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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]  # Aioredis is now in redis-py 4.2.0rc1+ 
pip install cashews[aioredis]  # Please install "redis" instead, unless you must maintain a legacy code.
pip install cashews[diskcache]
pip install cashews[speedup] # for bloom filters


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 configure 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)
  • Set ttl with string (2h5m) or with timedelta
  • Middlewares
  • Client-side cache (10x faster than simple cache with redis)
  • Bloom filters
  • Different cache invalidation techniques (time-based and function-call based)
  • Cache any objects securely with pickle (use hash key)
  • 2x faster then aiocache

Usage Example

from cashews import cache

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

# use a decorator-based API
@cache(ttl="3h", 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 two different ways:

from cashews import cache

# via url
# or via kwargs
cache.setup("redis://", db=1, wait_for_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 redis package.
Note: If you must support a legacy code that uses aioredis, then install aioredis instead.

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.

Any connections errors are suppressed, to disable it use safe=False If you would like to use client-side cache set client_side=True Client side cache will add cashews: prefix for each key, to customize it use client_side_prefix option.

cache.setup("redis://", prefix="func")
cache.setup("redis://", password="my_pass", socket_connect_timeout=0.1, retry_on_timeout=True, hash_key="my_secret")
cache.setup("redis://", client_side=True, client_side_prefix="my_prefix:")

For using secure connections to redis (over ssl) uri should have rediss as schema

cache.setup("rediss://", ssl_ca_certs="path/to/ca.crt", ssl_keyfile="path/to/client.key",ssl_certfile="path/to/client.crt",)


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 and cache.get_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=90, expire=60, exist=None)  # -> bool
await cache.set_raw(key="key", value="str")  # -> bool
await cache.get("key", default=None)  # -> Any
await cache.get_raw("key")
await cache.get_many("key1", "key2", default=None)
await cache.incr("key") # -> int
await cache.delete("key")
await cache.delete_match("pattern:*")
async for key in cache.scan("pattern:*"):
async for key, value in cache.get_match("pattern:*", batch_size=100, default=None):

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 failover

# note: the key will be "__module__.get_status:name:{name}"
@cache.failover(ttl="2h", exceptions=(ValueError, MyException))  
async def get_status(name):
    value = await api_call()
    return {"status": value}

If exceptions didn't get will catch all exceptions or use default if it set by:

cache.set_default_fail_exceptions(ValueError, MyException)

Hit cache

Expire cache after given numbers of call cache_hits.

from cashews import cache  # or: from cashews import hit

@cache.hit(ttl="2h", cache_hits=100, update_after=2)
async def get(name):


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="10m", early_ttl="7m")  
async def get(name):
    value = await api_call()
    return {"status": value}


Like a simple cache, but with a fail protection base on soft ttl.

from cashews import cache

# if you call this function after 7 min, cache will be updated and return a new result.
# If it fail on recalculation will return current cached value (if it not more then 10 min old)
@cache.soft(ttl="10m", soft_ttl="7m")  
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.

:warning: **Warning: this decorator will not cache the result To do so you can combine this decorator with any cache decorator or use parameter lock=True with @cache()

from cashews import cache  # or: from cashews import locked

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="1m", ttl="5m")
async def get(name):

Bloom filter (experimental)

Bloom filter

from cashews import cache

@cache.bloom(name="emails:{email}", capacity=10_000, false_positives=1)
async def email_exists(email):

for email in all_users_emails:
    await email_exists.set(email)

await email_exists("")

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, *args, version="v1", **kwargs):

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

await get_name("me", version="v2") 
# a key will be "__module__.get_name:user:me::version:v2"
await get_name("me", version="v1", foo="bar") 
# a key will be "__module__.get_name:user:me::version:v1:foo:bar"
await get_name("me", "opt", "attr", opt="opt", attr="attr")
# a key will be "__module__.get_name:user:me:opt:attr:version:v1:attr:attr:opt:opt"

The same with a class method

from cashews import cache

class MyClass:

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

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

await MyClass().get_name("me", version="v2") 
# a key will be "__module__:MyClass.get_name:self:<__module__.MyClass object at 0x105edd6a0>:user:me:version:v1"

As you can see, there is an ugly reference to the instance in the key. That is not what we expect to see. That cache will not work properly. There are 3 solutions to avoid it.) define __str__ magic method in our class

class MyClass:

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

    def __str__(self) -> str:
        return self._host

await MyClass(host="").get_name("me", version="v2") 
# a key will be "__module__:MyClass.get_name:self:"
  1. Set a key template
class MyClass:

    @cache(ttl="2h", key="{self._host}:name:{user}:{version}")
    async def get_name(self, user, version="v1"):

await MyClass(host="").get_name("me", version="v2") 
# a key will be ""
  1. Use noself or noself_cache if you want to exclude self from a key
from cashews import cache, noself, noself_cache

class MyClass:

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

    @noself_cache(ttl="2h")  # for python <= 3.8
    async def get_name(self, user, version="v1"):
# a key template will be "__module__:MyClass.get_name:user:{user}:version:{version}

await MyClass().get_name("me", version="v2") 
# a key will be "__module__:MyClass.get_name:user:me:version:v1"

Sometimes you may need to format the parameters or define your own template for the key and Cashews allows you to do this:

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()

def _decimal(value: Decimal) -> str:
    return value.quantize(Decimal("0.00"))

async def get_price(item):

await get_name(item) 
# a key will be "price-10.00:USD"

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 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, ))



To run tests you can use tox:

pip install tox
tox -e py  // tests for inmemory backend 
tox -e py-diskcache  // tests for diskcache backend 
tox -e py-redis  // tests for redis backend  - you need to run redis 
tox -e py-integration  // tests for integrations with aiohttp and fastapi 

tox // to run all tests for all python that is installed on your machine

Or use pytest, but 2 tests always fail, it is OK:

pip install .[tests,redis,diskcache,speedup] fastapi aiohttp requests

pytest // run all tests with all backends   
pytest -m "not redis" // all tests without tests for redis backend

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