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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…
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…
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…
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…
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…
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…
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,…
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…
