It works well enough for me. I can't say how large my index set is (because I honestly don't have the figures to hand) but on the current project, a search of around 50,000 products, 10,000 product families and a whole lot of associated data (product attributes, datasheets, etc), an uncached search takes around 65ms.

To put it in context, the psycopg2 calls to PostgreSQL take about 100ms to retrieve the associated data once I've found my search results with Whoosh.

(Most of my response time sits with SQLAlchemy ORM, building up matrices of data, which is why in production I'm caching the more complex queries with memcached).

Overall, for a project of this size (I can't imagine having to index more than a few hundred thousand objects) I'm very happy with Whoosh. If I get to the point of indexing millions of objects, I'll optimize it then.

not in my experience. i put id3 tags from ~32K songs in it and a query for an artist was around 700ms. same query in ES was like 10ms, same qry to sqlite db (select * from track where artist=...) was about the same as ES. the docs for whoosh make these claims that it's fast, but I haven't seen anything to backup the claims in unit tests or otherwise. I'm sure it's still useful for some purposes but ES is so easy to setup that I don't bother.