Metadata-Version: 2.1
Name: django-cache-memoize
Version: 0.2.0
Summary: Django utility for a memoization decorator that uses the Django cache framework.
Home-page: https://github.com/peterbe/django-cache-memoize
Author: Peter Bengtsson
Author-email: mail@peterbe.com
License: MPL-2.0
Description: ====================
        django-cache-memoize
        ====================
        
        * License: MPL 2.0
        
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        Django utility for a memoization decorator that uses the Django cache framework.
        
        For versions of Python and Django, check out `the tox.ini file`_.
        
        .. _`the tox.ini file`: https://github.com/peterbe/django-cache-memoize/blob/master/tox.ini
        
        Key Features
        ------------
        
        * Memoized function calls can be invalidated.
        
        * Works with non-trivial arguments and keyword arguments
        
        * Insight into cache hits and cache missed with a callback.
        
        * Ability to use as a "guard" for repeated execution when storing the function
          result isn't important or needed.
        
        
        Installation
        ============
        
        .. code-block:: python
        
            pip install django-cache-memoize
        
        Usage
        =====
        
        .. code-block:: python
        
            # Import the decorator
            from cache_memoize import cache_memoize
        
            # Attach decorator to cacheable function with a timeout of 100 seconds.
            @cache_memoize(100)
            def expensive_function(start, end):
                return random.randint(start, end)
        
            # Just a regular Django view
            def myview(request):
                # If you run this view repeatedly you'll get the same
                # output every time for 100 seconds.
                return http.HttpResponse(str(expensive_function(0, 100)))
        
        
        The caching uses `Django's default cache framework`_. Ultimately, it calls
        ``django.core.cache.cache.set(cache_key, function_out, expiration)``.
        So if you have a function that returns something that can't be pickled and
        cached it won't work.
        
            For cases like this, Django exposes a simple, low-level cache API. You can
            use this API to store objects in the cache with any level of granularity
            you like. You can cache any Python object that can be pickled safely:
            strings, dictionaries, lists of model objects, and so forth. (Most
            common Python objects can be pickled; refer to the Python documentation
            for more information about pickling.)
        
        See `documentation`_.
        
        
        .. _`Django's default cache framework`: https://docs.djangoproject.com/en/1.11/topics/cache/
        .. _`documentation`: https://docs.djangoproject.com/en/1.11/topics/cache/#the-low-level-cache-api
        
        
        Example Usage
        =============
        
        This blog post: `How to use django-cache-memoize`_
        
        It demonstrates similarly to the above Usage example but with a little more
        detail. In particular it demonstrates the difference between *not* using
        ``django-cache-memoize`` and then adding it to your code after.
        
        .. _`How to use django-cache-memoize`: https://www.peterbe.com/plog/how-to-use-django-cache-memoize
        
        Advanced Usage
        ==============
        
        ``args_rewrite``
        ~~~~~~~~~~~~~~~~
        
        Internally the decorator rewrites every argument and keyword argument to
        the function it wraps into a concatenated string. The first thing you
        might want to do is help the decorator rewrite the arguments to something
        more suitable as a cache key string. For example, suppose you have instances
        of a class whose ``__str__`` method doesn't return a unique value. For example:
        
        .. code-block:: python
        
            class Record(models.Model):
                name = models.CharField(max_length=100)
                lastname = models.CharField(max_length=100)
                friends = models.ManyToManyField(SomeOtherModel)
        
                def __str__(self):
                    return self.name
        
            # Example use:
            >>> record = Record.objects.create(name='Peter', lastname='Bengtsson')
            >>> print(record)
            Peter
            >>> record2 = Record.objects.create(name='Peter', lastname='Different')
            >>> print(record2)
            Peter
        
        This is a contrived example, but basically *you know* that the ``str()``
        conversion of certain arguments isn't safe. Then you can pass in a callable
        called ``args_rewrite``. It gets the same positional and keyword arguments
        as the function you're decorating. Here's an example implementation:
        
        .. code-block:: python
        
            from cache_memoize import cache_memoize
        
            def count_friends_args_rewrite(record):
                # The 'id' is always unique. Use that instead of the default __str__
                return record.id
        
            @cache_memoize(100, args_rewrite=count_friends_args_rewrite)
            def count_friends(record):
                # Assume this is an expensive function that can be memoize cached.
                return record.friends.all().count()
        
        
        ``prefix``
        ~~~~~~~~~~
        
        By default the prefix becomes the name of the function. Consider:
        
        .. code-block:: python
        
            from cache_memoize import cache_memoize
        
            @cache_memoize(10, prefix='randomness')
            def function1():
                return random.random()
        
            @cache_memoize(10, prefix='randomness')
            def function2():  # different name, same arguments, same functionality
                return random.random()
        
            # Example use
            >>> function1()
            0.39403406043780986
            >>> function1()
            0.39403406043780986
            >>> # ^ repeated of course
            >>> function2()
            0.39403406043780986
            >>> # ^ because the prefix was forcibly the same, the cache key is the same
        
        
        ``hit_callable``
        ~~~~~~~~~~~~~~~~
        
        If set, a function that gets called with the original argument and keyword
        arguments **if** the cache was able to find and return a cache hit.
        For example, suppose you want to tell your ``statsd`` server every time
        there's a cache hit.
        
