Full Text Search vs. LIKE in SQL: Choosing the Right Tool for the Job

• What it does: FTS is a powerful tool that analyzes and indexes individual words in a text column. This allows for efficient searching based on keywords, phrases, and even semantic similarity.
• Benefits:
  • Faster searches: FTS leverages the pre-built index to quickly find relevant documents, especially with large datasets.
  • Richer search capabilities: You can search for words with synonyms, plurals, and even stemming (e.g., "search" matches "searched" and "searching").
  • Relevance ranking: FTS can rank results based on their relevance to the search query, prioritizing documents containing more relevant keywords.

Example:

SELECT * FROM articles
WHERE CONTAINS(body, 'artificial intelligence');

This query searches the body column of the articles table for documents containing the term "artificial intelligence" and its related forms.

LIKE Operator:

• What it does: LIKE is a simpler operator that performs pattern matching within a text string. You can use wildcards like "%" to match any number of characters and "_" to match a single character.
• Benefits:
  • Simpler syntax: The LIKE operator is easier to understand and implement for basic string searches.
  • Precise control: You have more control over the exact matching pattern using wildcards.

SELECT * FROM products
WHERE name LIKE '%smartphone%';

This query searches the name column of the products table for entries containing the word "smartphone" anywhere within the name (e.g., "Android smartphone", "Latest smartphone").

Related Issues and Solutions:

• False positives: Both FTS and LIKE can sometimes return irrelevant results. FTS can mitigate this by using features like stop words (common words like "the" and "a") exclusion and stemming. For LIKE, you may need to refine your search pattern using more specific keywords or escaping special characters.
• Performance: While FTS is generally faster for large datasets, creating and maintaining the full-text index can be resource-intensive. For smaller datasets or simple searches, LIKE might be a more efficient option.

Choosing the right approach:

The best method for your specific scenario depends on your needs. Here's a general guideline:

• Use FTS for:
  • Large datasets with complex search requirements
  • When searching for related terms, synonyms, and plurals
  • When relevance ranking is important
• Use LIKE for:
  • Simple string searches with specific patterns
  • Smaller datasets where index creation overhead is a concern