pg-ulid VS postgres-elasticsearch-fd

Here's one[1], not actively maintained though.

[1] https://github.com/edoceo/pg-ulid

Yeah, just use a UUID unless the bits to store the UUID really are your driving limitation (they're not), having a UUID that is non-linear is almost always the most straight-forward option for identifying things, for the tradeoff of human readability (though you can get some of that back with prefixes and some other schemes). I'm not going to rehash the benefits that people have brought up for UUIDs, but they're in this thread. At this point what I'm concerned about is just... what is the best kind of UUID to use -- I've recently started using mostly v1 because time relationship is important to me (despite the unfortunate order issues) and v6[0] isn't quite so spread yet. Here's a list of other approaches out there worth looking at

- isntauuid[1] (mentioned in this thread, I've given it a name here)

- timeflake[2]

- HiLo[3][4]

- ulid[5]

- ksuid[6] (made popular by segment.io)

- v1-v6 UUIDs (the ones we all know and some love)

- sequential interval based UUIDs in Postgres[7]

Just add a UUID -- this almost surely isn't going to be what bricks your architecture unless you have some crazy high write use case like time series or IoT or something maybe.

[0]: http://gh.peabody.io/uuidv6/

[1]: https://instagram-engineering.com/sharding-ids-at-instagram-...

[2]: https://github.com/anthonynsimon/timeflake

[3]: https://en.wikipedia.org/wiki/Hi/Lo_algorithm

[4]: https://www.npgsql.org/efcore/modeling/generated-properties....

[5]: https://github.com/edoceo/pg-ulid

[6]: https://github.com/segmentio/ksuid

[7]: https://www.2ndquadrant.com/en/blog/sequential-uuid-generato...

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...