Python Memcached

Make Python Memcached super easy with Redis Enterprise

Using Memcached with Python

In order to use Memcached with Python you will need a Python Memcached client. In the following sections, we will demonstrate the use of bmemcached, a pure Python module (thread-safe) to access Memcached via its binary protocol with SASL auth support.

Installing bmemcached

bememcached’s installation instructions are given in the “Installing” section of its README file. Use pip to install bmemcached:

$ sudo pip install python-binary-memcached

Opening a Connection to Memcached Using bmemcached

The following code creates a connection to Memcached using MemJS:

import bmemcached

mc = bmemcached.Client('hostname:port', 'username', 'password')

To adapt this example to your code, make sure that you replace the following values with those of your bucket:

Reading and Writing Data with bmemcached

Once connected to Memcached, you can start reading and writing data. The following code snippet writes the value bar to the Memcached key foo, reads it back, and prints it:

# open a connection to Memcached

mc.set('foo', 'bar')
value = r.get('foo')

The output of the above code should be:

$ python

Redis Enterprise enables running Memcached buckets in a highly available and auto-scalable manner, with predictable top performance.

Redis Enterprise Software lets you install an enterprise grade Memcached cluster in your environment of choice, whether an on-premises data-center or your preferred cloud platform. It gives you full control of your data and configuration – no clustering or sharding knowledge required!

Memcached Cloud is a fully managed cloud service for hosting and running Memcached datasets in a highly available and scalable manner, with predictable and stable top performance. It provides a storage engine for standard Memcached, as well as in-memory replication and instant auto-failover within the same data center or across data centers. The service completely frees developers from dealing with nodes, clusters, scaling, data persistence issues or failure recovery.

For more information on using Redis’ products and services with Python please see the Howto page.