aiocache

name	aiocache
description	Configure or use the aiocache caching layer. Use when: adding cache reads/writes, configuring cache backends, working with TTLs, enabling/disabling caching, or understanding the NoOpCache fallback pattern.

aiocache Caching

context7: If the context7 tools are available, resolve and load the full aiocache documentation before making changes:

context7_resolve-library-id: "aiocache"
context7_query-docs: /aio-libs/aiocache

The caching layer is defined in {{cookiecutter.__package_slug}}/services/cache.py. It provides helper functions and a NoOpCache fallback for when caching is disabled.

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Cache Aliases

Three cache backends are configured by configure_caches():

Alias	Backend	Default TTL
memory	Always in-memory	cache_default_ttl (300s)
persistent	Redis if configured, else memory	cache_persistent_ttl (3600s)
default	Same as memory	cache_persistent_ttl (3600s)*

* Note: set_cached() applies cache_default_ttl only when alias == "memory", so default falls through to cache_persistent_ttl.

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Using the Helpers

from {{cookiecutter.__package_slug}}.services.cache import get_cached, set_cached, delete_cached, clear_cache

# Get (returns None on miss)
value = await get_cached("user:123")

# Set with default TTL
await set_cached("user:123", user_data)

# Set with custom TTL
await set_cached("user:123", user_data, ttl=600, alias="persistent")

# Delete
await delete_cached("user:123", alias="persistent")

# Clear entire cache
await clear_cache(alias="persistent")

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Direct Cache Access

For operations not covered by the helpers:

from {{cookiecutter.__package_slug}}.services.cache import get_cache

cache = get_cache("memory")
exists = await cache.exists("key")

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Cache Decorator

Use @cached to automatically cache function return values. The decorator takes a cache instance (not an alias string) as its first argument. Retrieve the instance with get_cache():

from aiocache import cached
from {{cookiecutter.__package_slug}}.services.cache import get_cache

@cached(get_cache("memory"), ttl=300, key_builder=lambda f, *args, **kwargs: f"user:{args[0]}")
async def get_user(user_id: int) -> dict[str, str] | None:
    # Expensive DB call — cached for 300s
    return await fetch_user_from_db(user_id)

Decorator parameters:

• First positional arg: cache instance from get_cache(alias) — required
• ttl: time-to-live in seconds (default: 60)
• key: static cache key (overrides key_builder if set)
• key_builder: callable that generates the cache key. Signature: lambda f, *args, **kwargs: str
• noself=True: use on class methods to share the cache across instances

Default key format: namespace__module__func_name(args)[kwargs] — e.g., api__main__fetch_user(1,)[]

Key builder patterns:

# Default key builder (uses function name + args)
@cached(get_cache("memory"), ttl=300)

# Static key (no key_builder needed)
@cached(get_cache("memory"), ttl=300, key="singleton:config")

# Custom key with positional arg
@cached(get_cache("memory"), ttl=300, key_builder=lambda f, *args, **kwargs: f"user:{args[0]}")

# Custom key with keyword arg
@cached(get_cache("memory"), ttl=300, key_builder=lambda f, *args, **kwargs: f"config:{kwargs.get('name')}")

# Hashed key for complex arguments
import hashlib
def hash_key_builder(f, *args, **kwargs):
    key = f"{f.__name__}:{args}:{sorted(kwargs.items())}"
    return hashlib.md5(key.encode()).hexdigest()

@cached(get_cache("memory"), ttl=300, key_builder=hash_key_builder)
async def search_products(query: , category:  |  = , page:  = ) -> []:
    ...

Bypassing the cache on individual calls:

# Skip cache read (always execute function)
result = await get_user(1, cache_read=False)

# Skip cache write (don't store result)
result = await get_user(2, cache_write=False)

# Non-blocking write (don't wait for cache to accept)
result = await get_user(3, aiocache_wait_for_write=False)

---

Multi-Cache Decorator

Use @multi_cached for functions that return dicts and need to cache individual keys. On subsequent calls, only missing keys are fetched:

from aiocache import multi_cached
from {{cookiecutter.__package_slug}}.services.cache import get_cache

@multi_cached(get_cache("memory"), keys_from_attr="user_ids", ttl=300)
async def get_users(user_ids: list[int]) -> dict[int, dict[str, str]]:
    # Only uncached user_ids will be passed here on subsequent calls
    return {uid: {"id": uid, "name": f"User {uid}"} for uid in user_ids}

# With custom key builder
@multi_cached(
    get_cache("persistent"),
    keys_from_attr="product_ids",
    key_builder=lambda key, f, *args, **kwargs: f"product:{key}",
    ttl=3600
)
async def get_products(product_ids: list[str]) -> dict[str, dict[str, str]]:
    ...

# Skip caching for certain values
@multi_cached()
  () -> [, [, ] | ]:
    ...

@multi_cached parameters:

• First positional arg: cache instance from get_cache(alias) — required
• keys_from_attr: name of the argument containing the list of keys to cache
• ttl: time-to-live in seconds (default: 60)
• key_builder: callable for custom key generation. Signature: lambda key, func, *args, **kwargs: str
• skip_cache_func: callable to skip caching specific values. Signature: lambda key, value: bool

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Initialization

Caches are automatically initialized by:

• FastAPI lifespan event (on startup)
• Celery on_after_configure signal
• QuasiQueue main entry point

For custom scripts or CLI commands, call manually:

from {{cookiecutter.__package_slug}}.services.cache import configure_caches
configure_caches()

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NoOpCache

When CACHE_ENABLED=False, all caches use NoOpCache — a transparent drop-in that satisfies the BaseCache interface without storing anything. This means code that uses the cache helpers works identically whether caching is enabled or not.

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Key Conventions

• Use meaningful, namespaced keys: user:v1:123 not just 123
• Always check for None returns — cache misses are normal
• Use memory for request-scoped data, persistent for cross-instance sharing
• Set CACHE_ENABLED=False in development to debug uncached behavior

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Style Checklist

• Cache keys use namespaced format (entity:version:id)
• Cache miss returns None — always have a fallback
• configure_caches() called before any cache operations in custom scripts
• memory alias for ephemeral data, persistent for shared data
• TTL explicitly set only when different from defaults
• @cached decorator receives a cache instance from get_cache(), not an alias string
• @cached only used on async functions — never synchronous
• noself=True set on class method decorators
• @multi_cached used for functions returning dicts of individually-cached items
• skip_cache_func set on @multi_cached to avoid caching None/inactive values

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Further Reading

• docs/dev/cache.md — Full caching developer guide
• aiocache Docs