# Caching

## Caching

**Can reduce the load on servers by storing the results of common operations and serving the precomputed answers to clients.**

For example, instead of retrieving data from database tables that rarely change, you can store the values in-memory.

A cache can be created **for multiple layers of the stack**.

## Caching backends

* [memcached](http://memcached.org/) is a common in-memory caching system.
* [Redis](http://redis.io/) is a key-value in-memory data store that can easily be configured for caching with libraries such as [django-redis-cache](https://github.com/sebleier/django-redis-cache) and the similarly-named, but separate project [django-redis](https://github.com/niwinz/django-redis).

## Caching resources

* [Caching at Reddit](https://redditblog.com/2017/01/17/caching-at-reddit/) is a wonderful in-depth post that goes into detail on how they handle caching their Python web app for [billions of pageviews each month](http://expandedramblings.com/index.php/reddit-stats/).
* "[Caching: Varnish or Nginx?](https://bjornjohansen.no/caching-varnish-or-nginx)" reviews some considerations such as SSL and SPDY support when choosing reverse proxy Nginx or Varnish.
* [Caching is Hard, Draw me a Picture](http://bizcoder.com/caching-is-hard-draw-me-a-picture) has diagrams of how web request caching layers work. The post is relevant reading even though the author is describing his Microsoft code as the impetus for writing the content.
* While caching is a useful technique in many situations, it's important to also note that there are [downsides to caching](https://msol.io/blog/tech/2015/09/05/youre-probably-wrong-about-caching/) that many developers fail to take into consideration.
* [Caching at Reddit](https://redditblog.com/2017/1/17/caching-at-reddit/) covers monitoring, tuning and scaling for the very high scale[Reddit.com](https://www.reddit.com/) website.
* [Mastering HTTP caching](https://blog.fortrabbit.com/mastering-http-caching) provides more advanced advice on caching dynamic as well as static content via CDNs and other configurations.

## Caching learning checklist

1. **Analyze your web application for the slowest parts**. It's likely there are complex database queries that can be **precomputed and stored in an in-memory data store**.
2. Leverage your existing in-memory data store already used for session data to cache the results of those complex database queries. A [**task queue**](https://www.fullstackpython.com/task-queues.html) **can often be used to precompute the results on a regular basis and save them in the data store**.
3. **Incorporate a cache invalidation scheme** so the precomputed results remain accurate when served up to the user.


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