Use of Memory Caching in Python

  1. Install Memcached Module
  2. Set and Get Cached Values Using Python
Use of Memory Caching in Python

When writing Python applications, caching is important. Using a cache to avoid recomposing data or accessing a slow database can boost your performance.

In Python, we can use the memcached module to include memory caching in our scripts. This article will discuss preparing memory caching operations and the primary memcached usage.

We will also learn about advanced patterns using Python cache and set.

Install Memcached Module

The Memcached package is available for many platforms:

  • For Linux, we can install it using yum install memcached or apt-get install memcached. This script will install the memcached package from a pre-built package.
  • For macOS, the easiest choice is to utilize Homebrew. After installing the Homebrew package manager, type brew install memcached.
  • For Windows, you would have to compile memcached yourself by going to the official memcached website.

Once installed, memcached can be launched by calling the memcached command:


Set and Get Cached Values Using Python

The memcached package is straightforward to grasp if you have never used it. In addition, it gives access to a sizable vocabulary over the network.

This dictionary differs from a traditional Python dictionary in a few ways, mainly:

  • Values and keys have to be in bytes data type
  • Values and keys are automatically deleted after a given expiration time

Therefore, set and get are the two fundamental procedures for dealing with memcached. They are employed to give a key a value or to get a value from a key, as we would have imagined.

The code below demonstrates how to utilize memcached as a network-distributed cache in your Python applications:

import memcache

mcobject = memcache.Client([""], debug=0)

mcobject.set("some_key", "Some value")
value = mc.get("some_key")

mcobject.set("another_key", 3)

mcobject.set("key", "1")

The memcached network protocol is straightforward. Due to its lightning-fast implementation, storing data that would otherwise take a long time to compute or get from the canonical source of data is advantageous.

While straightforward, this example allows storing key-value tuples across the network and accessing them through multiple, distributed, running copies of your application.

This process is simplistic yet powerful. And it’s a significant first step toward optimizing your application.

Marion Paul Kenneth Mendoza avatar Marion Paul Kenneth Mendoza avatar

Marion specializes in anything Microsoft-related and always tries to work and apply code in an IT infrastructure.


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