Welcome to the PostgreSQL Performance Optimization repository. This project documents a hands-on journey from a slow, unoptimized database to a high-performance, scalable architecture capable of handling millions of rows and thousands of concurrent users.
We have structured this guide into three progressive chapters. Each chapter builds upon the last, moving from basic query tuning to complex architectural patterns.
"The Needle in the Haystack"
- Goal: Reduce single-query latency from seconds to milliseconds.
- Key Concepts: B-Tree Indexes, Index Scans vs Seq Scans, Foreign Key Indexing.
- Result: Achieved 10,000x speedup (1.5s -> 0.15ms) on 5 million rows.
"The Traffic Jam"
- Goal: Maintain low latency under high user load (500+ concurrent clients).
- Key Concepts:
pgbench, Connection Pooling (PgBouncer),max_connections, Write Latency. - Result: Scaled from total failure at 500 users to 16,000 TPS using PgBouncer.
"Scaling to Infinity"
- Goal: Handle massive datasets and heavy reporting without slowing down the app.
- Key Concepts: Materialized Views, Table Partitioning (Range), Vacuum Tuning, Functional Indexes.
- Result: Reporting queries accelerated by 36,000x (1.8s -> 0.05ms).
To follow along with the experiments, you will need:
- PostgreSQL 14+ (Local or Docker)
- pgbench (Usually installed with Postgres)
- PgBouncer (For Chapter 2)
- Python 3 (Optional, for generating graphs)
We use pgbench and EXPLAIN (ANALYZE, BUFFERS) to measure everything. No guessing.
See the Benchmark Graph for a visual comparison of indexing improvements.
Created as part of a comprehensive PostgreSQL learning series.