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SQL Query Optimization Engine (QEO)
A local, offline-capable tool for analyzing and optimizing SQL queries (specifically PostgreSQL) with cost-based index recommendations. QEO examines SQL queries without executing them, ensuring it's safe to use on production databases.
Overview
QEO is a local, offline-capable tool for analyzing and optimizing SQL queries (specifically PostgreSQL) with cost-based index recommendations. It examines SQL queries without executing them, ensuring it's safe to use on production databases. By parsing query plans and simulating indexes, QEO provides actionable insights - for example, it might suggest: "Create this index and your query will be 94.5% faster!". The system can test hypothetical indexes using the HypoPG extension to estimate performance gains before any real changes are made.
From
slow
queries
to
optimized
performance
Key Features
Architecture and Usage
The QEO system is implemented in Python using FastAPI for the backend server (providing both the web UI and a REST API). Under the hood, it connects to a PostgreSQL instance (with the HypoPG extension) to analyze queries. The architecture is essentially a client–server model: the user's web browser interacts with the FastAPI server, which in turn consults PostgreSQL+HypoPG for query planning. This design allows QEO to remain self-contained and offline.
Quick Setup
To run QEO, users simply launch the provided Docker-based setup and navigate to the local web interface. Running the startup script brings up the service, and you can access QEO at `http://localhost:9000` in your browser. From there, you can load sample queries or input your own SQL, click "Optimize Query," and get instant index recommendations with estimated cost savings.
Technology Stack
Built with Python, FastAPI, PostgreSQL, and HypoPG extension. The system leverages Docker for containerization, making it easy to deploy and run across different environments. The web interface is built with modern HTML/CSS/JavaScript, providing an intuitive user experience for query analysis.
Safe,
fast,
and
actionable
insights
Example
For illustration, consider a query that selects orders by `user_id` with a sort on `created_at`. QEO might analyze this and report:
Performance Analysis
• Current cost: 1,910.68 (without index)
• With suggested index: 104.59
• Improvement: ~94.5% faster execution
Recommended Index
QEO would propose the exact index, e.g.: `CREATE INDEX CONCURRENTLY idx_orders_user_id_created_at ON orders (user_id, created_at);` to achieve this improvement. This example demonstrates how QEO provides concrete, quantitative advice to developers on optimizing their database performance.
Optimize
queries
without
risk
QEO demonstrates a powerful approach to database query optimization by providing developers with actionable, data-driven recommendations. The tool's ability to simulate indexes without modifying the database makes it an invaluable resource for production environments. By combining PostgreSQL's query planner with HypoPG's hypothetical indexing capabilities, QEO delivers instant insights that help developers make informed decisions about database performance improvements.
