Data is cached everywhere, from the client-facing side to backend systems. Let's look at the many caching layers:
Caching Layers
- • Client Apps: Browsers cache HTTP responses. Server responses include caching directives in headers. Upon subsequent requests, browsers may serve cached data if still fresh.
- • Content Delivery Networks: CDNs cache static content like images, stylesheets, and JavaScript files. They serve cached content from locations closer to users, reducing latency and load times.
- • Load Balancers: Some load balancers cache frequently requested data. This allows serving responses without engaging backend servers, reducing load and response times.
- • Message Brokers: Systems like Kafka can cache messages on disk per a retention policy. Consumers then pull messages according to their own schedule.
- • Services: Individual services often employ caching to improve data retrieval speeds, first checking in-memory caches before querying databases. Services may also utilize disk caching for larger datasets.
- • Distributed Caches: Systems like Redis cache key-value pairs across services, providing faster read/write capabilities compared to traditional databases.
- • Full-text Search Engines: Platforms like Elasticsearch index data for efficient text search. This index is effectively a form of cache, optimized for quick text search retrieval.
- • Databases: There are specialized mechanisms to enhance performance, some of which include caching concepts:
Database Caching Mechanisms
- • Bufferpool: This is a cache within the database that holds copies of data pages. It allows for quick reads and writes to temporary storage in memory, reducing the need to access data from disk.
- • Materialized Views: They are similar to caches in that they store the results of computationally expensive queries. The database can return these precomputed results quickly, rather than recalculating them.
