# Full-text search model

Learn how full-text search differs from literal matching, why SurrealDB fits FTS workloads, and where to find guides on analyzers, indexes, scoring, and non-FTS text operations.

A full-text search database is designed to index and retrieve text-based data (like articles, messages, or comments) based on tokenized and modified parts of the text itself, rather than exact, literal matches. This allows you to:

* Find documents containing certain keywords.
* Search for phrases or words with variants (e.g., “run,” “runs,” “running”).
* Rank results by relevance, not just by literal string matches.

As a multi-model database, SurrealDB has integrated full-text search capabilities so that you can store your data and query it with advanced text search features.

Note: SurrealDB has many other built-in ways of working with text besides full-text search. For more details, see [Other ways to work with text](/docs/learn/data-models/full-text-search/other-ways-to-work-with-text.md).

## How full-text search differs from keyword matching

In traditional databases, you might do something like:

```surql
SELECT * FROM articles WHERE 'fox' IN title;
```

This approach:

- Doesn’t rank results by relevance; it just returns every article containing “fox.”
- Ignores language variations, e.g., “Foxes,” “FoX,” or synonyms like “vixen.”
- May scan an entire table, making it slower for large datasets.

Full-text search, by contrast, uses an inverted index or other specialised structures for fast lookups and can handle a variety of linguistic transformations. It can highlight results and rank them by how relevant or frequent the terms are.

## Advantages of SurrealDB for FTS

- **Unified model**: You can keep your data, relationships, and search logic in a single engine.
- **Flexible schema**: SurrealDB can be schemaless, so adding new fields or text columns doesn’t require schema migrations.
- **Powerful query language**: SurrealQL blends SQL-like syntax with searching syntax (the [`@@` matching operator](/docs/reference/query-language/language-primitives/operators.md#matches) for FTS queries, advanced indexing features, and so on).
- **Real-time updates**: SurrealDB can handle real-time changes, so newly inserted or updated text becomes searchable quickly.

## Implementing full-text search

There are three steps involved in full-text search:

- Defining an analyzer
- Defining an index that uses the analyzer
- Querying using syntax that specifically uses full-text search

The guides in this section walk through each part.

## Where to go next

- [Analyzers and tokenizers](/docs/learn/data-models/full-text-search/analyzers-and-tokenizers.md): `DEFINE ANALYZER`, tokenizers, filters, stemming, and `search::analyze`.
- [Search indexes](/docs/learn/data-models/full-text-search/search-indexes.md): `FULLTEXT ANALYZER` on a single field per index.
- [Scoring and ranking](/docs/learn/data-models/full-text-search/scoring-and-ranking.md): the `@@` operator, BM25, highlights, and `search::score` / `search::highlight`.
- [Other ways to work with text](/docs/learn/data-models/full-text-search/other-ways-to-work-with-text.md): sorting and collating text, contains, fuzzy matching, regex, and related string tools.

For reference, see [`DEFINE INDEX`](/docs/reference/query-language/statements/define/indexes.md), [`DEFINE ANALYZER`](/docs/reference/query-language/statements/define/analyzer.md), and [Search functions](/docs/reference/query-language/functions/database-functions/search.md).