Improving storage engine throughput: MaxScale application case in MySQL

Improving the throughput of the storage engine: MaxScale application case in MySQL

Introduction:
In the current environment of big data and high concurrency, how to improve the throughput of the database has become a problem for many enterprises and Problems faced by developers. As a commonly used open source relational database, MySQL's performance optimization has always attracted much attention. This article will introduce a method to improve the throughput of MySQL database by using the MaxScale tool, as well as specific application cases.

1. Introduction to MaxScale
MaxScale is an open source database agent tool launched by MariaDB Company to improve the performance, reliability and scalability of the database. It can serve as an intermediate layer between the database and the client, responsible for distributing and routing database requests. MaxScale has features such as load balancing, failover, caching, query routing, and query filtering to increase database throughput without modifying the application.

2. MaxScale application case in MySQL
Suppose we have an online e-commerce platform, and a large number of users are browsing, placing orders, and paying for products every day. Due to the high read and write pressure on the database, we hope to improve the throughput of the database through the MaxScale tool.

1. Installing MaxScale
   First, we need to install MaxScale. The latest version of MaxScale can be downloaded and installed through the official website. During the installation process, you need to follow the prompts to configure, including specifying the connection information for the MySQL database, etc.
2. Configuring MaxScale
   The configuration file is located in the MaxScale installation directory, and the default is /etc/maxscale.cnf. After opening the file, we need to perform some configuration, such as specifying the listening port of the database, setting user authentication information, etc. The following is a simple configuration example:

[maxscale]
threads=4
log_info=1

[monitor]
module=mysqlmon
servers=primary,secondary
user=maxscale_user
passwd=maxscale_password

[listener]
type=server
service=db_service
protocol=MySQLClient
port=3306

[db_service]
type=service
router=readconnroute
servers=primary,secondary
user=db_user
passwd=db_password

[primary]
type=server
address=127.0.0.1
port=3306
protocol=MySQLBackend

[secondary]
type=server
address=127.0.0.2
port=3306
protocol=MySQLBackend

In the configuration file, we first define a monitor module to monitor the status of the database. Then a listener module is defined to listen for database connection requests. Then a db_service module is defined, which is used to define database-related parameters and connection pool information. Finally, two server modules are defined, corresponding to the master database and the slave database respectively. Modify the corresponding parameters according to the actual situation.

3. Start MaxScale
   After completing the configuration, we can start MaxScale by executing the following command:

maxscale -f /etc/maxscale.cnf

4. Test performance
   Complete the above steps Finally, we can test the effect of MaxScale on improving database throughput through concurrent requests. The following is a simple test code example:

import pymysql
import time
from concurrent.futures import ThreadPoolExecutor

def query_data():
    conn = pymysql.connect(host='127.0.0.1', user='maxscale_user', password='maxscale_password', database='test')
    cursor = conn.cursor()
    cursor.execute('SELECT * FROM table')
    rows = cursor.fetchall()
    conn.close()

def concurrent_test():
    start = time.time()
    executor = ThreadPoolExecutor(max_workers=100)
    futures = []

    for _ in range(1000):
        future = executor.submit(query_data)
        futures.append(future)

    executor.shutdown()

    for future in futures:
        result = future.result()

    end = time.time()
    print('Total time:', end - start)

if __name__ == '__main__':
    concurrent_test()

In the above code, we use Python's concurrent.futures module to implement concurrent requests. By adjusting the max_workers parameters and the number of cycles, you can simulate different concurrency situations.

Through testing, we can observe that after using MaxScale, the throughput of the database has been significantly improved compared to before. This is because MaxScale can automatically distribute requests to different database nodes to achieve load balancing, thus improving the processing capacity of the database.

Conclusion:
By using the MaxScale tool, we can increase the throughput of the MySQL database without modifying the application. MaxScale has functions such as load balancing, failover, caching, query routing, and query filtering, and can be configured and adjusted according to actual application scenarios. In a high-concurrency environment, reasonable use of MaxScale can help us improve the performance and reliability of the database.

Reference materials:

1. MaxScale official website: https://mariadb.com/products/skysql/maxscale
2. MaxScale documentation: https://mariadb. com/kb/en/mariadb-maxscale-21/
3. MySQL official website: https://www.mysql.com/

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