sql-optimizer

Analyzing MyBatis Mapper XML and annotation-based queries identifies missing indexes and N+1 problem patterns. It delivers a prioritized modification list with query rewriting and batch processing recommendations to reduce query latency and resource consumption.

Detecting full-table scans in MyBatis Mapper files involves parsing SQL statements from XML and annotations to identify anti-patterns. It provides specific optimization guidance to ensure efficient indexing and minimize full-table scans across Java projects.

Yes, you can run MyBatis SQL analysis safely within a CI pipeline or code review. The parsing and pattern detection execute safely in code review contexts, delivering actionable optimization guidance without modifying production data.

MyBatis query tuning works with both XML Mappers and annotation-based queries across Java projects. It analyzes SQL from both formats to identify performance bottlenecks and provides optimization recommendations like adding indexes or rewriting queries.

To generate a prioritized modification list for MyBatis SQL performance bottlenecks, analyze Mapper files to detect inefficient patterns. The output delivers executable optimization schemes including index adjustments and batch processing to lower query latency.

Your MyBatis query lacks efficient indexing due to anti-patterns in the SQL logic. You can fix it by parsing Mapper XML to detect the bottleneck and applying generated recommendations for adding or adjusting indexes.