Let's look at the many 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:
- 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.