This article details how OpenAI scaled PostgreSQL to handle the massive traffic from 800 million ChatGPT users. It discusses the challenges faced during high write loads, optimizations made to reduce strain on the primary database, and strategies for maintaining performance under heavy demand.

This article explores creative database optimization techniques in PostgreSQL, focusing on scenarios that bypass full table scans and reduce index size. It emphasizes using check constraints and function-based indexing to improve query performance without unnecessary overhead.

Efficient storage in PostgreSQL can be achieved by understanding data type alignment and padding bytes. By organizing columns in a specific order, one can minimize space waste while maintaining or even enhancing performance during data retrieval.

PostgreSQL's full-text search (FTS) can be significantly faster than often perceived, achieving a ~50x speed improvement with proper optimization techniques such as pre-calculating `tsvector` columns and configuring GIN indexes correctly. Misleading benchmarks may overlook these optimizations, leading to an unfair comparison with other search solutions. For advanced ranking needs, extensions like VectorChord-BM25 can further enhance performance.

PostgreSQL v18 introduces the ability to preserve optimizer statistics during major upgrades, enhancing performance and reducing downtime. This feature allows users to export optimizer statistics with `pg_dump` and ensures that statistics remain intact when using `pg_upgrade`, streamlining database upgrades.