Cache Algorithms

Cache Algorithms

Cache algorithms are essential techniques used in computer science to store and manage frequently accessed data efficiently. By keeping recently or frequently used data readily available, cache algorithms significantly improve system performance and reduce access time.

Overview

A cache is a hardware or software component that stores data so that future requests for that data can be served faster. The data stored in a cache might be the result of an earlier computation or a copy of data stored elsewhere. Cache algorithms determine which items to keep in the cache and which to evict when the cache is full.

Common Cache Eviction Policies

1. LRU (Least Recently Used)

  • Evicts the least recently accessed item when the cache is full
  • Assumes that data accessed recently is likely to be accessed again soon
  • Time Complexity: O(1) for both Get and Put operations
  • Implementation: Doubly-linked list + Hash map

2. LFU (Least Frequently Used)

  • Evicts the least frequently accessed item when the cache is full
  • Tracks the frequency of access for each item
  • Useful when access patterns favor frequently used items over recently used ones
  • Time Complexity: O(1) for both Get and Put operations
  • Implementation: Hash map + Frequency-based data structure

3. FIFO (First In, First Out)

  • Evicts the oldest item in the cache
  • Simple but may not be optimal for all access patterns

4. LIFO (Last In, First Out)

  • Evicts the most recently added item
  • Less common in practice

Key Concepts

Cache Hit vs Cache Miss

  • Cache Hit: When requested data is found in the cache
  • Cache Miss: When requested data is not in the cache and must be fetched from the slower storage

Trade-offs

  • Memory vs Performance: Larger caches improve hit rates but consume more memory
  • Complexity vs Efficiency: More sophisticated algorithms may provide better hit rates but require more complex implementation

Applications

  • Web Browsers: Caching web pages and resources
  • Operating Systems: Page replacement in virtual memory
  • Databases: Query result caching
  • CDNs: Content delivery networks
  • CPU Caching: L1, L2, L3 caches in processors
  • API Rate Limiting: Tracking request frequencies

Performance Metrics

  • Hit Rate: Percentage of requests served from cache
  • Miss Rate: Percentage of requests requiring data fetch
  • Eviction Rate: How often items are removed from cache
  • Latency: Time taken to serve a request

Explore the cache algorithms below to understand their implementations and use cases!