Cache Optimization and Distributed Locking in High-Concurrency Systems

This article discusses the critical role of caching in high-concurrency web applications, particularly for large-scale websites facing massive traffic during peak events like shopping festivals or back-to-school seasons. Without proper caching, database overload can cause system crashes.

The author demonstrates a practical approach using Spring Boot, starting with a simple controller that retrieves user data from a database. Using JMeter for load testing, the initial setup shows a throughput of 421 requests per second under 2000 concurrent requests in one second.

To optimize performance, the article introduces a basic HashMap-based cache implementation. The cache-first approach checks for data in memory before querying the database, significantly improving throughput. However, this local caching approach has limitations in distributed environments where multiple application instances would maintain separate caches, leading to data inconsistency.

The solution involves using Redis as a distributed cache. The article provides step-by-step instructions for setting up Redis using Docker, including configuration files and container deployment. It then shows how to integrate Redis with Spring Boot applications using the spring-boot-starter-data-redis dependency and StringRedisTemplate for cache operations.

Several cache-related challenges are addressed: cache penetration (repeated queries for non-existent data), cache avalanche (simultaneous cache expiration), and cache breakdown (hot key expiration under high concurrency). The article focuses on solving cache breakdown using distributed locking mechanisms.

Initially, a synchronized block is used for locking, but this only works in single-instance deployments. For distributed systems, Redis-based distributed locking is implemented using the SETNX command. The article explains the atomic nature of this operation and how it prevents multiple instances from simultaneously accessing the database.

The implementation evolves through several iterations, addressing issues like deadlock prevention through lock expiration, ensuring only the lock owner can release it, and using Lua scripts to prevent race conditions during lock release. Finally, the article introduces Redisson, a Redis-based Java in-memory data grid that simplifies distributed locking implementation.

Redisson provides automatic lock expiration, a watchdog mechanism that extends lock timeouts, and built-in support for read-write locks. The article demonstrates both exclusive write locks and shared read locks, ensuring data consistency while allowing concurrent reads.

The article concludes by discussing cache consistency challenges, comparing write-through and write-behind patterns, and recommending best practices including setting expiration times for all cached data and using Redisson's read-write locks to ensure atomic write operations.