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Home › Backends › L1-Only Mode (No Backend)

L1-Only Mode (backend=None)

Use backend=None to run cachekit as a pure in-memory cache — no Redis, no Memcached, no external services. This is cachekit's equivalent of functools.lru_cache, but with all the decorator features (TTL, namespacing, metrics, encryption).

Basic Usage

from cachekit import cache

@cache(backend=None, ttl=300)
def expensive_computation(x: int) -> dict:
    return {"result": x ** 2}

# First call: computes
result = expensive_computation(42)

# Second call: served from L1 in-memory cache (~50ns)
result = expensive_computation(42)

No environment variables needed. No services to run. Works everywhere.

When to Use

Use L1-only when:

  • Building CLI tools, scripts, or batch processors
  • Single-process applications (no multi-pod coordination needed)
  • Local development and testing
  • You want lru_cache but with TTL, metrics, and an upgrade path

When NOT to use:

  • Multi-pod deployments (L1 cache is per-process, not shared)
  • Need persistence across restarts (L1 is in-memory only)
  • Cache must be shared between workers/processes

How It Works

With backend=None, cachekit skips L2 entirely. The data flow is:

@cache(backend=None)
  └─ L1 In-Memory Cache (~50ns)
     ├─ Hit → return cached value
     └─ Miss → call function → store in L1 → return

No network calls. No serialization to bytes. No backend initialization.

With Intent Presets

All presets work with backend=None:

from cachekit import cache

# Speed-critical, no backend
@cache.minimal(backend=None, ttl=60)
def fast_lookup(key: str) -> dict:
    return fetch_data(key)

# With encryption, no backend (L1 stores ciphertext)
@cache.secure(backend=None, ttl=3600)
def sensitive_data(user_id: int) -> dict:
    return get_pii(user_id)

Upgrade Path

The key advantage over functools.lru_cache: when you're ready to scale, just remove backend=None:

# Development: L1-only
@cache(backend=None, ttl=300)
def get_user(user_id: int) -> dict:
    return db.fetch(user_id)

# Production: just remove backend=None
# Set REDIS_URL and cachekit auto-detects Redis
@cache(ttl=300)
def get_user(user_id: int) -> dict:
    return db.fetch(user_id)

No API changes. No code rewrite. Same decorator, same function signature.

Characteristics

  • Latency: ~50ns (in-memory, no network)
  • Shared across processes: No (per-process only)
  • Persistence: No (lost on restart)
  • TTL support: Yes
  • Encryption: Yes (L1 stores ciphertext)
  • Metrics: Yes (if monitoring configured)

See Also