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https://github.com/noripyt/django-cachalot

No effort, no worry, maximum performance.
https://github.com/noripyt/django-cachalot

cache django performance python

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No effort, no worry, maximum performance.

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README 

        
Django Cachalot

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



Caches your Django ORM queries and automatically invalidates them.



Documentation: http://django-cachalot.readthedocs.io



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.. image:: http://img.shields.io/pypi/v/django-cachalot.svg?style=flat-square&maxAge=3600

   :target: https://pypi.python.org/pypi/django-cachalot



.. image:: https://img.shields.io/pypi/pyversions/django-cachalot

    :target: https://django-cachalot.readthedocs.io/en/latest/



.. image:: https://github.com/noripyt/django-cachalot/actions/workflows/ci.yml/badge.svg

   :target: https://github.com/noripyt/django-cachalot/actions/workflows/ci.yml



.. image:: http://img.shields.io/coveralls/noripyt/django-cachalot/master.svg?style=flat-square&maxAge=3600

   :target: https://coveralls.io/r/noripyt/django-cachalot?branch=master



.. image:: http://img.shields.io/scrutinizer/g/noripyt/django-cachalot/master.svg?style=flat-square&maxAge=3600

   :target: https://scrutinizer-ci.com/g/noripyt/django-cachalot/



.. image:: https://img.shields.io/discord/773656139207802881

    :target: https://discord.gg/WFGFBk8rSU



----



Table of Contents:



- Quickstart

- Usage

- Hacking

- Benchmark

- Third-Party Cache Comparison

- Discussion



Quickstart

----------



Cachalot officially supports Python 3.7-3.11 and Django 3.2, 4.1, 4.2, 5.0, 5.1 with the databases PostgreSQL, SQLite, and MySQL.



Note: an upper limit on Django version is set for your safety. Please do not ignore it.



Usage

-----



#. ``pip install django-cachalot``

#. Add ``'cachalot',`` to your ``INSTALLED_APPS``

#. If you use multiple servers with a common cache server,

   `double check their clock synchronisation `_

#. If you modify data outside Django

   – typically after restoring a SQL database –,

   use the `manage.py command `_

#. Be aware of `the few other limits `_

#. If you use

   `django-debug-toolbar `_,

   you can add ``'cachalot.panels.CachalotPanel',``

   to your ``DEBUG_TOOLBAR_PANELS``

#. Enjoy!



Hacking

-------



To start developing, install the requirements

and run the tests via tox.



Make sure you have the following services:



* Memcached

* Redis

* PostgreSQL

* MySQL



For setup:



#. Install: ``pip install -r requirements/hacking.txt``

#. For PostgreSQL: ``CREATE ROLE cachalot LOGIN SUPERUSER;``

#. Run: ``tox --current-env`` to run the test suite on your current Python version.

#. You can also run specific databases and Django versions: ``tox -e py38-django3.1-postgresql-redis``



Benchmark

---------



Currently, benchmarks are supported on Linux and Mac/Darwin.

You will need a database called "cachalot" on MySQL and PostgreSQL.

Additionally, on PostgreSQL, you will need to create a role

called "cachalot." You can also run the benchmark, and it'll raise

errors with specific instructions for how to fix it.



#. Install: ``pip install -r requirements/benchmark.txt``

#. Run: ``python benchmark.py``



The output will be in benchmark/TODAY'S_DATE/



TODO Create Docker-compose file to allow for easier running of data.



Third-Party Cache Comparison

----------------------------



There are three main third party caches: cachalot, cache-machine, and cache-ops. Which do you use? We suggest a mix:



TL;DR Use cachalot for cold or modified <50 times per minutes (Most people should stick with only cachalot since you

most likely won't need to scale to the point of needing cache-machine added to the bowl). If you're an enterprise that

already has huge statistics, then mixing cold caches for cachalot and your hot caches with cache-machine is the best

mix. However, when performing joins with ``select_related`` and ``prefetch_related``, you can

get a nearly 100x speed up for your initial deployment.



Recall, cachalot caches THE ENTIRE TABLE. That's where its inefficiency stems from: if you keep updating the records,

then the cachalot constantly invalidates the table and re-caches. Luckily caching is very efficient, it's just the cache

invalidation part that kills all our systems. Look at Note 1 below to see how Reddit deals with it.



Cachalot is more-or-less intended for cold caches or "just-right" conditions. If you find a partition library for

Django (also authored but work-in-progress by `Andrew Chen Wang`_), then the caching will work better since sharding

the cold/accessed-the-least records aren't invalidated as much.



Cachalot is good when there are <50 modifications per minute on a hot cached table. This is mostly due to cache invalidation. It's the same with any cache,

which is why we suggest you use cache-machine for hot caches. Cache-machine caches individual objects, taking up more in the memory store but

invalidates those individual objects instead of the entire table like cachalot.



Yes, the bane of our entire existence lies in cache invalidation and naming variables. Why does cachalot suck when

stuck with a huge table that's modified rapidly? Since you've mixed your cold (90% of) with your hot (10% of) records,

you're caching and invalidating an entire table. It's like trying to boil 1 ton of noodles inside ONE pot instead of

100 pots boiling 1 ton of noodles. Which is more efficient? The splitting up of them.



Note 1: My personal experience with caches stems from Reddit's: https://web.archive.org/web/20210803213621/https://redditblog.com/2017/01/17/caching-at-reddit/



Note 2: Technical comparison: https://django-cachalot.readthedocs.io/en/latest/introduction.html#comparison-with-similar-tools



Discussion

----------



Help? Technical chat? `It's here on Discord `_.



Legacy chats:



- https://gitter.im/django-cachalot/Lobby

- https://join.slack.com/t/cachalotdjango/shared_invite/zt-dd0tj27b-cIH6VlaSOjAWnTG~II5~qw



.. _Andrew Chen Wang: https://github.com/Andrew-Chen-Wang



.. image:: https://raw.github.com/noripyt/django-cachalot/master/django-cachalot.jpg