Full-text search model

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.

How full-text search differs from keyword matching

In traditional databases, you might do something like:

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 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: DEFINE ANALYZER, tokenizers, filters, stemming, and search::analyze.

• Search indexes: FULLTEXT ANALYZER on a single field per index.

• Scoring and ranking: the @@ operator, BM25, highlights, and search::score / search::highlight.

• Other ways to work with text: sorting and collating text, contains, fuzzy matching, regex, and related string tools.

For reference, see DEFINE INDEX, DEFINE ANALYZER, and Search functions.