How to use MTR for performance regression testing of MySQL database?
Introduction:
MySQL is a widely used relational database management system. In order to ensure its normal operation and performance stability, developers often need to conduct performance regression testing. MTR (MySQL Test Runner) is a powerful testing tool that can be used for automated testing and performance regression testing. This article will introduce how to use MTR for performance regression testing of MySQL database, and provide code samples as a reference.
1. Introduction to MTR
MTR is a tool that comes with the MySQL source code. Its purpose is for automated testing and performance regression testing. It can simulate multiple clients accessing the MySQL server at the same time, collect performance indicators during the test, and finally generate a test report. MTR has strong flexibility and scalability, and can meet various testing needs by writing customized test scripts.
2. Performance Regression Testing Process
Performance regression testing is a method of comparing system performance under different versions or configurations. During the regression testing process, we will run the same test cases in different environments and compare the test results to discover performance changes or problems. The following is the basic process of using MTR for performance regression testing:
- Prepare the test environment:
First, we need to prepare the MySQL server and test cases. You can choose to install the MySQL database and create the corresponding database and tables according to the testing requirements. At the same time, write test cases, including query, insert, update and other operations for different scenarios. - Configuring MTR:
The configuration file of MTR is located in the mysql-test directory. You can specify the path of the test case, the parameters for connecting to the MySQL server and other configuration options by modifying the configuration file. -
Run the performance regression test:
Execute the following command in the command line to run the performance regression test:./mtr --force --retry=3 --max-test-fail=0 --suite=perf regression
The meaning of the parameters in the above command is as follows:
- --force: Indicates that the test is forced to run, even if a previous test failed.
- --retry=3: Indicates that the test will be retried up to 3 times when it fails.
- --max-test-fail=0: Indicates that if a test fails, stop test execution.
- --suite=perf: Specify the test suite. The perf suite is used here, which contains a series of performance test cases.
- Regression: Specify the type of test case that needs to be run.
- Analyze test results:
MTR will generate a test report after the test, including the execution results, performance indicators and error logs of each test case. Based on the changes in performance indicators and the output of the error log, we can draw conclusions to determine whether performance has improved or degraded.
3. Code Example
The following is a code example that uses MTR for MySQL performance regression testing. Suppose we need to test insertion performance:
-
Create test case file test_insert.test:
#创建测试表 CREATE TABLE test_table(id INT PRIMARY KEY AUTO_INCREMENT, data VARCHAR(100)); #插入性能测试 #插入1000条数据 INSERT INTO test_table(data) VALUES ("test data"); ... INSERT INTO test_table(data) VALUES ("test data"); SELECT COUNT(*) FROM test_table;
-
Edit MTR configuration file my.cnf:
[mysqld] mtr_query_timeout=1800
-
Run the performance regression test:
Execute the following command in the command line:./mtr --force --retry=3 --max-test-fail=0 --suite=perf regression test_insert
The execution results will include the execution time of each test case and the inserted record Number, you can compare the performance differences of different versions or configurations based on execution time.
Conclusion:
Using MTR for performance regression testing of MySQL database is an effective testing method. By automating testing and comparing the test results of different versions or configurations, you can evaluate the performance changes and stability of MySQL. I hope the introduction and code examples of this article can help readers better use MTR for performance regression testing.
The above is the detailed content of How to use MTR for performance regression testing of MySQL database?. For more information, please follow other related articles on the PHP Chinese website!

InnoDB uses redologs and undologs to ensure data consistency and reliability. 1.redologs record data page modification to ensure crash recovery and transaction persistence. 2.undologs records the original data value and supports transaction rollback and MVCC.

Key metrics for EXPLAIN commands include type, key, rows, and Extra. 1) The type reflects the access type of the query. The higher the value, the higher the efficiency, such as const is better than ALL. 2) The key displays the index used, and NULL indicates no index. 3) rows estimates the number of scanned rows, affecting query performance. 4) Extra provides additional information, such as Usingfilesort prompts that it needs to be optimized.

Usingtemporary indicates that the need to create temporary tables in MySQL queries, which are commonly found in ORDERBY using DISTINCT, GROUPBY, or non-indexed columns. You can avoid the occurrence of indexes and rewrite queries and improve query performance. Specifically, when Usingtemporary appears in EXPLAIN output, it means that MySQL needs to create temporary tables to handle queries. This usually occurs when: 1) deduplication or grouping when using DISTINCT or GROUPBY; 2) sort when ORDERBY contains non-index columns; 3) use complex subquery or join operations. Optimization methods include: 1) ORDERBY and GROUPB

MySQL/InnoDB supports four transaction isolation levels: ReadUncommitted, ReadCommitted, RepeatableRead and Serializable. 1.ReadUncommitted allows reading of uncommitted data, which may cause dirty reading. 2. ReadCommitted avoids dirty reading, but non-repeatable reading may occur. 3.RepeatableRead is the default level, avoiding dirty reading and non-repeatable reading, but phantom reading may occur. 4. Serializable avoids all concurrency problems but reduces concurrency. Choosing the appropriate isolation level requires balancing data consistency and performance requirements.

MySQL is suitable for web applications and content management systems and is popular for its open source, high performance and ease of use. 1) Compared with PostgreSQL, MySQL performs better in simple queries and high concurrent read operations. 2) Compared with Oracle, MySQL is more popular among small and medium-sized enterprises because of its open source and low cost. 3) Compared with Microsoft SQL Server, MySQL is more suitable for cross-platform applications. 4) Unlike MongoDB, MySQL is more suitable for structured data and transaction processing.

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

SublimeText3 Linux new version
SublimeText3 Linux latest version

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

Dreamweaver Mac version
Visual web development tools

Atom editor mac version download
The most popular open source editor