django-cache-memoize

==================== 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