernetes, allowing us to manage Wasm applications using +familiar `kubectl` commands. + +This should return something like + +``` + APPLICATION DEPLOYED VERSION STATUS + rust-hello-world 01JVPDW… Deployed + +``` + +Or use `wash` for a more detailed view: + +``` + wash app status rust-hello-world + +``` + +You should see output similar to the following: + +``` + rust-hello-world@ - Deployed + Name Kind Status + http_component SpreadScaler Deployed + http_component -(wasi:keyvalue)-> kvnats LinkScaler Deployed + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Running Wasm in Kubernetes with wasmCloud_** **211** + +``` +httpserver -(wasi:http)-> http_component LinkScaler Deployed +httpserver SpreadScaler Deployed +kvnats SpreadScaler Deployed + +``` + +This breakdown shows which parts of the application were deployed, how they’re +connected, and whether everything came up successfully—great for debugging or +just getting an overview. If something isn’t working, try running `kubectl` `describe` `app` + +`rust-hello-world` to check its metadata and events. + +Finally, to hit the app from outside the cluster, port forward the `wasmcloud-host` +deployment: + +``` +kubectl port-forward deployment/wasmcloud-host 8080 + +``` + +Then, in another terminal window, try it out: + +``` +curl http://localhost:8080 + +``` + +And you should get + +``` +Hello! I was called 1 times + +``` + +That’s it—we’ve just run a full wasmCloud application in Kubernetes. + +We reused the app from chapter 6, reused the deployment spec from earlier chapters, and deployed the wasmCloud host directly into our existing cluster. It’s a different +model than SpinKube, but it plays nicely with Kubernetes all the same—just from a +slightly different angle. + +The key difference comes down to architecture and tooling. SpinKube runs each +Wasm binary in its own pod, treating Wasm workloads as first-class Kubernetes citizens. This means you can use `kubectl` for everything—from deployment to debugging to monitoring. wasmCloud takes a platform approach, where components run +inside wasmCloud hosts, and you primarily interact through `wash` commands and `wadm` +manifests. + +This affects your operational workflow. With SpinKube, a failing Wasm workload +looks like any other failing pod—you debug it with standard Kubernetes tools. With +wasmCloud, you use commands like `wash` `app` `status` and work within the wasmCloud +ecosystem to manage your applications. + +Ultimately, the choice between SpinKube and wasmCloud on Kubernetes mirrors +the decision we explored in chapter 6 (section 6.5.2). The tradeoffs between Spin’s simplicity and wasmCloud’s flexibility remain the same—Kubernetes just becomes another +deployment target. If you chose Spin for its straightforward HTTP-focused approach, +SpinKube extends that simplicity to Kubernetes. If you chose wasmCloud for its component model and provider ecosystem, those same benefits carry over when running on +Kubernetes. + +Licensed to Ibrahim Mohammed Mohammed + +**212** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +_8.4_ _Running the SmartCMS example on Kubernetes_ + +The time has come to take our SmartCMS application from earlier chapters and get +it running on Kubernetes. The good news is we’ve already seen almost every piece +we need. The process for deploying SmartCMS is very similar to what we did with the + +`hello-world-wasmcloud` app—just with a bit more wiring. + +Here’s the general plan: + +1 Push the SmartCMS app to a registry so our cluster can pull it. + +2 Modify the existing `wadm.yaml` to work with Kubernetes. + +3 Deploy Ollama, the external large language model (LLM) backend we used in +chapter 6. + +Once that’s in place, we’ll fire it all up and run the app from a browser—just like we +did before, but this time fully managed by Kubernetes. + +_8.4.1_ _Preparing the SmartCMS image and manifest_ + +Start by pushing the SmartCMS component to a registry so that it can be accessed from +within our cluster. + +From inside the `chapter06/smart-cms/` folder, run + +``` + wash push ghcr.io//serverside-wasm-book-code/ +``` + +➥ `smart-cms:v1 ./build/smart_cms_s.wasm` + +[NOTE If you’d rather skip pushing your own image, you can use mine: https://](https://mng.bz/Owqa) +[mng.bz/Owqa.](https://mng.bz/Owqa) + +Next, in your `chapter08/` directory, create a new folder called `smart-cms/` . Copy over +the `index.html` and `wadm.yaml` files from `chapter06/smart-cms/` into this new directory. + +Now we’ll update the `wadm.yaml` manifest to reflect our Kubernetes setup. We need +to make several key adjustments to adapt this app for Kubernetes: + +¡ _Replace local references with registry images_ —The local component reference needs + +to be replaced with the registry image we just pushed so Kubernetes can pull it at +run time. + +¡ _Remove local-only configurations_ —The manual zone spread trait from our local + +setup is no longer needed; Kubernetes handles scheduling and fault tolerance +for us. + +¡ _Update networking for containers_ —The HTTP server must bind to `0.0.0.0` instead + +of `127.0.0.1` to be accessible from outside the container. + +¡ _Configure the Ollama provider_ —Add a host field pointing to the Ollama Kubernetes + +service (not localhost, which would refer to the container itself). + +¡ _Use prebuilt providers_ —Replace the local Ollama provider build with a prebuilt + +version from a registry. + +The following listing shows the updated manifest with these changes applied. + +Licensed to Ibrahim Mohammed Mohammed + +**_Running the SmartCMS example on Kubernetes_** **213** + +Listing 8.11 Updated SmartCMS application for Kubernetes + +``` +apiVersion: core.oam.dev/v1beta1 +kind: Application +metadata: +name: smart-cms +annotations: +description: "A smart CMS!" +spec: +components: +- name: http-component +type: component +properties: +image: ghcr.io/danbugs/serverside +traits: +- type: spreadscaler +``` + +**Removes the local-only** +``` +properties: +``` +**zone spread setup** +``` +instances: 100 +- type: link +properties: +namespace: wasi +package: keyvalue +interfaces: [store, atomics] +target: +name: kvnats +config: +- name: wasi-keyvalue-config +properties: +bucket: default +enable_bucket_auto_create: 'true' +- type: link +properties: +target: ollama +namespace: thomastaylor312 +package: ollama +interfaces: [generate] +target_config: +- name: ollama-conf +properties: +model_name: gurubot/tinystories-656k-q8 +host: 'http://ollama:11434' + +``` + +**Adds a host field for** +**the Ollama provider** + +**Changes from local Wasm** +**to registry image** + +**Removes the local-only** +**zone spread setup** + +``` +- name: httpserver +type: capability +properties: +image: ghcr.io/wasmcloud/http-server:0.23.2 +traits: +- type: link +properties: +target: http-component +namespace: wasi +package: http +interfaces: [incoming-handler] + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**214** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +``` +source_config: +- name: default-http +properties: +address: 0.0.0.0:8000 + +``` + +**Changes 127.0.0.1 to 0.0.0.0 to** +**enable container networking** + +``` +- name: kvnats +type: capability +properties: +image: ghcr.io/wasmcloud/keyvalue-nats:0.3.1 + +``` + +``` +- name: ollama +type: capability +properties: +image: ghcr.io/danbugs/serverside``` + +➥ `wasm-book-code/ollama-provider:v1` + +**Updates provider to** +**use a public image** + +WARNING At the time of writing, the provider image used here only supports + +`x86_64-linux` . If you’re running on a system with a different architecture— +like an ARM-based Mac—this example likely won’t work out of the box. You +can check which architectures are currently supported by running `wash` `par` + +`inspect` `ghcr.io/danbugs/serverside-wasm-book-code/ollama-provider:v1` . If +you’re interested in compiling the provider yourself for a different architecture, you can see the changes I made to support Kubernetes (i.e., vendoring +[OpenSSL, compiling to a MUSL target) here: https://github.com/danbugs/](https://github.com/danbugs/ollama-provider/pull/1) +[ollama-provider/pull/1. Just like regular Wasm components, providers can be](https://github.com/danbugs/ollama-provider/pull/1) +pushed to registries with `wash` `push` . + +_8.4.2_ _Preparing the Ollama dependency_ + +To fulfill requests from the Ollama provider, we’ll also need the Ollama service itself +running in our cluster. Create a file named `ollama.yaml` in the same `chapter08/smart` + +`-cms/` folder, and populate it with the contents shown in the following listing. + +Listing 8.12 Ollama deployment and service + +``` + apiVersion: apps/v1 + kind: Deployment + metadata: + name: ollama + namespace: default + spec: + replicas: 1 + selector: + matchLabels: + app: ollama + template: + metadata: + labels: + app: ollama + spec: + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Running the SmartCMS example on Kubernetes_** **215** + +``` +containers: +- name: ollama +image: ollama/ollama:latest +ports: +- containerPort: 11434 +env: +``` + +`- name: PRELOAD_MODELS` **Preloads the** +`value: "gurubot/tinystories-656k-q8"` **TinyStories model** +``` +- name: OLLAMA_KEEP_ALIVE +value: "12h" +volumeMounts: +- name: ollama-storage +mountPath: /root/.ollama +lifecycle: +postStart: +exec: +command: ["/bin/sh", "-c", "for model in +``` + +➥ `$PRELOAD_MODELS; do ollama run $model \"\"; done"]` +``` +volumes: +- name: ollama-storage +emptyDir: {} +--apiVersion: v1 +kind: Service + +namespace: default +spec: +selector: +app: ollama +ports: + +protocol: TCP + +``` + +**Preloads the** +**TinyStories model** + +**Service named “ollama”** +**to match our app spec** + +**Exposes port 11434 to** +**match our app spec** + +This setup deploys a single Ollama container running the official image. It preloads +the model we need, keeps it warm for reuse, and serves requests via HTTP. The service +ensures it’s accessible from other parts of our Kubernetes cluster—like the provider +component from the SmartCMS app. + +From inside the `chapter08/smart-cms/` folder, apply it to your cluster with + +``` + kubectl apply -f ollama.yaml + kubectl rollout status deployment/ollama --timeout=120s + +``` + +_8.4.3_ _Running the app_ + +With everything in place, we’re ready to deploy the SmartCMS app. + +From inside the `chapter08/smart-cms/` folder, run + +``` + kubectl apply -f wadm.yaml + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**216** Chapter 8 **_Scalability for Wasm with Kubernetes_** + +Then, in a separate terminal, if you haven’t already, forward the NATS port: + +``` + kubectl port-forward nats-0 4222 + +``` + +This lets us use `wash` to inspect app status: + +``` + wash app status smart-cms + +``` + +You should see all components listed with a status of “Deployed.” If anything shows +“Reconciling,” give it a moment—things are still coming online: + +``` + smart-cms@ - Deployed + + Name Kind Status + http_component SpreadScaler Deployed + http_component -(wasi:keyvalue)-> kvnats LinkScaler Deployed + http_component -(ollama)-> ollama LinkScaler Deployed + httpserver -(wasi:http)-> http_component LinkScaler Deployed + httpserver SpreadScaler Deployed + kvnats SpreadScaler Deployed + ollama SpreadScaler Deployed + +``` + +Once everything looks good, `port-forward` the app so you can use it locally: + +``` + kubectl port-forward deployment/wasmcloud-host 8000 + +``` + +Now just open the `index.html` file in your browser. You should be able to create stories, +retrieve them, and even generate new ones using the LLM backend—just like you did +in chapter 6. + +NOTE If something goes wrong, your first stop should be the logs: `kubectl` +`logs` `-l app.kubernetes.io/instance=wasmcloud-host -c wasmcloud-host` . + +That’s it—you just deployed your SmartCMS app, complete with LLM inference, onto +a Kubernetes cluster using wasmCloud. + +Take a moment to reflect on how far we’ve come. Back in chapter 2, we were manually working with Wasm memory just to use a string. In chapter 3, we had to handcraft +hosts to even run a Wasm component. Now we’re orchestrating an entire multiservice +Wasm application—complete with autoscaling, component linking, and model-backed +generation—on a distributed system! + +_Summary_ + +¡ Wasm can reuse mature, battle-tested components of the container ecosystem, + +like Kubernetes. This helps solve problems where there’s no good Wasm-native +solution yet. + +¡ CRDs and operators enable Kubernetes to manage noncontainer workloads, + +including Wasm apps, using native patterns for orchestration and scaling. + +Licensed to Ibrahim Mohammed Mohammed + +**_Summary_** **217** + +¡ SpinKube integrates Spin with Kubernetes, providing CRDs and an operator + +that understands how to deploy, scale, and manage Spin applications using +Kubernetes-­native primitives. + +¡ The Spin `kube` plugin streamlines deployment by scaffolding Kubernetes mani +fests for Spin applications, making it easy to move from local dev to production. + +¡ Kubernetes’ HPA can be integrated with Spin apps, allowing replicas to scale + +dynamically based on CPU and memory load. + +¡ Unlike SpinKube, wasmCloud does not rely on a specialized container runtime; + +instead, it deploys its full host environment inside the cluster and manages apps +through NATS and `wadm` . + +Licensed to Ibrahim Mohammed Mohammed + +##### _The future of Wasm_ +# ~~_9_~~ + +_This chapter covers_ + +¡ Applying Wasm to confidential computing + +scenarios + +¡ Securing Model Context Protocol AI agents with + +Wasm + +¡ Running Wasm at the edge for low-latency + +compute + +¡ WASI 0.3 async updates on the path to WASI 1.0 + +In the previous chapter, we walked through getting our Wasm applications running +inside a Kubernetes cluster. Pair that lesson with everything else you’ve learned— +starting from simple Wasm modules and custom hosts and stretching to Wasm components and generic hosts like wasmCloud—and you now have all the pieces you +need to put a Wasm app into production while talking to the services already living +in your cluster and the wider Kubernetes ecosystem. + +This chapter wraps up our journey with SmartCMS and pulls back the curtain +on new frontiers: confidential computing, AI/machine learning (ML), and edge + +**218** + +Licensed to Ibrahim Mohammed Mohammed + +**_SmartCMS closing remarks_** **219** + +compute. We’ll also take a look at the upcoming WASI 0.3 release and how it lays the +groundwork for a stable, long-term WASI 1.0. + +_9.1_ _SmartCMS closing remarks_ + +From a sketch on paper to a production-ready service, SmartCMS grew chapter by +chapter. We began in chapter 2 by drafting a minimal WebAssembly Interface Types +(WIT) contract that defined our core content API. Chapter 3 turned that contract +into code with a JS guest served by a custom Rust host. Chapter 4 added intelligence +with an ML guest that, through composition, ran alongside the original component. +Chapter 5 swapped our bespoke ML interface for the `wasi:nn` spec, giving the model a +portable runtime. Chapter 6 handed the application to wasmCloud for more generic +development—no custom hosts required—and chapter 8 wrapped up the journey by +containerizing the service and launching it inside Kubernetes. The result is the architecture shown in figure 9.1. + +User + +Figure 9.1 Final architecture of SmartCMS deployed in Kubernetes. The frontend interacts with a wasmCloud +host via an HTTP server. Inside the host, Wasm components use capability providers for storage and ML. The ML +provider delegates inference to Ollama, while persistent storage is handled by **`kvnats`** . + +Licensed to Ibrahim Mohammed Mohammed + +**220** Chapter 9 **_The future of Wasm_** + +If you’d like to take SmartCMS a step further, you’ve got a few options. One path is +adding new features by building your own provider or tapping into the many premade +[ones listed here: https://mng.bz/YZya. For example, you could experiment with the](https://mng.bz/YZya) +built-in logging provider or try swapping out the key-value store—maybe use Redis +instead of NATS! + +TIP To get started building your own provider, run `wash` `new` `provider` + +`` . You can also scaffold a provider from a specific template +using something like `wash` `new` `provider` `my-provider --template-name` `custom` + +`-template-go` . Check out other available template names here: [https://mng](https://mng.bz/GwqJ) +[.bz/GwqJ.](https://mng.bz/GwqJ) + +Another way to level up SmartCMS is by integrating it more deeply with the broader +Kubernetes ecosystem. For example, you could add observability using tools like Prometheus for metrics, Grafana for dashboards, or Jaeger for distributed tracing. You +might also bring in Argo CD to manage deployments through GitOps workflows or +use Gatekeeper to enforce cluster policies. These additions can help bring SmartCMS +closer to a production-grade deployment, giving you greater visibility, control, and +automation. + +Speaking of production—if you’re ready to get your SmartCMS into other people’s +hands, head over to appendix B. There we’ll walk through deploying SmartCMS to +the Azure Kubernetes Service. Or, if you just want to see it in action, check out my own +[deployment at https://sswasm.com!](https://sswasm.com) + +With that, thanks for joining the ride in developing and deploying SmartCMS. I +hope it gave you a practical, real-life lens on each chapter’s topics. Next up, let’s look at +some future areas where I see Wasm having an effect. + +_9.2_ _What’s next for Wasm?