PlanetScale Adds In-Database Full-Text Search to Postgres with TIN
PlanetScale announced TIN, a full-text search extension for Postgres, on September 16, 2026. The extension is generally available for all Postgres and Neki databases. PlanetScale describes it as a fast, full-featured, reliable full-text search extension for Postgres. PlanetScale
TIN stands for "Text INdex." Query support covers boolean expressions, phrase queries and span queries. Term matching includes fuzzy, wildcard and regular-expression matching, with case and accent folding. That combination covers structured text retrieval and tolerant human input in one engine.
The company documented the release in a blog post titled "Introducing TIN: full-text search for Postgres" and listed a changelog entry titled "TIN: Postgres full-text search" dated September 16, 2026.
On September 17, 2026, PlanetScale introduced Lead, a TIN-compatible full-text search extension for CI. Lead carries the same feature set and SQL statements as TIN. It is a non-production Postgres text-search extension for exercising TIN-compatible application SQL in development, test and CI. PlanetScale Lead repository Parity is the point.
Lead was documented in a blog post titled "Introducing Lead: TIN-compatible full-text search for CI" and a changelog entry titled "Lead: run full-text search queries in your CI" dated September 17, 2026.
Looking at what this means for teams running Postgres in production, the interest lies in keeping search close to the row store. External search clusters solve relevance and scale, but they introduce sync lag, dual writes and a second system to operate, secure and upgrade. An in-database extension with boolean logic, phrase and span operators, plus fuzzy and pattern matching, lets developers express product search, filtering and ranking without leaving transactional SQL. The name is literal. An index that understands text avoids exporting text to understand it.
In my view, the Lead companion matters as much as TIN itself for expert adopters. Full-text behavior is notoriously sensitive to tokenization, folding rules and query dialect. Tests that pass against a stub can fail against the real index, or worse, pass while relevance drifts. Offering identical SQL and feature coverage for development, test and CI narrows that gap. It allows migration scripts, application queries and ranking assumptions to be exercised on every commit rather than discovered after deploy. Worth flagging for operators: non-production means exactly that, and CI parity does not remove the need for load, relevance and failure testing against production-sized data.
The longer-term appeal here is workflow simplicity. One query language, one data copy for functional tests, fewer moving parts between laptop and production. If the extension holds up under concurrency and data growth, it gives small teams search without a search team, and gives larger teams a cleaner path to prototype before scaling out. That is a modest, useful kind of progress.
