Questions raised by the interviewer
During the interview process, the interviewer may ask the following questions:
"How do you index to improve the efficiency of text searches when working with database tables containing large segments of text?"
The key to the problem
The focus of this problem is to understand the challenges posed by large segments of text content to index creation, and how to choose the appropriate index type, creation strategy, and optimization methods based on MySQL's indexing mechanism to improve query performance. Interviewers need to demonstrate a deep understanding of the MySQL indexing mechanism and the ability to flexibly apply this knowledge in practical applications.
How does the interviewer answer
Interviewers can answer according to the following steps:
Analyze text content and query requirements:
First of all, I will analyze the characteristics of text content, such as text length, keyword distribution, etc., as well as query requirements, such as query frequency, query conditions, etc.Select the index type:
For large segments of text content, I would give priority to using full text indexing (FULLTEXT INDEX), because it supports full text search for text fields and is suitable for scenarios where you need to find keywords anywhere in the text. If the text content is short, or needs to match a specific string exactly, I would consider using a normal index (B-Tree INDEX) or hash index (HASH INDEX), but be aware that hash index does not support range queries.Create an index:
Before creating the index, I create the database and data table first and insert some sample data. Then, use the CREATE INDEX or ALTER TABLE statement to create an index for the selected text field and specify the appropriate index type.Execute queries and optimize:
When querying, I will use indexes to speed up the query process. For full-text indexing, I will use MATCH…AGAINST syntax for full-text search. At the same time, I will pay attention to avoid using query conditions that may cause index failure, such as using functions, implicit type conversions, etc.
Then we can take a look at the following case to try it out
-- Create a database
CREATE DATABASE blog_db;
-- Using database
USE blog_db;
-- Create a data table，Includes a long text field and other basic information
CREATE TABLE posts (
id INT AUTO_INCREMENT PRIMARY KEY, -- Primary key，Self-increasing
title VARCHAR(255) NOT NULL, -- title，longest255Characters
content TEXT, -- Long text content
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP -- Creation time
);
-- Insert some sample data
INSERT INTO posts (title, content) VALUES
('My First Post', 'This is the content of my first post. It has some long text...'),
('My Second Post', 'This post discusses more complex topics. It also contains a lot of text to index...');
-- forcontentField creation full text index
ALTER TABLE posts ADD FULLTEXT(content);
-- Perform full-text search query，Find keywords included'complex'Articles
SELECT * FROM posts
WHERE MATCH(content) AGAINST('complex' IN NATURAL LANGUAGE MODE);
- CREATE DATABASE blog_db;: Create a database named blog_db.
- USE blog_db;: Select to use the database you just created.
- CREATE TABLE posts (…): Create a data table called posts, including id, title, content, and created_at fields.
- INSERT INTO posts (title, content) VALUES (…): Insert some sample data into the table.
- ALTER TABLE posts ADD FULLTEXT(content);: Create a full text index for the content field.
- SELECT * FROM posts WHERE MATCH(content) AGAINST('complex' IN NATURAL LANGUAGE MODE);: Perform a full-text search query to find articles containing the keyword complex.
Summarize
This is the article about how to process MySQL large texts in interviews, which are the most important questions for major manufacturers. This is the end of this article. For more related content on processing MySQL large texts, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!