_ + +There are plenty of areas that stand to benefit from Wasm’s growth beyond the +browser—whether it’s for its language agnosticism, sandboxed execution, or portability. In this section, we’ll highlight a few of these spaces that either didn’t come up earlier in the book or deserve a bit more spotlight. + +_9.2.1_ _Confidential computing_ + +These days, just about everyone uses the cloud in some form—and with that shift, +a massive amount of personal and organizational data now lives on someone else’s +infrastructure. But it wasn’t always that way. When cloud computing first emerged, +the idea of storing sensitive data on someone else’s servers seemed risky—even +unacceptable. + +Now that cloud adoption is nearly universal, securing that data—whether it’s code or +content—is more critical than ever. Figure 9.2 introduces the data security triad, which +highlights the three key states where data is vulnerable to compromise: at rest, in transit, and in use. + +Licensed to Ibrahim Mohammed Mohammed + +**_What’s next for Wasm?_** **221** + +Figure 9.2 The three states +of data: at rest, in transit, +and in use. Each state +represents a potential point of +vulnerability where data can +be exposed or compromised if +not properly protected. + +Some parts of the triad are fairly well understood and protected today. _Data in transit_ —­ +like information moving between your browser and a server—can be encrypted using +protocols like TLS, keeping it safe from prying eyes during transmission. _Data at rest_ stored on disk or in the cloud—can be secured using encrypted filesystems or managed key storage systems offered by most cloud providers. + +But _data in use_ is a tougher nut to crack. This is data that’s actively being processed +in memory—when encryption can’t protect it and traditional defenses fall short. That’s +where _confidential computing_ comes in: it’s a growing area focused on keeping data +secure even while it’s being used. + +Think about it this way: right now, when a computer processes data, it typically pulls +it into memory (RAM) in clear text. At that point, the data could be read or copied by +anyone who can access and inspect RAM directly—like a malicious actor or even the +cloud provider itself. + +Confidential computing tackles this problem through something called a _trusted_ +_execution environment_ (TEE), or _enclave_ . These enclaves use special CPU extensions to +encrypt memory access on the fly, so even while the data is in use, it’s still protected +from threats outside the enclave. + +NOTE These CPU extensions for confidential computing are already supported by most major processor vendors. For example, Intel supports this +through SGX (Software Guard Extensions), AMD through SEV (Secure +Encrypted Virtualization), ARM provides TrustZone and the newer Confidential Compute Architecture (CCA), and RISC-V has support via Keystone. These +features are increasingly common in modern cloud environments. + +One powerful use case for confidential computing is _secure multiparty collaboration_ . +Imagine you work at an institution developing an AI model and you want to train it on +sensitive hospital patient data. Your model is proprietary, so handing it over to the hospital isn’t an option. On the flip side, the hospital can’t share its patient records with +you due to strict confidentiality regulations. + +This is where a TEE can help. By running the model and the data together inside a +secure enclave, neither party has to expose their sensitive assets. Figure 9.3 illustrates +how this setup works. + +Licensed to Ibrahim Mohammed Mohammed + +**222** Chapter 9 **_The future of Wasm_** + +Data owner + +Model owner + +Figure 9.3 Secure multiparty computation using a TEE. Sensitive data and proprietary models are +loaded into an enclave within an untrusted server. The computation runs securely inside the TEE, keeping +both inputs confidential from the hosting environment. + +An example of a system that enables this kind of secure enclave interaction is CoCoS.ai +[(https://cocos.ai/), an open source platform developed with partial support from the](https://cocos.ai/) +ELASTIC project—funded by the European Union’s Horizon Europe Research and +Innovation Program—and presented at WasmCon 2024. + +With CoCoS.ai, collaborators define who the data providers and result consumers +are up front. Then, approved data providers upload their data to the enclave, where +computation—­possibly supplied by a separate third party—is carried out securely. +Once the task completes, only the authorized result consumers can access the output, +closing the loop on a secure multiparty compute cycle. + +So where does Wasm fit into all this? A good way to think about a TEE is like a strippeddown, bare-metal machine. By default, it has no built-in tooling or environment—just a +minimal, secure shell. To run computations, we need to load everything ourselves. + +We could drop in a full Linux kernel and userspace, but that introduces a lot of complexity and potential vulnerabilities. In security terms, this expands the _trusted computing_ +_base_ _(TCB)_ —the entirety of the code and systems that need to be trusted to keep data +safe inside the enclave. + +Wasm is a great fit here because it can run in small, purpose-built runtimes with far +fewer moving parts. Instead of booting an OS, we can just drop in a Wasm module/ +component and a minimal runtime. This keeps the TCB tight and focused, which is +exactly what we want in confidential computing scenarios. + +But that’s not all. The CPU extensions that power these enclaves—like Intel SGX +and AMD SEV—are typically exposed through low-level C or C++ APIs, since they’re +designed to interact closely with hardware. That means if you’re not working in those +languages, you’re pretty much out of luck when it comes to using those features directly. + +Licensed to Ibrahim Mohammed Mohammed + +**_What’s next for Wasm?_** **223** + +This is another place where Wasm shines. Because it’s language agnostic, you can +write your application logic in a language you’re comfortable with—Rust, Go, you name +it—and compile it to Wasm. That Wasm binary can then run inside the enclave via a +compatible runtime, letting you interact with confidential computing features without +writing C code or dealing with the system-level APIs. + +Finally, one more benefit Wasm brings to TEEs is multitenancy. Because Wasm runs +in a sandboxed environment, it’s possible to execute multiple tenants’ code safely +within the same enclave. This opens the door to shared, confidential compute environments where different parties can run their logic side by side—without risking data +leakage or interference. + +Overall, confidential computing is still an emerging field, but it’s already showing +real promise—especially when paired with technologies like Wasm. By combining +hardware-enforced isolation with Wasm’s lightweight, portable, and language-agnostic +execution model, we get a practical foundation for building secure, privacy-­respecting +applications in sensitive environments. Whether it’s collaborative AI, secure data processing, or sandboxed services in the cloud, this space is ripe for innovation—and Wasm +is well positioned to play a central role. + +_9.2.2_ _AI and machine learning_ + +Earlier in the book, we used `wasi:nn` to connect a Wasm component to an ML backend. +But with AI moving as fast as it is, there’s a lot more ground to cover. In this section, +we’ll take a closer look at two efforts that are shaping how AI and Wasm interact: `wasi` + +`-gfx` and the Model Context Protocol (MCP). + +WASI-GFX + +`wasi-gfx` is a WASI proposal that bundles several packages— `wasi:webgpu`, `wasi:frame` + +`-buffer`, `wasi:surface`, and `wasi:graphics-context` —into a unified graphics and compute interface. Its aim is to bring the same security and portability benefits of Wasm +from the CPU world to the GPU. + +The proposal targets several key goals: secure GPU compute, Wasm-based UI rendering, graphics in GPU-less environments, and AI use cases not covered by `wasi:nn` . +Its sweet spot is in scenarios where you need low-level GPU access, want to work with +unsupported `wasi:nn` backends, or are doing things like training and fine-tuning models directly. + +To see it in action, check out the browser playground with example workloads here: +[https://wasi-gfx.github.io/playground/.](https://wasi-gfx.github.io/playground/) + +Model Context Protocol +One fast-growing area in AI is the rise of AI agents—autonomous or semiautonomous +models that can plan, reason, and act to achieve specific goals. These agents aren’t just +enhanced chatbots; they’re designed to interact with tools, access files, call APIs, and +sometimes even perform actions on the host machine. + +In November 2024, Anthropic introduced the MCP, a proposal aimed at standardizing +how agents interface with their environments. Think of MCP as doing for agents what + +Licensed to Ibrahim Mohammed Mohammed + +**224** Chapter 9 **_The future of Wasm_** + +REST did for web services: it defines a consistent model for accessing resources, tools, and +data. In MCP, a server exposes a set of resources and tools, and an agent (the client) interacts with them according to a clear, structured context. This setup ensures that an agent +knows exactly what it can access and how it should interact with its environment. + +But as with any powerful abstraction, there’s risk. If you give an agent access to a +system without guardrails, things can go sideways—fast. A common example is _prompt_ +_injection_, where a malicious file or input manipulates the agent into performing actions +it shouldn’t, like deleting data or exfiltrating secrets. + +Now imagine implementing the MCP server with Wasm. Because this server is responsible for exposing tools and resources to the agent, we can define those capabilities +precisely using WIT. This lines up perfectly with the only-what-you-need security model +from chapter 3—where each Wasm component is given access only to the specific interfaces it requires. On top of that, Wasm’s sandboxed execution ensures that even if the +agent misbehaves or is compromised, its interactions are limited to the tightly scoped +capabilities exposed by the server. It’s a strong foundation for building secure, predictable environments for intelligent agents—and one that’s well worth keeping an eye on. + +NOTE For an example of building MCP servers with Wasm components and +[WIT-defined capabilities, check out wassette: https://github.com/microsoft/](https://github.com/microsoft/wassette) +[wassette.](https://github.com/microsoft/wassette) + +_9.2.3_ _Compute at the edge_ + +The _edge_ is a layer of infrastructure that sits between end users and centralized cloud +servers. It’s made up of data centers distributed across many locations—often run by +Content Delivery Networks (CDNs) like Fastly, Cloudflare, or Akamai—and it exists +to reduce latency by bringing computation physically closer to users. In the context +of web applications, these edge nodes are the “in-between” spots, usually positioned +within or near ISPs. + +To make this concrete, imagine you’re running a blog. If it’s a static site with just +HTML files, serving it from the edge is straightforward—the CDN caches your files at +hundreds of locations worldwide, so readers in Tokyo get your content from a nearby +Tokyo server instead of your origin server in Virginia. + +Traditionally, CDNs used this edge layer only to cache and serve static content like +images, scripts, and HTML. But modern web experiences demand more than static files. +People expect real-time updates, dynamic personalization, and interactive features that +respond instantly. Consider adding a comment system to that blog: comments need to +be fetched from a database, chronologically ordered, nested for replies, and filtered +for spam. In the traditional model, every comment request would travel back to your +centralized database, adding latency that slows down page loads. + +This is where compute at the edge comes in—running actual application logic at +these distributed CDN locations. Instead of just serving cached files, edge computing platforms now let you execute code right where the request arrives. For our blog + +Licensed to Ibrahim Mohammed Mohammed + +**_What’s next for Wasm?_** **225** + +example, you could run your entire comment system at the edge, processing and serving comments from the same location that delivers your blog posts. Your readers get +both static and dynamic content from the nearest edge location, making the entire +experience feel instantaneous. + +While edge computing has many incarnations—from autonomous vehicles making split-second decisions locally to IoT sensors processing data before sending it +upstream—web applications focus on a specific pattern. Traditional web applications +receive requests at a CDN, do some lightweight processing (like blocking bots or malicious traffic), and then forward the request to a centralized server for the heavy lifting. +But with edge compute, you can handle more of that logic directly at the CDN location, +eliminating the roundtrip to your backend servers. This saves those precious milliseconds that make the difference between a sluggish and a snappy user experience. + +But edge computing isn’t a full replacement for your backend. Edge environments +often have limits: no full database access, smaller memory footprints, and stricter execution times. So, you typically use them for specific slices of logic—not your whole app. +You can see how this interaction plays out in figure 9.4. + +**The request first hits** +**the edge node, where** +**user-defined Wasm code** +**can perform computations.** + +Figure 9.4 Request flow with edge compute. Incoming requests first hit the edge node, where userdefined Wasm code can perform logic before routing to the centralized cloud backend. + +Wasm is a great fit for these environments. It offers a compact, secure way to ship multilanguage workloads to constrained environments like edge nodes. Platforms like Fastly +Compute compile your code to Wasm and then run it on edge nodes using runtimes +like Wasmtime. + +To understand the scale and purpose, consider a global e-commerce platform like an +online retailer serving millions of customers across continents. Major CDN providers + +Licensed to Ibrahim Mohammed Mohammed + +**226** Chapter 9 **_The future of Wasm_** + +typically operate 100 to 300 edge locations worldwide—Cloudflare has about 330 cities, +Fastly has about 430 strategic locations, and Akamai boasts over 4,000 locations. For our +e-commerce platform, this means their code runs in all these places simultaneously. + +Here’s how edge computing helps their operations: + +¡ _A/B testing_ —When launching a new checkout flow, they can instantly route 10% + +of, say, Japanese users to the experimental version while keeping everyone else +on the current flow **.