.. image:: https://img.shields.io/pypi/v/py-memoize.svg :target: https://pypi.org/project/py-memoize

.. image:: https://img.shields.io/pypi/pyversions/py-memoize.svg :target: https://pypi.org/project/py-memoize

.. image:: https://readthedocs.org/projects/memoize/badge/?version=latest :target: https://memoize.readthedocs.io/en/latest/?badge=latest

.. image:: https://github.com/DreamLab/memoize/actions/workflows/tox-tests.yml/badge.svg :target: https://github.com/DreamLab/memoize/actions/workflows/tox-tests.yml

.. image:: https://github.com/DreamLab/memoize/actions/workflows/non-test-tox.yml/badge.svg :target: https://github.com/DreamLab/memoize/actions/workflows/non-test-tox.yml

Extended docs (including API docs) available at memoize.readthedocs.io <https://memoize.readthedocs.io>_.

What & Why

What: Caching library for asyncio Python applications.

Why: Python deserves library that works in async world (for instance handles dog-piling <https://en.wikipedia.org/wiki/Cache_stampede>_ ) and has a proper, extensible API.

Etymology

In computing, memoization or memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again. (…) The term “memoization” was coined by Donald Michie in 1968 and is derived from the Latin word “memorandum” (“to be remembered”), usually truncated as “memo” in the English language, and thus carries the meaning of “turning [the results of] a function into something to be remembered.” ~ Wikipedia <https://en.wikipedia.org/wiki/Memoization>_

Getting Started

Installation

Basic Installation

To get you up & running all you need is to install:

.. code-block:: bash

   pip install py-memoize

Installation of Extras

To harness the power of ujson <https://pypi.org/project/ujson/>_ (if JSON SerDe is used) install extra:

.. code-block:: bash

pip install py-memoize[ujson]

Usage

Provided examples use default configuration to cache results in memory. For configuration options see Configurability_.

asyncio

To apply default caching configuration use:

..
    _example_source: examples/basic/basic.py

.. code-block:: python

    import asyncio
    import random
    from memoize.wrapper import memoize


    @memoize()
    async def expensive_computation():
        return 'expensive-computation-' + str(random.randint(1, 100))


    async def main():
        print(await expensive_computation())
        print(await expensive_computation())
        print(await expensive_computation())


    if __name__ == "__main__":
        asyncio.get_event_loop().run_until_complete(main())


Features
========

Async-first
-----------

Asynchronous programming is often seen as a huge performance boost in python programming.
But with all the benefits it brings there are also new concurrency-related caveats
like `dog-piling <https://en.wikipedia.org/wiki/Cache_stampede>`_.

This library is built async-oriented from the ground-up, what manifests in, for example,
in `Dog-piling proofness`_ or `Async cache storage`_.


Configurability
---------------

With *memoize* you have under control:

* timeout applied to the cached method;
* key generation strategy (see :class:`memoize.key.KeyExtractor`);
  already provided strategies use arguments (both positional & keyword) and method name (or reference);
* storage for cached entries/items (see :class:`memoize.storage.CacheStorage`);
  in-memory storage is already provided;
  for convenience of implementing new storage adapters some SerDe (:class:`memoize.serde.SerDe`) are provided;
* eviction strategy (see :class:`memoize.eviction.EvictionStrategy`);
  least-recently-updated strategy is already provided;
* entry builder (see :class:`memoize.entrybuilder.CacheEntryBuilder`)
  which has control over ``update_after``  & ``expires_after`` described in `Tunable eviction & async refreshing`_
* value post-processing (see :class:`memoize.postprocessing.Postprocessing`);
  noop is the default one;
  deep-copy post-processing is also provided (be wary of deep-copy cost & limitations,
  but deep-copying allows callers to safely modify values retrieved from an in-memory cache).

All of these elements are open for extension (you can implement and plug-in your own).
Please contribute!

