tlc.service.tlc_lrucache

Module Contents

Classes

Class	Description
LRUCacheStoreConfig	LRUCache backend configuration.
LRUCacheStore	In-memory LRU cache backend.
LRUCache	LRU cache where you can control how many slots are available, maximum memory to use for the cache, and a cache time out for the items.

Functions

Function	Description
LRUFuncCache	Decorator to add an LRU cache to a function.

API

class tlc.service.tlc_lrucache.LRUCacheStoreConfig(/, **data: typing.Any)

Bases: pydantic.BaseModel

LRUCache backend configuration.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

max_entries: int = 50000

Available slots

max_memory_in_bytes: int = 1073741824

Maximum memory to use for the cache

time_out_in_seconds: float = 3600

Cache time out for the items

class tlc.service.tlc_lrucache.LRUCacheStore(config: tlc.service.tlc_lrucache.LRUCacheStoreConfig)

Bases: litestar.stores.base.Store

In-memory LRU cache backend.

Initialize LRUCacheBackend

stats() → dict[str, int]

async get(key: str, renew_for: int | datetime.timedelta | None = None) → bytes | None

async set(key: str, value: str | bytes, expires_in: int | datetime.timedelta | None = None) → None

async delete(key: str) → None

async delete_all() → None

async exists(key: str) → bool

async expires_in(key: str) → int | None

Get the time in seconds key expires in. If no such key exists or no expiry time was set, return None.

tlc.service.tlc_lrucache.LRUFuncCache(max_entries: int, max_memory_in_bytes: int, time_threshold_in_seconds: float, time_out_in_seconds: float = 0.0) → Callable

Decorator to add an LRU cache to a function.

The decorator can control the number of cache slots (max_entries) and how much memory to use for cached element (max_memory).

In addition, the decorator can set how long a function execution must take before the result is cached (time_threshold), to avoid caching results that are fast to compute or retrieve, thus only using the cache for slower items.

The time_out parameter can be used to set how long each cached item should remain valid. If set to 0, the items will never expire.

class tlc.service.tlc_lrucache.LRUCache(max_entries: int, max_memory: int = 0, time_out: float = 0.0)

LRU cache where you can control how many slots are available, maximum memory to use for the cache, and a cache time out for the items.

Parameters:

• max_entries – The maximum number of entries the cache can hold.

• max_memory – The maximum memory to use for the cache, in bytes. If set to 0, the cache will not use memory limits.

• time_out – The time out for the items in the cache, in seconds. If set to 0, the items will never expire.

The stats() method will return a dictionary of important statistics about the cache. The clear() method will clear the cache and reset all statistics.

LRUEntry = namedtuple(...)

clear() → None

get(key: collections.abc.Hashable) → Any

set(key: collections.abc.Hashable, value: object) → None

delete(key: Any) → None

remove_oldest_item() → None

expires_in(key: str) → int | None

stats() → dict[str, int]