What is the difference between memoization and caching?

What is the difference between memoization and caching?

Memoization is a specific optimization technique that stores the results of expensive function calls and returns the cached result when the same inputs occur again. Caching, on the other hand, is a broader concept that involves storing data to speed up future requests, which can include storing results of function calls, database queries, or even web pages.

Can you give me an example of memoization in Python?

Sure! Here's a simple example of memoization in Python using a decorator:

def memoize(func):
    cache = {}
    def wrapper(*args):
        if args in cache:
            return cache[args]
        result = func(*args)
        cache[args] = result
        return result
    return wrapper

@memoize
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

How does caching improve performance in web applications?

Caching improves performance by storing frequently accessed data in a location that can be retrieved quickly, reducing the need to fetch data from slower sources like databases or external APIs. This leads to faster response times and reduced load on servers.

Are there any downsides to using caching?

Yes, there are some downsides to caching. It can lead to stale data if the cache is not updated properly, and it requires additional memory to store the cached data. Additionally, managing cache invalidation can add complexity to your application.