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
We will also learn about advanced patterns using Python
Memcached package is available for many platforms:
- For Linux, we can install it using
yum install memcachedor
apt-get install memcached. This script will install the
memcachedpackage 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
memcachedyourself by going to the official
memcached can be launched by calling the
Set and Get Cached Values Using Python
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
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(["127.0.0.1:11212"], debug=0) mcobject.set("some_key", "Some value") value = mc.get("some_key") mcobject.set("another_key", 3) mcobject.delete("another_key") mcobject.set("key", "1") mcobject.incr("key") mcobject.decr("key")
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.