** + +¡ _Rate limiting_ —During flash sales, malicious actors might try to buy up inventory + +with bots. Edge nodes can identify and block these patterns instantly, protecting +inventory for real customers without overwhelming the central order processing +system. + +¡ _Geo personalization_ —European visitors see prices in euros with VAT included, + +while US visitors see dollars with sales tax calculated by state—all computed at +the edge based on the user’s location. + +¡ _Auth filtering_ —Premium members get priority access during high-traffic events. + +The edge validates their membership tokens and routes them to dedicated backend resources, while nonmembers might see a “please wait” queue. + +The power isn’t just in any individual feature—it’s in how they compose. That same +e-commerce platform can combine these capabilities: validate membership, apply +regional pricing, enforce rate limits, and run experiments, all in milliseconds at the +edge location nearest to each customer. This creates a responsive, personalized experience that would be impossible if every decision required a roundtrip to a central server. + +In short, Wasm gives you a secure and portable way to run this custom logic across +hundreds of locations worldwide—without needing to manage full servers at each point +of presence. You write your logic once, then compile it to Wasm, and it runs identically +whether it’s serving customers in São Paulo, Stockholm, or Singapore. + +_9.3_ _What’s next for WASI?_ + +So far in the book, we’ve worked with WASI 0.1 and 0.2. These early versions helped +define how components can interact with system-like interfaces in a structured, portable way. WASI 0.2, in particular, was the first version to adopt the component model, +which we’ve leaned on throughout the SmartCMS example. + +The next major step is WASI 0.3, which is currently in development and may land +in late 2025. Its biggest contribution is native support for asynchronous operations. +Unlike earlier versions that relied on manually modeling async behavior using state +machines or resource juggling, WASI 0.3 brings asynchrony directly into the type system with built-in `future` and `stream` primitives. These types can appear in component function signatures, allowing async behavior to be expressed directly and naturally +across languages. + +The goal is to make asynchronous components interoperable across toolchains +without needing glue code. A Rust component using `async` / `await` could be used + +Licensed to Ibrahim Mohammed Mohammed + +**_Staying in the loop_** **227** + +from Python or C#, and the host runtime handles the async machinery behind the +scenes. WASI’s host supplies the runtime, while the guest interacts with it through a +standard ABI. Under the hood, this is managed using _waitables_ —handles that represent +in-progress operations. A guest can block on a waitable or return a special code to tell +the host to pause its task and resume it later, freeing up the host to run other work in the +meantime. + +This approach simplifies interface definitions. For example, the `wasi:http` interface in 0.2 needed over a dozen resource types to manually model async behavior. With +native `future` and `stream` support, that interface can be redefined with just a few types +and a single handler. + +WASI 0.3 won’t be a one-off release. The plan is to periodically release backward-­ +compatible updates (0.3.1, 0.3.2, etc.) as new features are added. These updates are +expected to include + +¡ _Cancellation_ - _S_ upport for canceling ongoing operations, integrated with language-­ + +native patterns like C#’s `CancellationToken` or JavaScript’s `AbortSignal.` + +¡ _Stream optimizations_ —Adding support for common stream patterns like splicing, + +skipping, and caller-provided buffers to improve performance and reduce memory copies. + +¡ _Threads_ —First cooperative (nonpreemptive) threads to allow concurrency within + +a single OS thread—important for getting toolchains like `wasi-libc` working +safely. Later, preemptive threading will be added via the _shared-everything-threads_ +[proposal (https://mng.bz/z2pA) to enable parallelism across CPU cores.](https://mng.bz/z2pA) + +These changes set the stage for WASI 1.0, which could arrive as early as 2026. That +release would mark the start of long-term stability guarantees, but it’s not just +about maturity—it’s about solidifying the foundations for building modern, high-­ +performance, server-side Wasm applications. + +_9.4_ _Staying in the loop_ + +Wasm is moving fast, and it can be tricky to keep up with all the progress—especially +around the component model and WASI. If you want to follow the latest developments, +the best place to start is the Bytecode Alliance blog: [https://bytecodealliance.org/](https://bytecodealliance.org/articles/) +[articles/. It covers major updates, proposals, and announcements from the team shap-](https://bytecodealliance.org/articles/) +ing much of the Wasm standards work. + +For deeper technical discussions or proposal feedback, you can follow relevant +[GitHub topics in the Bytecode Alliance org (https://github.com/bytecodealliance) or](https://github.com/bytecodealliance) +[even join in directly on the Bytecode Alliance’s Zulip chat: https://bytecodealliance](https://bytecodealliance.zulipchat.com/) +[.zulipchat.com/. It’s open to the public and a good space for asking questions or follow-](https://bytecodealliance.zulipchat.com/) +ing active design threads. + +If you’re more into hands-on examples or walkthroughs, I occasionally post tutorials +[and updates on my YouTube channel (http://youtube.com/c/danlogs), where I focus](http://youtube.com/c/danlogs) +on topics like Rust and Wasm in practice. + +Licensed to Ibrahim Mohammed Mohammed + +**228** Chapter 9 **_The future of Wasm_** + +[It’s also worth keeping an eye on community conferences like WasmCon (https://](https://events.linuxfoundation.org/wasmcon/) +[events.linuxfoundation.org/wasmcon/) and Wasm I/O (https://wasm.io/). These](https://events.linuxfoundation.org/wasmcon/) +events often feature demos of bleeding-edge tools, talks from folks building the standards, and real-world case studies that can help you stay grounded in how all this tech is +being used. + +Finally, thanks for coming with me on this journey. Whether you followed along +chapter by chapter or dropped in to skim a few sections, I hope this book helped you +understand what server-side Wasm is capable of—and maybe even inspired a few ideas +of your own. There’s a lot of exciting work ahead, and the ecosystem could use more +folks building, experimenting, and asking good questions. + +If you’ve got feedback on the book—typos, ideas, questions, or suggestions—you can +[drop a note in the book’s live forum (https://livebook.manning.com/forum?product](https://livebook.manning.com/forum?product=chiarlone) +[=chiarlone) or open an issue in the book’s GitHub repository (https://github.com/](https://livebook.manning.com/forum?product=chiarlone) +[danbugs/serverside-wasm-book-code). I’d love to hear what you think.](https://github.com/danbugs/serverside-wasm-book-code) + +_Summary_ + +¡ Wasm in confidential computing enables secure, multiparty execution using + +TEEs—hardware-enforced secure enclaves that protect data even while it’s being +processed. Wasm’s minimal runtime and language-agnostic nature make it ideal +for these constrained environments, keeping the TCB small while enabling multiple tenants to run securely side by side. + +¡ AI/ML use cases for Wasm are expanding beyond `wasi:nn` . The wasi-gfx proposal + +brings GPU compute capabilities for training and fine-tuning models, while the +MCP—a standard for how AI agents interact with tools and resources—pairs +naturally with Wasm’s sandboxed execution to create secure environments for +autonomous agents. + +¡ Wasm fits naturally into edge compute environments, where its compact size + +and sandboxed security match the constraints of CDN-distributed execution +across hundreds of global locations. Edge platforms like Fastly Compute compile your code to Wasm for use cases, including A/B testing, rate limiting, geo-­ +personalization, and auth filtering—all running milliseconds away from users +without managing full servers at each location. + +¡ WASI 0.3 introduces native async support via built-in `future` and `stream` + +types, eliminating the need for complex state machines and resource juggling. +This simplifies interface design, enables cross-language async interoperability, +and sets the foundation for WASI 1.0’s stability guarantees expected around +2026. + +Licensed to Ibrahim Mohammed Mohammed + +##### _appendix A_ _Required tools_ + +You can use this appendix as a quick reference for installing all the tools used +throughout the book. + +_A.1_ _Rust_ + +[To install Rust, see https://www.rust-lang.org/learn/get-started.](https://www.rust-lang.org/learn/get-started) + +NOTE We use `rustc` `1.84.0` `(9fc6b4312` `2025-01-07)` . If you already have +Rust installed, you can install this specific version with `rustup` `install` +`1.84.0` and then set it as default with `rustup` `default` `1.84.0` . For some Spin +sections, we use Rust version 1.85.0. You can follow the same steps to install +it and swap between different Rust versions as needed. + +_A.1.1_ _Target installation_ + +Here are the Rust targets used in this book and how to install them: + +¡ `wasm32-unknown-unknown` - `rustup target add wasm32-unknown-unknown` + +¡ `wasm32-wasip1` - `rustup target add wasm32-wasip1` + +¡ `wasm32-wasip2` - `rustup target add wasm32-wasip2` + +_A.1.2_ _Cargo installs_ + +These are the Cargo-based tools we’ll install as needed: + +¡ _Cargo component_ - `cargo install cargo-component@0.20.0 --locked` + +**229** + +Licensed to Ibrahim Mohammed Mohammed + +**230** Appendix A **_Required tools_** + +¡ _Wasm tools_ - `cargo install wasm-tools@1.227.1 --locked` + +¡ _Wasmtime_ - `cargo install wasmtime-cli@30.0.0 --locked` + +¡ _WASI-Virt_ - `cargo install wasi-virt --git https://github.com/bytecodealliance/` +``` + WASI-Virt --rev b662e419 --locked +``` + +¡ _WAC_ - `cargo install --locked wac-cli@0.6.1` + +¡ _Wasm Hex Dump_ - `cargo install wasm_hex_dump@0.1.0 --locked` + +¡ _Wash_ - `cargo install --locked wash-cli` (installs the latest version) + +¡ _Oha_ - `cargo install oha@1.4.7 --locked` + +¡ _Wkg_ - `cargo install wkg@0.10.0 --locked` + +_A.2_ _Git LFS_ + +[To install Git LFS, see https://git-lfs.com/.](https://git-lfs.com/) + +_A.3_ _Docker_ + +There are two ways you can install Docker: + +¡ _Docker Engine_ —Lightweight, CLI-focused: [https://docs.docker.com/engine/](https://docs.docker.com/engine/install/) + +[install/](https://docs.docker.com/engine/install/) + +¡ _Docker Desktop_ [—Includes Docker Engine with a GUI: https://docs.docker.com/](https://docs.docker.com/desktop/) + +[desktop/](https://docs.docker.com/desktop/) + +NOTE The recommended version is Docker 28.0.0, build f9ced58. You can +verify your version with `docker --version` . + +_A.4_ _JS toolchain_ + +If you’re on Linux, macOS, or Windows Subsystem for Linux, install nvm with + +``` + curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.2/install.sh | bash + +``` + +Restart your terminal; then install Node version 22 (along with npm) by running + +``` + nvm install 22 + +``` + +[If you’re on Windows, download the installer from https://github.com/coreybutler/](https://github.com/coreybutler/nvm-windows) +[nvm-windows, and follow the same steps.](https://github.com/coreybutler/nvm-windows) + +After setting up Node and npm, install JavaScript’s Wasm toolchain with + +¡ `jco` - `npm install -g @bytecodealliance/jco@1.10.2` + +¡ `componentize-js` - `npm install -g @bytecodealliance/componentize-js@0.17.0` + +_A.5_ _Python toolchain_ + +[To install Python, check out https://mng.bz/KwqO. At the time of writing, the book](https://mng.bz/KwqO) +uses Python version 3.12.3. + +Licensed to Ibrahim Mohammed Mohammed + +**_container2wasm_** **231** + +_A.6_ _Spin_ + +[Install Spin from https://developer.fermyon.com/spin/v3/install.](https://developer.fermyon.com/spin/v3/install) + +NOTE We use `spin` `3.2.0` `(c9be6d8` `2025-03-24)` . To stay consistent, use this +version while following along. + +To prepare for Kubernetes deployment, install the Spin plugin: + +``` + spin plugins update + spin plugins install kube + spin plugins list --installed + +``` + +You should see output similar to this (versions may vary): + +``` + cloud 0.11.0 [installed] + kube 0.4.0 [installed] + +``` + +_A.7_ _Ollama setup_ + +[Install instructions are at https://ollama.com/.](https://ollama.com/) + +Then pull the model used in the book with + +``` + ollama pull gurubot/tinystories-656k-q8 + +``` + +_A.8_ _regctl_ + +To install on Ubuntu using Snap, run + +``` + sudo snap install regclient + +``` + +At the time of writing, this installs regctl version 0.8.0. For other operating systems, +[follow https://regclient.org/install.](https://regclient.org/install) + +_A.9_ _container2wasm_ + +Install with + +``` + wget https://github.com/container2wasm/container2wasm/releases/download/ + v0.8.3/container2wasm-v0.8.3-linux-amd64.tar.gz + +``` + +NOTE If you’re on an arm64 Linux machine instead of amd64, make sure to +download the arm64 release by replacing amd64 in the link with arm64. You +[can find all available versions at https://mng.bz/9yqr.](https://mng.bz/9yqr) + +After downloading the release, extract the archive with + +``` + tar -xvf container2wasm-v0.8.3-linux-amd64.tar.gz + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**232** Appendix A **_Required tools_** + +Next, move the extracted binaries to a directory in your PATH so they can be used +system-wide: + +``` + sudo mv c2w c2w-net /usr/local/bin/ + +``` + +Ensure the binaries are executable by running + +``` + chmod +x /usr/local/bin/c2w /usr/local/bin/c2w-net + +``` + +Now verify that the installation was successful by checking the version: + +``` + c2w --version + +``` + +You should see output similar to the following: + +``` + c2w version v0.8.3 + +``` + +Finally, clean up by removing the downloaded archive: + +``` + rm container2wasm-v0.8.3-linux-amd64.tar.gz + +``` + +_A.10 Kubernetes setup_ + +Start by installing `k3d`, a Docker-based local Kubernetes cluster tool. It’s fast, lightweight, and great for development/testing. + +[We use version v5.8.3: https://k3d.io/v5.8.3/?h=installation#releases.](https://k3d.io/v5.8.3/?h=installation#releases) +Next, install `kubectl` to interact with your cluster: [https://kubernetes.io/docs/](https://kubernetes.io/docs/tasks/tools/) +[tasks/tools/.](https://kubernetes.io/docs/tasks/tools/) + +We use + +``` + kubectl version --client + Client Version: v1.33.0 + Kustomize Version: v5.6.0 + +``` + +[Then install Helm by following https://helm.sh/docs/intro/install/.](https://helm.sh/docs/intro/install/) + +We use + +``` + helm version + version.BuildInfo{Version:"v3.17.3", + GitCommit:"e4da49785aa6e6ee2b86efd5dd9e43400318262b", + GitTreeState:"clean", GoVersion:"go1.23.7"} + +``` + +Licensed to Ibrahim Mohammed Mohammed + +##### _appendix B_ _Deploying the SmartCMS_ _to Azure Kubernetes Service_ + +In this appendix, we’ll walk through how to deploy the SmartCMS app to Azure +Kubernetes Service (AKS). + +_B.1_ _Prerequisites_ + +For this section, you’ll need a few tools installed. Most are covered in appendix A. +The only new one is the Azure CLI, which you can install by following the steps here: +[https://mng.bz/jZNe.](https://mng.bz/jZNe) + +You’ll also need an Azure account and an active subscription. If you don’t already +[have one, you can create both by following this guide: https://mng.bz/WwQW.](https://mng.bz/WwQW) + +Next, you’ll need a Kubernetes cluster on Azure. To set it up, follow the instruc[tions at https://mng.bz/8XqD. Follow the guide up to, but not including, the Deploy](https://mng.bz/8XqD) +the Application step. + +Once your cluster is up and running, grab the public IP assigned to it with + +``` + az network public-ip list --query "[?starts_with(resourceGroup, 'MC_') && +``` + +➥ `ipAddress!=null].