py-cachify 3.1.0

Distributed locks, caching, and locking at hand

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Documentation: https://py-cachify.readthedocs.io/latest/

Source Code: https://github.com/EzyGang/py-cachify

FastAPI Integration Guide: Repo

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Py-Cachify is a robust library tailored for developers looking to enhance their Python applications with elegant caching and locking mechanisms. Whether you're building synchronous or asynchronous applications, Py-Cachify has you covered! It acts as a thin, backend-agnostic wrapper over your favorite cache client, letting you focus on business logic instead of juggling low-level get/set calls.

Key Features:

• Flexible Caching: Effortlessly cache your function results, dramatically reducing execution time for expensive computations and I/O-bound tasks. Utilize customizable keys and time-to-live (TTL) parameters.

• Distributed Locks: Ensure safe concurrent operation of functions with distributed locks. Prevent race conditions and manage shared resources effectively across both sync and async contexts.

• Backend Agnostic: Easily integrate with different cache backends. Choose between in-memory, Redis, DragonflyDB, or any custom backend that adheres to the provided client interfaces.

• Decorators for Ease: Use intuitive decorators like @cached() and @lock() to wrap your functions, maintain clean code, and benefit from automatic cache management.

• Type Safety & Documentation: Fully type-annotated for enhanced IDE support and readability, featuring comprehensive documentation and examples to guide you through various use cases.

• Production Ready: With 100% test coverage and usage in multiple commercial projects, Py-Cachify is trusted for production environments, ensuring reliability and stability for your applications.

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Table of Contents

• Installation
• How to use
• Basic examples
• Contributing
• License

Installation

$ pip install py-cachify

---> 100%
Successfully installed py-cachify

How to use

You can read more in-depth tutorials here.

First, to start working with the library, you will have to initialize it by using the provided init_cachify function:

from py_cachify import init_cachify

init_cachify()

This call:

• Configures the global client used by the top-level decorators: cached, lock, and once.
• Returns a Cachify instance, but you don't have to use it if you only work with the global decorators.
• Uses an in-memory cache by default (both for sync and async usage).

If you want to use Redis:

from py_cachify import init_cachify
from redis.asyncio import from_url as async_from_url
from redis import from_url


# Example: configure global cachify with Redis for both sync and async flows
init_cachify(
    sync_client=from_url(redis_url),
    async_client=async_from_url(redis_url),
)

Normally you wouldn't have to use both sync and async clients since an application usually works in a single mode i.e. sync/async. You can pass only sync_client or only async_client if that matches your usage.

Once initialized you can use everything that the library provides straight up without being worried about managing the cache yourself.

❗ If you forgot to call init_cachify with is_global=True at least once, using the global decorators (cached, lock, once) will raise CachifyInitError during runtime.

You can also create dedicated instances without touching the global client:

from py_cachify import init_cachify

# Global initialization for the top-level decorators
init_cachify()

# Local instance that does NOT touch the global client
local_cache = init_cachify(is_global=False, prefix='LOCAL-')

@local_cache.cached(key='local-{x}')
def compute_local(x: int) -> int:
    return x * 2

Basic examples

Caching

Caching by using @cached decorator utilizing the flexibility of a dynamic key:

# Cache the result of the following function with dynamic key
@cached(key='sum_two-{a}-{b}')
async def sum_two(a: int, b: int) -> int:
    # Let's put print here to see what was the function called with
    print(f'Called with {a} {b}')
    return a + b
    
    
# Reset the cache for the call with arguments a=1, b=2
await sub_two.reset(a=1, b=2)

Multi-layer Usage

It is possible to layer caches by stacking cached decorators (for example, a global cache inside a local instance cache).

from py_cachify import cached, init_cachify

# Global initialization for the top-level decorators
init_cachify()

# Local instance with a shorter TTL that wraps the global one
local = init_cachify(is_global=False, prefix='LOCAL-')

@local.cached(key='local-expensive-{x}', ttl=5)
@cached(key='expensive-{x}', ttl=60)
def expensive(x: int) -> int:
    return x * 10

Read more about @cached here.

Locking

Locking through context manager:

from py_cachify import lock


async_lock = lock('resource_key')
# Use it within an asynchronous context
async with async_lock:
    # Your critical section here
    print('Critical section code')

# Check if it's locked
await async_lock.is_alocked()

# Forcefully release
await async_lock.arelease()

# Use it within a synchronous context
with lock('resource_key'):
    # Your critical section here
    print('Critical section code')

Locking via decorator:

from py_cachify import lock

@lock(key='critical_function_lock-{arg}', nowait=False, timeout=10)
async def critical_function(arg: int) -> None:
    # critical code
    

# Check if it's locked for arg=5
await critical_function.is_locked(arg=5)

# Forcefully release for arg=5
await critical_function.release(arg=5)

Read more about lock here.

For a more detailed tutorial visit Tutorial or full API reference.

Contributing

If you'd like to contribute, please first discuss the changes using Issues, and then don't hesitate to shoot a PR which will be reviewed shortly.

License

This project is licensed under the MIT License - see the LICENSE file for details.