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Full-text search and other searches with Data Connect in Firebase: Web Architecture & Implementation Guide

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Alex Patterson
Alex Patterson

For years, choosing Firebase meant trading away relational SQL joins, strict foreign keys, and pgvector similarity search in favor of NoSQL document denormalization. Following the announcement of Full-text search and other searches with Data Connect by Andrea Wu on the Official Firebase Blog (2025-09-02), here is our hands-on CodingCat.dev breakdown of what actually shipped, how it works under the hood, and how we wire it into production TypeScript, Next.js, Astro, SvelteKit, React, and Angular applications. Note: Firebase Data Connect has been renamed to SQL Connect. Learn more in the SQL Connect announcement.

The Mental Model: Why Full-text search and other searches with Data Connect Matters for Web Devs

As a Google Developer Expert (GDE) for Firebase and Senior Solutions Engineer at CodingCat.dev, I evaluate every Firebase update through one lens: does it reduce boilerplate while keeping our runtime security and cloud bills predictable? Firebase Data Connect bridges that gap by pairing a managed Google Cloud SQL for PostgreSQL instance with a declarative GraphQL schema compiler that automatically generates type-safe TypeScript, React, and Angular SDKs alongside server-enforced @auth directives.

AJ Pro-Tip from Alex: Keep your authorization logic inside your Data Connect GraphQL operations using @auth(level: USER) and server-evaluated expressions like auth.uid and request.time rather than trusting client-supplied user IDs in query variables.

How It Works: Technical Breakdown & Implementation

Data Connect now supports full-text search, powered by Postgres. Full-text search lets you quickly and efficiently locate information within large datasets by searching for keywords and phrases across multiple columns at once.

There are many ways to search within a database, and using Data Connect, you can do search using:

  • String pattern filters: matches exact text or regular expressions
  • Vector similarity search: finds semantically similar rows for a single column (for example, recommendations or “More like this”)
  • Full-text search: looks for data across multiple columns based on words and phrases rooted in the rules of a particular language (think your favorite search engine)

Let’s use the movie app to talk about examples for each of these searches.

String pattern filters

When you use a string or a regular expression to search, the search results will only contain that exact string. To that note, capitalization is important, and the search will find exact character matches. For example, if you search “justice”, only movies containing “justice” will come up. You can add as many filters as you want, so on top of filtering for “justice”, you can also search for movies with release year between 1900 and 2030 with a rating between 1 and 10.

To perform string pattern filters, use where in your query:

query
  $input: 
  $minYear: 
  $maxYear: 
  $minRating: 
  $maxRating: 
) 
  moviesMatchingTitle
    where: {
      _and
        { 
        { 
        { 
        { 
        { 
      ]
    }
  ) {
    id
    title
    genre
    rating
    imageUrl
  }

Vector similarity search

Vector similarity search is a bit more advanced: with the help of Vertex AI, Data Connect will look for content that is similar to the searched text. Let’s say your user is in the mood for a “romantic and sciency” movie. To see which movie descriptions match this complex mood, you can use vector similarity search.

As a side note, each movie also has a genre. In the case of vector search, you can only find matches for one column - in this example, the search goes through the description column. If you want to look in the genre column, you can do that instead, but you can’t look at both at the same time.

To perform vector search, you need to add a new field of Vector type in your schema. Using the example above to look for “romantic and sciency” movies in the description, add a field to hold the vector embeddings associated with the movie description. In this schema, descriptionEmbedding is added to store vector embeddings for the description field:

type
   id: 
   title:
   description:
   descriptionEmbedding:
   # other fields
}

Full-text search

Full-text search is even more advanced, as it can search an entire table across columns to find words, phrases, or numbers. It performs lexical stemming, which helps match different forms or tenses of the same word.

  • string pattern filters, because it’s not looking for only exact matches
  • vector similarity search, because it can search only in one column and requires Vertex AI. Full-text search searches across multiple columns and doesn’t need Vertex AI.

When you use full-text search, there are different query formats you can use:

  • QUERY: familiar semantics to web search engines (e.g. quoted strings, AND and OR)
  • PLAIN: matches all of the words but not necessarily together (“brown dog” will match “the brown and white dog” or “the white and brown dog”)
  • PHRASE: matches an exact phrase (“brown dog” will match “the white and brown dog” but won’t match “brown and white dog”)
  • ADVANCED: you can create complex queries using the full set of tsquery operators.

QUERY is the default, and if you don’t specify a queryFormat in your query, this is the format that gets used.