{Name:name, ResourceGroup:resourceGroup, IP:ipAddress}"` +➥ `-o table` + +You’ll get output that looks like + +``` + Name ResourceGroup IP + ------------ ------------------------ ---------- 715a31e6-... MC_sswasm_sswasm_westus2 4.155.90.17 + +``` + +**233** + +Licensed to Ibrahim Mohammed Mohammed + +**234** Appendix B **_Deploying the SmartCMS to Azure Kubernetes Service_** + +Next, assign a DNS name to your cluster’s public IP: + +``` + az network public-ip update +``` + +➥ `--resource-group ` +➥ `--name ` +➥ `--dns-name ` +➥ `--query "dnsSettings.fqdn"` + +Replace `` with the resource group name, `` with the public IP’s name from the previous command (i.e., the value in the +leftmost column), and `` with a DNS name of your choice +(e.g., `danbugssmartcms` ). + +Make sure to save the FQDN output from this command (e.g., `danbugssmartcms` + +`.westus2.cloudapp.azure.com` )—you’ll need it for the Ingress configuration later. + +NOTE Aside from setting up the cluster, I’ve already built a Docker image for +the frontend and pushed it to GitHub’s Container Registry (GHCR). You can +find it here: `ghcr.io/danbugs/smartcms-frontend:v1` . The source code is avail[able at https://mng.bz/EwYR.](https://mng.bz/EwYR) + +_B.2_ _Preparing the cluster_ + +Now that your Kubernetes cluster is up and running, it’s time to set it up for SmartCMS. +We’ll install some tools, configure networking, and get things ready to run our app. +[Make sure you’re logged into GHCR, following https://mng.bz/NwEd if necessary.](https://mng.bz/NwEd) + +First, install the `wasmcloud-platform` Helm chart: + +``` + helm upgrade --install wasmcloud-platform +``` + +➥ `--values https://raw.githubusercontent.com/wasmCloud/wasmcloud/main/` +➥ `charts/wasmcloud-platform/values.yaml` +➥ `oci://ghcr.io/wasmcloud/charts/wasmcloud-platform:0.1.2` +➥ `--dependency-update` + +Next, set up the Ingress controller. This will allow HTTP traffic to flow into the cluster. +Replace `` with the IP address you obtained in the previous section: + +``` + helm upgrade --install ingress-nginx ingress-nginx/ingress-nginx +``` + +➥ `--namespace ingress-nginx --create-namespace` +➥ `--set controller.replicaCount=2` +➥ `--set controller.nodeSelector."kubernetes.io/os"=linux` +➥ `--set defaultBackend.nodeSelector."kubernetes.io/os"=linux` +➥ `--set controller.service.externalTrafficPolicy=Local` +➥ `--set controller.service.loadBalancerIP=""` + +After deploying, it may take a few minutes for the Ingress controller’s external IP to +show up. You can check its status with + +``` + kubectl get svc -n ingress-nginx + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Deploying the SmartCMS_** **235** + +To enable HTTPS, install `cert-manager` : + +``` + kubectl apply +``` + +➥ `-f https://github.com/cert-manager/cert-manager/releases/latest/` +➥ `download/cert-manager.yaml` +``` + kubectl wait --namespace cert-manager +``` + +➥ `--for=condition=Available deployment/cert-manager --timeout=120s` + +Once `cert-manager` is available, you’ll need a `ClusterIssuer` so it can request TLS certificates from Let’s Encrypt. + +NOTE Let’s Encrypt is a free certificate authority that issues trusted SSL/TLS +certificates. It uses the ACME protocol to prove that you control the domain. + +Replace `` with your email address to receive expiration warnings. + +Listing B.1 Let’s Encrypt **`ClusterIssuer`** for TLS + +``` + apiVersion: cert-manager.io/v1 + kind: ClusterIssuer + metadata: + name: letsencrypt-prod + spec: + acme: + email: + server: https://acme-v02.api.letsencrypt.org/directory + privateKeySecretRef: + name: letsencrypt-prod + solvers: + - http01: + ingress: + class: nginx + +``` + +Save this to a file called `cluster-issuer.yaml`, then apply it with + +``` + kubectl apply -f cluster-issuer.yaml + +``` + +Finally, deploy the wasmCloud host configuration. This gets your backend runtime set +up and ready: + +``` + kubectl apply -f https://raw.githubusercontent.com/danbugs/serverside-wasm book-code/refs/heads/main/app_c/setup/wasmcloud-host.yaml + +``` + +_B.3_ _Deploying the SmartCMS_ + +Now that the cluster and Ingress are ready, it’s time to deploy the SmartCMS system. +It consists of a few backend services, a frontend, and an Ingress that ties it all together. + +_B.3.1_ _Deploying the backend components_ + +Apply each of the backend YAML files: + +Licensed to Ibrahim Mohammed Mohammed + +**236** Appendix B **_Deploying the SmartCMS to Azure Kubernetes Service_** + +``` + kubectl apply + -f https://raw.githubusercontent.com/danbugs/ +``` + +➥ `serverside-wasm-book-code/refs/heads/main/app_c/backend/ollama.yaml` +``` + kubectl rollout status deployment/ollama --timeout=120s + + kubectl apply +``` + +➥ `-f https://raw.githubusercontent.com/danbugs/` +➥ `serverside-wasm-book-code/refs/heads/main/app_c/backend/wadm.yaml` +``` + kubectl apply +``` + +➥ `-f https://raw.githubusercontent.com/danbugs/` +➥ `serverside-wasm-book-code/refs/heads/main/app_c/backend/wasmcloud-api.yaml` + +NOTE Most of this is the same as chapter 8; one difference is that we now +expose the `wasmcloud-api` service through an Ingress route instead of using +`kubectl` `port-forward` . This is a more production-friendly approach and integrates with TLS. + +_B.3.2_ _Deploying the frontend_ + +Apply the frontend YAML file: + +``` + kubectl apply +``` + +➥ `-f https://raw.githubusercontent.com/danbugs/` +➥ `serverside-wasm-book-code/refs/heads/main/app_c/frontend/frontend.yaml` + +This deploys the static frontend, which will serve the UI for the SmartCMS. + +_B.3.3_ _Configuring Ingress_ + +To route external traffic to your services, create an Ingress configuration. Save the following listing as `ingress.yaml`, replacing `` with the FQDN we +obtained in the prerequisites section. + +Listing B.2 An example Ingress resource for SmartCMS + +``` + apiVersion: networking.k8s.io/v1 + kind: Ingress + metadata: + name: nginx + annotations: + cert-manager.io/cluster-issuer: letsencrypt-prod + spec: + ingressClassName: nginx + tls: + - hosts: + - + secretName: sswasm-tls + rules: + - host: + http: + paths: + - path: /api + +``` + +Licensed to Ibrahim Mohammed Mohammed + +**_Deploying the SmartCMS_** **237** + +``` +pathType: Prefix +backend: +service: +name: wasmcloud-api +port: +number: 8000 +- path: / +pathType: Prefix +backend: +service: +name: smartcms-frontend +port: +number: 80 + +``` + +Apply it with + +``` +kubectl apply -f ingress.yaml + +``` + +Once everything is set up, you’ll be able to access the SmartCMS frontend at + +`https://` . To see my own deployment, visit: [https://sswasm](https://sswasm.com/) +[.com/. For an overview of the deployed Smart CMS system, see figure B.1.](https://sswasm.com/) + +User + +Figure B.1 The full SmartCMS deployment architecture on Azure. The Wasm component runs inside a +wasmCloud host within an AKS cluster, communicating with persistent storage and a machine learning +provider. Traffic enters through an Ingress controller mapped to a public IP, routing API requests to an +HTTP server and frontend assets to the web UI. Inference is powered by an Ollama-hosted model. + +Licensed to Ibrahim Mohammed Mohammed + +**238** Appendix B **_Deploying the SmartCMS to Azure Kubernetes Service_** + +And that’s it! You’ve deployed a Wasm backend service to a Kubernetes cluster running +on a public cloud—and made it accessible to the world. You configured networking, +installed essential tooling like wasmCloud and `cert-manager`, and even set up HTTPS +using trusted TLS certificates. + +Licensed to Ibrahim Mohammed Mohammed + +##### _afterword_ + +When we first started working on WebAssembly in Firefox, along with our colleagues in +Chrome, Safari, and Edge, we were, as Dan mentioned at the beginning of this book, +primarily interested in providing a safe, portable, standards-based alternative to native +browser NPAPI, ActiveX, and PPAPI plugins. Already this was a challenging goal for +both technical and browser-political reasons—several attempts in this direction had +already been made, and all had failed to achieve cross-browser adoption. Fortunately, +with Emscripten and asm.js already being used in production, we had a nice, focused +goal for our initial release: provide something functionally equivalent but more efficient and less weird than asm.js that existing Emscripten users could target by simply +flipping a flag. + +At the same time, given the explosive growth of JavaScript _outside_ the browser, driven +by the adoption of Node.js, we thought that maybe, just _maybe_, WebAssembly might also +be useful outside the browser as well. For what? We weren’t sure; after all, outside the +browser there is no prohibition on running native code or the ever-present need for +sandboxing of malicious code, which had been the prime motivators for WebAssembly +inside the browser. But just in case, the WebAssembly standards group included “nonweb embedding” use cases in its initial goals and design documents. What this mostly +boiled down to was not baking in any hard dependencies on JavaScript or Web APIs into +core WebAssembly. Instead, all interactions between WebAssembly and the rest of the +browsers were carefully factored out into separate, optional, layered specifications that +nonbrowser embeddings were free to ignore. This layering discipline also helped us to +keep the core WebAssembly specification relatively small and self-contained, which in +turn has encouraged formalization, proofs of soundness, mechanized reasoning, and a +growing body of academic study and research. + +**239** + +Licensed to Ibrahim Mohammed Mohammed + +**240** afterword + +What has been pleasantly surprising since the release of WebAssembly in browsers is +that this initial suspicion—that WebAssembly could provide unique value even outside +the browser—has turned out to be true. As Dan explains in the preceding pages, compared to popular mainstream alternatives like containers, WebAssembly offers a smaller +size, lower cold start times, better portability, and simpler sandboxing, all of which can +lead to cost savings, which has become increasingly important in recent years. We’ve +seen these benefits play out in a number of other software domains, which Dan has +helpfully given us a tour of throughout this book. + +As exciting as it is to see WebAssembly spreading outside the browser, it has also +created the clear and present need to beef up the foundational standards to fill in the +gaps left by the absence of JavaScript and web APIs. This is the role of the Component +Model and WASI standards proposals that Dan has thoroughly introduced throughout +the book. It is my belief that as we progress from the release of 0.3, which Dan describes +in the final chapter, to the subsequent stable 1.0 release, we’ll see self-reinforcing, exponential growth as each popular language, framework, package, and platform provides +upstream, out-of-the-box support for WebAssembly as another instruction set and operating system—a special one based on vendor-neutral open standards, rigorous formal +foundations, capability-based security, and extreme portability. By explaining the concepts, big ideas, and nuts and bolts of WebAssembly, WASI, and the WebAssembly Component Model with approachable language and examples, this book is an invaluable +tool for kicking off this exponential growth. + +—Luke Wagner +cocreator of WebAssembly and distinguished engineer at Fastly + +Licensed to Ibrahim Mohammed Mohammed + +A + +ABI (application binary interface) 29 +A/B testing 226 +adapter component 68 +admission webhooks 194 +AI (artificial intelligence) 223 +AKS (Azure Kubernetes Service), deploying + +SmartCMS 233–235 +applications + +adding persistent storage 130–135 +creating production-grade, “Hello, World” + +application 124–130 +plugins 13 +production-grade 150, 155 +scaling 135–138 +atomics interface 131, 132 +auth filtering 226 + +B + +backend components, deploying 235 +BLOB type 99 + +literal 101 + +C + +cancellation, defined 227 +Canonical ABI 29, 54 + +##### _index_ + +Cargo, Rust installs 229 +cargo component 62, 64, 229 +CCA (Confidential Compute Architecture) 221 +CDNs (content delivery networks) 224 +cert-manager 194, 235 +cgroups (control groups) 168 +CLI (command-line interface), creating + +applications 62–64 +ClusterIssuer 235 +CMS (content management system) 30 +CNCF (Cloud Native Computing Foundation) 170 +cold start problem 11 +command world 62 +complex data types 28 +components + +benefits of 34–39 +building componentized “Hello, World” guest + +application with JavaScript 39–40 +converting modules to 65–68 +expanding example project with 43–52 +managing capabilities 69 +compute at edge 224–226 +confidential computing 220–223 +Config object 59 +containerd 170 + +setting up 173 +containerd-shim 170 + +**241** + +Licensed to Ibrahim Mohammed Mohammed + +**242** index + +containerd-shim-spin executor 198 +containerd-shim-wasm 171 +containers + +converting Linux containers to Wasm 183–187 +installing container2wasm 184 +using container2wasm 185–187 +Wasm vs. 168–169 +CRDs (Custom Resource Definitions) 191 +CRs (custom resources) 191 +cryptographic signing 129 + +D + +data at rest/in transit/in use 221 +databases, running Wasm inside 97–105 + +compiling Rust code as Wasm module 101–102 +creating UDFs 98–100 +running UDFs in libSQL 103–105 +decryption 98–103 +distributing, production-grade Wasm + +applications 138–142 +Docker 230 +Docker+Wasm Technical Preview 171 +Docker Compose 180–182 +Docker container 10–11 +Docker Desktop 230 +Docker Engine 230 +Dockerfile 172 + +E + +edge computing 11 +encryption 98–103 +Engine object 59 +EOS (end-of-sequence) token 110, 112 +example project, implementing as wasmCloud + +app 147 + +F + +f32 floating-point values 90, 99, 111 +f64 type 99 +FaaS (function as a service) 5 +frontend, deploying 236 + +G + +generate interface 159 +geo personalization 226 +GHCR (GitHub Container Registry) 175, 198 +Git LFS (Large File Storage) 230 + +greet component 57 +guest applications, building componentized “Hello, + +J + +JavaScript + +building componentized “Hello, World” guest + +application with 39–40 +building host for Wasm component 41–43 +jco 57 +jco transpile command 51 +jq command 177 + +World” guest application with +JavaScript 39–40 +guest components, implementing and running + +inside SmartCMS host 50–52 +guest-host architecture 19 + +H + +hardware agnosticism 11 +hello world definition 125 +Helm 192, 193 +HorizontalPodAutoscaler resource 203 +Host interface 47 +hosts, updating to use ONNX for wasi:nn 115 +Host trait 46 +HPA (Horizontal Pod Autoscaler) 200, 203 +http capability 57 +http-component 30, 127, 139, 148 +HTTP proxies, creating 65 +http-rust template 143 + +I + +i32 types 24 +IaaS (Infrastructure as a service) 5 +image crate 88 +impl block 46 +import/export interfaces 63 +incoming-handler 127 +Ingress, configuring 236 +INTEGER type 99 +interfacing + +expanding example project with WASI and + +composability 76–83 +Wasm with underlying system, composing + +components together 71–76 +IoView trait 58, 80 +ISA (instruction set architecture) 3 + +Licensed to Ibrahim Mohammed Mohammed + +JS toolchain 230 +JVM (Java Virtual Machine) 6 + +K + +k3d tool 192 +key-value store component 30 +key-value store interface 131, 132 +key-value stores, improving WIT file for 44 +KeyValue struct 46, 47, 48, 49 +kubectl tool 192 +kube plugin 192, 197, 198, 202 +Kubernetes 11 + +preparing cluster for SpinKube 193–195 +running SmartCMS example on 212–216 +running Wasm in with SpinKube 192, 197 +running Wasm in with wasmCloud 205–211 +scalability for Wasm with 189 + +scaling Wasm in Kubernetes with + +SpinKube 199 +setup 232 +Wasm and 190–192 +kvnats provider 132, 158 +kvstore interface 44, 46, 52 + +L + +labels in classification 87 +language agnosticism 6 +LFS (Git Large File Storage) 21 +libSQL, running UDFs in 103–105 +libsql_bindgen crate 98, 99, 104 +linear memory 18 +Linker object 59 +Linux containers, converting to Wasm 183–187 + +M + +machine learning component 31 +magic_crypt crate 99, 101 +manifest, modifying WIT and wadm 130–132 +MCP (Model Context Protocol) 223 +memory assumptions risks 28 +microcontrollers, defined 13 +ML (machine learning) 86, 109, 223 + +adding to SmartCMS 156, 159, 161, 164 +improving example project with wasi:nn 106 +MCP (Model Context Protocol) 223 +setting up project 106 + +index **243** + +updating host to use ONNX for wasi-nn 115 +using wasi-nn 86–97 +wasi-gfx 223 +writing Wasm component 109 +ML models 87 +ml world 89 +mobile and desktop applications 13 +mod bindings 73 +modules + +building server-side applications with, WIT + +28–30 +converting to components 65–68 + +N + +namespaces 192 +NATS lattice 127 +ndarray crate 88 +NPUs (neural processing units) 86 +nvm (Node Version Manager) 39 + +O + +setup 231 +ONNX (Open Neural Network Exchange) 87 + +updating host to use for wasi:nn 115 +Option type 50, 51 + +P + +PaaS (Platform as a service) 5 +PAT (personal access token) 175 +performance 9–11 + +Wasm app vs. short-running workload in + +container 9 +Wasm app vs. x86_64 app 9 +Wasm container vs. Docker container 10–11 +Wasm density in Kubernetes 11 +persistent storage + +adding to applications 130–135 +adding to SmartCMS 150–153, 155 + +OAM (Open Application Model) 142 +OCI (Open Container Initiative) 6, 89, 124, + +175–179, 189 +inspecting component OCI images 176–178 +packaging and publishing components as OCI + +images 175 +pulling and running OCI images 178 +oha tool 136, 230 +Ollama 156, 158 + +Licensed to Ibrahim Mohammed Mohammed + +**244** index + +POSIX (Portable Operating System Interface) 61 +probabilities vector 112 +production-grade applications + +Kubernetes 212–216 +running Wasm in Kubernetes with + +preparing cluster for SpinKube 193–195 +running SmartCMS example on + +adding machine learning to SmartCMS 156, + +159, 161, 164 +adding persistent storage 130–135 +adding persistent storage to SmartCMS 150, + +152, 153, 155 +creating, wasmCloud “Hello, World” + +application 124–130 +distributing 138–142 +implementing example project as wasmCloud + +SpinKube 192 +scaling Wasm in Kubernetes with SpinKube 199 +scaling applications 135–138 +SDKs (software development kits) 124 +secret-store interface 35 +Secret type 35–36 +Secure Encrypted Virtualization (SEV) 221 +secure multiparty collaboration 221 +security, overview 12 +server-side applications 17 + +app 147 +scaling 135–138 +Spin 142–147 +prompt injection 224 +Python + +building host for JavaScript bundled Wasm + +component 41–43 +toolchain 230 + +R + +rand crate 77 +rate limiting 226 +REAL type 99 +Request record 157 +Response record 157 +RNG (random number generator) 110, 112 +root world 63 +routes 152, 153 +RPC (Remote Procedure Call) 128 +RuntimeClass 194 +runwasi project 171 +Rust 229 + +Cargo installs 229 +compiling code into Wasm modules 25, 101–102 +creating library projects 21 +creating Wasm apps using 21–28 +executing Wasm modules with custom hosts + +26–27 +“Hello, World” application 28 +specifying compilation to Wasm modules 22 +target installation 229 + +S + +scalability + +for Wasm (WebAssembly) with Kubernetes 189, + +197 + +building with Wasm modules 30 +building with WIT 28–30 +creating Wasm apps using Rust 21–28 +guest-host architecture of 19 +SEV (Secure Encrypted Virtualization) 221 +SGX (Software Guard Extensions) 221 +SmartCMS + +creating “Hello, World” application 142–146 +wasmCloud vs. 146 +SpinApp 203 + +manifest 198 +resource 198 +SpinKube + +preparing cluster for 193–195 +running Wasm app in Kubernetes with 192, 197 + +adding machine learning to 156, 159, 161, 164 +adding persistent storage to 135, 150, 152–153, + +155 +closing remarks 219 +creating production-grade applications, + +implementing example project as +wasmCloud app 147 +deploying 235–236 +deploying to Azure Kubernetes Service 233 +expanding example project with WASI and + +composability 76–83 +expanding example project with Wasm + +components 43–52 +improving example project with wasi:nn 106 +machine learning, updating host to use ONNX + +for wasi:nn 115 +preparing cluster 234 +running example on Kubernetes 212–216 +Spin 142–147, 231 + +Licensed to Ibrahim Mohammed Mohammed + +index **245** + +scaling Wasm in Kubernetes with 199, 202, 204 +spreadscaler 140, 146 + +configuration 137 +trait 127, 149 +standards-based and vendor neutrality 12 +State structure 48, 58, 80, 115, 116 +stdout capability 69 +storage, persistent 135 +Store 59, 116 +stream optimizations 227 +String objects 99, 100, 167 + +T + +TCB (Trusted Computing Base) 222 +TEE (Trusted Execution Environment) 221 +tensors, defined 87 +testing, production-grade applications 155, 164 +TEXT type 99 +threads, defined 227 +TLS certificates 194 +tokenizers library 107 +toolchain, installing Rust 41 +tools 229 + +container2wasm 231 +Docker 230 +JS toolchain 230 +Kubernetes setup 232 +Ollama setup 231 +Python toolchain 230 +Spin 231 +transpile/interfaces 51 +Triangle filter 92 + +U + +UDFs (user-defined functions) 97 + +creating 98–100 +running in libSQL 103–105 +underlying system, interfacing with, converting + +module to component 65–68 +unwrap call 47 + +V + +VM (virtual machine) 5 + +W + +WAC 75, 230 +wadm + +modifying manifest 130–132 +monitor 127 +file 127 +wadm.yaml file 126, 129, 150, 181, 182 +waitables, defined 227 +wash 230 +WASI (WebAssembly System Interface) 5, 55–62, + +creating component 89–93 +running component 95–97 +setting up project 87 +WasiNnCtx 115, 116 +wasi_nn_example/ folder 96 +wasi_nn_example project 95, 111, 112 +WasiView trait 58, 80 +WASI-Virt 230 +Wasm architecture 178 +wasm-builder 174 +wasmCloud 179–183 + +creating “Hello, World” application 124–130 + +123, 125, 169 +cli run function 88, 95 +expanding example project with 76–83 +frame-buffer 223 +future of 226 +graphics-context 223 +http interface 227 +keyvalue-backed key-value store 148 +nn interface 88, 89, 97 +surface 223 +wasi-gfx 223 +webgpu 223 +what can be built with 61–65 +WASI 0.2 125 +wasi:cli/run function 64 +was::keyvalue::* dependency 151 +wasi:keyvalue interface 130, 131, 132, 133, 134, + +135, 142 +wasi:nn (WebAssembly System Interface for neural + +networks) +improving example project with 106 +interface 84 +updating host to use ONNX for 115 +wasi:sql-embed proposal 98 +WasiCtx 59, 116 +wasi-gfx 223 +wasi-nn 86–97 + +Licensed to Ibrahim Mohammed Mohammed + +**246** index + +implementing example project as wasmCloud + +app 147 +running Wasm in Kubernetes with 205–211 +Spin vs. 146 +using custom WIT interfaces with 159 +Wasm components, overview 54 +Wasm containers 167, 170 + +running 172–175 +running inside traditional containers 172 +wasmCloud and 179–183 +wasm_hex_dump tool 101, 102, 104, 230 +Wasm modules, overview 53 +wasm-opt tool 102 +Wasmtime 230 +wasmtime-py 57 +wasm-tools 63, 65, 230 +Wasm (WebAssembly) 3 + +advantages of 6–13 + +hardware agnosticism 11 +language agnosticism 6 +performance 9–11 +security 12 +standards-based and vendor neutrality 12 +Wasm app vs. short-running workload in + +container 9 +Wasm app vs. x86_64 app 9 +Wasm container vs. Docker container 10–11 +Wasm density in Kubernetes 11 +building blocks of 18 +components 33–52 +containers 168–169, 175–17, 183–187 +containers vs. 168–169 +converting module to component 65–68 +creating apps using Rust 21–28 +creating production-grade applications + +for developers 1 +future of 220–227 +interfacing with underlying system 53, 65–68, + +71–76 +Kubernetes and 190–192 +machine learning, updating host to use ONNX + +for wasi:nn 115 +managing component capabilities 69 +navigating book 15 +running in Kubernetes with SpinKube 192 +running inside databases 97–105 +scalability for, running Wasm app in Kubernetes + +with SpinKube 197 +server side 5 +WASI (WebAssembly System Interface) 55–61, + +85 +when and where to use 13–15 +WAT (WebAssembly Text Format) 101, 104 +WeightedIndex 112 +WIT (WebAssembly Interface Types) 28–30, 34, 54, + +89, 123, 125, 167 +adding persistent storage 135 +improving for key-value store 44 +modifying manifest 130–132 +using custom interfaces with wasmCloud 159 +wRPC (component-native remote procedure call) + +protocol 128 +WSL (Windows Subsystem for Linux) 173 + +X + +x86_64 app 9 + +Y + +YAML manifests 192 + +123–135, 142–147 + +Licensed to Ibrahim Mohammed Mohammed + +RELATED MANNING TITLES + +_WebAssembly in Action_ +by Gerard Gallant + +ISBN 9781617295744 +448 pages, $49.99 +November 2019 + +_Rust Servers, Services, and Apps_ +by Prabhu Eshwarla + +ISBN 9781617298608 +328 pages, $59.99 +July 2023 + +_Rust Web Development_ +by Bastian Gruber + +ISBN 9781617299001 +400 pages, $49.99 +December 2022 + +_Build an Orchestrator in Go (From Scratch)_ +by Tim Boring + +ISBN 9781617299759 +288 pages, $59.99 +March 2024 + +_For ordering information, go to www.manning.com_ + +Licensed to Ibrahim Mohammed Mohammed + +**A new online reading experience** + +liveBook, our online reading platform, adds a new dimension to your Manning books, +with features that make reading, learning, and sharing easier than ever. A liveBook +version of your book is included FREE with every Manning book. + +This next generation book platform is more than an online reader. It’s packed with +unique features to upgrade and enhance your learning experience. + + - Add your own notes and bookmarks + + - One-click code copy + + - Learn from other readers in the discussion forum + + - Audio recordings and interactive exercises + + - Read all your purchased Manning content in any browser, anytime, anywhere + +As an added bonus, you can search every Manning book and video in liveBook—even +ones you don’t yet own. Open any liveBook, and you’ll be able to browse the content and +read anything you like.* + +**Find out more at www.manning.com/livebook-program.** + +_*Open reading is limited to 10 minutes per book daily_ + +Licensed to Ibrahim Mohammed Mohammed + +**Final SmartCMS architecture: Practical example from server-side WebAssembly** + +User + +Licensed to Ibrahim Mohammed Mohammed + +PROGRAMMING LANGUAGES AND STYLES/SYSTEM PROGRAMMING + +Server-Side WebAssembly + +Danilo Chiarlone ● Foreword by Brendan Burns ● Afterword by Luke Wagner + +ebAssembly (Wasm) revolutionized frontend development by enabling secure, high-performance code +in the browser. Now it’s doing the same for backend +## W +infrastructure as a platform for lightweight, portable applications that run anywhere. In this book, you’ll learn how to +build production-grade server-side apps using Wasm. + +Server-Side WebAssembly teaches you to use WebAssembly +components and the WebAssembly System Interface (WASI) +to build web back ends. You’ll start with Wasm fundamentals +and then quickly start building a realistic content management system complete with data storage, HTTP servers, and +AI-generated content. As you go, you’ll see how Wasm integrates code from multiple languages and connects naturally +with databases, AI/ML systems, and deployment platforms +like Kubernetes. + +What’s Inside + + - Integrate components from multiple languages + + - Deploy Wasm applications on Kubernetes + + - Connect Wasm apps to databases and ML systems + +For intermediate web developers. Code samples in Rust, +JavaScript and Python. + +Danilo Chiarlone works at Microsoft on cloud and virtualization technologies and is a contributor to several WebAssembly +System Interface (WASI) proposals. + +For print book owners, all digital formats are free: + +https://www.manning.com/freebook + +**M** **A N** **N** **I** **N** **G** diff --git a/tree/specialized_tooling/webassembly/pdf/wasm-performance.md b/tree/specialized_tooling/webassembly/pdf/wasm-performance.md new file mode 100644 index 0000000..44fb8d3 --- /dev/null +++ b/tree/specialized_tooling/webassembly/pdf/wasm-performance.md @@ -0,0 +1,1711 @@ +--- +title: wasm-performance +source: sources/specialized_tooling/webassembly/pdf/wasm-performance.pdf +source_type: paper +source_hash: 318558a22e3ab158f00ff34c9d242693977d2a89b3a79fbbc2198ee336737db2 +tags: +- specialized_tooling +- webassembly +- paper +extracted: '2026-09-07' +--- + +## **Not So Fast:** **Analyzing the Performance of WebAssembly vs. Native Code** + +Abhinav Jangda, Bobby Powers, Emery D. Berger, and Arjun Guha + +_University of Massachusetts Amherst_ + +**Abstract** +All major web browsers now support WebAssembly, a lowlevel bytecode intended to serve as a compilation target for +code written in languages like C and C++. A key goal of WebAssembly is performance parity with native code; previous +work reports near parity, with many applications compiled +to WebAssembly running on average 10% slower than native code. However, this evaluation was limited to a suite +of scientific kernels, each consisting of roughly 100 lines of +code. Running more substantial applications was not possible because compiling code to WebAssembly is only part of +the puzzle: standard Unix APIs are not available in the web +browser environment. To address this challenge, we build +BROWSIX-WASM, a significant extension to BROWSIX [29] +that, for the first time, makes it possible to run unmodified +WebAssembly-compiled Unix applications directly inside the + +browser. We then use BROWSIX-WASM to conduct the first +large-scale evaluation of the performance of WebAssembly +vs. native. Across the SPEC CPU suite of benchmarks, we +find a substantial performance gap: applications compiled to +WebAssembly run slower by an average of 45% (Firefox) to + +55% (Chrome), with peak slowdowns of 2 _._ 08 _×_ (Firefox) and +2 _._ 5 _×_ (Chrome). We identify the causes of this performance +degradation, some of which are due to missing optimizations +and code generation issues, while others are inherent to the +WebAssembly platform. + +**1** **Introduction** + +Web browsers have become the most popular platform for run +ning user-facing applications, and until recently, JavaScript +was the only programming language supported by all major +web browsers. Beyond its many quirks and pitfalls from the +perspective of programming language design, JavaScript is +also notoriously difficult to compile efficiently [12, 17, 30, +31]. Applications written in or compiled to JavaScript typically run much slower than their native counterparts. To +address this situation, a group of browser vendors jointly +developed _WebAssembly_ . + +WebAssembly is a low-level, statically typed language +that does not require garbage collection, and supports interoperability with JavaScript. The goal of WebAssembly +is to serve as a universal compiler target that can run in a +browser [15, 16, 18]. 1 Towards this end, WebAssembly is +designed to be fast to compile and run, to be portable across +browsers and architectures, and to provide formal guarantees +of type and memory safety. Prior attempts at running code at +native speed in the browser [4, 13, 14, 38], which we discuss +in related work, do not satisfy all of these criteria. + +WebAssembly is now supported by all major browsers [8, +34] and has been swiftly adopted by several programming +languages. There are now backends for C, C++, C#, Go, +and Rust [1, 2, 24, 39] that target WebAssembly. A curated +list currently includes more than a dozen others [10]. Today, +code written in these languages can be safely executed in +browser sandboxes across any modern device once compiled +to WebAssembly. + +A major goal of WebAssembly is to be faster than +JavaScript. For example, the paper that introduced WebAssembly [18] showed that when a C program is compiled +to WebAssembly instead of JavaScript (asm.js), it runs 34% +faster in Google Chrome. That paper also showed that the performance of WebAssembly is competitive with native code: +of the 24 benchmarks evaluated, the running time of seven +benchmarks using WebAssembly is within 10% of native +code, and almost all of them are less than 2 _×_ slower than +native code. Figure 1 shows that WebAssembly implementations have continuously improved with respect to these benchmarks. In 2017, only seven benchmarks performed within +1.1 _×_ of native, but by 2019, this number increased to 13. +These results appear promising, but they beg the question: +_are_ _these_ _24_ _benchmarks_ _representative_ _of_ _WebAssembly’s_ +_intended use cases_ ? + +1The WebAssembly standard is undergoing active development, with +ongoing efforts to extend WebAssembly with features ranging from SIMD +primitives and threading to tail calls and garbage collection. This paper +focuses on the initial and stable version of WebAssembly [18], which is +supported by all major browsers. + +**The** **Challenge** **of** **Benchmarking** **WebAssembly** The +aforementioned suite of 24 benchmarks is the PolybenchC +benchmark suite [5], which is designed to measure the effect of polyhedral loop optimizations in compilers. All the +benchmarks in the suite are small scientific computing kernels rather than full applications (e.g., matrix multiplication +and LU Decomposition); each is roughly 100 LOC. While +WebAssembly is designed to accelerate scientific kernels on + +the Web, it is also explicitly designed for a much richer set of +full applications. + +The WebAssembly documentation highlights several intended use cases [7], including scientific kernels, image editing, video editing, image recognition, scientific visualization, +simulations, programming language interpreters, virtual machines, and POSIX applications. Therefore, WebAssembly’s +strong performance on the scientific kernels in PolybenchC +do not imply that it will perform well given a different kind +of application. + +We argue that a more comprehensive evaluation of WebAssembly should rely on an established benchmark suite of +large programs, such as the SPEC CPU benchmark suites. +In fact, the SPEC CPU 2006 and 2017 suite of benchmarks include several applications that fall under the intended +use cases of WebAssembly: eight benchmarks are scientific applications (e.g., 433.milc, 444.namd, 447.dealII, +450.soplex, and 470.lbm), two benchmarks involve image +and video processing (464.h264ref and 453.povray), and +all of the benchmarks are POSIX applications. + +Unfortunately, it is not possible to simply compile a sophisticated native program to WebAssembly. Native programs, +including the programs in the SPEC CPU suites, require operating system services, such as a filesystem, synchronous I/O, +and processes, which WebAssembly and the browser do not +provide. The SPEC benchmarking harness itself requires a file +system, a shell, the ability to spawn processes, and other Unix +facilities. To overcome these limitations when porting native +applications to the web, many programmers painstakingly +modify their programs to avoid or mimic missing operating +system services. Modifying well-known benchmarks, such +as SPEC CPU, would not only be time consuming but would +also pose a serious threat to validity. + +The standard approach to running these applications today +is to use Emscripten, a toolchain for compiling C and C++ to +WebAssembly [39]. Unfortunately, Emscripten only supports + +the most trivial system calls and does not scale up to largescale applications. For example, to enable applications to use +synchronous I/O, the default Emscripten MEMFS filesystem +loads the entire filesystem image into memory before the +program begins executing. For SPEC, these files are too large +to fit into memory. + +A promising alternative is to use BROWSIX, a framework +that enables running unmodified, full-featured Unix applications in the browser [28, 29]. BROWSIX implements a +Unix-compatible kernel in JavaScript, with full support for + +25 + +20 + +15 + +10 + +5 + +0 + +|Col1|Col2|Col3|Col4|Col5| +|---|---|---|---|---| +||PLDI 20|7
