What are the differences between caching and memoization?

What are the differences between caching and memoization?

Caching and memoization are both optimization techniques, but they serve different purposes. Caching stores the results of expensive function calls and reuses them when the same inputs occur again, typically across different function calls. Memoization, on the other hand, is a specific form of caching that stores results of function calls based on their input parameters, usually within the same function call context.

Can you give me an example of each?

Sure! For caching, imagine a web application that fetches user data from a database. If the same user data is requested multiple times, caching stores that data so it doesn't have to be fetched again. For memoization, consider a recursive function that calculates Fibonacci numbers. By storing previously calculated Fibonacci values, memoization avoids redundant calculations for the same inputs.

When should I use caching over memoization?

Use caching when you want to optimize data retrieval across different function calls or sessions, especially for data that doesn't change often. Use memoization when you're dealing with pure functions where the same inputs will always yield the same outputs, and you want to optimize repeated calculations within a single function call.

Are there any downsides to using these techniques?

Yes, both techniques can consume memory. Caching can lead to stale data if not managed properly, while memoization can increase memory usage significantly if the input space is large. It's important to implement strategies for cache invalidation and to limit the size of memoization storage.