A distributed cache is a system that stores data in memory across multiple servers, allowing applications to access frequently used information quickly. Unlike a traditional cache that resides on a single machine, a distributed cache spans multiple nodes, enabling scalability and fault tolerance. It works by keeping copies of data closer to the application layer, reducing the need to fetch it from slower backend sources like databases. For example, a web application might use a distributed cache to store user session data, product catalog details, or API responses, ensuring rapid access for users across different regions or servers.
In distributed databases, a distributed cache acts as a performance optimization layer. When a database is spread across multiple nodes (e.g., in a sharded or replicated setup), querying data can introduce latency due to network overhead or disk I/O. A distributed cache mitigates this by storing hot data—frequently accessed or recently used records—in memory. For instance, in a social media platform, user profile data might be cached to avoid repeated database lookups during high-traffic periods. When a query is made, the application first checks the cache. If the data is present (a “cache hit”), it’s returned immediately. If not (a “cache miss”), the database is queried, and the result is stored in the cache for future requests. Tools like Redis or Memcached are often used for this purpose, providing features like expiration policies to evict stale data.
Key considerations when using a distributed cache include consistency and invalidation. Since data might be updated in the database, the cache must reflect those changes to avoid serving outdated information. Techniques like write-through caching (updating the cache and database simultaneously) or time-to-live (TTL) settings help manage this. Additionally, distributed caches often partition data using strategies like consistent hashing to distribute load evenly. For example, in a distributed key-value store like Amazon ElastiCache, data is split across nodes based on hashed keys, ensuring scalability. By reducing database load and accelerating read operations, distributed caches play a critical role in building responsive, high-throughput distributed systems.