Let’s say you wanted to look for movies that have “justice AND not detective”. You’ll use QUERY in this case:

Now, let’s say you want to search for “crimson rising” - you can use either PHRASE or PLAIN, depending on what result you want. If you’re looking for an exact match of the phrase, use PHRASE, otherwise use PLAIN. Here, using PLAIN shows a result of “Rise of the Crimson Empire”:

To add full-text search, include the @searchable directive to the String in your schema that you want to search over. For example:

type
  @table

  # The fields you want to search over
  title
  genre
  description
  tags

  # Some other fields that aren't searched over
  rating
  imageUrl
  releaseYear
}

Once you add this directive, you can then perform full-text search by adding the <pluralType>_search field to a query. In this case, it’ll be movies_search:

query
  movies_search(query: $query, queryFormat:
    id
    title
    imageUrl
    releaseYear
    genre
    rating
    tags
    description
  }
}

As mentioned before, queryFormat can be QUERY, PHRASE, PLAIN, or ADVANCED. If queryFormat is not specified, QUERY is the default.

Why not always use full-text search?

If full-text search is the most advanced search and seemingly covers the use cases for string pattern filters and vector similarity search, why not just always use full-text search?

That’s a great question. There are two main main reasons:

  • vector similarity search is better for when you want the semantic meaning of your search words to matter more than the actual words used to perform the search

Vector similarity search works better than full-text search when the meaning behind the search is more important than the actual words used for the search. For example, if you wanted to search for car insurance, vector similarity search might return results that include the words “vehicle” or “automobile”. Full-text search returns results with “car”, because it doesn’t look for words with similar meanings to perform your search nor does it use the underlying idea or concept of a query. Vector similary search does.

As is common in software development, more advanced solutions usually come at a cost of resources - and that’s one of the considerations when choosing which search to use.

String pattern filters use the least memory and disk space since it does not require any indexes or generated columns. However, it is less performant when searching larger documents.

Full-text search and vector similarity search are both performant over large documents, but full-text search adds memory and storage overhead to store a generated column and an index for each search.

As a result, while full-text search is ultimately a good choice for searching, it does come with tradeoffs. If memory and storage overhead (which usually translates into additional expenses for apps with many users) is a concern, you might choose either string pattern filters or vector similarity search, depending on what you’re searching for.

The documentation summarizes the differences among full-text search, vector similarity search, and string pattern filters.

Production Web Architecture & Security Checklist

Whether we are deploying on Firebase App Hosting, Cloudflare Workers, or Vercel, shipping AI features to production requires defense-in-depth across three layers:

  • Enforce Firebase App Check & Attestation: Protect your web endpoints, Cloud Functions, Data Connect GraphQL resolvers, and Firebase AI Logic calls against unauthorized replay or billing abuse using Firebase App Check with reCAPTCHA Enterprise.
  • Validate Locally with the Firebase Emulator Suite: Run deterministic integration tests against local Firestore, Auth, Functions, and Data Connect PostgreSQL emulators before promoting schema or rule changes to production.
  • Keep Server & Client Boundaries Type-Safe: Use the modular Firebase Web SDK and generated TypeScript bindings so SSR frameworks like Next.js, Astro, SvelteKit, and Angular serialize state cleanly without hydration flickers.

Conclusion

Accelerate your full-stack web and AI development by pairing Full-text search and other searches with Data Connect with strict Security Rules, Firebase App Check, and local emulator testing. Come tell us on X (@CodingCatDev) and LinkedIn (CodingCat.dev) how you are using Firebase in your stack, and subscribe to the CodingCat.dev YouTube Channel so you don't miss what we build next!

Watch Our Related Firebase Video Tutorial on YouTube

Want to see how we architect and debug Firebase web applications live? Watch our related CodingCat.dev session Firebase SQL with Data Connect below, and make sure to Subscribe to CodingCat.dev on YouTube for weekly full-stack web and AI deep dives:

Resources, Official Docs & Related CodingCat.dev Guides

Frequently Asked Questions (FAQ)

What are the key capabilities introduced in Full-text search and other searches with Data Connect?

Full-text search and other searches with Data Connect (announced 2025-09-02 by the Firebase team) enhances the platform for Firebase, Google Cloud, Web Dev, AI, SQL workloads. Note: Firebase Data Connect has been renamed to SQL Connect. Learn more in the SQL Connect announcement. Developers can review the primary announcement on the Firebase Blog alongside our implementation patterns and production checklist above.

How do web developers use Full-text search and other searches with Data Connect with Next.js, SvelteKit, React, or Angular?

Web developers can integrate these features using the modular Firebase JavaScript/TypeScript SDK, Firebase Data Connect web bindings, and Firebase App Hosting. Always pair client-side SDK initialization with Firebase App Check and strict Security Rules, and test locally with the Firebase Local Emulator Suite before deploying.

Where can I watch video tutorials on Firebase Web and AI development?

You can watch our hands-on Firebase video walkthroughs on the CodingCat.dev YouTube Channel, including Firebase SQL with Data Connect, and follow us on X (@CodingCatDev) for weekly full-stack tutorials.