This project explores how SQL queries are executed and optimized in database systems. It focuses on understanding how query structure, indexing, and execution plans impact performance at scale.
The goal is to build intuition for why some queries are fast and others become bottlenecks, even when they return the same results.
As datasets grow, poorly written or unoptimized queries can cause severe performance degradation. Full table scans, inefficient joins, and missing indexes can significantly increase latency and resource usage.
This project explores how to analyze and optimize SQL queries by studying execution plans, indexing strategies, and query restructuring techniques.
- Understand how SQL queries are executed internally
- Learn how query planners choose execution strategies
- Explore indexing and its impact on performance
- Understand join strategies and their tradeoffs
- Learn how to identify slow queries using execution plans
- Explore techniques for rewriting queries for efficiency
- Build intuition for database performance tuning
- Inspect query execution plans
- Identify full table scans vs index scans
- Analyze join strategies (nested loop, hash join, merge join)
- Add and evaluate indexes
- Rewrite inefficient queries
- Reduce unnecessary data scanning
- Optimize WHERE, JOIN, and GROUP BY clauses
- Compare query execution times
- Evaluate impact of indexing changes
- Measure improvements from query rewrites
- Analyze query cost estimates
- Parses SQL queries
- Generates execution plans
- Applies optimization rules
- Stores tables and indexes
- Supports row and column retrieval strategies
- B-tree or conceptual index structures
- Speeds up lookup and filtering operations
- Tradeoffs between read and write performance
- Index maintenance overhead
- Choosing correct indexing strategies
- Understanding query planner heuristics
- Cost of joins on large datasets
- Impact of data distribution on query plans
- ETL Pipeline (query-heavy transformations)
- PostgreSQL Deep Dive (real-world execution behavior)
- Data Modeling (schema design impact on queries)
- Load Testing (query performance under stress)
- Backend services (API response optimization)
🟡 Planned