This article introduces MybatisPlusPro, an extension of MyBatis‑Plus that automates controller‑level CRUD, pagination, dynamic queries, data auditing, permission control, and performance optimizations, showing how developers can dramatically reduce code and improve efficiency in Java backend projects.
The article introduces mybatis-mate, an enterprise‑grade MyBatis module that supports sharding, multi‑datasource, data auditing, field encryption, sensitive‑data masking, automatic DDL maintenance, performance logging, and fine‑grained data‑scope control, providing code examples and configuration guidelines for Spring Boot projects.
This article introduces mybatis-mate, an official MyBatis‑Plus extension that provides enterprise‑grade features such as sharding, multi‑datasource support, data auditing, field encryption, sensitive‑data masking, automatic DDL maintenance, dynamic datasource switching, distributed transaction logging, and fine‑grained data permission control, with detailed usage examples and configuration snippets for Spring Boot projects.
This article introduces Mybatis-Mate, an enterprise‑level MyBatis extension that provides sharding, data audit, sensitive‑word filtering, field encryption, dictionary binding, data permission, dynamic DDL generation and other features, and shows how to integrate and use them with practical code examples.
This article introduces MyBatis‑Mate, an official MyBatis‑Plus extension that provides enterprise‑grade features such as sharding, data auditing, field encryption, sensitive data masking, dictionary binding, dynamic DDL maintenance, and flexible data‑source switching, complete with usage examples and code snippets.
The article presents mybatis-mate, an enterprise‑grade MyBatis module that provides sharding, data auditing, sensitive‑word filtering, field encryption, dictionary write‑back, data permission, automatic DDL maintenance, dynamic multi‑datasource switching, and distributed transaction logging, offering developers a more agile and elegant way to handle data.
This article explains how to achieve non‑intrusive, efficient full‑record data change auditing in a Spring‑based backend by leveraging Aspect‑Oriented Programming, custom annotations, and reflection to automatically capture before‑and‑after values for both primary and related tables.
This article explains how Inceptor’s data auditing feature works, showing how to enable or disable table‑level error logging, interpret and query the Log Error Table, and manage its access permissions to ensure accurate query results despite dirty data.
