cashews 0.17.0

cache tools with async power

CASHEWS 🥔

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

pip install cashews[redis]

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, memcache by request)
• Can cache any objects securely with pickle (use hash key).
• Simple configuring and API
• cache invalidation autosystem and API
• Cache usage detection API

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

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

Also read about dynamic disabling at simple cache section

Backends

Memory

Store values in a dict, have 2 strategies to expire keys: deferred task to remove key, can overload loop by big amount of async tasks, that's why use strategy with storing expiration time is prefer This strategy check expiration on 'get' and periodically purge expired keys Also size of memory cache limit with size parameter (default 1000):

cache.setup("mem://?size=500")
cache.setup("mem://?check_interval=10&size=10000") # using strategy with expiration store, we increase check_interval be

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 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

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

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):
    ...

:param ttl: duration in seconds or in timedelta object or a callable object to store a result

:param func_args: arguments of call that will be used in key, can be tuple or dict with argument name as a key and callable object as a transform function for value of this argument

@cache(ttl=100, func_args=("arg", "token"))
async def long_running_function(arg, user: User, token: str = "token"):
    ...

await long_running_function("name", user=user, token="qdfrevt")  # key will be like "long_running_function:arg:name:token:qdfrevt

But what if we want to user argument define a cache key or want to hide token from cache

@cache(ttl=100, func_args={"arg": True, "token": get_md5, "user": attrgetter("uid")})
async def long_running_function(arg, user: User, token: str = "token"):
    ...

await long_running_function("name", user=user, token="qdfrevt")  # key will be like "long_running_function:arg:name:token:7ea802f0544ff108aace43e2d3752a28:user:51e6da60-2553-45ec-9e56-d9538b9614c8

:param key: custom cache key, may contain alias to args or kwargs passed to a call (like 'key_{token}/{arg}{user}')

:param condition: callable object that determines whether the result will be saved or not

:param prefix: custom prefix for key

Fail cache

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

:param ttl: duration in seconds or in timedelta object or a callable object to store a result

:param exceptions: exceptions at which returned cache result

:param func_args: see simple cache params

:param key: custom cache key, may contain alias to args or kwargs passed to a call

:param prefix: custom prefix for key, default "fail"

Example

from cashews import cache  # or from cashews import fail

@cache.fail(ttl=timedelta(hours=2))
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'

:param ttl: duration in seconds or in timedelta object or a callable object to store a result

:param cache_hits: number of cache hits till cache will dropped

:param update_before: number of cache hits before cache will update

:param func_args: see simple cache params

:param key: custom cache key, may contain alias to args or kwargs passed to a call

:param condition: callable object that determines whether the result will be saved or not

:param prefix: custom prefix for key, default "hit"

Example

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

:param ttl: duration in seconds or in timedelta object or a callable object to store a result

:param func_args: see simple cache params

:param key: custom cache key, may contain alias to args or kwargs passed to a call

:param trace_size: the number of calls that are involved

:param perf_condition: callable object that determines whether the result will be cached, default if doubled mean value of time execution less then current

:param prefix: custom prefix for key, default 'perf'

import cashews.decorators.perf
from cashews import cache   # or from cashews import perf

@cashews.decorators.perf.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, if ttl is None

:param ttl: duration to lock wrapped function call

:param func_args: see simple cache params

:param key: custom cache key, may contain alias to args or kwargs passed to a call

:param prefix: custom prefix for key, default 'early'

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 background near expiration time Warning! Not good at cold cache

:param ttl: seconds in int or as timedelta object to store a result

:param func_args: see simple cache params

:param key: custom cache key, may contain alias to args or kwargs passed to a call

:param condition: callable object that determines whether the result will be saved or not

:param prefix: custom prefix for key, default 'early'

from cashews import cache  # or from cashews import early

@cache.early(ttl=timedelta(minutes=10))
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

:param limit: number of calls

:param period: Period

:param ttl: time to ban, default == period

:param func_args: see simple cache params

:param action: call when rate limit reached, default raise RateLimitException

:param prefix: custom prefix for key, default 'rate_limit'

import cashews.decorators.rate
from cashews import cache  # or from cashews import rate_limit

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

Circuit breaker

Circuit breaker

:param errors_rate: Errors rate in percents

:param period: Period

:param ttl: seconds in int or as timedelta to keep circuit breaker switched

:param func_args: arguments that will be used in key

:param exceptions: exceptions at which returned cache result

:param key: custom cache key, may contain alias to args or kwargs passed to a call

:param prefix: custom prefix for key, default "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::```python

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):
    ...

@cashews.cache_utils.invalidate.invalidate("module:items:page:*")  # the same as @cache.invalidate(items)
@cashews.cache_utils.invalidate.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. Сertainly 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 simple API:

from cashews import context_cache_detect

context_cache_detect.start()
response = await decorated_function()
keys = context_cache_detect.get()
print(keys)
# >>> {"key": {"ttl": 10}, "fail_key": {"ttl": timedelta(hours=10), "exc": RateLimit}}

# OR
from cashews import CacheDetect

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

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

You can use it in your web app:

@app.middleware("http")
async def add_from_cache_headers(request: Request, call_next):
    context_cache_detect.start()
    response = await call_next(request)
    keys = context_cache_detect.get()
    if keys:
        key = list(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)
        response.headers["X-From-Cache-TTL"] = str(keys[key]["ttl"].total_seconds())
        if "exc" in keys[key]:
            response.headers["X-From-Cache-Exc"] = str(keys[key]["exc"])
    return response

Fail cache - disable next set to avoide update cache by wrong values Info by key template Hit rate + https://www.datadoghq.com/blog/how-to-monitor-redis-performance-metrics/

Invalidate without scan (may be separate keys by db ) Client side cache for redis backend