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KNOWN_LIMITATIONS.md

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Known Limitations & Production Status

This document provides an assessment of the current capabilities of the SQL Auditor.

Current Status: Production Ready ✅

SQL Auditor has evolved from a prototype into a robust, production-ready tool with 90% test coverage and comprehensive monitoring.

What Works Well ✅

Static Analysis (Fully Tested)

  • ✅ SQL Parsing: SQLGlot-based AST parsing handles complex queries, CTEs, subqueries.
  • ✅ 10+ Detection Rules: All rules tested and working (SELECT *, unused joins, cartesian products, etc.).
  • ✅ Index Recommendations: AST-based analysis for WHERE, JOIN, ORDER BY, GROUP BY clauses.
  • ✅ Cost Estimation: Heuristic-based scoring with 96% test coverage.
  • ✅ Test Coverage: 90% overall across the entire codebase.

EXPLAIN & Performance Validation

  • ✅ Real EXPLAIN Integration: Supports SQLite and PostgreSQL EXPLAIN plans.
  • ✅ Performance Validation: Measures actual execution time improvements in test databases.
  • ✅ Automated Benchmarking: Compares original vs. optimized query performance.

Monitoring & Infrastructure

  • ✅ Prometheus Metrics: Tracks query counts, errors, and performance gains.
  • ✅ Grafana Dashboards: Visualizes system health and optimization impact.
  • ✅ Alerting: Configured alerts for high error rates or budget overruns.
  • ✅ Docker Support: Production-ready Docker and Docker Compose configurations.

LLM Integration & Cost Tracking

  • ✅ OpenAI Integration: Hardened with retry logic and error handling.
  • ✅ Cost Tracking: Real-time token usage tracking and cost estimation.
  • ✅ Budgeting: Enforces daily/monthly budget limits for LLM usage.

Remaining Limitations ⚠️

Heuristic-Based Analysis

While we now support real EXPLAIN plans, the initial static analysis still uses heuristic rules.

  • Initial index recommendations are based on AST patterns, which are then validated by real EXPLAIN plans if a database connection is available.

Dialect Support

  • Currently optimized for PostgreSQL and SQLite. Other dialects (MySQL, SQL Server) are supported via SQLGlot but may have less accurate cost models.

Complexity

  • Extremely large queries (>2000 lines) or deeply nested recursive CTEs may still challenge the parser or LLM context limits.

Roadmap

  1. User Authentication: Implement JWT/OAuth for multi-user support.
  2. Audit History Persistence: Move from local storage to a dedicated PostgreSQL database.
  3. CI/CD Automation: Fully automate the 139-test suite in GitHub Actions.
  4. Slack/Teams Integration: Direct notifications for critical performance regressions.

Final Assessment

SQL Auditor is now a production-ready tool. It provides a unique combination of static analysis, LLM-driven explanation, and real-world performance validation.

Use it to audit your production queries, but always verify optimizations in a staging environment before applying to live databases.