Using Memcached to Speed up Your Python Applications
Python is a great tool for processing data. Unfortunately, if you are dealing with large datasets, then you may find that your applications perform rather slowly. Using a cache to avoid having to reperform calculations or access a slow database repeatedly could massively improve the performance of your apps.
There are some useful caching tools built-in to python, but their use is limited to local datasets. If you are working with cloud data, then you may need something more powerful, such as memcached.
Memcached and Distributed Data
Memcached is an open source tool which is available for Linux, Windows and macOS. To make use of memcached, you will need to install it and then also install the client library for Python.
Once you have set it up, using memcached is quite easy. In essence, memcached works like a dictionary. It has keys and values, which are set when you update the cache, and that will expire after a pre-defined time (to ensure that the data in the cache is current). You can set the expiration time, in seconds, for the data in memcached to stay valid. After that time expires, the old data is removed from the cache. Depending on the nature of the data that you ar working with the expiration time could be as short as a few seconds, or it could be several hours. The important thing to remember is that the data should not be allowed to become stale. If the cache can hold data for longer than it takes to perform the calculations, then you should see a performance increase.
Why Flush Memcached?
Caches should be flushed for two reasons. Firstly, if the data becomes stale then the application won’t provide users with accurate information. The cache should be a relatively recent reflection of what is in the main database. Secondly, the cache cannot continue to grow and grow because eventually, the server will run out of memory.
Canonical vs Cache
The data in the main database in the cloud is known as the canonical data source. The data in the cache is simply a reflection of that. Sometimes, when you go to query the cache you might find that keys are missing because they have been flushed, and you will need to query the cache to update that. Your Python Memcache client will have a class that can do that. If you are using pyememcache, for example, there is a fallback class that allows you to refresh your cached data if the cache is empty.
Caching and Scalability
Caching is just one element of scaling your Python applications. If you have a database on a remote server, multiple cloud instances, and are working with distributed computing then you will need to optimize your code for that. The simple scripts you used while learning Python may not be robust enough to handle huge cloud applications. Caching is at the cornerstone of scalability, however, and can greatly speed up data-intensive web applications or apps that work over the Internet.