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This article explains how to build a two‑level cache architecture using Caffeine as a local cache and Redis as a remote cache in a Spring Boot project, covering manual implementation, Spring cache annotations, and a custom AOP‑based solution while discussing advantages, consistency challenges, and best‑practice code examples.
Learn how to implement a two‑level caching architecture in Spring Boot using Caffeine as a local cache and Redis as a remote cache, covering manual implementations, annotation‑driven approaches with @Cacheable/@CachePut/@CacheEvict, and a custom @DoubleCache annotation to minimize code intrusion while improving response times.
This article explains how to design and implement a two‑level caching architecture in Spring Boot by combining a local Caffeine cache with a remote Redis cache, covering manual approaches, annotation‑driven management with Spring Cache, and a custom AOP solution to minimize code intrusion.
An overview of two-level cache architecture explains why local in-memory caches are needed, outlines essential cache features, compares implementations using ConcurrentHashMap, Guava Cache, Caffeine, and Ehcache with code examples, and discusses consistency, hit-rate improvement, and technology selection, recommending Caffeine for best performance.
This article explains how to replace manual cache handling with Spring Cache annotations, introduces the concepts of L1 (Caffeine) and L2 (Redis) caches, discusses design considerations for consistency, null handling, eviction, and provides practical Maven and configuration examples for Java backend applications.
This article explains the design and implementation of a two‑level caching architecture using local Caffeine cache as L1 and remote Redis as L2 in Spring Boot, covering manual cache handling, annotation‑based management with Spring Cache, and a custom annotation with AOP to minimize code intrusion.
This article explains how to design and build a simple two‑level caching solution in Java, covering generic cache interfaces, local caching with Guava, distributed caching with Redis, configuration details, version‑controlled cache keys, and practical usage within a Spring Boot application.