Scenic VS Searchkick

To model our Deliverable class, we will need a view. We will use the popular scenic gem, which provides some useful generators for creating views with their respective migrations, and utilities to handle views versioning.

+1 for scenic - https://github.com/scenic-views/scenic

If the data doesn't need to be close to real-time, and if your DB can handle a bit of load, I'd use a "batch" approach. To do this, I'd create a materialized view in your relational DB that you'd then refresh periodically. The easiest way to do this is with the `scenic` gem. Once you've done this, you can simply create a new model and set the `table_name` to the name of the materialized view, and then treat it as a regular model.

I use the scenic gem to manage views which uses raw sql files: https://github.com/scenic-views/scenic

add scenic

TIL about fx gem for storing triggers in schema.rb. That makes me so happy because scenic gem for creating database views is one of my favorites. Postgres is very powerful and it's great to see tools for exposing that through Rails.

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.