How does database caching work in distributed systems?

1 Caching Basics

Database caching is a technique that stores a copy of frequently accessed data in a temporary storage area known as the cache. This is beneficial because memory retrieval is significantly faster than disk retrieval. When a request is made, the system first checks the cache for the data. If it's there, known as a cache hit, the data can be served immediately. If not, a cache miss occurs, and the system retrieves the data from the database and then stores it in the cache for future requests.

2 Distributed Systems

In distributed systems, where databases are spread across multiple servers or locations, caching becomes more complex but also more critical. Caching can occur at various levels, including local caches on each server, a distributed cache layer, or even in-client caching. The goal is to reduce latency and network traffic, which is especially important when servers are geographically dispersed. Properly implemented caching in distributed systems can significantly enhance performance and scalability.

3 Cache Invalidation

Cache invalidation is the process of updating or removing data in the cache that is no longer valid due to changes in the underlying database. It's crucial to ensure that users receive the most current information. Strategies for cache invalidation include time-to-live (TTL), where data expires after a certain period, or write-through caching, where updates to the database are simultaneously written to the cache. Choosing the right strategy depends on the specific needs of your application.

4 Consistency Models

Ensuring consistency between the cache and the database is a challenge in distributed systems. Consistency models like eventual consistency allow for some lag between data updates and cache refreshes, which can be acceptable for applications that don't require real-time accuracy. Strong consistency, on the other hand, ensures that all users see the same data at all times but can be more resource-intensive to maintain.

5 Cache Eviction Policies

When the cache becomes full, it's necessary to determine which items to remove to make room for new ones. This is where cache eviction policies come in. Least Recently Used (LRU) and First In, First Out (FIFO) are common policies that help maintain an effective cache size while attempting to keep the most relevant data available. The choice of eviction policy can greatly affect the performance of your caching strategy.