First things first, head over to https://supabase.io/ and sign up for an account. Once you're in, create a new project. Make sure to jot down your project's URL and API key from the dashboard. You'll need these later.

Next, go to the "Table Editor" in the Supabase dashboard and whip up a new table. For full-text search, you'll want one or more columns to be of type text. Let's say you create a table called articles with columns id, title, and content.

create table articles (
  id serial primary key,
  title text,
  content text
);

Time to add some sample data to your table. This will help you test the full-text search. You can do this using SQL in the SQL Editor:

insert into articles (title, content) values
('Supabase Full-text Search', 'Learn how to implement full-text search with Supabase.'),
('Introduction to Supabase', 'Supabase is an open-source Firebase alternative.'),
('Advanced Supabase Queries', 'Explore advanced queries in Supabase.');

Full-text search in PostgreSQL relies on indexes. So, let's create one for your articles table. Use the SQL Editor for this:

create index articles_search_idx on articles using gin(to_tsvector('english', title || ' ' || content));

This command sets up a GIN (Generalized Inverted Index) on the combined title and content fields, making searches faster.

Now, you can run full-text search queries. For a basic search, use the @@ operator with to_tsquery in the SQL Editor:

select * from articles
where to_tsvector('english', title || ' ' || content) @@ to_tsquery('full-text & search');

Feel free to tweak the search term 'full-text & search' to whatever you need.

To bring full-text search into your app, use Supabase client libraries. For JavaScript, start by installing the Supabase client:

npm install @supabase/supabase-js

Then, set up the client and make a query:

import { createClient } from '@supabase/supabase-js'

const supabaseUrl = 'https://your-supabase-url.supabase.co'
const supabaseKey = 'your-public-anon-key'

const supabase = createClient(supabaseUrl, supabaseKey)

async function searchArticles(query) {
  const { data, error } = await supabase
    .from('articles')
    .select('*')
    .filter('tsvector', 'fts', query)

  if (error) {
    console.error('Error fetching articles:', error)
    return []
  }

  return data
}

searchArticles('full-text & search')
  .then(articles => {
    console.log('Search Results:', articles)
  })

Don't forget to replace your-supabase-url and your-public-anon-key with your actual Supabase project's URL and API key.

For larger datasets, you might want to optimize index creation and query formulation to strike a balance between performance and accuracy. Keep your indexes updated regularly to maintain search efficiency.

You can use plainto_tsquery for simple searches or phraseto_tsquery for phrase matching to make the search more intuitive:

select * from articles
where to_tsvector('english', title || ' ' || content) @@ plainto_tsquery('full-text search');

select * from articles
where to_tsvector('english', title || ' ' || content) @@ phraseto_tsquery('full-text search');

And there you have it!