This project explores the internal behavior, performance characteristics, and advanced features of PostgreSQL. The focus is on understanding how a relational database engine executes queries, manages storage, and optimizes performance under real-world workloads.
Relational databases often behave as “black boxes” to application developers. This project aims to demystify PostgreSQL by exploring how queries are planned and executed, how indexes affect performance, and how internal mechanisms like MVCC and buffering influence system behavior.
- Understand PostgreSQL architecture and query execution flow
- Learn how the query planner selects execution strategies
- Explore indexing strategies and their performance impact
- Understand storage, buffering, and caching behavior
- Learn how MVCC enables concurrency
- Analyze query execution plans (EXPLAIN / ANALYZE)
- Identify and resolve performance bottlenecks
- Build intuition for database-level optimizations
- Query parsing and planning
- Execution plans (EXPLAIN / ANALYZE)
- Join strategies (nested loop, hash join, merge join)
- B-Tree indexes
- Composite indexes
- Partial indexes
- Index-only scans
- When indexes help vs hurt
- Table storage structure
- Pages and buffers
- Sequential vs index scans
- WAL (Write-Ahead Logging) concepts
- Multi-Version Concurrency Control
- Snapshot isolation
- Vacuum process
- Transaction visibility rules
- Query optimization strategies
- Index selection tradeoffs
- Identifying slow queries
- Cost-based optimization concepts
- How query plans affect real-world latency
- Tradeoffs between write performance and read performance
- Index overhead vs query speed improvements
- Memory usage and caching behavior
- System-level bottlenecks in database workloads
- Data Modeling repository (schema design impacts)
- Query Optimization repository (applied tuning)
- ETL Pipeline (bulk data performance considerations)
- Backend Services (real-world query behavior)
- Transactions repository (MVCC and isolation behavior)
🟡 Planned