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cache tools with async power

Project description


Async cache utils with simple API to build fast and reliable applications

pip install cashews
pip install cashews[redis]
pip install cashews[diskcache]


Cache plays a significant role in modern applications and everybody want to use all power of async programming and cache. There are a few advanced techniques with cache and async programming that can help you build simple, fast, scalable and reliable applications. This library intends to make it easy to implement such techniques.


  • Easy to configurate and use
  • Decorator-based API, just decorate and play
  • Different cache strategies out-of-the-box
  • Support for multiple storage backends (In-memory, Redis, DiskCache)
  • Middlewares
  • Client-side cache
  • Different cache invalidation techniques (time-based and function-call based)
  • Cache any objects securely with pickle (use hash key)
  • Cache usage API

Usage Example

from datetime import timedelta

from cashews import cache

cache.setup("mem://")  # configure as in-memory cache, but redis is also supported

# use a decorator-based API
@cache(ttl=timedelta(hours=3), key="user:{request.user.uid}")
async def long_running_function(request):

# or for fine-grained control, use it directly in a function
async def cache_using_function(request):
    await cache.set(key=request.user.uid, value=request.user, expire=60)

Table of Contents


cashews provides a default cache, that you can setup in a two different ways:

from cashews import cache

# via url
# or via kwargs
cache.setup("redis://", db=1, create_connection_timeout=0.5, safe=False, hash_key=b"my_key", enable=True)

Alternatively, you can create cache instance yourself:

from cashews import Cache

cache = Cache()

Optionally, you can disable cache with enable parameter:

cache.setup("mem://?size=500", enable=False)

You can setup different Backends based on a prefix:

cache.setup("mem://?size=500", prefix="user")

await cache.get("accounts")  # will use redis backend
await cache.get("user:1")  # will use memory backend

Available Backends


In-memory cache uses fixed-sized LRU dict to store values. It checks expiration on get and periodically purge expired keys.



Requires aioredis package.

This will use Redis as a storage.

This backend uses pickle module to store values, but the cashes can store values with sha1-keyed hash. Use hash_key parameter to protect your application from security vulnerabilities.

To supress any connections errors use safe parameter.

You can set parameters for redis pool with minsize or maxsize parameters.

If you would like to use client-side cache set client_side=True

cache.setup("redis://", prefix="func")
cache.setup("redis://", hash_key=None, prefix="super", index_name="user", index_field="user_uid")
cache.setup("redis://", db=1, password="my_pass", create_connection_timeout=0.1, safe=1, hash_key="my_secret", client_side=True)


Requires diskcache package.

This will use local sqlite databases (with shards) as storage.

It is a good choice if you don't want to use redis, but you need a shared storage, or your cache takes a lot of local memory. Also, it is good choice for client side local storage.

You cat setup disk cache with FanoutCache parameters

** Warning ** cache.keys_match does not work with this storage (works only if shards are disabled)

cache.setup("disk://?directory=/tmp/cache&timeout=1&shards=0")  # disable shards
Gb = 1073741824
cache.setup("disk://", size_limit=3 * Gb, shards=12)

Basic API

There are few basic methods to work with cache:

from cashews import cache

cache.setup("mem://")  # configure as in-memory cache

await cache.set(key="key", value={"any": True}, expire=60, exist=None)  # -> bool
await cache.get("key")  # -> Any
await cache.get_many("key1", "key2")
await cache.incr("key") # -> int
await cache.delete("key")
await cache.expire("key", timeout=10)
await cache.get_expire("key")  # -> int seconds to expire
await  # -> bytes
await cache.clear()
await cache.is_locked("key", wait=60)  # -> bool
async with cache.lock("key", expire=10):
await cache.set_lock("key", value="value", expire=60)  # -> bool
await cache.unlock("key", "value")  # -> bool


Simple cache

This is typical cache strategy: execute, store and return from cache until it expired.

from datetime import timedelta

from cashews import cache

@cache(ttl=timedelta(hours=3), key="user:{request.user.uid}")
async def long_running_function(request):

Fail cache (Failover cache)

Return cache result, if one of the given exceptions is raised (at least one function call should be succeed prior that).

from cashews import cache  # or: from cashews import fail

# note: the key will be "__module__.get_status:name:{name}", exceptions=(ValueError, MyException))  
async def get_status(name):
    value = await api_call()
    return {"status": value}

Hit cache

Expire cache after given numbers of call cache_hits.

from cashews import cache  # or: from cashews import hit

@cache.hit(ttl=timedelta(hours=2), cache_hits=100, update_before=2)
async def get(name):

Performance downgrade detection

Trace time execution of target and throw exception if it downgrades to given condition

from cashews import cache   # or: from cashews import perf

async def get(name):
    value = await api_call()
    return {"status": value}


Decorator that can help you to solve Cache stampede problem. Lock following function calls until the first one will be finished. This guarantees exactly one function call for given ttl.

from cashews import cache  # or: from cashews import locked

async def get(name):
    value = await api_call()
    return {"status": value}


Cache strategy that tries to solve Cache stampede problem with a hot cache recalculating result in a background.

from cashews import cache  # or: from cashews import early

# if you call this function after 7 min, cache will be updated in a background 
@cache.early(ttl=timedelta(minutes=10), early_ttl=timedelta(minutes=7))  
async def get(name):
    value = await api_call()
    return {"status": value}

