How to optimize database queries by using php code testing function

How to optimize database queries by using PHP code testing functions

Introduction:
Database queries are an essential part of web development, and optimizing database queries It is an important part of improving web application performance and user experience. In PHP development, we can optimize database queries by using the code testing function. This article will introduce how to use PHP code testing to optimize database queries and provide relevant code examples.

1. Database query performance issues
In web applications, database query performance issues may cause the website to run slowly, especially when the number of visits is large. Common database query performance problems include:

1. Complex query statements: A query statement may contain multiple tables, multiple association conditions, multiple sorting rules, etc. Such query statements will increase the number of database queries. Query complexity and query time.
2. Unreasonable indexes or lack of indexes: Indexes are the key to improving database query performance. Unreasonable indexes or lack of indexes will lead to low query efficiency.
3. Unnecessary queries or repeated queries: Sometimes we may make unnecessary queries or repeat queries, which will waste database resources and reduce query efficiency.

2. Use PHP code to test database queries
In order to optimize database query performance, we can use PHP code testing to analyze the execution efficiency of the query, identify possible problems and make improvements. The following are some commonly used PHP code testing skills:

1. Use the EXPLAIN statement to analyze the query execution plan
   The EXPLAIN statement can help us analyze the query execution plan and understand how the query statement is executed. It can help We identify problems and improve query efficiency by optimizing query statements or indexes. The following is an example of using the EXPLAIN statement:

$sql = "SELECT * FROM users WHERE age > 20";
$result = $mysqli->query("EXPLAIN " . $sql);
while ($row = $result->fetch_assoc()) {
    print_r($row);
}

2. Use SQL_CALC_FOUND_ROWS and FOUND_ROWS() functions to optimize paging queries
   When performing paging queries, you can use the SQL_CALC_FOUND_ROWS and FOUND_ROWS() functions to Optimize query performance. SQL_CALC_FOUND_ROWS tells the database to return the total number of matching rows, and the FOUND_ROWS() function can obtain this value without the need for another COUNT(*) query. The following is an example of using the SQL_CALC_FOUND_ROWS and FOUND_ROWS() functions:

$sql = "SELECT SQL_CALC_FOUND_ROWS * FROM users LIMIT 10, 20";
$result = $mysqli->query($sql);
$total_rows_result = $mysqli->query("SELECT FOUND_ROWS() AS total_rows");
$total_rows = $total_rows_result->fetch_assoc()['total_rows'];

3. Using prepared statements and bound parameters
   Preprocessed statements can help us improve query efficiency and prevent SQL Injection attack. You can use preprocessing functions provided by extensions such as mysqli or PDO. The following is an example of using prepared statements and bound parameters:

$stmt = $mysqli->prepare("SELECT * FROM users WHERE age > ?");
$age = 20;
$stmt->bind_param("i", $age);
$stmt->execute();
$result = $stmt->get_result();
while ($row = $result->fetch_assoc()) {
    // 处理查询结果
}
$stmt->close();

3. Summary
By using PHP code to test functions, we can discover database query performance problems and optimize them to improve Web Application performance and user experience. This article introduces techniques such as using the EXPLAIN statement to analyze execution plans, using the SQL_CALC_FOUND_ROWS and FOUND_ROWS() functions to optimize paging queries, and using prepared statements and bind parameters to optimize database queries. I hope the content of this article will be helpful to you in optimizing database queries.

Reference materials:

• PHP manual: https://www.php.net/manual/zh/index.php
• MySQL manual: https:/ /dev.mysql.com/doc/
• MariaDB Manual: https://mariadb.com/kb/en/
• Stack Overflow: https://stackoverflow.com/

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