The cacheing from breid48

cacheing - Pure Python Cacheing Library

Resources

Installation
Updating
Basic Usage
Benchmark
Performance
Callback Functions

Supported Caches

LFUCache
LRUCache
VolatileCache(s)
TTLCache
VTTLCache
RandomCache

Motivation

The initial motivation behind this package was twofold: fix the long insertion/eviction times in cachetools.LFUCache and provide an alternative to the cachetools.TTLCache offering variable per-key TTL's.

Installation

pip install cacheing

And then in your python interpreter:

import cacheing

Updating

pip install -U cacheing

Basic Usage

from cacheing import LFUCache

cache = LFUCache(capacity=2)

cache[1] = 2
cache[2] = 3
cache[3] = 4

>>> cache
LFUCache{2: 3, 3: 4}

Benchmark

cacheing provides a benchmarking library found in ./benchmark.

$ python3 ./benchmark.py --help

usage: benchmark [-h] [--cache [CACHE [CACHE ...]]] [--method [{get,set,delete} [{get,set,delete} ...]]]

arguments:
  -h, --help            show this help message and exit
  --cache [CACHE [CACHE ...]], -c [CACHE [CACHE ...]]
                        cache(s) to benchmark. example: cacheing.LRUCache.
  --method [{get,set,delete} [{get,set,delete} ...]], -m [{get,set,delete} [{get,set,delete} ...]]
                        method(s) to benchmark.

Run the Benchmarks:

$ cd benchmark

$ python3 ./benchmark.py --cache cachetools.LRUCache cacheing.LRUCache --method set get delete

Performance

All benchmark times were measured using the provided benchmark library. See the benchmark section for details. The default benchmarking configuration executes 100,000 get operations, 20,000 set operations and n = cache_size delete operations. The median, p90, and p99 times for each operation, measured in microseconds, or 1e-6, are displayed in the figures below.

Get (LFU Cache) Delete (LFU Cache)

Set (LFU Cache) Set - Cross Section (LFU Cache)

Set (LRU Cache) Get (LRU Cache)

Delete (LRU Cache)

Callback Functions

A callback function can be specified in the body of each cache constructor. The callback function is automatically invoked when an item is evicted from the cache. The callback function signature should take two arguments mapping to the key and value of the evicted item.

import datetime

from cacheing import LFUCache

# Define a Callback-Function
def _cache_log(key, value):
    print(f"<{datetime.now()}> [Evict] {{{key}: {value}}}")

# Register Callback in the cache constructor
cache = LFUCache(capacity=2, callback=_cache_log)

cache[1] = 2
cache[2] = 3
cache[1]  # Get
cache[2]  # Get
cache[3] = 4

>>> cache
<2023-01-09 21:52:11.109161> [Evict] {1: 2}
LFUCache{2: 3, 3: 4}

LFUCache

Evictions are performed using a "Least Frequently Used" eviction policy. Cache items are "ranked" based on their overall usage. When the cache is at capacity and a request is made to insert a new item, the item with the least overall usage is evicted.

Usage is tracked by-key. Gets and Sets are considered usage qualifiers.

See: LFU Benchmarks

from cacheing import LFUCache

cache = LFUCache(capacity=2)

cache[1] = 2
cache[2] = 3
cache[1]  # Get
cache[2]  # Get
cache[3] = 4

>>> cache
LFUCache{2: 3, 3: 4}

LRUCache

Evictions are performed using a "Least Recently Used" eviction policy. Cache items are "ranked" based on how recently they were used. This is the reccomended cache to use when your data has high locality. When the cache is at capacity and a request is made to insert a new item, the least-recently accessed item is evicted.

Usage is tracked by-key. Gets and Sets are considered usage qualifiers.

See: LRU Benchmarks

from cacheing import LRUCache

cache = LRUCache(capacity=2)

cache[1] = 2
cache[2] = 3
cache[2]  # Get
cache[1]  # Get
cache[3] = 4

>>> cache
LRUCache{1: 2, 3: 4}

VolatileCaches

VolatileCache's are inspired by the Redis API's of the same name. See Redis Eviction Policies.

VolatileCache's are variants of LFU, LRU, Random, and TTL Cache's offering optional item eviction. Specific cache items can be held in-memory by setting their unique expire fields to False.

Volatile variant supported:

VolatileLFUCache
VolatileLFUCache
VolatileTTLCache
VolatileRandomCache

from cacheing import VolatileLRUCache

cache = VolatileLRUCache(capacity=2)

cache[1, False] = 2
cache[2, False] = 3
cache[3, True] = 4

>>> cache
VolatileLRUCache{1: 2, 2: 3}

TTLCache

TTLCache is a time-aware cache implementation. The ttl field defines the global time-to-live binding each item in the cache. The cache uses an underlying LRUCache to handle evictions. When the cache is at capacity and a request is made to insert a new item, the least recently used item is evicted.

import asyncio
from cacheing import TTLCache

cache = TTLCache(capacity=2, ttl=5)

cache[1] = 2
cache[2] = 3

asyncio.sleep(5) # Some task

cache[3] = 4

>>> cache
TTLCache{3: 4}

VTTLCache

Like TTLCache, VTTLCache is a time-aware cache implementation. Time to lives are explicitly assigned per-key. The cache uses an underlying LRUCache to handle evictions. When the cache is at capacity and a request is made to insert a new item, the least recently used item is evicted.

import asyncio
from cacheing import VTTLCache

cache = VTTLCache(capacity=2)

# cache[key, ttl] = value
cache[1, 5] = 2
cache[2, 6] = 3

asyncio.sleep(5) # Some task

cache[3, 5] = 4

>>> cache
VTTLCache{2: 3, 3: 4}

RandomCache

Randomly select an item for eviction.

from cacheing import RandomCache

cache = RandomCache(capacity=2)

cache[1] = 2
cache[2] = 3
cache[1]  # Get
cache[2]  # Get
cache[3] = 4

>>> cache
RandomCache{1: 2, 3: 4}

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