Tag: redis

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 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…