Example how to customize default config (everything gets overridden):

..
    _example_source: examples/configuration/custom_configuration.py

.. code-block:: python

    from datetime import timedelta

    from memoize.configuration import MutableCacheConfiguration, DefaultInMemoryCacheConfiguration
    from memoize.entrybuilder import ProvidedLifeSpanCacheEntryBuilder
    from memoize.eviction import LeastRecentlyUpdatedEvictionStrategy
    from memoize.key import EncodedMethodNameAndArgsKeyExtractor
    from memoize.postprocessing import DeepcopyPostprocessing
    from memoize.storage import LocalInMemoryCacheStorage
    from memoize.wrapper import memoize

    @memoize(
        configuration=MutableCacheConfiguration
        .initialized_with(DefaultInMemoryCacheConfiguration())
        .set_method_timeout(value=timedelta(minutes=2))
        .set_entry_builder(ProvidedLifeSpanCacheEntryBuilder(update_after=timedelta(minutes=2),
                                                             expire_after=timedelta(minutes=5)))
        .set_eviction_strategy(LeastRecentlyUpdatedEvictionStrategy(capacity=2048))
        .set_key_extractor(EncodedMethodNameAndArgsKeyExtractor(skip_first_arg_as_self=False))
        .set_storage(LocalInMemoryCacheStorage())
        .set_postprocessing(DeepcopyPostprocessing()),
        update_statuses=InMemoryLocks(update_lock_timeout=timedelta(minutes=5))
    )
    async def cached():
        return 'dummy'


Still, you can use default configuration which:

* sets timeout for underlying method to 2 minutes;
* uses in-memory storage;
* uses method instance & arguments to infer cache key;
* stores up to 4096 elements in cache and evicts entries according to least recently updated policy;
* refreshes elements after 10 minutes & ignores unrefreshed elements after 30 minutes;
* does not post-process cached values.

If that satisfies you, just use default config:

..
    _example_source: examples/configuration/default_configuration.py

.. code-block:: python

    from memoize.configuration import DefaultInMemoryCacheConfiguration
    from memoize.wrapper import memoize


    @memoize(configuration=DefaultInMemoryCacheConfiguration())
    async def cached():
        return 'dummy'

Also, if you want to stick to the building blocks of the default configuration, but need to adjust some basic params:

..
    _example_source: examples/configuration/default_customized_configuration.py

.. code-block:: python

    from datetime import timedelta

    from memoize.configuration import DefaultInMemoryCacheConfiguration
    from memoize.wrapper import memoize


    @memoize(configuration=DefaultInMemoryCacheConfiguration(capacity=4096, method_timeout=timedelta(minutes=2),
                                                             update_after=timedelta(minutes=10),
                                                             expire_after=timedelta(minutes=30)))
    async def cached():
        return 'dummy'

Tunable eviction & async refreshing
-----------------------------------

Sometimes caching libraries allow providing TTL only. This may result in a scenario where when the cache entry expires
latency is increased as the new value needs to be recomputed.
To mitigate this periodic extra latency multiple delays are often used. In the case of *memoize* there are two
(see :class:`memoize.entrybuilder.ProvidedLifeSpanCacheEntryBuilder`):

* ``update_after`` defines delay after which background/async update is executed;
* ``expire_after`` defines delay after which entry is considered outdated and invalid.

This allows refreshing cached value in the background without any observable latency.
Moreover, if some of those background refreshes fail they will be retried still in the background.
Due to this beneficial feature, it is recommended to ``update_after`` be significantly shorter than ``expire_after``.

Dog-piling proofness
--------------------

If some resource is accessed asynchronously `dog-piling <https://en.wikipedia.org/wiki/Cache_stampede>`_ may occur.
Caches designed for synchronous python code
(like built-in `LRU <https://docs.python.org/3.3/library/functools.html#lru_cache>`_)
will allow multiple concurrent tasks to observe a miss for the same resource and will proceed to flood underlying/cached
backend with requests for the same resource.


As it breaks the purpose of caching (as backend effectively sometimes is not protected with cache)
*memoize* has built-in dog-piling protection.

Under the hood, concurrent requests for the same resource (cache key) get collapsed to a single request to the backend.
When the resource is fetched all requesters obtain the result.
On failure, all requesters get an exception (same happens on timeout).

An example of what it all is about:

..
    _example_source: examples/dogpiling/dogpiling.py

.. code-block:: python

    import asyncio
    from datetime import timedelta

    from aiocache import cached, SimpleMemoryCache  # version 0.11.1 (latest) used as example of other cache implementation

    from memoize.configuration import DefaultInMemoryCacheConfiguration
    from memoize.wrapper import memoize

    # scenario configuration
    concurrent_requests = 5
    request_batches_execution_count = 50
    cached_value_ttl_ms = 200
    delay_between_request_batches_ms = 70

    # results/statistics
    unique_calls_under_memoize = 0
    unique_calls_under_different_cache = 0


    @memoize(configuration=DefaultInMemoryCacheConfiguration(update_after=timedelta(millisec