How does index cardinality affect query performance in MySQL?

Index cardinality has a significant impact on MySQL query performance. High cardinality indexes can locate data faster and optimize queries; low cardinality indexes may lead to full table scanning. Query performance can be effectively improved by regularly updating statistics, selecting the appropriate index type, avoiding over-index and using over-index.

introduction

In MySQL, the impact of the cardinality of the index on query performance is the focus of our discussion today. As a senior database engineer, I know that understanding these details is essential to optimizing database performance. Through this article, you will learn how to evaluate the cardinality of an index, understand its impact on query performance, and master some practical optimization techniques.

Review of basic knowledge

In MySQL, indexing is a key tool used to speed up data retrieval. The cardinality of an index refers to the number of unique values ​​in the index. Simply put, if the cardinality of a column is high, the values ​​of this column are more scattered; conversely, if the cardinality is low, the values ​​are more concentrated. Understanding these concepts is crucial for our subsequent discussion.

Core concept or function analysis

Definition and function of index cardinality

Index cardinality refers to the number of different values ​​in the index column. High cardinality index means that the column's values ​​are very scattered, which usually helps locate data faster. For example, in a user table, the user ID usually has a high cardinality because the ID of each user is unique. Conversely, gender columns usually have low cardinality, as there are only a few possible values.

Let's look at a simple example:

 CREATE TABLE users (
    id INT PRIMARY KEY,
    username VARCHAR(50),
    email VARCHAR(100),
    gender ENUM('M', 'F')
);

CREATE INDEX idx_username ON users(username);
CREATE INDEX idx_gender ON users(gender);

In this example, the cardinality of username index will usually be higher than the gender index, because the username is usually unique, and the gender only has two possible values.

How it works

When MySQL executes a query, it decides which index to use based on the index's cardinality. High cardinality indexes usually reduce the data more effectively, thereby improving query performance. MySQL uses statistics to estimate the cardinality of the index, which can be updated by ANALYZE TABLE command.

For example, suppose we want to query the user with a specific username:

 SELECT * FROM users WHERE username = 'john_doe';

MySQL will select the idx_username index because it has a higher cardinality and can be positioned to john_doe faster.

However, low cardinality indexes can in some cases lead to full table scans. For example, if we query all male users:

 SELECT * FROM users WHERE gender = 'M';

Because of the low cardinality of gender column, MySQL may decide not to use the idx_gender index, but to do a full table scan, as this may be faster.

Example of usage

Basic usage

Let's look at a basic query example that shows how to use indexes to improve query performance:

 -- Create a table with a large amount of data CREATE TABLE large_table (
    id INT PRIMARY KEY,
    value INT
);

-- Insert large amount of data INSERT INTO large_table (id, value) 
SELECT a.id, FLOOR(RAND() * 1000000) 
FROM (SELECT id FROM information_schema.columns LIMIT 1000000) a;

-- Create index CREATE INDEX idx_value ON large_table(value);

-- Query specific value EXPLAIN SELECT * FROM large_table WHERE value = 12345;

In this example, we create a table with one million rows and create an index on value column. Through the EXPLAIN command, we can see whether MySQL uses indexes and the execution plan of the query.

Advanced Usage

Now, let's look at a more complex example showing how to optimize complex queries using index cardinality:

 -- Create a table with multiple columns CREATE TABLE complex_table (
    id INT PRIMARY KEY,
    category VARCHAR(50),
    subcategory VARCHAR(50),
    value INT
);

-- Insert data INSERT INTO complex_table (id, category, subcategory, value) 
SELECT a.id, 
       CASE WHEN a.id % 3 = 0 THEN 'A' WHEN a.id % 3 = 1 THEN 'B' ELSE 'C' END,
       CASE WHEN a.id % 5 = 0 THEN 'X' WHEN a.id % 5 = 1 THEN 'Y' ELSE 'Z' END,
       FLOOR(RAND() * 1000000)
FROM (SELECT id FROM information_schema.columns LIMIT 1000000) a;

-- Create composite index CREATE INDEX idx_category_subcategory_value ON complex_table(category, subcategory, value);

-- Query the values ​​under specific categories and subcategories EXPLAIN SELECT * FROM complex_table 
WHERE category = 'A' AND subcategory = 'X' AND value = 12345;

In this example, we create a composite index containing category , subcategory , and value columns. Through the EXPLAIN command, we can see how MySQL uses this composite index to optimize queries.

Common Errors and Debugging Tips

Common errors when using indexes include:

• Index not used : Sometimes MySQL may decide not to use indexes, which may be due to inaccurate statistics or the query conditions are not suitable for indexes. Indexes can be used forcefully via FORCE INDEX , but this should be used with caution.

• Too many indexes : Creating too many indexes increases the overhead of insertion and updates, because the index needs to be updated every time the data changes. You can view the index status of the current table through SHOW INDEX command and adjust it according to actual needs.

• Inaccurate index cardinality estimation : If the index cardinality estimation is inaccurate, MySQL may make incorrect optimization decisions. Statistics can be updated through ANALYZE TABLE command to ensure the accuracy of cardinality estimation.

Performance optimization and best practices

In practical applications, optimizing index cardinality and query performance requires a comprehensive consideration of a variety of factors. Here are some practical optimization tips and best practices:

• Regularly update statistics : Use the ANALYZE TABLE command to regularly update statistics for tables to ensure that MySQL can make accurate optimization decisions.

• Select the right index type : Choose the right index type according to the characteristics of the data. For example, the B-Tree index is suitable for range query, while the hash index is suitable for exact matches.

• Avoid over-index : Create indexes only on necessary columns, avoid excessive indexes that lead to degraded insertion and update performance.

• Using Overlay Index : When possible, using Overlay Index can reduce table back operations and improve query performance. For example:

 CREATE INDEX idx_value_id ON large_table(value, id);
EXPLAIN SELECT id FROM large_table WHERE value = 12345;

In this example, the idx_value_id index overwrites all columns required for the query, avoiding the table back operation.

• Monitoring and Adjustment : Use EXPLAIN and EXPLAIN ANALYZE commands to monitor the execution plan of the query, and adjust the index and query according to actual conditions.

Through these tips and practices, you can better understand and optimize index cardinality in MySQL, thereby significantly improving query performance. In actual projects, I have optimized a query that originally took several minutes to only take a few seconds by optimizing the index cardinality, which not only improves the user experience, but also greatly reduces the server load.

I hope this article can help you understand the impact of index cardinality on MySQL query performance and flexibly apply this knowledge in practical applications.

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