Last Updated: October 2, 2025 Project Status: Phase 1 Complete - Foundation Established ✅
QueryGrade is a Django-based SQL query analysis and grading platform powered by machine learning. This roadmap outlines completed work, current capabilities, and planned features through Q3 2026.
Status: Production Ready Completion: October 2025
- 9 Specialized Analyzers: SELECT, JOIN, WHERE, ORDER BY, GROUP BY, Indexing, Subquery, MySQL, PostgreSQL
- 18+ Issue Detection Types: High/Medium/Low severity classification
- 27+ Recommendation Types: Actionable optimization suggestions with examples
- Letter Grading: A-F grades with numerical scores (0-100)
- Database Support: MySQL, PostgreSQL, SQLite, Oracle, SQL Server
Status: Production Ready Completion: October 2025
- 41+ Feature Extraction: Query structure, complexity, performance indicators
- Hybrid Predictions: Rule-based + ML with confidence-weighted blending
- Multiple Algorithms: Random Forest, Gradient Boosting, XGBoost, LightGBM
- Automated Training: Learns from user feedback continuously
- Model Versioning: Track performance across model iterations
Status: Production Ready Completion: October 2025
- Complete Auth Flow: Registration, login, logout, session management
- Password Management: Reset via email, change password, secure validation
- Account Management: User profile, query history, preferences
- Security Features: CSRF protection, rate limiting, XSS prevention
- Custom Error Pages: 404, 500 with user-friendly messaging
Status: Production Ready Completion: October 2025
- Query History Tracking: All user queries with timestamps and grades
- Deletion API: Secure bulk deletion with user ownership validation
- Frontend Integration: Smooth animations, CSRF handling, notifications
- Pagination: Efficient browsing of large query histories
Status: Production Ready Completion: October 2025
- Quick Feedback: Thumbs up/down for instant quality rating
- Detailed Feedback: Multi-criteria ratings (accuracy, usefulness, clarity)
- User Reliability Scoring: Weight feedback by user consistency
- ML Training Integration: Convert feedback to training samples automatically
Status: Production Ready Completion: October 2025
- Batch Processing: Analyze multiple queries simultaneously
- Query Comparison: Side-by-side comparison of 2-3 queries
- Async Processing: Celery-based background jobs for heavy workloads
- Progress Tracking: Real-time status updates via WebSocket (planned)
Status: Production Ready Completion: October 2025
- JWT Authentication: Token-based API access
- Query Grading Endpoints: Single and batch analysis
- History Management: List, retrieve, delete query history
- Feedback Submission: Programmatic feedback collection
- User Statistics: Analytics and insights
Status: Production Ready Completion: October 2025
- Performance Analysis: Comprehensive metrics for ensemble voting
- Redis Cache Integration: Time-window filtering for analysis
- Actionable Recommendations: Confidence, quality, variance warnings
- Multiple Strategies: Simple average, weighted, confidence-based, adaptive
Status: Production Ready Completion: October 2025
- 100+ Tests: Unit, integration, ML tests across codebase
- Test Coverage: Critical paths fully covered
- CI/CD Ready: Automated test execution
- Documentation: TESTING.md with examples and best practices
Timeline: January - March 2026 Focus: Build foundation for advanced ML features Milestone: Q1 2026 - Infrastructure & Monitoring
Priority: High | Type: Testing | Area: ML System
Goals:
- Expand test coverage for ML training pipeline
- Add integration tests for feedback collection
- Test model deployment and versioning
- Benchmark ML performance metrics
Success Criteria:
- ≥90% test coverage for ML modules
- All ML components have integration tests
- Performance benchmarks established
Priority: High | Type: ML Improvement | Area: Infrastructure
Goals:
- Implement real-time ML model performance monitoring
- Set up alerting for model degradation
- Track prediction confidence trends
- Monitor user satisfaction metrics
Features:
- Dashboard for ML metrics visualization
- Slack/email alerts for anomalies
- Automated model retraining triggers
- A/B testing framework for model comparison
Success Criteria:
- Real-time monitoring dashboard live
- Alerting system operational
- Model degradation detected within 24 hours
Priority: High | Type: Feature | Area: Query Grading
Goals:
- Analyze queries to suggest optimal indexes
- Detect missing indexes causing performance issues
- Recommend composite indexes for complex queries
- Estimate performance improvement from indexes
Features:
- Index suggestion engine
- Cost-benefit analysis for indexes
- DDL generation for recommended indexes
- Integration with database introspection
Success Criteria:
- Accurately suggest indexes for 85%+ queries
- Provide performance improvement estimates
- Generate valid DDL for all major databases
Timeline: April - June 2026 Focus: Major ML enhancements and semantic understanding Milestone: Q2 2026 - Advanced ML Features
