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<!DOCTYPE html>
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<h1>Caching</h1>
<p>Caching can reduce the load on servers by storing the results of common
operations and serving the precomputed answers to clients. </p>
<p>For example, instead of retrieving data from database tables that rarely
change, you can store the values in-memory. Retrieving values from an
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<p>A cache can be created for multiple layers of the stack. </p>
<h2>Caching backends</h2>
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<p><a href="http://memcached.org/">memcached</a> is a common in-memory caching system.</p>
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<p><a href="http://redis.io/">Redis</a> is a key-value in-memory data store that can
easily be configured for caching with libraries such as
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<h2>Caching resources</h2>
<ul>
<li>
<p>"<a href="https://bjornjohansen.no/caching-varnish-or-nginx">Caching: Varnish or Nginx?</a>"
reviews some considerations such as SSL and SPDY support when choosing
reverse proxy Nginx or Varnish.</p>
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<p><a href="http://bizcoder.com/caching-is-hard-draw-me-a-picture">Caching is Hard, Draw me a Picture</a>
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.</p>
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<h2>Caching learning checklist</h2>
<p><i class="fa fa-check-square-o"></i>
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.</p>
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Leverage your existing in-memory data store already used for session data
to cache the results of those complex database queries.
A <a href="/task-queues.html">task queue</a> can often be used to precompute the results
on a regular basis and save them in the data store.</p>
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Incorporate a cache invalidation scheme so the precomputed results remain
accurate when served up to the user.</p>
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<a href="/">Full Stack Python</a> is an open book that explains
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2014
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