How To Implement Caching In Python?

March 28, 2023

Python cache is a not-so-secret hack that enables developers to streamline app development. Caching in Python lets us readily access frequently used data and curtail time spent on redundant operations.

Table of Contents

This article shares why Python caching is a must, the most common types, how to implement caching in Python, and some best practices to know before you turn to code.

Why Use Caching in Python?

Python cache is a magic spell to get the data you need from the language database or the web in a blink of an eye. Pre-stored database queries and web requests in a Python cache help developers code seamlessly.

Not to forget, the practice also slashes server load and improves performance by caching the results of computationally expensive functions. Further, Python caching helps developers wave goodbye to the hassle of serializing and de-serializing objects.

However, it is essential to use caching judiciously, as the process consumes extra memory and often proves ineffective. It is also needed to ensure the Python cache doesn’t entail stale or invalid data.

Types of Python Cache

Multiple caching techniques exist in Python that help developers better performance and save time. The most common ones are below:

Memory Caching – This type stores data in memory, typically in a data structure like a dictionary or a cache library like cachetools. Memory caching is fast yet relies upon available memory.

File-Based Caching – This type stores data in files, typically on disk or in a distributed file system like Hadoop’s HDFS. File-based caching is slower than the first but can work with larger-sized datasets.

Database Caching – This type stores data in a database, typically in key-value stores like Redis or Memcached. Database caching is also slower than memory type but more persistent and lasts across multiple sessions.

Function Caching – Specific to functional calls, this type eradicates the need to re-execute the function. The built-in functools.lru_cache decorator serves the purpose perfectly.

HTTP Caching – Solely for web apps, this type stores HTTP requests to minimize the need to access web servers. The HTTP headers Cache_Control and ETag work the best for HTTP caching.

How To Implement Python Caching?

There are different ways to implement Python cache depending upon the type. Here is a general step-by-step guide:

Step 1 – Identify Data or Function

You must know which data or function would be required repeatedly in the code before you turn the same into a cache to speed development.

Step 2 – Choose A Cache Library or Module

As per the type selected, you can choose from multiple Python caching libraries or modules, like cachetools, redis, memcached, or sqlite3.

Step 3 – Begin Implementation

With a library or module selected, the next step is to create a cache object and configure it desirably. Configuration includes defining the maximum cache size or expiration time.

Step 4 – Integrate Cache To Code

To use caching in Python, you must add it to the code to check if the data is cached. Else, generate it and store it in the cache. You can use a conditional statement to check whether or not data is cached.

Step 5 – Use Python Cache

Completed implementation? Now it is time to use the cached data to speed up development. Here again, conditional statements could come in handy to check whether data is cached or not.

Caching in Python Example

Here is an example of using the cachetools library to implement memory caching in Python:

from cachetools import cached, TTLCache

# create a cache object with a maximum size of 100 and an expiration time of 60 seconds

cache = TTLCache(maxsize=100, ttl=60)

# define a function to be cached

@cached(cache)

def expensive_function(argument):

    # perform expensive calculations here

    return result

In the example above, the cached decorator from the cachetools library is used to wrap expensive_function. The process cache the function’s output using the TTLCache object. The object will, by default, remove any cached data older than 60 seconds or if the maximum size reaches 100 items.

Best Practices for Caching in Python

1) Choose the appropriate caching strategy.

2) Identify and cache frequently accessed data.

3) Set appropriate cache expiration time.

4) Limit the size of the cache.

5) Use a consistent cache key format.

6) Monitor cache usage.

7) Test performance impact repeatedly.

The Final Word

Implementing a Python cache can cut development time drastically and help write codes efficiently. Remember, it is vital to stay ever-ready to automate as that’s the only way to turn pro and leverage maximum from any app development framework, be it Python or anything else.

Read Also: What Is A Static Method In Python?