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Table of Contents
What is Full-Text Search?
Common implementation methods in databases
Use scenarios and precautions
How to start using it?
Home Database SQL Text Search in SQL: Full-Text Search Capabilities

Text Search in SQL: Full-Text Search Capabilities

Jul 19, 2025 am 01:27 AM

Full-Text Search instead of LIKE when handling complex text searches in SQL, because full-text search supports multi-word matching, relevance sorting and other functions through word segmentation and indexes, while LIKE only supports wildcard matching; common database implementations include MySQL's FULLTEXT index and MATCH AGAINST syntax, PostgreSQL's tsvector and tsquery types, independent full-text search services that SQL Server needs to be configured additionally, and SQLite's FTS3/FTS4 extension module; when using it, you should note that not all databases enable this function by default, full-text index takes up more storage space, data insertion or update will have performance overhead, and Chinese needs to configure additional word segmentation to avoid search results missed; if using MySQL, you can create a table with FULLTEXT index and use MATCH AGAINST query, in actual applications, you can also optimize the query effect by combining weights, Boolean patterns, etc.

Text Search in SQL: Full-Text Search Capabilities

When doing text search in SQL, many people think of LIKE statements first. But when you really need to deal with complex text retrieval needs, LIKE is not enough. At this time, you need to understand the full-text search function of the database.

Text Search in SQL: Full-Text Search Capabilities

Simply put, it is a technique specifically used to match keywords in large segments of text. The difference between LIKE is that LIKE can only do wildcard matching, while full-text search will first tokenize the text and then create an index, which can support more complex queries, such as "including multiple words" and "sort by correlation" operations.

For example, if you look for "database optimization" in the article library, LIKE will scan progressively; and if you search with full text, it can directly find articles containing these two words from the index and tell you which documents are the most relevant.

Text Search in SQL: Full-Text Search Capabilities

Common implementation methods in databases

The implementation methods of different database systems are slightly different:

  • MySQL : Use FULLTEXT index, and after creation, you can query using MATCH AGAINST syntax.
  • PostgreSQL : Supports full-text retrieval through tsvector and tsquery types.
  • SQL Server : Provides independent full-text search service, which needs to be enabled and configured additionally.
  • SQLite : There is an FTS3/FTS4 extension module that supports basic full-text search functions.

Although the syntax is different, the core idea is the same: first create an index, and then use specific functions or syntax to query.

Text Search in SQL: Full-Text Search Capabilities

Use scenarios and precautions

Full-text search is more suitable for the following situations:

  • Need to quickly find keywords in large amounts of text
  • It is required to support advanced functions such as multi-word combination, synonyms, and fuzzy matching.
  • Results need to be sorted according to correlation

But you have to pay attention to:

  • Not all databases enable this feature by default
  • Full-text index takes up more storage space than normal index
  • Additional performance overhead when inserting or updating data
  • Word segmentation method will affect search results, especially in Chinese, you need to configure an additional word segmentation device.

Sometimes you think "it should be found", but if you miss it, it may be a question of word participle.

How to start using it?

If you are using MySQL, you can try this:

  1. Add FULLTEXT index when creating the table:

     CREATE TABLE articles (
     id INT PRIMARY KEY AUTO_INCREMENT,
     title VARCHAR(200),
     content TEXT,
     FULLTEXT (content)
    );
  2. Insert several test data

  3. Query:

     SELECT * FROM articles 
    WHERE MATCH(content) AGAINST('Database Optimization');

Of course this is just the basic usage. In practical applications, you may also need to add weights, combine Boolean patterns, or jointly query with other conditions.

Basically that's it. If you want to really use the full text search, it depends on your specific data structure and query needs.

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