Comprehensive Guide to Effective Caching Strategies and Best Practices

Before using a cache you must confirm that your project truly needs it; typical indicators are high CPU consumption (e.g., frequent regex processing) and saturated database connection pools.

Choosing the right cache – In‑process caches include ConcurrentHashMap (good for small, stable data), LRUMap (lightweight eviction), Ehcache (heavyweight, persistent, clustered), Guava Cache (lightweight, widely used), and Caffeine (high hit‑rate and performance). For distributed caches, the article compares MemCache, Redis, and Tair, highlighting their strengths and suitable scenarios.

Multi‑level caching – Combine a local cache (e.g., Caffeine) with a distributed cache (e.g., Redis) to reduce latency and improve availability. The typical flow is: check Caffeine → if miss, check Redis → if miss, query MySQL and populate both caches.

Cache update strategies – Two common patterns are (1) delete cache then update the database, which can cause stale reads, and (2) update the database then delete the cache (recommended), which minimizes inconsistency but may still need careful handling of race conditions.

Common cache pitfalls – Cache penetration (queries for non‑existent keys), cache breakdown (hot key expiration causing massive DB load), and cache avalanche (simultaneous expiration of many keys). Mitigations include caching null results, using Bloom filters, applying distributed locks, asynchronous loading, randomizing TTLs, and employing multi‑level caches.

Cache pollution – Modifying objects retrieved from the cache without proper synchronization can corrupt cached data; the solution is to avoid mutating cached objects directly and enforce strict code reviews and testing.

Serialization issues – Incompatible or evolving object schemas can cause deserialization failures. Recommendations: comprehensive testing across JVM versions, careful field ordering, using versioned keys, and employing double‑write strategies during migrations.

GC tuning – Heavy local caching can increase GC pause times. Advice includes monitoring GC metrics, using CMS with pre‑remark YGC, or switching to G1 with appropriate pause‑time goals.

Cache monitoring – Track metrics such as hit/miss rates, expiration, and failures (e.g., via Cat) to fine‑tune cache size and TTL.

Choosing a good framework – The article recommends JetCache (JSR‑107 compliant) combined with Spring Cache, Cat for monitoring, and Rhino for circuit‑breaking, providing a clean separation between business logic and caching concerns.

Conclusion – Effective caching requires understanding workload characteristics, selecting suitable in‑process and distributed solutions, handling updates safely, and continuously monitoring performance; a solid framework simplifies these tasks.

//jdk 7
class A{
    int a;
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}
//jdk 8
class A{
    int b;
    int a;
}