The folks at thoughtbot have a great article about Full Text Search optimizations, where they add a pre-calculated tsvector column, keeping it up-to-date with triggers. This tells me that the slowness is in re-parsing and normalizing the document into a tsvector data type every single time the query is executed. SELECT to_tsvector ( 'english', description ) FROM jobs - ~130ms SELECT description FROM jobs - ~15ms There are a number of ways you can configure pg_search: From support for prefixes and negation, to specifying which language dictionary to use when normalizing the document, as well as adding multiple, weighted columns.īy default pg_search uses the simple dictionary, which does zero normalization, but if we wanted to normalize our document using the english dictionary, searching across both the title and description, it would look like: "id" AS pg_search_id, (ts_rank ( (to_tsvector ( 'simple', coalesce ( "jobs". * FROM "jobs" INNER JOIN ( SELECT "jobs". Let's add the Gem to our Gemfile and get started: We will be focusing on the Search Scope approach in this article, as it lets us dive into the configuration options available when working with Full Text Search in Rails. Search Scope: Search within a single model, but with greater flexibility. This would be perfect for adding federated search functionality to your app. Using Multi Search we could search across all of them at the same time, seeing a single set of search results. Imagine having three models: Product, Brand, and Review. Multi Search: Search across multiple models and return a single array of results. We will be using the pg_search Gem, which can be used in two ways: With a basic understanding of Full Text Search under our belts, it's time to take our knowledge over to Rails. Implementing Postgres Full Text Search in Rails Later, we'll see how to give additional weight (precedence) to the title column. This allows us to search both the title and the description at the same time. This query is actually concatenating (using ||) two tsvector fields together. Taking the text “looking for the right words”, we can see how Postgres stores this data internally, using the to_tsvector function: Let's break down the basics of Full Text Search, defining and explaining some of the most common terms you'll run into. If you are interested in efficient Full Text Search in Postgres with Django, you can read our article about it. Instructions on how to run this application locally and how to load the sample data referenced within this article can be found in the README. The full source code used in this article can be found here. We will learn how to search multiple columns at once, to give one column precedence over another, and how to optimize our Full Text Search implementation, taking a single query from 130ms to 7ms. In this article, we are going to learn about the inner workings of Full Text Search in Postgres and how to easily integrate Full Text Search into your Rails application using a fantastic gem named pg_search. To summarize, here is a quick overview of popular built-in Postgres search options: Postgres Feature
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