Relational databases handle full-text search through specialized indexing and querying techniques designed to efficiently search large text fields. Unlike standard SQL queries that rely on exact matches or simple pattern matching (e.g., LIKE), full-text search enables keyword-based searching with features like stemming, ranking, and phrase matching. Databases like PostgreSQL, MySQL, and SQL Server implement full-text search using inverted indexes, which map keywords to their locations in the text. For example, PostgreSQL uses tsvector to store processed text (tokenized, normalized, and stripped of stop words) and tsquery to represent search conditions. These structures allow the database to quickly locate documents containing specific terms without scanning entire tables.
When a full-text index is created, the database parses the text into tokens, applies language-specific rules (e.g., removing plurals or verb conjugations), and builds an index that maps terms to their source records. For instance, MySQL’s FULLTEXT index breaks text into words based on spaces and punctuation, while PostgreSQL’s to_tsvector function converts text into a normalized format. Queries use operators like @@ (in PostgreSQL) or MATCH() AGAINST() (in MySQL) to search the indexed terms. For example, SELECT * FROM articles WHERE to_tsvector('english', content) @@ to_tsquery('english', 'database & performance') finds articles containing both “database” and “performance.” The database also ranks results by relevance, often using algorithms like TF-IDF (Term Frequency-Inverse Document Frequency) to prioritize documents where terms appear frequently but aren’t overly common across the dataset.
However, relational databases have limitations in full-text search compared to dedicated engines like Elasticsearch. They may struggle with advanced features like fuzzy matching, synonym expansion, or handling large-scale distributed data. For example, while PostgreSQL supports fuzzy search via the pg_trgm extension, it requires manual configuration and isn’t as seamless as Elasticsearch’s built-in analyzers. Additionally, maintaining real-time full-text indexes on high-write tables can impact performance. Developers often use relational databases for basic full-text needs (e.g., product descriptions or blog posts) but switch to dedicated search systems when scalability, complex queries, or advanced linguistics are required. Choosing the right approach depends on balancing simplicity, performance, and feature requirements.