The article recounts a real‑world case where using ShardingJDBC, Elasticsearch and Logstash dramatically increased system complexity and caused a pagination bug, then walks through hardware, software and SQL‑level optimization techniques and suggests more balanced alternatives to blind sharding.
This article introduces ShardingJDBC, a Java middleware that simplifies sharding, read/write separation, and distributed transactions, and provides a step‑by‑step Spring Boot example with configuration, code snippets, and verification of its core features.
This article explains how to expand a sharded database from three to four nodes, compares five migration schemes—including stop‑service, stop‑write, log‑based, dual‑write, and smooth 2N approaches—and provides step‑by‑step instructions for MariaDB installation, master‑slave configuration, Keepalived high‑availability setup, and dynamic data‑source integration using ShardingJDBC.
This article explains how to set up MySQL master‑slave replication for read‑write splitting, covering configuration of master and slave servers, binary log usage, troubleshooting common issues, and demonstrates implementing the setup in a Java project using Sharding‑JDBC.
This article explains why a single MySQL instance can become a bottleneck, introduces asynchronous master‑slave replication using binary logs, provides detailed configuration commands for both master and slave servers, and shows how to achieve read‑write splitting in Java projects with ShardingJDBC, including dependency setup, YAML configuration, and testing procedures.
This article examines why large‑scale MySQL deployments encounter CPU, I/O and memory bottlenecks, compares vertical and horizontal data‑sharding strategies, evaluates open‑source middleware ShardingJdbc versus MyCat, and provides a detailed Spring Boot integration design with routing rules and practical use‑case examples.