        .. code-block:: python
        
            from cache_memoize import cache_memoize
        
            def _cache_hit(user, **kwargs):
                statsdthing.incr(f'cachehit:{user.id}', 1)
        
            @cache_memoize(10, hit_callable=_cache_hit)
            def calculate_tax(user, tax=0.1):
                return ...
        
        
        ``miss_callable``
        ~~~~~~~~~~~~~~~~~
        
        Exact same functionality as ``hit_callable`` except the obvious difference
        that it gets called if it was *not* a cache hit.
        
        ``store_result``
        ~~~~~~~~~~~~~~~~
        
        This is useful if you have a function you want to make sure only gets called
        once per timeout expiration but you don't actually care that much about
        what the function return value was. Perhaps because you know that the
        function returns something that would quickly fill up your ``memcached`` or
        perhaps you know it returns something that can't be pickled. Then you
        can set ``store_result`` to ``False``. This is equivalent to your function
        returning ``True``.
        
        .. code-block:: python
        
            from cache_memoize import cache_memoize
        
            @cache_memoize(1000, store_result=False)
            def send_tax_returns(user):
                # something something time consuming
                ...
                return some_none_pickleable_thing
        
            def myview(request):
                # View this view as much as you like the 'send_tax_returns' function
                # won't be called more than once every 1000 seconds.
                send_tax_returns(request.user)
        
        ``cache_exceptions``
        ~~~~~~~~~~~~~~~~~~~~
        
        This is useful if you have a function that can raise an exception as valid
        result. If the cached function raises any of specified exceptions is the
        exception cached and raised as normal. Subsequent cached calls will
        immediately re-raise the exception and the function will not be executed.
        ``cache_exceptions`` accepts an Exception or a tuple of Exceptions.
        
        
        This option allows you to cache said exceptions like any other result.
        Only exceptions raised from the list of classes provided as cache_exceptions
        are cached, all others are propagated immediately.
        
        .. code-block:: python
        
            >>> from cache_memoize import cache_memoize
        
            >>> class InvalidParameter(Exception):
            ...     pass
        
            >>> @cache_memoize(1000, cache_exceptions=(InvalidParameter, ))
            ... def run_calculations(parameter):
            ...     # something something time consuming
            ...     raise InvalidParameter
        
            >>> run_calculations(1)
            Traceback (most recent call last):
            ...
            InvalidParameter
        
            # run_calculations will now raise InvalidParameter immediately
            # without running the expensive calculation
            >>> run_calculations(1)
            Traceback (most recent call last):
            ...
            InvalidParameter
        
        ``cache_alias``
        ~~~~~~~~~~~~~~~
        
        The ``cache_alias`` argument allows you to use a cache other than the default.
        
        .. code-block:: python
        
            # Given settings like:
            # CACHES = {
            #     'default': {...},
            #     'other': {...},
            # }
        
            @cache_memoize(1000, cache_alias='other')
            def myfunc(start, end):
                return random.random()
        
        
        Cache invalidation
        ~~~~~~~~~~~~~~~~~~
        
        When you want to "undo" some caching done, you simply call the function
        again with the same arguments except you add ``.invalidate`` to the function.
        
        .. code-block:: python
        
            from cache_memoize import cache_memoize
        
            @cache_memoize(10)
            def expensive_function(start, end):
                return random.randint(start, end)
        
            >>> expensive_function(1, 100)
            65
            >>> expensive_function(1, 100)
            65
            >>> expensive_function(100, 200)
            121
            >>> exensive_function.invalidate(1, 100)
            >>> expensive_function(1, 100)
            89
            >>> expensive_function(100, 200)
            121
        
        An "alias" of doing the same thing is to pass a keyword argument called
        ``_refresh=True``. Like this:
        
        .. code-block:: python
        
            # Continuing from the code block above
            >>> expensive_function(100, 200)
            121
            >>> expensive_function(100, 200, _refresh=True)
            177
            >>> expensive_function(100, 200)
            177
        
        There is no way to clear more than one cache key. In the above example,
        you had to know the "original arguments" when you wanted to invalidate
        the cache. There is no method "search" for all cache keys that match a
        certain pattern.
        
