How does full-text search work in relational databases, and when is it necessary to switch to search engines?

Full-text search in relational databases allows for efficiently searching words and phrases in text columns without using the slow LIKE operator with wildcards. Regular pattern matching forces the database to scan the entire table row by row, whereas full-text search uses pre-built inverted indexes. These indexes contain lists of words and references to the rows where these words appear, providing fast access to relevant data.

To implement such a search, modern DBMS offer special data types and functions that account for language morphology, stop words, and result relevance. The setup process typically includes the following stages:

Built-in full-text search handles medium-complexity tasks within standard web applications remarkably well, sparing developers from the need to integrate third-party services. If the volume of text data is moderate and search requirements are limited to simple product description searches or blog post lookups, the capabilities of a relational database will be entirely sufficient.

However, as a project grows, limitations emerge that require a transition to specialized search engines like Elasticsearch or OpenSearch. These limitations include complex fuzzy queries with typo correction, real-time faceted filtering across multiple parameters, and the need to horizontally scale the search cluster independently of the main database.