pg_search VS Pagy

On the backend, we'll need a few tools. Apart from the classics (ActiveRecord scopes and the pg_search gem), you’ll see how the (yet officially unreleased but production-tested) all_futures gem, built by SR authors, will act as an ideal ephemeral object to temporarily store our filter params and host our search logic. Finally, we’ll use pagy for pagination duties.

You can take a look at pg_search if you’re using Postgres

This was a really good read, thanks. I've got into the habit of jumping straight to PgSearch but could definitely apply this approach to some existing projects.

Cleaner, more performant database queries: Definitely don't just leave your query sitting in the controller! For production use cases, you'd want to consider an option like pg_search

If you are using Rails with Postgres you can use pg_search gem to build the named scopes to take advantage of full text search.

https://github.com/Casecommons/pg_search

Hey guys. Looking to release an app for mobile that will be using a rails API. The app will heavily rely on search. I know the go-to is to use elasticsearch but wanted to see if there was enough user demand for the MVP before shelling out $50/mo for the heroku add on. In the mean time I've been using pg_search. From the eye test it's performing okay but will be adding a table that houses over 350K records. With this in mind I was wondering if you all had any tips for increasing the overall speed for search from the model and controller level. Also should note that I'm open to any other free search gems if they deem bette fit.

There are two basic search configurations with pg_search, a Single Model search scope or a multi Model configuration. In my case I am only using the Single Model configuration, but you can read more about multi-search in the documentation.

Now, let's dive into the pagination part of this post: setting up Pagy for handling pagination in our Rails application. If you haven't included the Pagy gem in your project, you'll need to add it manually. Here's how you can do it:

Several gems are available for pagination in Rails, but the Pagy gem is one of the most popular and efficient. It is a fast and lightweight library that provides a simple and flexible API. In this article, we’ll explore how to use it to implement pagination in Rails.

> Am I understanding correctly that there’s a significant difference in performance between using a ViewComponent + a partial vs. a ViewComponent which renders html via a tag - from inside the component?

I don't think there will be much difference at all in everyday use, but some libraries that value performance don't avoid templates for that reason, Pagy for example.

https://github.com/ddnexus/pagy

Personally I omit them in my projects whenever we want to customise attributes, I hate seeing stuff like this in templates:

    
Some header

The next step was about backporting the templates, adding Pagy gem for handling pagination and creating the controller. I was then able to show the listings with the models, but the filtering was not working.

On the backend, we'll need a few tools. Apart from the classics (ActiveRecord scopes and the pg_search gem), you’ll see how the (yet officially unreleased but production-tested) all_futures gem, built by SR authors, will act as an ideal ephemeral object to temporarily store our filter params and host our search logic. Finally, we’ll use pagy for pagination duties.

Pagination

In our application, we will use Pagy to implement pagination. Let’s install Pagy now, following along with the Pagy quick start guide.

For pagination I tend not to use gems like pagy or kaminari, instead implement this functionality just using limit and offset.

Kaminari hooks onto ActiveRecord associations and makes it super easy to page them. Pagy is another option that seems to have a solid API but I haven't tried it yet.

But how is this implemented? Well, if you use pagination in your index methods with gems like pagy, kaminari or will_paginate you will find that the same idea is happening here, they are using the power of SQL's LIMIT and OFFSET to fetch only a portion of the data each time. So in the 1 million users example, find_each will perform 1000 thousand queries with a limit of 1000 while changing the offset properly so we don't miss any record.