Metadata-Version: 2.1
Name: py-memoize
Version: 1.0.4
Summary: Caching library for asynchronous Python applications (both based on asyncio and Tornado) that handles dogpiling properly and provides a configurable & extensible API.
Home-page: https://github.com/DreamLab/memoize
Author: Michal Zmuda
Author-email: zmu.michal@gmail.com
Maintainer: DreamLab
License: Apache License 2.0
Description: .. image:: https://img.shields.io/pypi/v/py-memoize.svg
            :target: https://pypi.org/project/py-memoize
        
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            :target: https://memoize.readthedocs.io/en/latest/?badge=latest
        
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        Extended docs (including API docs) available at `memoize.readthedocs.io <https://memoize.readthedocs.io>`_.
        
        What & Why
        ==========
        
        **What:** Caching library for asynchronous 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
        ~~~~~~~~~~~~~~~~~~~~~~
        
        If you are going to use ``memoize`` with tornado add a dependency on extra:
        
        .. code-block:: bash
        
           pip install py-memoize[tornado]
        
        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`_.
        
        You can use ``memoize`` with both `asyncio <https://docs.python.org/3/library/asyncio.html>`_
        and `Tornado <https://github.com/tornadoweb/tornado>`_ -  please see the appropriate example:
        
        asyncio
        ~~~~~~~
        
        To apply default caching configuration use:
        
        
        .. 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())
        
        
        Tornado
        ~~~~~~~
        
        If your project is based on Tornado use:
        
        .. code-block:: python
        
            import random
        
            from tornado import gen
            from tornado.ioloop import IOLoop
        
            from memoize.wrapper import memoize
        
        
            @memoize()
            @gen.coroutine
            def expensive_computation():
                return 'expensive-computation-' + str(random.randint(1, 100))
        
        
            @gen.coroutine
            def main():
                result1 = yield expensive_computation()
                print(result1)
                result2 = yield expensive_computation()
                print(result2)
                result3 = yield expensive_computation()
                print(result3)
        
        
            if __name__ == "__main__":
                IOLoop.current().run_sync(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`_.
        
        
        Tornado & asyncio support
        -------------------------
        
        No matter what are you using, build-in `asyncio <https://docs.python.org/3/library/asyncio.html>`_
        or its predecessor `Tornado <https://github.com/tornadoweb/tornado>`_
        *memoize* has you covered as you can use it with both.
        **This may come handy if you are planning a migration from Tornado to asyncio.**
        
        Under the hood *memoize* detects if you are using *Tornado* or *asyncio*
        (by checking if *Tornado* is installed and available to import).
        
        If have *Tornado* installed but your application uses *asyncio* IO-loop,
        set ``MEMOIZE_FORCE_ASYNCIO=1`` environment variable to force using *asyncio* and ignore *Tornado* instalation.
        
        
        Configurability
        ---------------
        
        With *memoize* you have under control:
        
        * timeout applied to the cached method;
        * key generation strategy (see `memoize.key.KeyExtractor`);
          already provided strategies use arguments (both positional & keyword) and method name (or reference);
        * storage for cached entries/items (see `memoize.storage.CacheStorage`);
          in-memory storage is already provided;
          for convenience of implementing new storage adapters some SerDe (`memoize.serde.SerDe`) are provided;
        * eviction strategy (see `memoize.eviction.EvictionStrategy`);
          least-recently-updated strategy is already provided;
        * entry builder (see `memoize.entrybuilder.CacheEntryBuilder`)
          which has control over ``update_after``  & ``expires_after`` described in `Tunable eviction & async refreshing`_
        
        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):
        
        .. 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.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())
                     )
            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.
        
        If that satisfies you, just use default config:
        
        .. code-block:: python
        
            from memoize.configuration import DefaultInMemoryCacheConfiguration
            from memoize.wrapper import memoize
        
        
            @memoize(configuration=DefaultInMemoryCacheConfiguration())
            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 `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:
        
        .. 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 MutableCacheConfiguration, DefaultInMemoryCacheConfiguration
            from memoize.entrybuilder import ProvidedLifeSpanCacheEntryBuilder
            from memoize.wrapper import memoize
        
            # scenario configuration
            concurrent_requests = 5
            request_batches_execution_count = 50
            cached_value_ttl_millis = 200
            delay_between_request_batches_millis = 70
        
            # results/statistics
            unique_calls_under_memoize = 0
            unique_calls_under_different_cache = 0
        
        
            @memoize(configuration=MutableCacheConfiguration
                .initialized_with(DefaultInMemoryCacheConfiguration())
                .set_entry_builder(
                    ProvidedLifeSpanCacheEntryBuilder(update_after=timedelta(milliseconds=cached_value_ttl_millis))
                ))
            async def cached_with_memoize():
                global unique_calls_under_memoize
                unique_calls_under_memoize += 1
                await asyncio.sleep(0.01)
                return unique_calls_under_memoize
        
        
            @cached(ttl=cached_value_ttl_millis / 1000, cache=SimpleMemoryCache)
            async def cached_with_different_cache():
                global unique_calls_under_different_cache
                unique_calls_under_different_cache += 1
                await asyncio.sleep(0.01)
                return unique_calls_under_different_cache
        
        
            async def main():
                for i in range(request_batches_execution_count):
                    await asyncio.gather(*[x() for x in [cached_with_memoize] * concurrent_requests])
                    await asyncio.gather(*[x() for x in [cached_with_different_cache] * concurrent_requests])
                    await asyncio.sleep(delay_between_request_batches_millis / 1000)
        
                print("Memoize generated {} unique backend calls".format(unique_calls_under_memoize))
                print("Other cache generated {} unique backend calls".format(unique_calls_under_different_cache))
                predicted = (delay_between_request_batches_millis * request_batches_execution_count) // cached_value_ttl_millis
                print("Predicted (according to TTL) {} unique backend calls".format(predicted))
        
                # Printed:
                # Memoize generated 17 unique backend calls
                # Other cache generated 85 unique backend calls
                # Predicted (according to TTL) 17 unique backend calls
        
            if __name__ == "__main__":
                asyncio.get_event_loop().run_until_complete(main())
        
        
        Async cache storage
        -------------------
        
        Interface for cache storage allows you to fully harness benefits of asynchronous programming
        (see interface of `memoize.storage.CacheStorage`).
        
        
        Currently *memoize* provides only in-memory storage for cache values (internally at *RASP* we have others).
        If you want (for instance) Redis integration, you need to implement one (please contribute!)
        but *memoize* will optimally use your async implementation from the start.
Keywords: python cache tornado asyncio
Platform: Any
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Framework :: AsyncIO
Classifier: Intended Audience :: Developers
Description-Content-Type: text/x-rst
Provides-Extra: tornado
Provides-Extra: ujson
