The article explains how Spring Cache, introduced after Spring 3.1, uses the @Cacheable annotation to declaratively cache method results, detailing each attribute such as cacheNames, key, keyGenerator, and unless, with code examples illustrating key generation and conditional caching.
This article analyzes a severe N+1 query performance disaster in a Java Spring Boot project, explains why the traditional recursive approach is slow, and presents a production‑tested solution that reduces database calls to one, uses O(n) tree construction, and adds multi‑level caching to achieve a 100‑fold speedup.
This article explains why caching is a performance trade‑off, outlines essential cache features such as size limits, eviction policies, TTL, configuration, integration APIs, and distributed versus local modes, and provides a comprehensive checklist for evaluating cache providers.
This article explains how to use Spring Boot Actuator to monitor and control Spring Cache, covering essential cache annotations, a complete CRUD example with caching, cache management endpoints, custom CacheManager configuration, and optional Redis integration for production‑grade caching.
This tutorial explains how to enable and use Spring Cache with annotations like @Cacheable, @CacheEvict, and @CachePut, integrates JSR‑107 and Ehcache, provides full Maven and configuration examples, and demonstrates caching behavior through a series of practical code snippets and tests.
This tutorial walks through the evolution from manual Redis calls to Spring Cache abstraction, explains AOP‑based proxying, details core annotations, demonstrates Caffeine and Redisson integration, explores list caching, and shows how to build a custom two‑level cache for high‑performance Java back‑ends.
Dragonfly DB is a high‑performance, Memcached‑ and Redis‑compatible in‑memory database that reportedly delivers up to 25× Redis throughput, with a shared‑nothing, multithreaded architecture; this guide shows quick Docker deployment on Linux/macOS, client access methods, Spring cache integration, and key usage notes.
This article introduces a two-level cache framework designed to address issues with Spring-Cache, providing enhanced caching strategies and improved performance in Java-based backend systems.
This article introduces Redis, explains how to install it on various platforms, demonstrates integrating Redis with Spring Boot using Spring Cache annotations, configures connection pools for Lettuce and Jedis, and provides custom Redis service implementations for flexible cache operations.
This article walks through adapting a custom Redis client to Spring Cache, explains the cache abstraction, demonstrates annotation‑driven caching, shows how to integrate Caffeine and Redisson, and builds a simple two‑level cache to illustrate advanced scenarios for Spring Cache users.
This article introduces Spring Cache as a unified caching solution that abstracts away the specifics of Redis, Ehcache, and other providers, explains its core concepts, annotations, configuration, and demonstrates practical usage with code examples to eliminate manual cache handling.
This article explains why caching is essential for Java distributed applications, introduces Redis as an in‑memory data store, and demonstrates how the Redisson framework provides distributed cache implementations—including RMapCache, Spring Cache integration, and JCache support—complete with code examples and configuration details.
This comprehensive guide walks you through installing Redis on Linux and Windows, configuring Spring Cache annotations, setting up JSON serialization with RedisTemplate, using connection pools, and implementing a flexible RedisService with controller examples, providing all the essential steps to integrate Redis into your Spring Boot applications.
This article explains why caching is essential for Java distributed applications, introduces Redis and Redisson, and demonstrates how to implement distributed caches using Redisson Maps, Spring Cache integration, and the JCache API with practical code examples.
This article explains Spring's annotation‑based caching, analyzes why the default cache key collides in multi‑tenant scenarios, shows how to embed tenant identifiers into keys, and provides a custom CacheManager implementation that parses TTL from the cache name and applies it to Redis entries.
This article explains the concept of caching in Java, the importance of reducing database I/O, introduces Spring Cache annotations and their limitations, demonstrates conditional caching with code examples, and provides configuration details for integrating Ehcache and Redis as cache providers.
