Indexing for Full-Text Search

In this lesson, you will learn how to optimize high-performance string matching using Full-Text Search (FTS) indexes instead of traditional LIKE patterns.

The Problem with LIKE

When you search for a substring in a large text column using WHERE content LIKE '%database%', the database engine performs a full table scan. Even with a B-Tree index, a leading wildcard (%) prevents the engine from using the index, leading to linear performance degradation as your table grows.

The FTS Solution

A Full-Text index creates a specialized inverted index. Think of this like the index at the back of a textbook: instead of scanning every page for the word "database," the engine consults an internal lookup table that points directly to the rows containing that specific term.

In MySQL and MariaDB, you implement this by adding a FULLTEXT index to your schema:

-- Create a table with a Full-Text index
CREATE TABLE articles (
    id INT AUTO_INCREMENT PRIMARY KEY,
    title VARCHAR(255),
    body TEXT,
    FULLTEXT(title, body)
);

-- Perform an optimized search
SELECT * FROM articles 
WHERE MATCH(title, body) AGAINST('indexing performance');

How it Works

When you query using MATCH() ... AGAINST(), the database engine breaks the search string into individual "tokens" (words). It then rapidly identifies rows that contain these tokens. This is significantly faster than regex-based matching because the inverted index structure is specifically designed to handle unstructured text data.

Important Considerations

1. Stopwords: By default, engines ignore common words (like "the", "is", "at") to keep the index size manageable and performance high.
2. Minimum Word Length: MySQL usually ignores words shorter than 3 or 4 characters by default (innodb_ft_min_token_size).
3. Natural Language vs. Boolean: While Natural Language mode ranks results by relevance, Boolean mode allows you to use operators like + (required) and - (excluded) to fine-tune search precision.

Unlike traditional B-Tree indexes that store keys in sorted order, Full-Text indexes store a map of unique words to their document locations, enabling sub-second search results across massive text datasets.

Key Takeaway: Replace slow LIKE '%...%' queries with FULLTEXT indexes to enable lightning-fast token-based searches on unstructured string data.