postgres-elasticsearch-fd VS Searchkick

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...

I run a large scale production application that does something along these lines. If the data needs to be close to real-time, I'd say use `searchkick` + Elasticsearch, and use `searchkick`'s async feature to "stream" the data from your table to the ES index. Your dashboard will then just query from the ES index via searchkick.

You're right, that's actually what we implemented, application-level hooks, but they needed development and maintenance effort that come for free with the adapter we're using for OpenSearch integration, which also comes with welcome features: synonyms, partial matches, and many others.

Spoiler, the adapter is Searchkick: https://github.com/ankane/searchkick

Searchkick

Normally for Rails applications you would use a gem like searchkick since it greatly reduces the initial Elasticsearch complexity.

We lean heavily on Elasticsearch at CompanyCam. One of it's primary use cases is serving our highly filterable project feed. It is incredibly fast, even when you apply multiple filters to your query and are searching a largish data set. Our primary interface for interacting with Elasticsearch is using the Searchkick gem. Searchkick is a powerhouse and provides so many features out of the box. One place where we bump up against the edges is when trying to reindex a large collection.

Convinced? Ok read on and I’ll show you what switching from Elasticsearch to Meilisearch looked like for a real production app — ScribeHub. We also moved from Ankane’s excellent Searchkick gem to the first party meilisearch-rails gem and I’ll show you the changes there as well.

If you want to use a look-aside pattern (like you might have used with Searchkick + Elasticsearch), you should check out ActiveStash: https://github.com/cipherstash/activestash

Searchkick learns what your users are looking for. As more people search, it gets smarter and the results get better. It’s friendly for developers - and magical for your users. BONUS: it's written and supported by "ankane" who has flawless reputation amongst the Ruby community.