Distributed Caching Strategies with Redis and Spring Boot
Patterns and best practices for implementing distributed caching in Spring Boot microservices.
Overview
Effective caching is critical for high-performance backend systems. This post explores caching patterns in the Spring Boot + Redis ecosystem.
Cache Patterns
Cache-Aside
The most common pattern: the application checks cache first, then database. Simple and gives full control over cache population.
Write-Through
Data is written to cache and database simultaneously for consistency. Good for read-heavy workloads where cache freshness matters.
Write-Behind
Writes are buffered and asynchronously flushed to the database for maximum throughput. Ideal for write-heavy scenarios.
Key Takeaways
Choose cache pattern based on consistency vs performance trade-offs. Use TTLs strategically to prevent stale data. Monitor cache hit rates and adjust eviction policies.