Django utility for a memoization decorator that uses the Django cache framework.
- 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.
pip install django-cache-memoize
# 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.
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.
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:
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:
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()
By default the prefix becomes the name of the function. Consider:
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
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.
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 ...
Exact same functionality as hit_callable
except the obvious difference
that it gets called if it was not a cache hit.
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
.
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)
The cache_alias
argument allows you to use a cache other than the default.
# Given settings like:
# CACHES = {
# 'default': {...},
# 'other': {...},
# }
@cache_memoize(1000, cache_alias='other')
def myfunc(start, end):
return random.random()
When you want to "undo" some caching done, you simple call the function
again with the same arguments except you add .invalidate
to the function.
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, 200)
>>> 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:
# 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.
- Python 3.4, 3.5, 3.6, 3.7
- Django 1.11, 2.0, 2.1
Check out the tox.ini file for more up-to-date compatibility by test coverage.
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
.
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
:
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...
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))
The most basic thing is to clone the repo and run:
pip install -e ".[dev]"
tox
All code has to be formatted with Black
and the best tool for checking this is
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:
tox -e lint-py36
To install the therapist
pre-commit hook simply run:
therapist install
When you run therapist run
it will only check the files you've touched.
To run it for all files use:
therapist run --use-tracked-files
And to fix all/any issues run:
therapist run --use-tracked-files --fix