April 2018|This paper|| +|||||| +|||||| +|||||| +|||||| +|||||| +|||||| +|||||| +|||||| +|||||| +|||||| +|||||| +|||||| + +Performance relative to native + +Figure 1: Number of PolyBenchC benchmarks performing +within _x×_ of native. In 2017 [18], seven benchmarks performed within 1.1 _×_ of native. In April 2018, we found that +11 performed within 1.1 _×_ of native. In May 2019, 13 performed with 1.1 _×_ of native. + +processes, files, pipes, blocking I/O, and other Unix features. Moreover, it includes a C/C++ compiler (based on +Emscripten) that allows programs to run in the browser unmodified. The BROWSIX case studies include complex applications, such as L A TEX, which runs entirely in the browser +without any source code modifications. + +Unfortunately, BROWSIX is a JavaScript-only solution, +since it was built before the release of WebAssembly. Moreover, BROWSIX suffers from high performance overhead, +which would be a significant confounder while benchmarking. +Using BROWSIX, it would be difficult to tease apart the poorly +performing benchmarks from performance degradation introduced by BROWSIX. + +**Contributions** + +_•_ **BROWSIX-WASM:** We develop BROWSIX-WASM, a +significant extension to and enhancement of BROWSIX +that allows us to compile Unix programs to WebAssembly and run them in the browser with no modifications. In addition to integrating functional extensions, +BROWSIX-WASM incorporates performance optimizations that drastically improve its performance, ensuring +that CPU-intensive applications operate with virtually +no overhead imposed by BROWSIX-WASM (§2). + +_•_ **BROWSIX-SPEC:** We develop BROWSIX-SPEC, a harness that extends BROWSIX-WASM to allow automated +collection of detailed timing and hardware on-chip performance counter information in order to perform detailed measurements of application performance (§3). + +_•_ **Performance** **Analysis** **of** **WebAssembly:** Using +BROWSIX-WASM and BROWSIX-SPEC, we conduct +the first comprehensive performance analysis of WebAssembly using the SPEC CPU benchmark suite (both +2006 and 2017). This evaluation confirms that WebAssembly does run faster than JavaScript (on average +1.3 _×_ faster across SPEC CPU). However, contrary to +prior work, we find a substantial gap between WebAssembly and native performance: code compiled to +WebAssembly runs on average 1.55 _×_ slower in Chrome + +and 1.45 _×_ slower in Firefox than native code (§4). + +_•_ **Root** **Cause** **Analysis** **and** **Advice** **for** **Implementers:** +We conduct a forensic analysis with the aid of perfor +mance counter results to identify the root causes of this +performance gap. We find the following results: + +1. The instructions produced by WebAssembly have +more loads and stores than native code (2.02 _×_ +more loads and 2.30 _×_ more stores in Chrome; +1.92 _×_ more loads and 2.16 _×_ more stores in Firefox). We attribute this to reduced availability of +registers, a sub-optimal register allocator, and a +failure to effectively exploit a wider range of x86 +addressing modes. + +2. The instructions produced by WebAssembly have +more branches, because WebAssembly requires +several dynamic safety checks. + +3. Since WebAssembly generates more instructions, +it leads to more L1 instruction cache misses. + +We provide guidance to help WebAssembly imple +menters focus their optimization efforts in order to close +the performance gap between WebAssembly and native +code (§5 _,_ 6). + +BROWSIX-WASM and BROWSIX-SPEC are available at +[https://browsix.org.](https://browsix.org) + +**2** **From BROWSIX to BROWSIX-WASM** + +BROWSIX [29] mimics a Unix kernel within the browser and +includes a compiler (based on Emscripten [33, 39]) that compiles native programs to JavaScript. Together, they allow +native programs (in C, C++, and Go) to run in the browser +and freely use operating system services, such as pipes, processes, and a filesystem. However, BROWSIX has two major +limitations that we must overcome. First, BROWSIX compiles +native code to JavaScript and not WebAssembly. Second, the +BROWSIX kernel has significant performance issues. In particular, several common system calls have very high overhead in +BROWSIX, which makes it hard to compare the performance +of a program running in BROWSIX to that of a program running natively. We address these limitations by building a new + +in-browser kernel called BROWSIX-WASM, which supports +WebAssembly programs and eliminates the performance bot +tlenecks of BROWSIX. + +**Emscripten Runtime Modifications** BROWSIX modifies +the Emscripten compiler to allow processes (which run in +WebWorkers) to communicate with the BROWSIX kernel +(which runs on the main thread of a page). Since BROWSIX +compiles native programs to JavaScript, this is relatively +straightforward: each process’ memory is a buffer that is +shared with the kernel (a SharedArrayBuffer), thus system +calls can directly read and write process memory. However, +this approach has two significant drawbacks. First, it precludes growing the heap on-demand; the shared memory +must be sized large enough to meet the high-water-mark heap +size of the application for the entire life of the process. Second, JavaScript contexts (like the main context and each web +worker context) have a fixed limit on their heap sizes, which +is currently approximately 2.2 GB in Google Chrome [6]. +This cap imposes a serious limitation on running multiple processes: if each process reserves a 500 MB heap, BROWSIX +would only be able to run at most four concurrent processes. +A deeper problem is that WebAssembly memory cannot be +shared across WebWorkers and does not support the Atomic +API, which BROWSIX processes use to wait for system calls. + +BROWSIX-WASM uses a different approach to processkernel communication that is also faster than the BROWSIX +approach. BROWSIX-WASM modifies the Emscripten runtime +system to create an auxiliary buffer (of 64MB) for each process that is shared with the kernel, but is distinct from process +memory. Since this auxiliary buffer is a SharedArrayBuffer +the BROWSIX-WASM process and kernel can use Atomic API +for communication. When a system call references strings +or buffers in the process’s heap (e.g., writev or stat), its +runtime system copies data from the process memory to the +shared buffer and sends a message to the kernel with locations +of the copied data in auxiliary memory. Similarly, when a +system call writes data to the auxiliary buffer (e.g., read), +its runtime system copies the data from the shared buffer to +the process memory at the memory specified. Moreover, if +a system call specifies a buffer in process memory for the +kernel to write to (e.g., read), the runtime allocates a corresponding buffer in auxiliary memory and passes it to the +kernel. In case the system call is either reading or writing +data of size more than 64MB, BROWSIX-WASM divides this +call into several calls such that each call only reads or writes +at maximum 64MB of data. The cost of these memory copy +operations is dwarfed by the overall cost of the system call +invocation, which involves sending a message between process and kernel JavaScript contexts. We show in §4.2.1 that +BROWSIX-WASM has negligible overhead. + +**Performance** **Optimization** While building BROWSIXWASM and doing our preliminary performance evaluation, + +|WebWorker
401.bzip2
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shared mem|Col2|Col3|Col4|Col5|Col6|Col7|Col8| +|---|---|---|---|---|---|---|---| +|_WebWorker_
**Browsix-Wasm userspace**
401.bzip2
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**Browsix-Wasm userspace**
401.bzip2
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WebAssembly module|**Browsix-**
**Wasm kernel**
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401.bzip2
WebAssembly module
shared mem|**Browsix-Wasm userspace**
shared m|**Browsix-Wasm userspace**
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shared m|**Browsix-Wasm userspace**
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**Browsix-Wasm userspace**
401.bzip2
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shared m|shared m|em|o|ry|segment| +|_WebWorker_
**Browsix-Wasm userspace**
401.bzip2
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shared m||||||| + +(7) + +Figure 2: The framework for running SPEC benchmarks in browsers. **Bold** components are new or heavily modified (§3). + +we discovered several performance issues in parts of the + +BROWSIX kernel. Left unresolved, these performance issues +would be a threat to the validity of a performance comparison +between WebAssembly and native code. The most serious +case was in the shared filesystem component included with +BROWSIX/BROWSIX-WASM, BROWSERFS. Originally, on +each append operation on a file, BROWSERFS would allocate +a new, larger buffer, copying the previous and new contents +into the new buffer. Small appends could impose substantial +performance degradation. Now, whenever a buffer backing a +file requires additional space, BROWSERFS grows the buffer +by at least 4 KB. This change alone decreased the time the +464.h264ref benchmark spent in BROWSIX from 25 seconds +to under 1.5 seconds. We made a series of improvements that +reduce overhead throughout BROWSIX-WASM. Similar, if +less dramatic, improvements were made to reduce the number of allocations and the amount of copying in the kernel +implementation of pipes. + +**3** **BROWSIX-SPEC** + +To reliably execute WebAssembly benchmarks while capturing performance counter data, we developed BROWSIXSPEC. BROWSIX-SPEC works with BROWSIX-WASM to +manage spawning browser instances, serving benchmark assets (e.g., the compiled WebAssembly programs and test +inputs), spawning perf processes to record performance +counter data, and validating benchmark outputs. + +We use BROWSIX-SPEC to run three benchmark suites +to evaluate WebAssembly’s performance: SPEC CPU2006, +SPEC CPU2017, and PolyBenchC. These benchmarks are +compiled to native code using Clang 4.0, and WebAssembly +using BROWSIX-WASM. We made no modifications to +Chrome or Firefox, and the browsers are run with their standard sandboxing and isolation features enabled. BROWSIXWASM is built on top of standard web platform features and +requires no direct access to host resources – instead, benchmarks make standard HTTP requests to BROWSIX-SPEC. + +**3.1** **BROWSIX-SPEC Benchmark Execution** + +Figure 2 illustrates the key pieces of BROWSIX-SPEC in play +when running a benchmark, such as 401.bzip2 in Chrome. +First (1), the BROWSIX-SPEC benchmark harness launches +a new browser instance using a WebBrowser automation tool, +Selenium. 2 (2) The browser loads the page’s HTML, harness JS, and BROWSIX-WASM kernel JS over HTTP from +the benchmark harness. (3) The harness JS initializes the +BROWSIX-WASM kernel and starts a new BROWSIX-WASM +process executing the runspec shell script (not shown in Figure 2). runspec in turn spawns the standard specinvoke +(not shown), compiled from the C sources provided in SPEC +2006. specinvoke reads the speccmds.cmd file from the +BROWSIX-WASM filesystem and starts 401.bzip2 with the +appropriate arguments. (4) After the WebAssembly module has been instantiated but before the benchmark’s main +function is invoked, the BROWSIX-WASM userspace runtime +does an XHR request to BROWSIX-SPEC to begin recording performance counter stats. (5) The benchmark harness +finds the Chrome thread corresponding to the Web Worker +401.bzip2 process and attaches perf to the process. (6) At +the end of the benchmark, the BROWSIX-WASM userspace +runtime does a final XHR to the benchmark harness to end +the perf record process. When the runspec program exits +(after potentially invoking the test binary several times), the +harness JS POSTs (7) a tar archive of the SPEC results directory to BROWSIX-SPEC. After BROWSIX-SPEC receives +the full results archive, it unpacks the results to a temporary +directory and validates the output using the cmp tool provided +with SPEC 2006. Finally, BROWSIX-SPEC kills the browser +process and records the benchmark results. + +**4** **Evaluation** + +We use BROWSIX-WASM and BROWSIX-SPEC to evaluate + +the performance of WebAssembly using three benchmark + +[2https://www.seleniumhq.org/](https://www.seleniumhq.org/) + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~Go~~|~~gle~~|~~ hro~~|~~ e~~||~~ozill~~|~~ Fir~~|~~ fox~~||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +SPEC CPU Benchmarks + +(b) + +3.5 + +3.0 + +2.5 + +2.0 + +1.5 + +1.0 + +0.5 + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17|Col18|Col19|Col20|Col21|Col22|Col23|Col24|Col25| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +|||||||||~~oo~~|~~le~~|~~ Ch~~|~~o~~|~~e~~||~~M~~|~~zil~~|~~a~~|~~ ire~~|~~ ox~~||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| +|||||||||||||||||||||||||| + +PolyBenchC Benchmarks + +(a) + +3.5 + +3.0 + +2.5 + +2.0 + +1.5 + +**1.0** + +0.5 + +Figure 3: The performance of the PolyBenchC and the SPEC CPU benchmarks compiled to WebAssembly (executed in Chrome +and Firefox) relative to native, using BROWSIX-WASM and BROWSIX-SPEC. The SPEC CPU benchmarks exhibit higher +overhead overall than the PolyBenchC suite, indicating a significant performance gap exists between WebAssembly and native. + +suites: SPEC CPU2006, SPEC CPU2017, and PolyBenchC. +We include PolybenchC benchmarks for comparison with + +the original WebAssembly paper [18], but argue that these +benchmarks do not represent typical workloads. The SPEC +benchmarks are representative and require BROWSIX-WASM +to run successfully. We run all benchmarks on a 6-Core Intel +Xeon E5-1650 v3 CPU with hyperthreading and 64 GB of +RAM running Ubuntu 16.04 with Linux kernel v4.4.0. We run +all benchmarks using two state-of-the-art browsers: Google +Chrome 74.0 and Mozilla Firefox 66.0. We compile benchmarks to native code using Clang 4.0 3 and to WebAssembly +using BROWSIX-WASM (which is based on Emscripten with +Clang 4.0). 