Priority: High | Type: ML Improvement | Area: ML System
Goals:
- Implement NLP-based query intent detection
- Understand user's business logic from SQL
- Detect semantic anti-patterns
- Provide context-aware recommendations
Features:
- Query intent classification (reporting, transaction, analytics)
- Business logic extraction from complex queries
- Semantic similarity detection for duplicate logic
- Context-aware grading based on query purpose
Technologies:
- Transformers (BERT, GPT-based models)
- Sentence embeddings for query similarity
- Custom NLP models trained on SQL corpus
Success Criteria:
- 80%+ accuracy on intent classification
- Semantic recommendations for 70%+ queries
- Duplicate logic detection with ≥90% precision
Priority: High | Type: ML Improvement | Area: Query Grading
Goals:
- Predict query execution plan without database access
- Estimate query cost and performance
- Identify plan optimization opportunities
- Recommend query rewrites for better plans
Features:
- ML-based execution plan prediction
- Cost estimation model (CPU, I/O, memory)
- Plan comparison for query alternatives
- Optimizer hint recommendations
Technologies:
- Deep learning models for plan prediction
- Transfer learning from database internals
- Reinforcement learning for query rewriting
Success Criteria:
- Plan prediction accuracy ≥75%
- Cost estimates within 20% of actual
- Recommend 3+ alternative plans per query
Priority: High | Type: Feature | Area: Query Grading
Goals:
- Connect to live databases for schema introspection
- Analyze queries in context of actual schema
- Detect schema-specific optimizations
- Recommend schema improvements
Features:
- Database connection management (MySQL, PostgreSQL, etc.)
- Schema caching and refresh mechanisms
- Context-aware query grading with schema
- Schema optimization recommendations
Security:
- Read-only database access
- Encrypted credential storage
- Connection pooling and rate limiting
- User-owned database connections only
Success Criteria:
- Support 5+ major databases
- Schema analysis within 5 seconds
- Context-aware grading for 100% queries
- Zero data leakage or security issues
Timeline: July - September 2026 Focus: User experience and personalized learning Milestone: Q3 2026 - Personalization
Priority: Medium | Type: Feature | Area: ML System
Goals:
- Track user skill progression over time
- Recommend learning resources based on weak areas
- Adaptive difficulty for query challenges
- Personalized best practices based on user patterns
Features:
- User skill profile (beginner, intermediate, expert)
- Learning path recommendations
- Personalized query challenges
- Progress tracking and achievements
- Custom best practices for user's database/use case
Technologies:
- Collaborative filtering for recommendations
- Skill assessment via query analysis
- Personalized content ranking
Success Criteria:
- Skill profiles for 100% active users
- 60%+ users engage with learning paths
- Measurable skill improvement over 3 months
- 4+ star user satisfaction rating
- Test Coverage: 100+ tests, all passing ✅
- Zero Technical Debt: No TODO/stub code ✅
- Code Quality: Modular architecture, 7+ refactored packages ✅
- Documentation: 7 comprehensive docs (CLAUDE.md, TESTING.md, etc.) ✅
- User Base: 1,000+ active users
- Query Analysis: 100,000+ queries graded
- ML Accuracy: ≥85% prediction accuracy
- User Satisfaction: 4.5+ star average rating
- API Usage: 10,000+ API requests/month
- Test Coverage: ≥90% across all modules
- Response Time: <500ms average query grading
- Uptime: 99.9% availability
- Backend: Django 4.0+, Python 3.12
- ML: scikit-learn, XGBoost, LightGBM
- Database: PostgreSQL (production), SQLite (dev)
- Cache: Redis with 4 separate cache databases
- Async: Celery with Redis broker
- API: Django REST Framework, JWT authentication
- Frontend: Django templates, vanilla JavaScript
- Testing: Django Test, unittest, pytest
- NLP: Transformers, BERT, sentence-transformers
- Deep Learning: TensorFlow, PyTorch
- Monitoring: Prometheus, Grafana
- APM: Sentry for error tracking
- Real-time: WebSocket (Django Channels)
- Frontend Framework: React or Vue.js (evaluation phase)
- Visit the application (deployment URL TBD)
- Register for an account
- Paste your SQL query
- Review grade, issues, and recommendations
- Provide feedback to improve the system
- Clone:
git clone https://github.com/ringo380/QueryGrade.git - Install:
pip install -r requirements.txt - Migrate:
python manage.py migrate - Run:
python manage.py runserver - Test:
python manage.py test
See DOCUMENTATION_MAP.md for comprehensive development guides.
- GitHub Issues: Report bugs or request features
- GitHub Projects: Track development progress
- Milestones: View progress on Q1, Q2, Q3 goals
[License information to be added]
Note: This roadmap is subject to change based on user feedback, technical discoveries, and strategic priorities. Check the GitHub Project Board for the most up-to-date status.