my2sql is a Go-based command-line tool designed for parsing MySQL binlog files. It can generate various SQL statements from binlog data, including original query logs, rollback SQL for point-in-time recovery, and SQL statements without primary key constraints for data manipulation. It is built upon the my2fback and binlog_rollback tools for improved performance and wider data type support. The tool utilizes the binary log to recreate database state and perform various analytical tasks.
my2sql stands out due to its speed and support for a wide range of MySQL data types, including JSON, BLOB, and text. It efficiently handles large binlog files, significantly faster than binlog2sql. It also meticulously handles both time and position based parsing, providing the convenience to parse MySQL binlogs with or without providing the initial --start-pos and --start-datetime to start parsing.
- SQL Generation: Generates various SQL formats: original, rollback, and DML statistics.
- Format Support: Supports various binlog formats and options like
replandfilemodes. - Performance: Optimized for speed, significantly faster than alternatives like
binlog2sqlin certain scenarios. - Data Type Handling: Supports JSON, BLOB, TEXT, and other data types.
- Flexibility: Handles queries that include
insert intoandupdatestatements. - Transaction Analysis: Provides detailed DML and transaction analysis capabilities.
- Usage Cases: Data recovery, point-in-time recovery, audit trails, and troubleshooting.
my2sql is actively maintained and under development. The project has a solid foundation with proven functionality and a growing user base. It's considered reliable for routine use cases, with continuous updates to address bug fixes and improve performance. Recent commits show ongoing development and bug fixes.
my2sql benefits database administrators and developers by simplifying data recovery, facilitating auditing, and enabling performance analysis directly from MySQL binlog files. It's ideal for scenarios requiring data restoration after errors, tracking database changes, and identifying performance bottlenecks. It provides a faster, more versatile alternative to existing tools, especially when dealing with complex data types.