How can I optimize performance using LRU caching?

How can I optimize performance using LRU caching?

To optimize performance with LRU caching, you can implement a cache that stores frequently accessed data, evicting the least recently used items when the cache reaches its limit.

What are the benefits of using LRU caching over other caching strategies?

LRU caching is efficient for workloads with temporal locality, as it keeps the most recently accessed items in memory, reducing access time.

Can you give me an example of how to implement LRU caching in Python?

Sure! You can use the collections.OrderedDict to create an LRU cache. Here's a simple implementation:

from collections import OrderedDict

class LRUCache:
    def __init__(self, capacity):
        self.cache = OrderedDict()
        self.capacity = capacity

    def get(self, key):
        if key not in self.cache:
            return -1
        else:
            self.cache.move_to_end(key)
            return self.cache[key]

    def put(self, key, value):
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.capacity:
            self.cache.popitem(last=False)

What are some common pitfalls to avoid when using LRU caching?

Common pitfalls include setting the cache size too small, which leads to frequent evictions, and not considering thread safety in multi-threaded applications.