Metadata-Version: 2.1
Name: cashews
Version: 1.1.2
Summary: cache tools with async power
Home-page: https://github.com/Krukov/cashews
Author: Dmitry Kryukov
Author-email: glebov.ru@gmail.com
License: UNKNOWN
Download-URL: https://github.com/Krukov/cashews/tarball/1.1.2
Keywords: cache aio async multicache aiocache
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Framework :: AsyncIO
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: System Administrators
Classifier: Operating System :: OS Independent
Classifier: Topic :: Internet
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development
Classifier: Typing :: Typed
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
Requires-Dist: aioredis (>=1.0.0)
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: codecov ; extra == 'dev'
Requires-Dist: coverage ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: pylint ; extra == 'dev'
Requires-Dist: pytest-asyncio ; extra == 'dev'

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](#memory), [Redis](#redis))
- Can cache any objects securely with pickle (use [hash key](#redis)). 
- Simple configuring and API
- cache invalidation autosystem and API 
- Cache usage detection API
- Client Side cache
- Stats for usage

## API
- [simple cache](#simple-cache)
- [fail cache](#fail-cache)
- [hit-rate cache](#hit-cache)
- [perf-rate cache](#performance-downgrade-detection)
- [rate-limit](#rate-limit)
- [cache with early expiration/rebuilding](#early)
- [lock](#locked) 
- [circuit](#circuit-breaker) 
- [api for key storage/backend](#basic-api)
- [auto invalidation](#invalidation)
- [detect cache usage](#detect-source-of-a-result)

Usage
-----

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

```python
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 
```python

from cashews import Cache

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

You can disable cache by 'enable' parameter:
```python

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](#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):

```python
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 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](https://aioredis.readthedocs.io/en/v1.3.0/api_reference.html#aioredis.create_pool) by backend setup    
Also if you would like to use [client side cache](https://redis.io/topics/client-side-caching) set `client_side=True` 

```python
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::

```python

from cashews import cache
from datetime import timedelta

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


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


Example
-------
```python
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'

Example
-------
```python
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


```python
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, if ttl is None

```python

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

```python
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


```python
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

```python
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:

```python
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:
```python

@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:
```python
@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`:
```python
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:
```python
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:
```python
@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)
        if "exc" in keys[key]:
            response.headers["X-From-Cache-Exc"] = str(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)


