cashews 2.5.0

cache tools with async power

CASHEWS 🥔

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

pip install cashews

Why

Cache plays significant role in modern applications and everybody wanna use all power of async programming and cache.. There are a few advance techniques with cache and async programming that can help you to build simple, fast, scalable and reliable applications. Caches

Features

• Decorator base api, just decorate and play
• Cache invalidation by time, 'ttl' is a required parameter to avoid storage overflow and endless cache
• Support Multi backend (Memory, Redis)
• Can cache any objects securely with pickle (use hash key).
• Simple configuring and API
• cache invalidation avtosystem and API
• Cache usage detection API
• Client Side cache
• Stats for usage

API

• simple cache
• fail cache
• hit-rate cache
• perf-rate cache
• rate-limit
• cache with early expiration/rebuilding
• lock
• circuit
• api for key storage/backend
• auto invalidation
• detect cache usage

Usage

Configure

Cache object is a single object that can be configured in one place by url::

from cashews import cache

cache.setup("redis://0.0.0.0/?db=1&create_connection_timeout=0.5&safe=0&hash_key=my_sicret&enable=1")
# or
cache.setup("mem://", prefix="fail:user") # for inmemory cache
cache.setup("redis://0.0.0.0/", db=1, create_connection_timeout=0.5, safe=False, hash_key=b"my_key", enable=True)

if you dont like global objects or prefer more manageable way you can work with cache class

from cashews import Cache

cache = Cache()
cache.setup("mem://?size=500")

You can disable cache by 'enable' parameter:

cache.setup("redis://redis/0?enable=1")
cache.setup("mem://?size=500", enable=False)
cache.setup("://", prefix="early")
cache.setup("redis://redis?enable=True")

Also read about dynamic disabling at simple cache section

Backends

Memory

Store values in a LRU dict with given size (default 1000). Check expiration on 'get' and periodically purge expired keys

cache.setup("mem://")
cache.setup("mem://?check_interval=10&size=10000")

Redis

Required aioredis package Store values in a redis key-value storage. Use 'safe' parameter to avoid raising any connection errors, command will return None in this case. This backend use pickle to store values, but the cashes can store values with sha1 keyed hash. So you should set 'hash_key' parameter to protect your application from security vulnerabilities. You can set parameters for redis pool by backend setup
Also if you would like to use client side cache set client_side=True

cache.setup("redis://0.0.0.0/?db=1&minsize=10&safe=1&hash_key=my_secret")
cache.setup("redis://0.0.0.0/", db=1, password="my_pass", create_connection_timeout=0.1, safe=0, hash_key="my_secret", client_side=True)

Simple cache

Typical cache strategy: execute, store and return cached value till expiration::

from cashews import cache
from datetime import timedelta

@cache(ttl=timedelta(hours=3))
async def long_running_function(arg, kward):
    ...

Fail cache (Failover cache)

Return cache result (at list 1 call of function call should be succeed) if call raised one of the given exceptions,

from cashews import cache  # or from cashews import fail

@cache.fail(ttl=timedelta(hours=2), exceptions=(ValueError, MyException))
async def get(name):
    value = await api_call()
    return {"status": value}

Hit cache

Cache call results and drop 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 downgrade to given condition

from cashews import cache   # or from cashews import perf

@cache.perf(ttl=timedelta(hours=2))
async def get(name):
    value = await api_call()
    return {"status": value}

Locked

Decorator that can help you to solve Cache stampede problem (https://en.wikipedia.org/wiki/Cache_stampede), Lock following function calls till first one will be finished Can guarantee that one function call for given ttl

from cashews import cache  # or from cashews import locked

@cache.locked(ttl=timedelta(minutes=10))
async def get(name):
    value = await api_call()
    return {"status": value}

Early

Cache strategy that try to solve Cache stampede problem (https://en.wikipedia.org/wiki/Cache_stampede), With a hot cache recalculate a result in a background

from cashews import cache  # or from cashews import early

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

Rate limit

Rate limit for function call. Do not call function if rate limit is reached, and call given action

from cashews import cache  # or from cashews import rate_limit

# no more then 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 rate_limit

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

Basic api

There are 13 basic methods to work with key-storage:

from cashews import cache

cache.setup("mem://")

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 cache.ping(message=None)  # -> 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

Invalidation

Cache invalidation - on of the main Computer Science well known problem. That's why ttl is a required parameter for all cache decorators Another strategy to cache invalidation implement in next api:

from cashews import cache
from datetime import timedelta

@cache(ttl=timedelta(days=1))
async def user_items(user_id, fresh=False):
    ...

@cache(ttl=timedelta(hours=3))
async def items(page=1):
    ...

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

Also you may face problem with invalid cache arising on code changing. For example we have:

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

Than we did changes

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

There is no way simple way to automatically detect that kind of cache invalidity, because it is a dict. Certainly we can add prefix for this cache:

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

but usually we forget to do it... The best defense against such errors is to use objects like dataclasses for operating with structures, it adds distinctness and cashews can detect changes in this structure automatically by checking representation (repr) of object. So you can you use your own datacontainer with defined __repr__ method that rise AttributeError:

from dataclasses import dataclass

@dataclass()
class User:
    name: str
    surname: str
# OR
class User:

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

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

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

Detect source of a result

Decorators give to us very simple api but it makes difficult to understand what led to this result - cache or direct call To solve this problem cashews have a next API:

from cashews import context_cache_detect

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

# OR
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

You can use it in your web app:

@app.middleware("http")
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

https://www.datadoghq.com/blog/how-to-monitor-redis-performance-metrics/

• Invalidate without scan (index?)
• Cache strategy based on history of execution (fail too match - add fail cache, too friquent - add cache )