Performing a full-text search in SQL allows you to efficiently query large text-based data, such as documents or product descriptions, using specialized indexing and search functions. Unlike simple LIKE or = operators, full-text search handles natural language queries, supports partial word matches, and improves performance by leveraging pre-built indexes. To use it, you typically need to create a full-text index on the target column(s), then query using functions like CONTAINS (in SQL Server) or MATCH ... AGAINST (in MySQL). The exact syntax varies by database system, but the core principles remain consistent.
First, you must set up a full-text index on the column(s) you want to search. For example, in SQL Server, you’d create a full-text catalog and index using statements like:
CREATE FULLTEXT CATALOG ft_catalog;
CREATE FULLTEXT INDEX ON Products(Description) KEY INDEX PK_Products ON ft_catalog;
This index tokenizes the text, breaking it into searchable terms. In MySQL, you’d use ALTER TABLE Products ADD FULLTEXT(Description);. PostgreSQL uses a tsvector column type and a GIN index for similar functionality. Index creation is a one-time process, but it requires planning—columns must be explicitly included, and some databases require enabling full-text features beforehand.
Once the index is ready, query using full-text functions. In SQL Server, CONTAINS(Description, 'organic AND sustainable') finds rows where both terms appear. The FREETEXT function handles synonyms and inflectional forms, like searching for “running” and matching “ran.” In MySQL, WHERE MATCH(Description) AGAINST('+organic +sustainable' IN BOOLEAN MODE) performs a boolean search. PostgreSQL uses to_tsquery and @@ operators, such as WHERE Description @@ to_tsquery('organic & sustainable'). These functions support advanced features like proximity searches, weighting terms, and excluding words.
Full-text search also considers language-specific rules (e.g., stemming, stopwords). For instance, searching for “mouse” in English might ignore “mice,” but this depends on the database’s configuration. Performance is generally better than LIKE because indexes avoid full-table scans. However, maintaining indexes during data updates adds overhead, so balance query needs with write frequency. Always test with real-world data to optimize settings like index refresh intervals or custom stopword lists.