Caching is an essential optimization technique for improving the performance of any software system. Understanding how caching works is a fundamental step toward incorporating it effectively in your applications.

In this video course, you learned:

What the different caching strategies are and how they work
How to use Python’s @lru_cache decorator
How to create a new decorator to extend the functionality of @lru_cache
How to measure your code’s runtime using the time module
What recursion is and how to solve a problem using it

The next step to implementing different caching strategies in your applications is looking at the cachetools module. This library provides several collections and decorators covering some of the most popular caching strategies that you can start using right away.

For more information on the concepts covered in this course, check out:

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00:00 In the previous lesson, I showed you how to augment the LRU cache to add time-based expiration. In this lesson, I’ll summarize the course and point you at some areas of further investigation.

00:13 You’ve seen how caching can make a big performance difference speed-wise. In fact, you saw a six order of magnitude difference when calculating the first thirty-six values of the Fibonacci sequence. This speed-up comes at the cost of memory.

00:26 Life is all about trade-offs. Early on in the course, I showed you several different caching policies but focused on LRU, the Least Recently Used policy.