Tag: cache

Why Refreshing L1 Cache Using a Scheduler is a Bad Idea

Building Scalable and Consistent Microservices Caching Architectures Caching is one of the most effective techniques for improving application performance. Modern microservices often implement a multi-level cache architecture: L1 Cache: In-memory cache inside the application (EhCache, Caffeine) L2 Cache: Distributed cache such as Redis or GemFire Database: The system of record While implementing L1 caching, many teams adopt a seemingly simple…

Building an Annotation-Driven Enterprise Cache Framework for Spring Boot Microservices

Introduction In the previous articles, we built a reusable caching library that provides: L1 Cache using EhCache L2 Cache using Redis or GemFire Distributed Locking Cache Stampede Protection Cache Warming Background Refresh Kafka Based Invalidation Spring Event Support Metrics & Monitoring While the framework is already powerful, developers still need to write code like: return cacheManager.get( "product:" + productId, ()…

Extending the Platform Cache Library: Distributed Locking, Cache Warming and Cache Stampede Protection

Introduction In the previous article, we built a reusable caching library that provides: L1 Cache using EhCache L2 Cache using Redis or GemFire Strategy Pattern based provider selection Kafka and Spring Event based invalidation Auto configuration Metrics and monitoring While this solution works well for most workloads, large-scale production systems face additional challenges: Multiple pods rebuilding the same cache simultaneously…

Building a Pluggable Multi-Level Cache Library for Spring Boot Microservices (EhCache + Redis/GemFire)

Introduction As organizations move toward microservices, caching becomes a critical component for performance, scalability, and cost optimization. Unfortunately, many teams implement caching differently: Different cache providers Different key conventions Different invalidation mechanisms No cache metrics No standard configuration This creates operational challenges and inconsistent behavior across services. To solve this problem, platform teams can build a reusable caching library that…

Redis vs GemFire vs EhCache: Choosing the Right Caching Strategy for Spring Boot Microservices

Introduction Caching is one of the most effective techniques for improving application performance. However, choosing the right caching technology can be challenging. Many development teams start with local in-memory caching and later discover the need for distributed caching as their applications scale across multiple instances, Kubernetes pods, or cloud regions. Common questions include: Should I use EhCache? Is Redis enough?…

Building High-Performance Distributed Caching with Apache Geode (GemFire) in Spring Boot

Introduction As enterprise applications scale, databases often become the primary bottleneck. Frequently accessed data such as customer profiles, product catalogs, pricing information, configuration settings, and reference data are repeatedly fetched from backend databases, resulting in increased latency and unnecessary database load. Distributed caching helps address this challenge by storing frequently used data in memory, significantly improving application performance and scalability.…

Building High-Performance Distributed Caching with Redis in Spring Boot

Introduction Modern applications often retrieve the same data repeatedly from databases. Product information, customer profiles, configuration settings, and reference data are commonly accessed by multiple users and services. Without caching, every request travels through the application layers and eventually reaches the database, increasing response times and placing unnecessary load on backend systems. Redis provides a highly performant in-memory data store…

Building a Pluggable Distributed Cache Library for Spring Boot Microservices Using EhCache and Redis

Introduction As organizations scale their Spring Boot microservices on Kubernetes, caching becomes a critical component for performance and cost optimization. While EhCache works well for single-node deployments, it introduces cache inconsistency when applications scale across multiple pods. To solve this challenge, we can build a reusable cache library that provides: Pluggable cache providers In-memory caching using EhCache Distributed caching using…