Focused on Java backend technologies, sharing fundamentals, multithreading, JVM, the Spring ecosystem, microservices, distributed systems, high concurrency, source‑code analysis, and practical experience. Continuously delivers high‑quality original content, interview guides, and learning roadmaps to help Java developers progress from beginner to advanced, enhancing technical skills and core competitiveness.
This article explains why naive for‑loop DB operations fail on large data sets, introduces Spring Batch’s chunk, transaction, retry, skip and restart features, and provides step‑by‑step SpringBoot configurations, code samples for Tasklet and Chunk jobs, database and CSV readers/writers, and manual or scheduled job triggering.
In high‑concurrency internet architectures, a MySQL master‑slave setup with read‑write splitting is the baseline for high availability, but static routing suffers from node failures and lag; this article explains how ShardingSphere provides health checks, auto‑failover, load‑balancing, and degradation to achieve resilient read‑write separation.
This article explains why single‑database, single‑table architectures hit performance limits at millions of rows, introduces ShardingSphere as the preferred zero‑deployment sharding solution for SpringBoot, and walks through vertical and horizontal splitting, configuration, custom algorithms, read/write splitting, and distributed transactions with practical code examples.
The article explains how layered caching in Java back‑ends—combining MyBatis first‑ and second‑level caches with a service‑layer Caffeine + Redis cache—affects cache granularity, consistency, distribution, and performance, and provides concrete configuration examples, code snippets, and best‑practice guidelines.
SpringBoot developers can dramatically boost throughput and cut latency by pairing a microsecond‑level local cache (Caffeine or Guava) with Redis, using a two‑level architecture that isolates hot data in JVM memory, reduces network and serialization overhead, and provides configurable eviction policies for various use cases.
This article explains why local locks like synchronized and ReentrantLock fail in microservice clusters, introduces the fundamentals and essential properties of distributed locks, walks through five iterative Redis lock implementations that address dead‑lock, atomicity, and timeout issues, and finally shows how to adopt the production‑grade Redisson library with best‑practice guidelines for key naming, lock scope, and lock type selection.
The article explains why the default SpringBoot cache manager is insufficient for production, then walks through creating custom Caffeine and Redis CacheManager beans, configuring expiration, key prefixes, serialization, and multi‑level caching to solve issues like cache penetration, key collisions, and performance bottlenecks.
This article explains how microservice architectures suffer from cascading failures and demonstrates how to use Sentinel for rate limiting, circuit breaking, and degradation—including architecture, configuration, code examples, and best‑practice tips—to achieve high‑availability Spring Boot services.
The article explains why placing rate limiting at the Spring Cloud Gateway layer, using Redis and Lua scripts, provides a high‑performance, distributed defense against traffic spikes, and walks through three algorithms, configuration parameters, code examples, and custom error handling for robust backend services.
In a micro‑service architecture, a centralized gateway eliminates the need for front‑ends to call dozens of services by handling authentication, rate‑limiting, logging, CORS, and security, and Spring Cloud Gateway—built on WebFlux and Netty—offers a non‑blocking, high‑throughput alternative to Zuul with detailed configuration and code examples for production use.