Rate limit

Rate limit for a function call - do not call a function if rate limit is reached

from cashews import cache  # or: from cashews import rate_limit

# no more than 10 calls per minute or ban for 10 minutes
@cache.rate_limit(limit=10, period=timedelta(minutes=1), ttl=timedelta(minutes=10))
async def get(name):
    return {"status": value}

Circuit breaker

Circuit breaker

from cashews import cache  # or: from cashews import circuit_breaker

@cache.circuit_breaker(errors_rate=10, period=timedelta(minutes=1), ttl=timedelta(minutes=5))
async def get(name):

Template Keys

Often, to compose a key, you need all the parameters of the function call. By default, Cashews will generate a key using the function name, module names and parameters

from cashews import cache

async def get_name(user, version="v1"):

# a key template will be "__module__.get_name:user:{user}:version:{version}"

await get_name("me", version="v2") 
# a key will be "__module__.get_name:user:me:version:v2"

Sometimes you need to format the parameters or define your own template for the key and Cashews allows you to do this:"name:{user.uid}")
async def get_name(user, version="v1"):

await get_name(user, version="v2") 
# a key will be "fail:name:me"

@cache.hit(key="user:{token:jwt(user_name)}", prefix="new")
async def get_name(token):

await get_name(token) 
# a key will be "new:user:alex"

from cashews import default_formatter, cache

def _upper(value):
    return value.upper()

async def get_name(user):

await get_name("alex") 
# a key will be "name-ALEX"

Cache invalidation

Cache invalidation - one of the main Computer Science well known problem. That's why ttl is a required parameter for all cache decorators.

Sometimes, you want to invalidate cache after some action is triggered. Consider this example:

from datetime import timedelta

from cashews import cache

async def user_items(user_id, fresh=False):

async def items(page=1):

@cache.invalidate("module:items:page:*")  # or: @cache.invalidate(items)
@cache.invalidate(user_items, {"user_id": lambda user:}, defaults={"fresh": True})
async def create_item(user):

Here, cache for user_items and items will be invalidated every time create_item is called.

Cache invalidation on code change

Often, you may face a problem with invalid cache after code is changed. For example:

@cache(ttl=timedelta(days=1), key="user:{user_id}")
async def get_user(user_id):
    return {"name": "Dmitry", "surname": "Krykov"}

Then, returned value was changed to:

-    return {"name": "Dmitry", "surname": "Krykov"}
+    return {"full_name": "Dmitry Krykov"}

Since function returning a dict, there is no way simple way to automatically detect that kind of cache invalidity

One way to solve the problem is to add a prefix for this cache:

@cache(ttl=timedelta(days=1), prefix="v2")
async def get_user(user_id):
    return {"full_name": "Dmitry Krykov"}

but it is so easy to forget to do it...

The best defense against this problem is to use your own datacontainers, like dataclasses, with defined __repr__ method. This will add distinctness and cashews can detect changes in such structures automatically by checking object representation.

from dataclasses import dataclass

from cashews import cache

class User:
    name: str
    surname: str

# or define your own class with __repr__ method

class User:

    def __init__(self, name, surname):, self.surname = name, surname

    def __repr__(self):
        return f"{} {self.surname}"

# Will detect changes of a structure
@cache(ttl=timedelta(days=1), prefix="v2")
async def get_user(user_id):
    return User("Dima", "Krykov")

Detect the source of a result

Decorators give us a very simple API but also make it difficult to understand where result is coming from - cache or direct call.

To solve this problem cashews has context_cache_detect context manager:

from cashews import context_cache_detect

with context_cache_detect as detector:
    response = await decorated_function()
    keys = detector.get()
# >>> {"my:key": [{"ttl": 10, "name": "simple", "backend": "redis"}, ], "fail:key": [{"ttl": timedelta(hours=10), "exc": RateLimit}, "name": "fail", "backend": "mem"],}

or you can use CacheDetect class:

from cashews import CacheDetect

cache_detect = CacheDetect()
await func(_from_cache=cache_detect)
assert cache_detect.keys == {}

await func(_from_cache=cache_detect)
assert len(cache_detect.keys) == 1

A simple middleware to use it in a web app:

async def add_from_cache_headers(request: Request, call_next):
    with context_cache_detect as detector:
        response = await call_next(request)
        if detector.keys:
            key = list(detector.keys.keys())[0]
            response.headers["X-From-Cache"] = key
            expire = await mem.get_expire(key)
            if expire == -1:
                expire = await cache.get_expire(key)
            response.headers["X-From-Cache-Expire-In-Seconds"] = str(expire)
            if "exc" in detector.keys[key]:
                response.headers["X-From-Cache-Exc"] = str(detector.keys[key]["exc"])
    return response


Cashews provide the interface for a "middleware" pattern:

import logging
from cashews import cache

logger = logging.getLogger(__name__)

async def logging_middleware(call, *args, backend=None, cmd=None, **kwargs):
    key = args[0] if args else kwargs.get("key", kwargs.get("pattern", ""))"=> Cache request: %s ", cmd, extra={"command": cmd, "cache_key": key})
    return await call(*args, **kwargs)

cache.setup("mem://", middlewares=(logging_middleware, ))

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