cacheme 0.2.0

async caching framework

Cacheme

Asyncio cache framework with multiple cache storages.

• Better cache management: Cache configuration with node class, you can apply different cache strategies on different nodes.
• Multiple cache storages: in-memory/redis/mongodb/postgres..., also support chain storages.
• Multiple serializers: Pickle/Json/Msgpack serializers.
• Type annotated: All cacheme API are type annotated with generics.
• High hit ratio in-memory cache: TinyLFU written in Rust with little memory overhead.
• Thundering herd protection: Simultaneously requests to same key will blocked by asyncio Event and only load from source once.
• Cache stats API: Stats of each node and colected automatically.

Related projects:

• Rust tiny-lfu/lru/bloomfilter used in cacheme: https://github.com/Yiling-J/cacheme-utils
• Benchmark(auto updated): https://github.com/Yiling-J/cacheme-benchmark

Table of Contents

• Installation
• Add Node
• Register Storage
• Cacheme API
• Cache Node
  • Key
  • Meta Class
  • Serializers
  • DoorKeeper
• Cache Storage
  • Local Storage
  • Redis Storage
  • MongoDB Storage
  • Sqlite Storage
  • PostgreSQL Storage
  • MySQL Storage
• Benchmarks

Requirements

Python 3.7+

Installation

pip install cacheme

Multiple storages are supported by drivers. You can install the required drivers with:

pip install cacheme[redis]
pip install cacheme[aiomysql]
pip install cacheme[motor]
pip install cacheme[asyncpg]

Add Node

Node is the core part of your cache, each node contains:

• Key attritubes and key method, which generate the cache key. Here the UserInfoNode is a dataclass, so the __init__ method are created automatically.
• Async load method, which will be called to load data from data source on cache missing. This method can be omitted if you use Memoize decorator only.
• Meta class, node cache configurations. See Cache Node

import cacheme
from dataclasses import dataclass
from cacheme.serializer import MsgPackSerializer

@dataclass
class UserInfoNode(cacheme.Node):
    user_id: int

    def key(self) -> str:
        return f"user:{self.user_id}:info"

    async def load(self) -> Dict:
        user = get_user_from_db(self.user_id)
        return serialize(user)

    class Meta(cacheme.Node.Meta):
        version = "v1"
        caches = [cacheme.Cache(storage="my-redis", ttl=None)]
        serializer = MsgPackSerializer()

This simple example use a cache storage called "my-redis", which will be registered next step. Also we use MsgPackSerializer here to dump and load data from redis. See [Cache Node] for more details.

Register Storage

Register a redis storage called "my-redis", which you can use in node meta data. The register_storage is asynchronous and will try to establish connection to cache store. See [Cache Storage] for more details.

import cacheme

await cacheme.register_storage("my-redis", cacheme.Storage(url="redis://localhost:6379"))

Cacheme API

get: get data from single node.

user = await cacheme.get(UserInfoNode(user_id=1))

get_all: get data from multiple nodes, same node type.

users = await cacheme.get_all([UserInfoNode(user_id=1), UserInfoNode(user_id=2)])

invalidate: invalidate a node, remove data from cache.

await cacheme.invalidate(UserInfoNode(user_id=1))

refresh: reload node data using load method.

await cacheme.refresh(UserInfoNode(user_id=1))

Memoize: memoize function with this decorator.

Decorate your function with cacheme.Memoize decorator and cache node. Cacheme will load data using the decorated function and ignore load method. Because your function may contain variable number of args/kwargs, we need one more step to map between args/kwargs to node. The decorated map function should have same input signature as memoized function, and return a cache node.

@cacheme.Memoize(UserInfoNode)
async def get_user_info(user_id: int) -> Dict:
    return {}

# function name is not important, so just use _ here
@get_user_info.to_node
def _(user_id: int) -> UserInfoNode:
    return UserInfoNode(user_id=user_id)

nodes: list all nodes.

nodes = cacheme.nodes()

stats: get node stats.

metrics = cacheme.stats(UserInfoNode)

metrics.request_count() # total request count
metrics.hit_count() # total hit count
metrics.hit_rate() # hit_count/request_count
metrics.miss_count() # (request_count - hit_count)/request_count
metrics.miss_rate() # miss_count/request_count
metric.load_success_count() # total load success count
metrics.load_failure_count() # total load fail count
metrics.load_failure_rate() # load_failure_count/load_count
metrics.load_count() # total load count
metrics.total_load_time() # total load time in nanoseconds
metrics.average_load_time() # total_load_time/load_count

set_prefix: set prefix for all keys. Default prefix is cacheme. Change prefix will invalid all keys, because prefix is part of the key.

cacheme.set_prefix("mycache")

Cache Node

Key

Generated cache key will be: {prefix}:{key()}:{Meta.version}. So change version will invalid all keys automatically.

