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This article provides a detailed tutorial on database sharding (分库分表), covering concepts, common sharding strategies, a step‑by‑step MyCAT configuration, Java JDBC implementation, and key considerations such as data distribution, complexity, transaction handling, and development cost.
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The SQLE 2.2306.0-pre1 release introduces support for TDSQL InnoDB and Mycat data sources in enterprise edition, adds new features, UI optimizations, bug fixes, and provides detailed usage instructions and links to documentation, source code, and demo environments.
This article details a low‑impact migration from MyCat to DBLE, covering required parameter adjustments, sharding function replacement, data node handling, and business‑SQL compatibility issues, while providing concrete configuration examples and practical recommendations for a smooth transition.
This article explains why high‑traffic systems need database sharding, compares table‑level and database‑level partitioning methods, presents concrete MySQL table‑creation scripts and Java service code, and discusses the advantages, drawbacks, and common middleware solutions such as Sharding‑JDBC and Mycat.
This article explains what database sharding (horizontal and vertical partitioning) is, why it is needed, the implementation strategies, and compares two popular solutions—Mycat middleware and Sharding-JDBC—highlighting their advantages, drawbacks, and typical use cases.
This tutorial explains how to set up MySQL master‑slave replication, configure MyCat as a database middleware, install and configure MyCat, and integrate it with a SpringBoot application using dynamic data sources and AOP to achieve transparent read‑write separation.
This tutorial explains two approaches to achieve MySQL read/write splitting—first by manually implementing routing logic in Spring Boot with MyBatis interceptors and dynamic data sources, and second by configuring the MyCat middleware for automatic load balancing, fault‑tolerance, and multi‑node support, complete with code samples and configuration files.
This article analyzes a community‑raised issue where a left join between a global table and a sharded table produces incorrect results in Mycat but works as expected in DBLE, explaining the underlying execution‑plan differences and how DBLE correctly merges results to avoid duplicate data.
The iKesheng community’s weekly roundup shares upcoming events like the DBLE Beijing meet‑up, free Mycat diagnosis services, detailed DBLE and DTLE weekly reports, and technical articles on global lock detection, slave data search, and hash algorithm choices, offering valuable insights for database professionals.
This article explains DBLE's hash‑based sharding algorithm, detailing how logical shards are derived via modulo, mapped to physical shards through a rule.xml configuration and mapFile, compares it with MyCat, and provides development, operational, and configuration best practices along with code examples.
This article reviews the shortcomings of MyCat in handling sharding, joins, inserts, and session variables, demonstrates how the DBLE middleware addresses those issues with better correctness, performance, security, and operational management, and discusses code‑quality improvements and automated testing practices.
A detailed case study investigates why DBLE middleware achieves only about 70% of MyCat's performance, revealing that excessive sorting CPU usage, a cmp‑function inefficiency, and a delete‑loop bug under REPEATABLE_READ isolation cause the slowdown, and demonstrates how adjusting isolation levels and fixing the cmp implementation restores expected performance.
This article presents a step‑by‑step comparison of DBLE and Mycat distributed middleware by setting up a test environment, defining table schemas and sharding rules, inserting sample data, executing the same cross‑node join query on both systems, and analyzing the differing results and execution plans to explain why Mycat omits expected rows while DBLE returns the correct result.
This talk details SF Express’s journey from heterogeneous legacy databases to standardized open‑source, distributed architectures and intelligent operations, covering standardization, migration to open‑source, scaling with Mycat, automated resource pooling, and the ThinkDB platform that drives proactive, automated DBA workflows.
The article explains the concept of distributed architecture, its key characteristics such as cohesion and transparency, showcases how MySQL and middleware like Mycat are used in e‑commerce platforms, and outlines the evolution, practical implementations, and challenges of building scalable distributed database systems.
The article introduces MyCat, an open‑source distributed database middleware that provides transparent sharding, multi‑backend support, AI‑driven catlet extensions, and advanced read‑write splitting to enable low‑cost migration of single‑node databases to cloud environments while addressing performance and scalability challenges.
This article introduces Mycat, an open‑source distributed database middleware that implements the MySQL protocol, explains its core concepts such as sharding, read/write separation, high availability, deployment architectures, optimization strategies, and provides practical configuration examples for schema, rule, and server files.
MyCat is an open‑source distributed database middleware that transparently shreds tables across multiple backend databases, solves connection overload, provides ER‑based sharding, global partitioning, AI‑driven catlets, and advanced read‑write separation, enabling low‑cost migration of single‑node databases to the cloud.