1 views collected around this technical thread.
Combining Redis’s distributed, high‑capacity cache with Caffeine’s ultra‑fast local cache creates a two‑level strategy where most reads hit the in‑process cache, fall‑backs use Redis, and only rare misses query the database, dramatically cutting latency, boosting throughput, and easing DB load.
This article explains the role of local in‑memory caches in high‑performance service architectures, compares implementations such as ConcurrentHashMap, Guava Cache, Caffeine, and Ehcache, discusses cache consistency and hit‑rate challenges, and recommends Caffeine as the preferred solution for multi‑level caching with Redis or Memcached.
An overview of two‑level caching architecture, the motivations for using local in‑memory caches, essential features of a local cache, comparative analysis of implementations using ConcurrentHashMap, Guava Cache, Caffeine, and Ehcache, and strategies for consistency, hit‑rate improvement, and practical code examples.
This article explains the inner workings of Caffeine's cache, covering the getIfPresent method, the design of ReadBuffer and StripedBuffer, eviction policies, the maintenance cycle, and the climb algorithm that dynamically adjusts window and protected partitions for optimal performance.
The article details practical monitoring strategies for both remote Redis and local Caffeine caches in high‑traffic game services, including prefix‑based Redis key tracking, Aspect‑oriented instrumentation, Caffeine statistics collection, and real‑world case studies that illustrate how these metrics identify hot‑keys, cache‑miss spikes, and reduce system load.
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.
This article explains how Caffeine implements element expiration using three built‑in policies and a custom TimeWheel strategy, detailing the construction of the timer wheel, bucket selection, scheduling, advancing, and eviction logic within the cache's maintenance cycle.
This article provides a comprehensive walkthrough of Caffeine cache's architecture, explaining its fixed-size eviction policy, underlying data structures such as ConcurrentHashMap, MPSC buffers, Count‑Min Sketch frequency tracking, and the dynamic window‑probation‑protected zones, while detailing key methods like put, getIfPresent, and maintenance.
This article introduces the Caffeine Java caching library, detailing its features, core classes and parameters, various loading strategies—including manual, automatic, and asynchronous loading—eviction policies, removal listeners, and cache statistics, accompanied by practical code examples for integration in backend applications.
This article details a step‑by‑step performance improvement journey for a category‑tree query in a SpringBoot‑Thymeleaf system, covering Redis caching, scheduled jobs, local Caffeine cache, Gzip compression, and data slimming with byte‑array storage to resolve latency and large‑key issues.
This article introduces the high‑performance Caffeine caching library for Spring Boot 3.2, explains its core features, demonstrates detailed performance benchmarks, and provides practical code examples covering synchronous and asynchronous operations, eviction policies, removal listeners, refresh mechanisms, and statistics collection.
This article presents a real‑world case study of a high‑concurrency data‑processing interface whose response time was reduced from 30 seconds to under one second by diagnosing SQL bottlenecks, refactoring MyBatis code, applying PostgreSQL array aggregation, and introducing a Caffeine cache, while also discussing the limits of relational databases and promoting related AI services.
This article presents a real‑world case of optimizing a high‑concurrency transaction‑statistics API, detailing diagnosis of a 30‑second latency, SQL refactoring with array_agg and unnest, Java‑level improvements, and Caffeine caching, while also sharing promotional offers for ChatGPT services and a developer community.
This article introduces the Java Caffeine caching library, explains its manual, loading, and async cache APIs, details eviction and refresh strategies, shows how to collect statistics, and demonstrates full integration with Spring Boot using Maven dependencies, cache annotations, constant definitions, configuration classes, and usage examples.
This article explains how to build a high‑performance, dynamically configurable rate‑limiting utility for Java applications using Caffeine cache with a 1‑second refresh interval, covering data structures, limit‑checking logic, dynamic configuration, and sample code with test results.
This article reviews three popular Java in‑process caching libraries—Guava, Caffeine, and Ehcache—explaining their configuration options, performance characteristics, monitoring capabilities, advantages and disadvantages, and provides a side‑by‑side comparison to help developers choose the most suitable local cache for their applications.
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 introduces J2Cache, a two‑level caching framework for Spring Boot, explains its L1 (Caffeine/Ehcache) and L2 (Redis/Memcached) architecture, and provides a complete hands‑on example covering Maven dependencies, configuration, core service classes, REST controller, and testing procedures.
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.
This article explains how upgrading Java and Groovy dependencies caused an IllegalAccessException in Caffeine's asynchronous cache due to module encapsulation, and details the attempted fixes including JVM '--add-opens' arguments and dependency version adjustments.