Anatomy of a (Postgres) search engine

Full-text search relies on inverted indexes rather than conventional b-tree indexes, which efficiently handle exact, range, and prefix matches but cannot search terms in the middle of text. An inverted index maps tokenized terms to sorted, compressed postings lists of document or row identifiers, optionally storing positional information for phrase and proximity searches and frequency statistics for ranked results using methods such as BM25. Index design involves trade-offs among size, speed, precision, and supported features, including tokenization, stemming, stop-word handling, bitmap encoding, and top-k query optimizations. To accommodate changing data, indexes are commonly divided into immutable segments plus mutable storage, with background merges consolidating segments and removing tombstoned documents created by deletes or updates. Implementing such an index in PostgreSQL adds database-specific requirements including row identifiers, transaction visibility, WAL logging, VACUUM compatibility, storage reuse, background maintenance, and query-planner integration with conventional SQL indexes; PlanetScale’s TIN extension is presented as an effort to address these challenges for PostgreSQL full-text search.

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