Document databases handle caching by storing frequently accessed data in memory to reduce the need for repeated disk reads, improving performance. Most document databases, like MongoDB or CouchDB, include built-in caching mechanisms that automatically manage which data is kept in memory based on usage patterns. For example, MongoDB uses its WiredTiger storage engine, which maintains an internal cache configured to hold a portion of the working data set in RAM. This cache prioritizes frequently queried documents and indexes, allowing faster access compared to retrieving them from disk. The database typically uses algorithms like Least Recently Used (LRU) to determine which data to evict when the cache fills up.
Developers can also implement application-level caching strategies to complement the database’s built-in mechanisms. For instance, a common approach is to use an external caching layer like Redis or Memcached to store results of complex queries or frequently accessed documents. Suppose an application retrieves user profiles from a document database for every login request. By caching these profiles in Redis, subsequent requests can skip the database entirely, reducing latency. Some document databases support write-through caching, where data is simultaneously written to the cache and the database, ensuring consistency. However, this requires careful configuration to avoid stale data if updates occur outside the caching layer.
Cache invalidation and consistency are critical challenges. Document databases often provide time-to-live (TTL) settings to automatically expire cached data after a period, which works well for transient data like session information. For example, MongoDB’s TTL indexes can automatically remove documents after a specified duration, which aligns with cache expiration logic. However, ensuring real-time consistency between the cache and the database remains complex. Developers might use event-driven patterns, like change streams in MongoDB, to notify the caching layer when documents are updated, triggering cache refreshes. This balances performance gains with data accuracy, though it adds operational overhead.