This repository contains the practical implementation and analysis of query optimization, indexing, and performance evaluation using PostgreSQL.
The project explores how database systems efficiently execute queries using query optimization techniques and indexing strategies. Through this, I analyzed and improved query performance using real-world datasets and execution plans.
Query-Optimization-and-Performance-Analysis/ ├── Assignment1/ │ ├── task1/ │ ├── task2/ │ └── task3/ ├── Assignment2/ │ ├── task1/ │ ├── task2/ │ ├── task3/ │ └── task4/
- SQL Query Optimization (EXPLAIN ANALYZE)
- Indexing Techniques (B-Tree, index scans)
- Query Execution Plans (sequential vs index scan)
- Performance Analysis (runtime comparison)
- Database Internals (cost models, cardinality)
- PostgreSQL
- SQL
- Python
- JSON
- Analyzed query performance before and after indexing
- Demonstrated improvements using optimized queries
- Explored execution plans and optimizer decisions
- Conducted experiments on datasets
- Execution plans matter more than just SQL syntax
- Indexes improve performance but must be used carefully
- Optimizers rely on cost estimation and statistics
- Understanding scans and joins is critical
- Add benchmarking scripts
- Improve visualization of query plans
- Extend to distributed databases
Krishnan R
B.Tech CSE, IIT Hyderabad