Full-text index (FULLTEXT) configuration and fuzzy query optimization

Full-text index: Let your database fly up, and it may also make you fall into a pit

Many friends think that full-text index is a good thing, which can quickly search and improve user experience. This is true. However, the configuration and optimization of the full text index cannot be done with just a click of the mouse, and there are many tricks inside! In this article, let’s dig into the full text indexing things so that you can not only use it well, but also avoid those headache-prone pits.

The purpose of this article is very simple, which is to allow you to thoroughly understand the configuration of the full-text index and fuzzy query optimization. After reading it, you can easily deal with various search scenarios like a database expert. You will learn how to choose the right index type, how to write efficient query statements, and how to deal with some common performance problems.

Let’s start with the basics. To put it bluntly, the full text index allows the database to quickly search for the index of text content. It is different from ordinary B-tree indexes. Ordinary indexes can only match exactly, while full-text indexes can support fuzzy matching, such as including a certain keyword, or similar words, etc. Common database systems, such as MySQL, PostgreSQL, and even Elasticsearch, support full-text indexing, but the specific implementation details may be slightly different. In MySQL, you may use FULLTEXT index, and PostgreSQL may use GIN index or tsvector type. Remember, it is very important to choose the right index type, which is directly related to your query efficiency. If you choose the wrong one, the index will slow you down!

Next, we will explore in-depth how FULLTEXT index works. It is usually based on inverted indexing technology. Simply put, it is to establish a mapping relationship between each word and the document position where it is located. In this way, when you want to search for a word, the database can directly find all documents containing the word, and the efficiency will naturally be high. However, this is not perfect. The construction and maintenance of FULLTEXT indexes require resource consumption, and its processing of stop words (such as "the", "yes", "in") also requires careful consideration. If you handle stop words inappropriately, the index will be large and the query efficiency will decrease. Worse, if you have huge data volumes, the time to build a full-text index may make you doubt your life.

Let's use MySQL as an example to see the basic usage of FULLTEXT index:

 <code class="language-sql">CREATE TABLE articles (</code><pre class='brush:php;toolbar:false;'> id INT AUTO_INCREMENT PRIMARY KEY,
title VARCHAR(255),
content TEXT,
FULLTEXT INDEX ft_idx (title, content)

);

SELECT FROM articles WHERE MATCH (title, content) AGAINST ('Database Optimization' IN BOOLEAN MODE);

This code creates an articles table and creates a FULLTEXT index for title and content columns ft_idx . MATCH...AGAINST statement is used to perform full text searches. IN BOOLEAN MODE means searching using Boolean mode, you can use '' to represent words that must be included, '-' to represent words that must be excluded, and ' ' to represent wildcard characters.

There are many advanced uses, such as using stemming, synonym replacement, etc. These technologies can improve the accuracy and recall of searches. However, the configuration and use of these advanced features require you to have a deeper understanding of full-text indexing. Moreover, too many advanced features may also bring performance problems.

Common errors? Too many! For example, improper selection of index fields leads to inefficient index efficiency; for example, poorly written query statements lead to a large amount of data scanning of the database; and, in addition, the stop word processing is ignored, resulting in huge index volume. Debugging skills? First, you need to use the database's performance analysis tool to find out the bottlenecks of the query; then, adjust the index strategy based on the analysis results, optimize the query statement, or improve the stop word processing method. Remember, optimization is an iterative process that requires constant testing and adjustment.

Lastly, regarding performance optimization and best practices, I want to emphasize that full-text indexing is not omnipotent. For some specific search scenarios, other technical solutions may be more efficient, such as using a specialized search engine such as Elasticsearch. In addition, the readability and maintainability of the code are also very important. Don’t write difficult code to pursue the ultimate performance. Clear and concise code, easier to maintain and optimize. Remember, you can only get twice the result with half the effort by choosing the right tools and techniques.

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