How to implement distributed caching

Short answer

Distributed caching stores frequently accessed data across multiple cache nodes so reads hit the cache instead of the database. Use Redis Cluster or Memcached for horizontal scaling. Apply cache-aside or write-through patterns for consistency. For caching fundamentals, see how caching improves system performance.

Cache patterns

• Cache-aside: Application checks cache first, on miss loads from DB and writes to cache
• Write-through: Writes go to cache and DB simultaneously
• Write-behind: Writes go to cache first, asynchronously flushed to DB
• Refresh-ahead: Cache proactively refreshes before expiration

Eviction policies

• LRU: Evict least recently used items
• LFU: Evict least frequently used items
• TTL: Set expiration time per key

Redis Cluster setup

Redis Cluster shards data across multiple nodes using hash slots. It provides automatic failover when a node goes down. Each key maps to one of 16,384 hash slots distributed across nodes.

Tips

• Always set TTL to prevent stale data accumulation
• Use consistent hashing for cache node scaling without mass invalidation
• For CDN-level caching, see how CDNs speed up content delivery