4 Each benchmark was executed five times. We +report the average of all running times and the standard error. +The execution time measured is the difference between wall +clock time when the program starts, i.e. after WebAssembly +JIT compilation concludes, and when the program ends. + +**4.1** **PolyBenchC Benchmarks** + +Haas et al. [18] used PolybenchC to benchmark WebAssembly implementations because the PolybenchC benchmarks do not make system calls. As we have already argued, +the PolybenchC benchmarks are small scientific kernels that +are typically used to benchmark polyhedral optimization techniques, and do not represent larger applications. Nevertheless, +it is still valuable for us to run PolybenchC with BROWSIXWASM, because it demonstrates that our infrastructure for + +3The flags to Clang are -O2 -fno-strict-aliasing. +4BROWSIX-WASM runs Emscripten with the flags -O2 +-s TOTAL_MEMORY=1073741824 -s ALLOW_MEMORY_GROWTH=1 +-fno-strict-aliasing. + +system calls does not have any overhead. Figure 3a shows the +execution time of the PolyBenchC benchmarks in BROWSIXWASM and when run natively. We are able to reproduce the +majority of the results from the original WebAssembly paper [18]. We find that BROWSIX-WASM imposes a very low +overhead: an average of 0.2% and a maximum of 1.2%. + +**4.2** **SPEC Benchmarks** + +We now evaluate BROWSIX-WASM using the C/C++ bench +marks from SPEC CPU2006 and SPEC CPU2017 (the new +C/C++ benchmarks and the speed benchmarks), which use +system calls extensively. We exclude four data points that +either do not compile to WebAssembly 5 or allocate more +memory than WebAssembly allows. 6 Table 1 shows the absolute execution times of the SPEC benchmarks when running +with BROWSIX-WASM in both Chrome and Firefox, and when +running natively. + +WebAssembly performs worse than native for all benchmarks except for 429.mcf and 433.milc. In Chrome, WebAssembly’s maximum overhead is 2.5 _×_ over native and 7 +out of 15 benchmarks have a running time within 1.5 _×_ of native. In Firefox, WebAssembly is within 2.08 _×_ of native and +performs within 1.5 _×_ of native for 7 out of 15 benchmarks. +On average, WebAssembly is 1.55 _×_ slower than native in +Chrome, and 1.45 _×_ slower than native in Firefox. Table 2 +shows the time required to compile the SPEC benchmarks + +5400.perlbench, 403.gcc, 471.omnetpp, and 456.hmmer from SPEC +CPU2006 do not compile with Emscripten. + +6From SPEC CPU2017, the ref dataset of 638.imagick_s and +657.xz_s require more than 4 GB RAM. However, these benchmarks +do work with their test dataset. + +**Google** +**Benchmark** **Native** +**Chrome** + +**Mozilla** + +**Firefox** + +401.bzip2 370 _±_ 0.6 864 _±_ 6.4 730 _±_ 1.3 +429.mcf 221 _±_ 0.1 180 _±_ 0.9 184 _±_ 0.6 +433.milc 375 _±_ 2.6 369 _±_ 0.5 378 _±_ 0.6 +444.namd 271 _±_ 0.8 369 _±_ 9.1 373 _±_ 1.8 +445.gobmk 352 _±_ 2.1 537 _±_ 0.8 549 _±_ 3.3 +450.soplex 179 _±_ 3.7 265 _±_ 1.2 238 _±_ 0.5 +453.povray 110 _±_ 1.9 275 _±_ 1.3 229 _±_ 1.5 +458.sjeng 358 _±_ 1.4 602 _±_ 2.5 580 _±_ 2.0 +462.libquantum 330 _±_ 0.8 444 _±_ 0.2 385 _±_ 0.8 +464.h264ref 389 _±_ 0.7 807 _±_ 11.0 733 _±_ 2.4 +470.lbm 209 _±_ 1.1 248 _±_ 0.3 249 _±_ 0.5 +473.astar 299 _±_ 0.5 474 _±_ 3.5 408 _±_ 1.0 +482.sphinx3 381 _±_ 7.1 834 _±_ 1.8 713 _±_ 3.6 +641.leela_s 466 _±_ 2.7 825 _±_ 4.6 717 _±_ 1.2 +644.nab_s 2476 _±_ 11 3639 _±_ 5.6 3829 _±_ 6.7 +**Slowdown: geomean** - **1.55** _×_ **1.45** _×_ +**Slowdown: median** - **1.53** _×_ **1.54** _×_ + +Table 1: Detailed breakdown of SPEC CPU benchmarks execution times (of 5 runs) for native (Clang) and WebAssembly +(Chrome and Firefox); all times are in seconds. The median +slowdown of WebAssembly is 1 _._ 53 _×_ for Chrome and 1 _._ 54 _×_ +for Firefox. + +using Clang and Chrome. (To the best of our knowledge, Firefox cannot report WebAssembly compile times.) In all cases, +the compilation time is negligible compared to the execution +time. However, the Clang compiler is orders of magnitude +slower than the WebAssembly compiler. Finally, note that +Clang compiles benchmarks from C++ source code, whereas +Chrome compiles WebAssembly, which is a simpler format +than C++. + +**4.2.1** **BROWSIX-WASM Overhead** + +It is important to rule out the possibility that the slowdown that +we report is due to poor performance in our implementation +of BROWSIX-WASM. In particular, BROWSIX-WASM implements system calls without modifying the browser, and system calls involve copying data (§2), which may be costly. To +quantify the overhead of BROWSIX-WASM, we instrumented +its system calls to measure all time spent in BROWSIX-WASM. +Figure 4 shows the percentage of time spent in BROWSIXWASM in Firefox using the SPEC benchmarks. For 14 of +the 15 benchmarks, the overhead is less than 0.5%. The +maximum overhead is 1.2%. On average, the overhead of +BROWSIX-WASM is only 0.2%. Therefore, we conclude that +BROWSIX-WASM has negligible overhead and does not substantially affect the performance counter results of programs +executed in WebAssembly. + +|Benchmark|Clang 4.0|Google Chrome| +|---|---|---| +|401.bzip2
429.mcf
433.milc
444.namd
445.gobmk
450.soplex
453.povray
458.sjeng
462.libquantum
464.h264ref
470.lbm
473.astar
482.sphinx3
641.leela_s
644.nab_s|1.9_ ±_ 0.018
0.3_ ±_ 0.003
2.2_ ±_ 0.02
4.6_ ±_ 0.02
12.1_ ±_ 0.2
6.9_ ±_ 0.01
15.3_ ±_ 0.03
1.9_ ±_ 0.01
6.9_ ±_ 0.03
10.3_ ±_ 0.06
0.3_ ±_ 0.001
0.73_ ±_ 0.005
3.0_ ±_ 0.04
4.3_ ±_ 0.05
4.1_ ±_ 0.03|0.53_ ±_ 0.005
0.15_ ±_ 0.005
0.3_ ±_ 0.003
0.78_ ±_ 0.004
1.4_ ±_ 0.014
1.2_ ±_ 0.009
1.2_ ±_ 0.012
0.35_ ±_ 0.001
0.15_ ±_ 0.002
1.0_ ±_ 0.03
0.14_ ±_ 0.004
0.24_ ±_ 0.004
0.48_ ±_ 0.007
0.74_ ±_0.003
0.41_ ±_0.001| + +Table 2: Compilation times of SPEC CPU benchmarks (average of 5 runs) for Clang 4.0 and WebAssembly (Chrome); all +times are in seconds. + +2.0 + +1.5 + +**1.0** + +0.5 + +0.0 + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17|Col18| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||||||||||||||| +||||||||||||||||||| +||||||||||||||||||| +||||||||||||||||||| +||||||||||||||||||| +||||||||||||||||||| +||||||||||||||||||| +||||||||||||||||||| +||||||||||||||||||| + +SPEC CPU Benchmarks + +Figure 4: Time spent (in %) in BROWSIX-WASM calls in +Firefox for SPEC benchmarks compiled to WebAssembly. +BROWSIX-WASM imposes a mean overhead of only 0.2%. + +**4.2.2** **Comparison of WebAssembly and asm.js** + +A key claim in the original work on WebAssembly was that +it is significantly faster than asm.js. We now test that claim +using the SPEC benchmarks. For this comparison, we modified BROWSIX-WASM to also support processes compiled to +asm.js. The alternative would have been to benchmark the +asm.js processes using the original BROWSIX. However, as +we discussed earlier, BROWSIX has performance problems +that would have been a threat to the validity of our results. +Figure 5 shows the speedup of the SPEC benchmarks using +WebAssembly, relative to their running time using asm.js + +using both Chrome and Firefox. WebAssembly outperforms +asm.js in both browsers: the mean speedup is 1.54 _×_ in + +3.0 + +2.5 + +2.0 + +1.5 + +**1.0** + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~Goo~~|~~gle~~|~~ hro~~|~~ e~~|~~M~~|~~ozill~~|~~ Fir~~|~~ fox~~||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +SPEC CPU Benchmarks + +Figure 5: Relative time of asm.js to WebAssembly for +Chrome and Firefox. WebAssembly is 1.54 _×_ faster than +asm.js in Chrome and 1.39 _×_ faster than asm.js in Firefox. + +2.25 + +2.00 + +1.75 + +1.50 + +1.25 + +1.00 + +0.75 + +0.50 + +0.25 + +0.00 + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +SPEC CPU Benchmarks + +Figure 6: Relative _best_ time of asm.js to the best time of +WebAssembly. WebAssembly is 1.3 _×_ faster than asm.js. + +Chrome and 1.39 _×_ in Firefox. + +Since the performance difference between Chrome and +Firefox is substantial, in Figure 6 we show the speedup of +each benchmark by selecting the best-performing browser for +WebAssembly and the best-performing browser of asm.js +(i.e., they may be different browsers). These results show +that WebAssembly consistently performs better than asm.js, +with a mean speedup of 1.3 _×_ . Haas et al. [18] also found that +WebAssembly gives a mean speedup of 1.3 _×_ over asm.js + +using PolyBenchC. + +**5** **Case Study: Matrix Multiplication** + +In this section, we illustrate the performance differences between WebAssembly and native code using a C function that +performs matrix multiplication, as shown in Figure 7a. Three + +matrices are provided as arguments to the function, and the results of A ( _NI ×_ _NK_ ) and B ( _NK ×_ _NJ_ ) are stored in C ( _NI ×_ _NJ_ ), +where _NI,_ _NK,_ _NJ_ are constants defined in the program. + +In WebAssembly, this function is 2 _×_ –3 _._ 4 _×_ slower than +native in both Chrome and Firefox with a variety of matrix +sizes (Figure 8). We compiled the function with -O2 and +disabled automatic vectorization, since WebAssembly does +not support vectorized instructions. + +Figure 7b shows native code generated for the matmul +function by clang-4.0. Arguments are passed to the function +in the rdi, rsi, and rdx registers, as specified in the System +V AMD64 ABI calling convention [9]. Lines 2 - 26 are the +body of the first loop with iterator i stored in r8d. Lines 5 + +- 21 contain the body of the second loop with iterator k stored + +in r9d. Lines 10 - 16 comprise the body of the third loop +with iterator j stored in rcx. Clang is able to eliminate a cmp +instruction in the inner loop by initializing rcx with _−NJ_, +incrementing rcx on each iteration at line 15, and using jne +to test the zero flag of the status register, which is set to 1 +when rcx becomes 0. + +Figure 7c shows x86-64 code JITed by Chrome for the +WebAssembly compiled version of matmul. This code has + +been modified slightly – nops in the generated code have been +removed for presentation. Function arguments are passed in +the rax, rcx, and rdx registers, following Chrome’s calling +convention. At lines 1– 3, the contents of registers rax, rdx, +and rcx are stored on the stack, due to registers spills at lines 7 + +- 9. Lines 7–45 are the body of the first loop with iterator i + +stored in edi. Lines 18–42 contain the body of second loop +with iterator k stored in r11. Lines 27–39 are the body of +the third loop with iterator j stored in eax. To avoid memory +loads due to register spilling at lines 7– 9 in the first iteration +of the first loop, an extra jump is generated at line 5. Similarly, +extra jumps are generated for the second and third loops at +line 16 and line 25 respectively. + +**5.1** **Differences** + +The native code JITed by Chrome has more instructions, suffers from increased register pressure, and has extra branches +compared to Clang-generated native code. + +**5.1.1** **Increased Code Size** + +The number of instructions in the code generated by Chrome +(Figure 7c) is 53, including nops, while clang generated code +(Figure 7b) consists of only 28 instructions. The poor instruction selection algorithm of Chrome is one of the reasons for +increased code size. + +Additionally, Chrome does not take advantage of all available memory addressing modes for x86 instructions. In Figure 7b Clang uses the add instruction at line 14 with register +addressing mode, loading from and writing to a memory address in the same operation. Chrome on the other hand loads + +1 void matmul (int C[NI][NJ], + +2 int A[NI][NK], + +3 int B[NK][NJ]) { + +4 for (int i = 0; i < NI; i++) { + +5 for (int k = 0; k < NK; k++) { + +6 for (int j = 0; k < NJ; j++) { + +7 C[i][j] += A[i][k] * B[k][j]; + +8 } + +9 } + +10 } + +11 } + +(a) matmul source code in C. + +1 xor r8d, r8d #i <- 0 + +2 L1: #start first loop + +3 mov r10, rdx + +4 xor r9d, r9d #k <- 0 + +5 L2: #start second loop + +6 imul rax, 4*NK, r8 + +7 add rax, rsi + +8 lea r11, [rax + r9*4] + +9 mov rcx, -NJ #j <- -NJ + +10 L3: #start third loop + +11 mov eax, [r11] + +12 mov ebx, [r10 + rcx*4 + 4400] + +13 imul ebx, eax + +14 add [rdi + rcx*4 + 4*NJ], ebx + +15 add rcx, 1 #j <- j + 1 + +16 jne L3 #end third loop + +17 + +18 add r9, 1 #k <- k + 1 + +19 add r10, 4*NK + +20 cmp r9, NK + +21 jne L2 #end second loop + +22 + +23 add r8, 1 #i <- i + 1 + +24 add rdi, 4*NJ + +25 cmp r8, NI + +26 jne L1 #end first loop + +27 pop rbx + +28 ret + +(b) Native x86-64 code for matmul generated by Clang. + +1 mov [rbp-0x28],rax + +2 mov [rbp-0x20],rdx + +3 mov [rbp-0x18],rcx + +4 xor edi,edi #i <- 0 + +5 jmp L1’ + +6 L1: #start first loop + +7 mov ecx,[rbp-0x18] + +8 mov edx,[rbp-0x20] + +9 mov eax,[rbp-0x28] + +10 L1’: + +11 imul r8d,edi,0x1130 + +12 add r8d,eax + +13 imul r9d,edi,0x12c0 + +14 add r9d,edx + +15 xor r11d,r11d #k <- 0 + +16 jmp L2’ + +17 L2: #start second loop + +18 mov ecx,[rbp-0x18] + +19 L2’: + +20 imul r12d,r11d,0x1130 + +21 lea r14d,[r9+r11*4] + +22 add r12d,ecx + +23 xor esi,esi #j <- 0 + +24 mov r15d,esi + +25 jmp L3’ + +26 L3: #start third loop + +27 mov r15d,eax + +28 L3’: + +29 lea eax,[r15+0x1] #j <- j + 1 + +30 lea edx,[r8+r15*4] + +31 lea r15d,[r12+r15*4] + +32 mov esi,[rbx+r14*1] + +33 mov r15d,[rbx+r15*1] + +34 imul r15d,esi + +35 mov ecx,[rbx+rdx*1] + +36 add ecx,r15d + +37 mov [rbx+rdx*1],ecx + +38 cmp eax,NJ #j < NJ + +39 jnz L3 #end third loop + +40 add r11,0x1 #k++ + +41 cmp r11d,NK #k < NK + +42 jnz L2 #end second loop + +43 add edi,0x1 #i++ + +44 cmp edi,NI #i < NI + +45 jnz L1 #end first loop + +46 retl + +(c) x86-64 code JITed by Chrome from WebAssembly matmul. + +Figure 7: Native code for matmul is shorter, has less register pressure, and fewer branches than the code JITed by Chrome. §6 +shows that these inefficiencies are pervasive, reducing performance across the SPEC CPU benchmark suites. + +the address in ecx, adds the operand to ecx, finally storing +ecx at the address, requiring 3 instructions rather than one on + +lines 35 _−_ 37. + +3.5 + +3.0 + +2.5 + +2.0 + +1.5 + +1.0 + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11| +|---|---|---|---|---|---|---|---|---|---|---| +||||~~Goo~~|~~gle Chr~~|~~ me~~|~~Mozill~~|~~a Firef~~|~~ x~~||| +|||||||||||| +|||||||||||| +|||||||||||| +|||||||||||| +|||||||||||| +|||||||||||| +|||||||||||| +|||||||||||| +|||||||||||| +|||||||||||| +|||||||||||| + +Size(NIxNKxNJ) + +Figure 8: Performance of WebAssembly in Chrome and Firefox for different matrix sizes relative to native code. WebAssembly is always between 2 _×_ to 3.4 _×_ slower than native. + +**perf Event** **Wasm Summary** +all-loads-retired (r81d0) (Figure 9a) Increased register +all-stores-retired (r82d0) (Figure 9b) pressure +branches-retired (r00c4) (Figure 9c) More branch +conditional-branches (r01c4) (Figure 9d) statements +instructions-retired (r1c0) (Figure 9e) Increased code size +cpu-cycles (Figure 9f) +L1-icache-load-misses (Figure 10) + +Table 3: Performance counters highlight specific issues with +WebAssembly code generation. When a raw PMU event + +descriptor is used, it is indicated by r _n_ . + +**5.1.2** **Increased Register Pressure** + +Code generated by Clang in Figure 7b does not generate any +spills and uses only 10 registers. On the other hand, the code +generated by Chrome (Figure 7c) uses 13 general purpose +registers – all available registers (r13 and r10 are reserved by +V8). As described in Section 5.1.1, eschewing the use of the +register addressing mode of the add instruction requires the +use of a temporary register. All of this register inefficiency +compounds, introducing three register spills to the stack at +lines 1–3. Values stored on the stack are loaded again into +registers at lines 7–9 and line 18. + +**5.1.3** **Extra Branches** + +Clang (Figure 7b) generates code with a single branch per +loop by inverting the loop counter (line 15). In contrast, +Chrome (Figure 7c) generates more straightforward code, +which requires a conditional jump at the start of the loop. In +addition, Chrome generates extra jumps to avoid memory +loads due to register spills in the first iteration of a loop. For +example, the jump at line 5 avoids the spills at lines 7– 9. + +**6** **Performance Analysis** + +We use BROWSIX-SPEC to record measurements from all + +supported performance counters on our system for the SPEC +CPU benchmarks compiled to WebAssembly and executed in +Chrome and Firefox, and the SPEC CPU benchmarks compiled to native code (Section 3). + +Table 3 lists the performance counters we use here, along +with a summary of the impact of BROWSIX-WASM performance on these counters compared to native. We use these +results to explain the performance overhead of WebAssembly +over native code. Our analysis shows that the inefficiences +described in Section 5 are pervasive and translate to reduced +performance across the SPEC CPU benchmark suite. + +**6.1** **Increased Register Pressure** + +This section focuses on two performance counters that show +the effect of increased register pressure. Figure 9a presents +the number of _load_ instructions retired by WebAssemblycompiled SPEC benchmarks in Chrome and Firefox, relative +to the number of load instructions retired in native code. Similarly, Figure 9b shows the number of _store_ instructions retired. +Note that a “retired” instruction is an instruction which leaves +the instruction pipeline and its results are correct and visible +in the architectural state (that is, not speculative). + +Code generated by Chrome has 2.02 _×_ more load instructions retired and 2.30 _×_ more store instructions retired than +native code. Code generated by Firefox has 1.92 _×_ more load +instructions retired and 2.16 _×_ more store instructions retired +than native code. These results show that the WebAssemblycompiled SPEC CPU benchmarks suffer from increased register pressure and thus increased memory references. Below, +we outline the reasons for this increased register pressure. + +**6.1.1** **Reserved Registers** + +In Chrome, matmul generates three register spills but does not +use two x86-64 registers: r13 and r10 (Figure 7c, lines 7– 9). +This occurs because Chrome reserves these two registers. 