🔗 A Hitchhiker's guide to PostgreSQL text search

One essential insight is how search performance can dramatically affect user experience. At Cortex, the team recognized that their previous search architecture, reliant on Lucene, faltered as data scales increased. Originally effective for smaller datasets, it became cumbersome with larger index sizes, resulting in out-of-memory exceptions and long latency for cold start queries. To remedy this, Cortex decided to leverage Postgres for search capabilities, a familiar yet effective solution given its widespread availability across cloud platforms.

The article provides a deep dive into the implementation of a text search system utilizing Postgres, including query parsing and building. It highlights their custom syntax for search expressions, translation to SQL queries, and handling of indexed documents. Key functions, like to_tsvector and tsquery, are discussed alongside techniques for scoring search results. This approach aimed for efficiency while managing the operational constraints presented by various environments.