        
        Compatibility
        =============
        
        * Python 3.8, 3.9, 3.10 & 3.11
        
        * Django 3.2, 4.1 & 4.2
        
        Check out the `tox.ini`_ file for more up-to-date compatibility by
        test coverage.
        
        .. _`tox.ini`: https://github.com/peterbe/django-cache-memoize/blob/master/tox.ini
        
        Prior Art
        =========
        
        History
        ~~~~~~~
        
        `Mozilla Symbol Server`_ is written in Django. It's a web service that
        sits between C++ debuggers and AWS S3. It shuffles symbol files in and out of
        AWS S3. Symbol files are for C++ (and other compiled languages) what
        sourcemaps are for JavaScript.
        
        This service gets a LOT of traffic. The download traffic (proxying requests
        for symbols in S3) gets about ~40 requests per second. Due to the nature
        of the application most of these GETs result in a 404 Not Found but instead
        of asking AWS S3 for every single file, these lookups are cached in a
        highly configured `Redis`_ configuration. This Redis cache is also connected
        to the part of the code that uploads new files.
        
        New uploads are arriving as zip file bundles of files, from Mozilla's build
        systems, at a rate of about 600MB every minute, each containing on average
        about 100 files each. When a new upload comes in we need to quickly be able
        find out if it exists in S3 and this gets cached since often the same files
        are repeated in different uploads. But when a file does get uploaded into S3
        we need to quickly and confidently invalidate any local caches. That way you
        get to keep a really aggressive cache without any stale periods.
        
        This is the use case ``django-cache-memoize`` was built for and tested in.
        It was originally written for Python 3.6 in Django 1.11 but when
        extracted, made compatible with Python 2.7 and as far back as Django 1.8.
        
        ``django-cache-memoize`` is also used in `SongSear.ch`_ to cache short
        queries in the autocomplete search input. All autocomplete is done by
        Elasticsearch, which is amazingly fast, but not as fast as ``memcached``.
        
        
        .. _`Mozilla Symbol Server`: https://symbols.mozilla.org
        .. _`Redis`: https://redis.io/
        .. _`SongSear.ch`: https://songsear.ch
        
        
        "Competition"
        ~~~~~~~~~~~~~
        
        There is already `django-memoize`_ by `Thomas Vavrys`_.
        It too is available as a memoization decorator you use in Django. And it
        uses the default cache framework as a storage. It used ``inspect`` on the
        decorated function to build a cache key.
        
        In benchmarks running both ``django-memoize`` and ``django-cache-memoize``
        I found ``django-cache-memoize`` to be **~4 times faster** on average.
        
        Another key difference is that ``django-cache-memoize`` uses ``str()`` and
        ``django-memoize`` uses ``repr()`` which in certain cases of mutable objects
        (e.g. class instances) as arguments the caching will not work. For example,
        this does *not* work in ``django-memoize``:
        
        .. code-block:: python
        
            from memoize import memoize
        
            @memoize(60)
            def count_user_groups(user):
                return user.groups.all().count()
        
            def myview(request):
                # this will never be memoized
                print(count_user_groups(request.user))
        
        However, this works...
        
        .. code-block:: python
        
            from cache_memoize import cache_memoize
        
            @cache_memoize(60)
            def count_user_groups(user):
                return user.groups.all().count()
        
            def myview(request):
                # this *will* work as expected
                print(count_user_groups(request.user))
        
        
        .. _`django-memoize`: http://pythonhosted.org/django-memoize/
        .. _`Thomas Vavrys`: https://github.com/tvavrys
        
        
        Development
        ===========
        
        The most basic thing is to clone the repo and run:
        
        .. code-block:: shell
        
            pip install -e ".[dev]"
            tox
        
        
        Code style is all black
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        All code has to be formatted with `Black <https://pypi.org/project/black/>`_
        and the best tool for checking this is
        `therapist <https://pypi.org/project/therapist/>`_ since it can help you run
        all, help you fix things, and help you make sure linting is passing before
        you git commit. This project also uses ``flake8`` to check other things
        Black can't check.
        
        To check linting with ``tox`` use:
        
        .. code:: bash
        
            tox -e lint-py36
        
        To install the ``therapist`` pre-commit hook simply run:
        
        .. code:: bash
        
            therapist install
        
        When you run ``therapist run`` it will only check the files you've touched.
        To run it for all files use:
        
        .. code:: bash
        
            therapist run --use-tracked-files
        
        And to fix all/any issues run:
        
        .. code:: bash
        
            therapist run --use-tracked-files --fix
        
Keywords: django,memoize,cache,decorator
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Web Environment :: Mozilla
Classifier: Framework :: Django
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Internet :: WWW/HTTP
Requires-Python: >=3.8
Provides-Extra: dev