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MySQLTuner-perl Project Rules

Core Constitution

Make mysqltuner.pl the most stable, portable, and reliable performance tuning advisor for MySQL, MariaDB, and Percona Server.

Key Pillars

  • Production Stability: Every recommendation must be safe for production environments.
  • Single-File Architecture: Strict enforcement of a single-file structure. Modules or splitting are prohibited.
  • Zero-Dependency Portability: The script must remain self-contained and executable on any server with a base Perl installation (Core modules only).
  • Universal Compatibility: Support the widest possible range of MySQL-compatible versions (Legacy 5.5 to Modern 11.x).
  • Regression Limit: Proactively identify and prevent regressions through exhaustive automated testing.

Execution Rules & Constraints

  1. SINGLE FILE: Spliting mysqltuner.pl into modules is strictly prohibited.
  2. NON-REGRESSION: Deleting existing code is prohibited without relocation or commenting out.
  3. TDD MANDATORY: Use a TDD approach. Validate solutions by creating test cases before final submission.
  4. SAFE COMMANDS: Always use absolute paths. Monitor every command for exit code 0.
  5. CREDENTIAL HYGIENE: NEVER hardcode credentials.
  6. VERSION CONSISTENCY: Version numbers MUST be synchronized across CURRENT_VERSION.txt, Changelog, and all occurrences within mysqltuner.pl.
  7. CONVENTIONAL COMMITS: All commit messages MUST follow the Conventional Commits specification.
  8. NO DIRECT COMMIT: All changes SHOULD be committed via npm run commit or git cz.

Best Practices

  1. Multi-Version Validation: Test diagnostic logic changes against at least one "Legacy" version (e.g. MySQL 8.0) and one "Modern" version (e.g. MariaDB 11.4).
  2. System Call Resilience: Every external command MUST check for binary existence and handle non-zero exit codes. Use execute_system_command.
  3. "Zero-Dependency" CPAN Policy: Use ONLY Perl "Core" modules.
  4. Audit Trail: Every recommendation MUST be documented in code with a comment pointing to official documentation.
  5. Memory-Efficient Parsing: Process logs line-by-line; NEVER load large files into memory.
  6. SQL Modeling: Use the Modeling array to collect schema design findings (naming, constraints, data types).