7 + +For the JavaScript garbage collector, Chrome reserves r13 +to point to an array of GC roots at all times. In addition, +Chrome uses r10 and xmm13 as dedicated scratch registers. +Similarly, Firefox reserves r15 as a pointer to the start of the +heap, and r11 and xmm15 are JavaScript scratch registers. 8 + +None of these registers are available to WebAssembly code. + +**6.1.2** **Poor Register Allocation** + +Beyond a reduced set of registers available to allocate, both +Chrome and Firefox do a poor job of allocating the registers + +[7https://github.com/v8/v8/blob/7.4.1/src/x64/](https://github.com/v8/v8/blob/7.4.1/src/x64/register-x64.h) + +[register-x64.h](https://github.com/v8/v8/blob/7.4.1/src/x64/register-x64.h) + +[8https://hg.mozilla.org/mozilla-central/file/tip/js/src/](https://hg.mozilla.org/mozilla-central/file/tip/js/src/jit/x64/Assembler-x64.h) + +[jit/x64/Assembler-x64.h](https://hg.mozilla.org/mozilla-central/file/tip/js/src/jit/x64/Assembler-x64.h) + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~Goo~~|~~gle~~|~~ hro~~|~~ e~~|~~M~~|~~zilla~~|~~ Fire~~|~~ fox~~||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +SPEC CPU Benchmarks + +(b) all-stores-retired + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~Go~~|~~gle~~|~~ Chro~~|~~ me~~||~~ozill~~|~~a Fir~~|~~ fox~~||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +SPEC CPU Benchmarks + +(e) instructions-retired + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~Go~~|~~gle~~|~~ hro~~|~~ e~~|~~M~~|~~ozill~~|~~ Fir~~|~~ fox~~||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~Go~~|~~gle~~|~~ hro~~|~~ e~~|~~M~~|~~ozill~~|~~ Fir~~|~~ fox~~||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +3.0 + +2.5 + +2.0 + +1.5 + +**1.0** + +0.5 + +3.5 + +3.0 + +2.5 + +2.0 + +1.5 + +**1.0** + +3.5 + +3.0 + +2.5 + +2.0 + +1.5 + +**1.0** + +SPEC CPU Benchmarks + +(a) all-loads-retired + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~Go~~|~~gle~~|~~ hro~~|~~ e~~|~~M~~|~~ozill~~|~~ Fir~~|~~ fox~~||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +SPEC CPU Benchmarks + +(d) conditional-branches + +8 + +7 + +6 + +5 + +4 + +3 + +2 + +**1** + +3.0 + +2.5 + +2.0 + +1.5 + +1.0 + +3.5 + +3.0 + +2.5 + +2.0 + +1.5 + +**1.0** + +SPEC CPU Benchmarks + +(c) branch-instructions-retired + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~Go~~|~~gle~~|~~ hro~~|~~ e~~|~~M~~|~~ozill~~|~~ Fir~~|~~ fox~~||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| +|||||||||||||||||| + +SPEC CPU Benchmarks + +(f) cpu-cycles + +Figure 9: Processor performance counter samples for WebAssembly relative to native code. + +they have. For example, the code generated by Chrome for +matmul uses 12 registers while the native code generated by +Clang only uses 10 registers (Section 5.1.2). This increased +register usage—in both Firefox and Chrome—is because of +their use of fast but not particularly effective register allocators. Chrome and Firefox both use a linear scan register +allocator [36], while Clang uses a greedy graph-coloring register allocator [3], which consistently generates better code. + +**6.1.3** **x86 Addressing Modes** + +The x86-64 instruction set offers several addressing modes +for each operand, including a _register_ mode, where the instruction reads data from register or writes data to a register, +and memory address modes like _register_ _indirect_ or _direct_ +_offset_ addressing, where the operand resides in a memory +address and the instruction can read from or write to that +address. A code generator could avoid unnecessary register +pressure by using the latter modes. However, Chrome does +not take advantage of these modes. For example, the code +generated by Chrome for matmul does not use the register indirect addressing mode for the add instruction (Section 5.1.2), +creating unnecessary register pressure. + +**6.2** **Extra Branch Instructions** + +This section focuses on two performance counters that measure the number of branch instructions executed. Figure 9c +shows the number of branch instructions retired by WebAssembly, relative to the number of branch instructions retired +in native code. Similarly, Figure 9d shows the number of _con-_ +_ditional_ branch instructions retired. In Chrome, there are +1 _._ 75 _×_ and 1 _._ 65 _×_ more unconditional and conditional branch +instructions retired respectively, whereas in Firefox, there are +1 _._ 65 _×_ and 1 _._ 62 _×_ more retired. These results show that all the +SPEC CPU benchmarks incur extra branches, and we explain +why below. + +**6.2.1** **Extra Jump Statements for Loops** + +As with matmul (Section 5.1.3), Chrome generates unnecessary jump statements for loops, leading to significantly more +branch instructions than Firefox. + +**6.2.2** **Stack Overflow Checks Per Function Call** + +A WebAssembly program tracks the current stack size with a +global variable that it increases on every function call. The +programmer can define the maximum stack size for the program. To ensure that a program does not overflow the stack, + +both Chrome and Firefox add stack checks at the start of each +function to detect if the current stack size is less than the maximum stack size. These checks includes extra comparison +and conditional jump instructions, which must be executed +on every function call. + +**6.2.3** **Function Table Indexing Checks** + +WebAssembly dynamically checks all indirect calls to ensure + +that the target is a valid function and that the function’s type +at runtime is the same as the type specified at the call site. +In a WebAssembly module, the function table stores the list +of functions and their types, and the code generated by WebAssembly uses the function table to implement these checks. +These checks are required when calling function pointers and +virtual functions in C/C++. The checks lead to extra comparison and conditional jump instructions, which are executed +before every indirect function call. + +**6.3** **Increased Code Size** + +The code generated by Chrome and Firefox is considerably +larger than the code generated by Clang. We use three performance counters to measure this effect. (i) Figure 9e shows the +number of _instructions retired_ by benchmarks compiled to +WebAssembly and executed in Chrome and Firefox relative + +to the number of instructions retired in native code. Similarly, +Figure 9f shows the relative number of _CPU cycles_ spent by +benchmarks compiled to WebAssembly, and Figure 10 shows +the relative number of _L1 instruction cache load misses_ . + +Figure 9e shows that Chrome executes an average of 1.80 _×_ +more instructions than native code and Firefox executes an +average of 1.75 _×_ more instructions than native code. Due to +poor instruction selection, a poor register allocator generating +more register spills (Section 6.1), and extra branch statements +(Section 6.2), the size of generated code for WebAssembly +is greater than native code, leading to more instructions being executed. This increase in the number of instructions +executed leads to increased L1 instruction cache misses in +Figure 10. On average, Chrome suffers 2.83 _×_ more I-cache +misses than native code, and Firefox suffers from 2.04 _×_ more +L1 instruction cache misses than native code. More cache +misses means that more CPU cycles are spent waiting for the +instruction to be fetched. + +We note one anomaly: although 429.mcf has 1.6 _×_ more +instructions retired in Chrome than native code and 1.5 _×_ +more instructions retired in Firefox than native code, it runs +_faster_ than native code. Figure 3b shows that its slowdown +relative to native is 0.81 _×_ in Chrome and 0.83 _×_ in Firefox. +The reason for this anomaly is attributable directly to its lower +number of L1 instruction cache misses. 429.mcf contains a +main loop and most of the instructions in the loop fit in the L1 +instruction cache. Similarly, 433.milc performance is better +due to fewer L1 instruction cache misses. In 450.soplex + +|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17|Col18|Col19| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +||||||~~oog~~|~~le Ch~~|~~le Ch~~|~~ rom~~|~~ rom~~||~~Mozi~~|~~lla Fi~~|~~ refox~~|~~ refox~~||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| +|||||||||||||||||||| + +SPEC CPU Benchmarks + +Figure 10: L1-icache-load-misses samples counted for +SPEC CPU compiled to WebAssembly executed in Chrome +and Firefox, relative to native. 458.sjeng not shown in the +graph exhibits 26.5 _×_ more L1 instruction cache misses in +Chrome and 18.6 _×_ more in Firefox. The increased code size +generated for WebAssembly leads to more instruction cache +misses. + +|Performance Counter|Chrome|Firefox| +|---|---|---| +|all-loads-retired
all-stores-retired
branch-instructions-retired
conditional-branches
instructions-retired
cpu-cycles
L1-icache-load-misses|2.02_×_
2.30_×_
1.75_×_
1.65_×_
1.80_×_
1.54_×_
2.83_×_|1.92_×_
2.16_×_
1.65_×_
1.62_×_
1.75_×_
1.38_×_
2.04_×_| + +Table 4: The geomean of performance counter increases for +the SPEC benchmarks using WebAssembly. + +there are 4.6 _×_ more L1 instruction cache misses in Chrome +and Firefox than native because of several virtual functions +being executed, leading to more indirect function calls. + +**6.4** **Discussion** + +It is worth asking if the performance issues identified here +are fundamental. We believe that two of the identified issues are not: that is, they could be ameliorated by improved +implementations. WebAssembly implementations today use +register allocators (§6.1.2) and code generators (§6.2.1) that +perform worse than Clang’s counterparts. However, an offline +compiler like Clang can spend considerably more time to +generate better code, whereas WebAssembly compilers must +be fast enough to run online. Therefore, solutions adopted +by other JITs, such as further optimizing hot code, are likely +applicable here [19, 32]. + +The four other issues that we have identified appear to + +10 + +8 + +6 + +4 + +2 + +1 + +0 + +arise from the design constraints of WebAssembly: the stack +overflow checks (§6.2.2), indirect call checks (§6.2.3), and +reserved registers (§6.1.1) have a runtime cost and lead to increased code size (§6.3). Unfortunately, these checks are necessary for WebAssembly’s safety guarantees. A redesigned +WebAssembly, with richer types for memory and function + +pointers [23], might be able to perform some of these checks +at compile time, but that could complicate the implementation of compilers that produce WebAssembly. Finally, a WebAssembly implementation in a browser must interoperate with +a high-performance JavaScript implementation, which may +impose its own constraints. For example, current JavaScript +implementations reserve a few registers for their own use, +which increases register pressure on WebAssembly. + +**7** **Related Work** + +**Precursors to WebAssembly** There have been several attempts to execute native code in browsers, but none of them +met all the design criteria of WebAssembly. + +ActiveX [13] allows web pages to embed signed x86 libraries, however these binaries have unrestricted access to +the Windows API. In contrast, WebAssembly modules are +sandboxed. ActiveX is now a deprecated technology. + +Native Client [11, 37] (NaCl) adds a module to a web application that contains platform specific machine code. NaCl +introduced sandboxing techniques to execute platform specific machine code at near native speed. Since NaCl relies on +static validation of machine code, it requires code generators +to follow certain patterns, hence, supporting only a subset +of the x86, ARM, and MIPS instructions sets in the browser. +To address the inherent portability issue of NaCl, Portable +NaCl (PNaCl) [14] uses LLVM Bitcode as a binary format. +However, PNaCl does not provide significant improvement +in compactness over NaCl and still exposes compiler and/or +platform-specific details such as the call stack layout. Both +have been deprecated in favor of WebAssembly. + +asm.js is a subset of JavaScript designed to be compiled +efficiently to native code. asm.js uses type coercions to +avoid the dynamic type system of JavaScript. Since asm.js +is a subset of JavaScript, adding all native features to asm.js +such as 64-bit integers will first require extending JavaScript. +Compared to asm.js, WebAssembly provides several improvements: (i) WebAssembly binaries are compact due to +its lightweight representation compared to JavaScript source, +(ii) WebAssembly is more straightforward to validate, (iii) +WebAssembly provides formal guarantees of type safety and + +isolation, and (iv) WebAssembly has been shown to provide +better performance than asm.js. + +WebAssembly is a stack machine, which is similar to the +Java Virtual Machine [21] and the Common Language Runtime [25]. However, WebAssembly is very different from +these platforms. For example WebAssembly does not support +objects and does not support unstructured control flow. + +The WebAssembly specification defines its operational semantics and type system. This proof was mechanized using +the Isabelle theorem prover, and that mechanization effort +found and addressed a number of issues in the specification [35]. RockSalt [22] is a similar verification effort for +NaCl. It implements the NaCl verification toolchain in Coq, +along with a proof of correctness with respect to a model of +the subset of x86 instructions that NaCl supports. + +**Analysis of SPEC Benchmarks using performance coun-** +**ters** Several papers use performance counters to analyze +the SPEC benchmarks. Panda et al. [26] analyze the SPEC +CPU2017 benchmarks, applying statistical techniques to identify similarities among benchmarks. Phansalkar et al. perform +a similar study on SPEC CPU2006 [27]. Limaye and Adegija +identify workload differences between SPEC CPU2006 and +SPEC CPU2017 [20]. + +**8** **Conclusions** + +This paper performs the first comprehensive performance +analysis of WebAssembly. We develop BROWSIX-WASM, +a significant extension of BROWSIX, and BROWSIX-SPEC, +a harness that enables detailed performance analysis, to let +us run the SPEC CPU2006 and CPU2017 benchmarks as +WebAssembly in Chrome and Firefox. We find that the mean + +slowdown of WebAssembly vs. native across SPEC benchmarks is 1.55 _×_ for Chrome and 1.45 _×_ for Firefox, with peak +slowdowns of 2.5 _×_ in Chrome and 2.08 _×_ in Firefox. We +identify the causes of these performance gaps, providing actionable guidance for future optimization efforts. + +**Acknowledgements** We thank the reviewers and our shepherd, Eric Eide, for their constructive feedback. This work +was partially supported by NSF grants 1439008 and 1413985. + +**References** + +[1] Blazor. 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