Meta Class

• version[str]: Version of node, will be used as suffix of cache key.
• caches[List[Cache]]: Caches for node. Each Cache has 2 attributes, storage[str] and ttl[Optional[timedelta]]. storage is the name you registered with register_storage and ttl is how long this cache will live. Cacheme will try to get data from each cache from left to right. In most cases, use single cache or [local, remote] combination.
• serializer[Optional[Serializer]]: Serializer used to dump/load data. If storage type is local, serializer is ignored. See Serializers.
• doorkeeper[Optional[DoorKeeper]]: See DoorKeeper.

Multiple caches example. Local cache is not synchronized, so set a much shorter ttl compared to redis one. Then we don't need to worry too much about stale data.

import cacheme
from dataclasses import dataclass
from datetime import timedelta
from cacheme.serializer import MsgPackSerializer

@dataclass
class UserInfoNode(cacheme.Node):
    user_id: int

    def key(self) -> str:
        return f"user:{self.user_id}:info"

    async def load(self) -> Dict:
        user = get_user_from_db(self.user_id)
        return serialize(user)

    class Meta(cacheme.Node.Meta):
        version = "v1"
        caches = [
            cacheme.Cache(storage="local", ttl=timedelta(seconds=30)),
            cacheme.Cache(storage="my-redis", ttl=timedelta(days=10))
        ]
        serializer = MsgPackSerializer()

Serializers

Cacheme provides serveral builtin serializers, you can also write your own serializer.

• PickleSerializer: All python objects.
• JSONSerializer: Use pydantic_encoder and json, support python primitive types, dataclass, pydantic model. See pydantic types.
• MsgPackSerializer: Use pydantic_encoder and msgpack, support python primitive types, dataclass, pydantic model. See pydantic types.

serializer with compression, use zlib level-3

• CompressedPickleSerializer
• CompressedJSONSerializer
• CompressedMsgPackSerializer

DoorKeeper

Idea from TinyLfu paper.

The Doorkeeper is a regular Bloom filter placed in front of the cahce. Upon item arrival, we first check if the item is contained in the Doorkeeper. If it is not contained in the Doorkeeper (as is expected with first timers and tail items), the item is inserted to the Doorkeeper and otherwise, it is inserted to the cache.

from cacheme import BloomFilter

@dataclass
class UserInfoNode(cacheme.Node):

    class Meta(cacheme.Node.Meta):
        # size 100000, false positive probability 0.01
        doorkeeper = BloomFilter(100000, 0.01)

BloomFilter is cleared automatically when requests count == size.

Cache Storage

Local Storage

Local storage uses dictionary to store data. A policy is used to evicate keys when cache is full.

# lru policy
Storage(url="local://lru", size=10000)

# tinylfu policy
Storage(url="local://tlfu", size=10000)

Parameters:

• url: local://{policy}. 2 policies are currently supported:

  • lru
  • tlfu: TinyLfu policy, see https://arxiv.org/pdf/1512.00727.pdf

• size: size of the storage. Policy will be used to evicate key when cache is full.

Redis Storage

Storage(url="redis://localhost:6379")

# cluster
Storage(url="redis://localhost:6379", cluster=True)

Parameters:

• url: redis connection url.
• cluster: bool, cluster or not, default False.
• pool_size: connection pool size, default 100.

MongoDB Storage

To use mongodb storage, create index first. See mongo.js

Storage(url="mongodb://test:password@localhost:27017",database="test",collection="cache")

Parameters:

• url: mongodb connection url.
• database: mongodb database name.
• collection: mongodb collection name.
• pool_size: connection pool size, default 50.

Sqlite Storage

To use sqlite storage, create table and index first. See sqlite.sql

Storage(url="sqlite:///test", table="cache")

Parameters:

• url: sqlite connection url.
• table: cache table name.
• pool_size: connection pool size, default 50.

PostgreSQL Storage

To use postgres storage, create table and index first. See postgresql.sql

Storage(url="postgresql://username:password@127.0.0.1:5432/test", table="cache")

Parameters:

• url: postgres connection url.
• table: cache table name.
• pool_size: connection pool size, default 50.

MySQL Storage

To use mysql storage, create table and index first. See mysql.sql

Storage("mysql://username:password@localhost:3306/test", table="cache")

Parameters:

• url: mysql connection url.
• table: cache table name.
• pool_size: connection pool size, default 50.

Benchmarks

• Local Storage Hit Ratios(hit_count/request_count) source code

• Throughput Benchmark of different storages

  See benchmark