val-town-mirror VS rum

Well this is fun...

    git clone https://github.com/pomdtr/val-town-mirror

the rum index has worked well for us on roughly 1TB of pdfs. written by postgrespro, same folks who wrote core text search and json indexing. not sure why rum not in core. we have no problems.

   https://github.com/postgrespro/rum

If you need ranking, and you have the possibility to install PostgreSQL extensions, then you can consider an extension providing RUM indexes: https://github.com/postgrespro/rum. Otherwise, you'll have to use an "external" FTS engine like ElasticSearch.

>Reduce the memory usage of prepared queries

Yes query plan reuse like every other db, this still blows me away PG replans every time unless you explicitly prepare and that's still per connection.

Better full-text scoring is one for me that's missing in that list, TF/IDF or BM25 please see: https://github.com/postgrespro/rum

for postgres, i highly recommend the rum index over the core fts. rum is written by postgrespro, who also wrote core fts and json indexing in pg.

    https://github.com/postgrespro/rum

My experience with Postgres FTS (did a comparison with Elastic a couple years back), is that filtering works fine and is speedy enough, but ranking crumbles when the resulting set is large.

If you have a large-ish data set with lots of similar data (4M addresses and location names was the test case), Postgres FTS just doesn't perform.

There is no index that helps scoring results. You would have to install an extension like RUM index (https://github.com/postgrespro/rum) to improve this, which may or may not be an option (often not if you use managed databases).

If you want a best of both worlds, one could investigate this extensions (again, often not an option for managed databases): https://github.com/matthewfranglen/postgres-elasticsearch-fd...

Either way, writing something that indexes your postgres database into elastic/opensearch is a one time investment that usually pays off in the long run.

Mandatory mention of the RUM extension (https://github.com/postgrespro/rum) if this caught your eye. Lots of tutorials and conference presentations out there showcasing the advantages in terms of ranking, timestamps...

You might be just fine adding an unindexed tsvector column, since you've already filtered down the results.

The GIN indexes for FTS don't really work in conjunction with other indices, which is why https://github.com/postgrespro/rum exists. Luckily, it sounds like you can use your existing indices to filter and let postgres scan for matches on the tsvector.

I suggest to have a look at https://github.com/postgrespro/rum if you haven’t yet. It solves the issue of slow ranking in PostgreSQL FTS.