What are the best practices for caching in Python?

What are the best practices for caching in Python?

Caching in Python can significantly improve performance. Some best practices include using libraries like functools.lru_cache, implementing cache expiration, and choosing the right storage backend.

Can you explain how functools.lru_cache works?

Sure! functools.lru_cache is a decorator that caches the results of a function based on its input arguments. It uses a Least Recently Used (LRU) strategy to limit the cache size.

What are some common caching backends I can use?

Common caching backends include Redis, Memcached, and in-memory caching with Python dictionaries. Each has its own use cases and benefits.

How do I handle cache invalidation?

Cache invalidation can be handled by setting expiration times, using versioning, or implementing manual invalidation strategies when data changes.

Are there any performance metrics I should monitor?

Yes, you should monitor cache hit rates, latency, and memory usage to ensure your caching strategy is effective.