PostgreSQL continues to dominate the database landscape—over 84,000 companies rely on it as of 2025, and that number keeps growing.
Here's the thing: keyword search isn't going away. It remains fundamental for most applications. But here's where it gets interesting—Tiger Data recently open-sourced pg_textsearch, which brings BM25-ranked search capabilities directly into Postgres. This isn't just another search tool; it works seamlessly with pgvector to enable hybrid search workflows.
For developers building performant search layers, this combination changes the game. You get efficient full-text search powered by BM25 algorithms while maintaining vector similarity capabilities in the same database. No need to juggle multiple systems—everything lives in Postgres.
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alpha_leaker
· 6h ago
No, no, pg_textsearch is really amazing. I finally don't have to mess around with that bunch of broken Elasticsearch stuff anymore.
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ThreeHornBlasts
· 11h ago
It's just about pushing search capabilities into Postgres; the real issue is that most people are still too lazy to optimize their queries...
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TokenVelocityTrauma
· 11h ago
The PostgreSQL ecosystem is getting competitive again. Pairing pg_textsearch with pgvector allows for a one-stop solution, eliminating the need for multiple systems. Truly convenient.
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UnluckyValidator
· 11h ago
The operation of pg_textsearch is interesting; BM25+pgvector directly eliminated a bunch of search solutions.
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AllTalkLongTrader
· 11h ago
Wow, the combination of pg_textsearch + pgvector is really awesome. One library handles all search needs, no more juggling multiple systems.
PostgreSQL continues to dominate the database landscape—over 84,000 companies rely on it as of 2025, and that number keeps growing.
Here's the thing: keyword search isn't going away. It remains fundamental for most applications. But here's where it gets interesting—Tiger Data recently open-sourced pg_textsearch, which brings BM25-ranked search capabilities directly into Postgres. This isn't just another search tool; it works seamlessly with pgvector to enable hybrid search workflows.
For developers building performant search layers, this combination changes the game. You get efficient full-text search powered by BM25 algorithms while maintaining vector similarity capabilities in the same database. No need to juggle multiple systems—everything lives in Postgres.