postgres-elasticsearch-fd VS zombodb

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.

I used a foreign data wrapper to query elasticsearch indexes from within postgres.[0]

It pushed alot of complexity down away from higher-level app developers not familiar with ES patterns.

[0]: https://github.com/matthewfranglen/postgres-elasticsearch-fd...

And lots of interesting extensions use it, like

https://github.com/tembo-io/pgmq

https://github.com/zombodb/zombodb

https://github.com/supabase/pg_jsonschema

Point 2 is generally solvable via engineering effort and careful dedicated code. From the existing tools, PGSync is an open source project that aims to specifically solve this problem. ZomboDB is an interesting Postgres extension that tackles point 2 (and I think partially point 3), by controlling and querying Elasticsearch through Postgres. I haven't yet tried either of these two projects, so I can't comment on their trade-offs, but I wanted to mention them.

Curious, did you try zombodb? [https://www.zombodb.com/]

What about zombodb, do you think that quickwit has all the necessary APIs?

No. Haha. Was just the right name for https://github.com/zombodb/zombodb at the time. Software where the only limit is yourself!

> The hardest part of building any search engine is keeping the index up-to-date with changes made to the underlying data store.

This deserves mention, as it solves that problem: https://github.com/zombodb/zombodb

From the README:

> ZomboDB brings powerful text-search and analytics features to Postgres by using Elasticsearch as an index type. Its comprehensive query language and SQL functions enable new and creative ways to query your relational data.

> From a technical perspective, ZomboDB is a 100% native Postgres extension that implements Postgres' Index Access Method API. As a native Postgres index type, ZomboDB allows you to CREATE INDEX ... USING zombodb on your existing Postgres tables. At that point, ZomboDB takes over and fully manages the remote Elasticsearch index and guarantees transactionally-correct text-search query results.

I find other things also hard in search engines: dealing with the plethora of human languages and all the requirements we may have to processing them. A mature solution like ES therefor is almost a must in the more demanding cases.

You can read more about it on our GitHub repo, but basically it brings most of the power of elasticsearch’s searching and analytics abilities straight into Postgres.