The EXPLAIN statement can be used to analyze and improve SQL query performance. 1. Execute the EXPLAIN statement to view the query plan. 2. Analyze the output results, pay attention to access type, index usage and JOIN order. 3. Create or adjust indexes based on the analysis results, optimize JOIN operations, and avoid full table scanning to improve query efficiency.

introduction

In database optimization, how to efficiently analyze and improve query performance has always been a big concern for us programmers. Today, I want to talk to you about how to use EXPLAIN statements to peek into the internal mechanism of SQL queries, which is one of my secret weapons when tuning databases over the years. Through this article, you will learn how to use EXPLAIN statement to diagnose query performance problems, and master some practical skills that I have summarized from actual combat.

Review of basic knowledge

The EXPLAIN statement can be said to be a good helper for database tuning players. It can help us understand the decision-making process of the database when executing queries, such as what kind of index is selected, how to scan tables, and estimated execution costs. Simply put, EXPLAIN allows us to see the query plan, which is crucial for optimizing queries.

Before using EXPLAIN, make sure you have a certain understanding of the basic SQL syntax and database execution plan, such as what are indexes, table scanning, JOIN operations, etc. These are the basis for understanding the output of EXPLAIN.

Core concept or function analysis

Definition and function of EXPLAIN

The purpose of the EXPLAIN statement is to let you see what happens inside the database when executing your query. It returns a series of information to help you analyze the execution plan of the query and identify performance bottlenecks.

For example, in MySQL, you can use EXPLAIN like this:

 EXPLAIN SELECT * FROM users WHERE age > 30;

This command will return the execution plan of the query, including the index used, the scanning method, the estimated number of rows, etc.

How it works

When you execute the EXPLAIN statement, the database analyzes your query and then generates an execution plan. This plan includes steps the database will take when executing a query, such as:

• Selected access method (such as index scan, full table scan)
• Estimated row count and cost
• Index used
• The order of JOIN operations

By analyzing this information, you can determine whether the query effectively utilizes the index, whether there are unnecessary full table scans, and whether the order of JOIN operations can be optimized.

When analyzing, pay special attention to type field, which represents the access type. ALL means full table scanning, with the worst performance; index means index scanning, with average performance; ref , eq_ref , etc. represent search using indexes, with good performance.

Example of usage

Basic usage

Let's look at a simple example, suppose we have an orders table and we want to find all orders with status shipped :

 EXPLAIN SELECT * FROM orders WHERE status = 'shipped';

The output might look like this:

 ---- ------------- -------- ------------ ------ --------------- ------ --------- ------- ------ ---------- ------- 
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
 ---- ------------- -------- ------------ ------ --------------- ------ --------- ------- ------ ---------- ------- 
| 1 | SIMPLE | orders | NULL | ALL | status_index | NULL | NULL | NULL | 1000 | 10.00 | Using where |
 ---- ------------- -------- ------------ ------ --------------- ------ --------- ------- ------ ---------- -------

From this output, we can see that the database has selected a full table scan ( type is ALL ) and no index is used ( key is NULL ). This suggests that we may need to create an index on status field to optimize the query.

Advanced Usage

Sometimes, queries involve JOIN operations of multiple tables, and EXPLAIN can help us optimize the JOIN order. Suppose we have two tables, orders and customers , we want to query customer information for all shipped orders:

 EXPLAIN SELECT c.name, o.order_date FROM customers c JOIN orders o ON c.id = o.customer_id WHERE o.status = 'shipped';

The output might look like this:

 ---- ------------- ------- ------------ ------ --------------- ---------- --------- ------------------- ------ ---------- ------------- 
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
 ---- ------------- ------- ------------ ------ --------------- ---------- --------- ------------------- ------ ---------- ------------- 
| 1 | SIMPLE | o | NULL | ref | status_index | status_index | 768 | const | 100 | 100.00 | Using where |
| 1 | SIMPLE | c | NULL | eq_ref | PRIMARY | PRIMARY | 4 | db.o.customer_id | 1 | 100.00 | NULL |
 ---- ------------- ------- ------------ ------ --------------- ---------- --------- ------------------- ------ ---------- -------------

From the output, we can see that the database first scans the orders table, and then looks up customers table through customer_id index. This JOIN order is reasonable because it first filters out orders that meet the criteria and then looks for the corresponding customer information.

Common Errors and Debugging Tips

When using EXPLAIN, there are some common misunderstandings that need to be noted:

• Misunderstanding output result : The output result of EXPLAIN needs to be interpreted in combination with actual situation. For example, the rows field represents the estimated number of rows, but may vary when actually executed.
• Ignore JOIN order : When multi-table JOIN, the JOIN order has a great impact on performance. Through EXPLAIN, the JOIN order can be adjusted to optimize queries.
• Not using indexes : If EXPLAIN shows that there is no index used, you may need to create an index on the relevant fields.

Debugging skills include:

• Adjust index : Create or adjust indexes to optimize queries based on the output of EXPLAIN.
• Rewrite queries : Sometimes you can optimize performance by rewriting queries, such as avoiding subqueries.
• Analyze the JOIN order : Adjust the JOIN order through EXPLAIN to ensure that the minimum data is filtered out first and then perform the JOIN operation.

Performance optimization and best practices

In practical applications, how to use EXPLAIN to optimize query performance? Here are some suggestions I have summarized from years of experience:

• Index optimization : Based on the output of EXPLAIN, make sure that the query uses the appropriate index. If the index is not used, you may need to create an index on the relevant fields.
• Avoid full table scans : Try to avoid full table scans ( type ALL ), which can be optimized by creating indexes or rewriting queries.
• Optimize JOIN operations : Analyze the order of JOIN operations through EXPLAIN, ensuring that the minimum data is filtered out before performing JOIN operations.
• Using Overlay Index : Using Overlay Index ( Extra field is Using index ) can significantly improve query performance if possible.

In an actual project, I once encountered a case: a complex query involves JOIN operations of multiple tables. Through EXPLAIN analysis, it was found that the order of JOINs was unreasonable, resulting in performance problems. By adjusting the JOIN order and creating the appropriate index, the query time is finally reduced from minutes to seconds.

In short, the EXPLAIN statement is a powerful tool for us database tuners. Through it, we can have an in-depth understanding of the execution plan of the query, thereby finding performance bottlenecks and optimizing. I hope this article can help you better master the skills of EXPLAIN and improve query performance in actual projects.

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