[fatbird]

I used it at 9.4 for a document management system with thousands, not millions, of PDFs that got indexed on upload, and it worked extremely well at that scale--fast, and with all the basic text search features well-covered (tokenization, stemming, etc.). A big win for me was that doing it well in Postgres meant the site could stay a simple Django site rather than adding another service.

[ngrilly]

Did you store the plain text of each PDF in PostgreSQL or just the ts_vector resulting from the plain text?

[fatbird]

IIRC, I stored the plain text too because the engine can return contextually marked up plaintext after finding it in the ts_vector.

[ngrilly]

You're right, PostgreSQL needs the plain text to highlight it with ts_headline. It's similar to Elasticsearch keeping the original document in the _source attribute. Thanks!

[pumainmotion]

Curious to know since you mentioned that it was fast for thousands of PDFs... any rough timing information on some of your queries for that kind of dataset?

[fatbird]

I'm really reaching here to recall, but the short version is that actual searches never took more than a second. All I really cared about was how noticeable a delay to expect, and it was never more than that.

On a bulk import of 1,000+, it took a couple minutes to ingest them. This was all on a $20/month VPS.