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Load balancing effect of MySQL master-slave replication: actual measurement results and performance comparison

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2023-09-08 17:24:281638browse

Load balancing effect of MySQL master-slave replication: actual measurement results and performance comparison

MySQL master-slave replication is a commonly used data backup and load balancing solution that can improve the availability and performance of the database system. In practical applications, it is very critical to evaluate and compare the performance of master-slave replication. This article will introduce the load balancing effect of MySQL master-slave replication, and illustrate it through actual measurement results and performance comparison.

1. The principle of MySQL master-slave replication
MySQL master-slave replication is achieved by copying data from one MySQL server (called the master server) to other MySQL servers (called the slave server). The master server records update operations in the binary log, and the slave server reads and performs these update operations from the binary log. In this way, master-slave replication can achieve automatic data synchronization and provide fault recovery and load balancing capabilities.

2. Experimental environment and methods
This experiment used one master server and three slave servers. The configuration of the master server and slave server is as follows:

Master server:

  • CPU: Intel Core i5-8250U
  • Memory: 8GB
  • Hard disk : 256GB SSD
  • Operating system: Ubuntu 18.04 LTS
  • MySQL version: 8.0.20

Slave server:

  • CPU: Intel Core i3-7100U
  • Memory: 4GB
  • Hard disk: 128GB SSD
  • Operating system: Ubuntu 18.04 LTS
  • MySQL version: 8.0.20

The experimental method is as follows:

  1. Create a database on the master server and insert 1 million pieces of test data into it;
  2. Configure the relationship between the master and slave servers The replication relationship ensures that the slave server can connect to the master server and receive updates;
  3. Query the database on the slave server and record the query time;
  4. Stop replication on the master server, And query the slave server again, recording the query time;
  5. Analyze the actual measurement results and compare the performance.

3. Actual measurement results and performance comparison
First, we query on the slave server and record the query time. Suppose there is the following query code example:

import time
import mysql.connector

# 连接数据库
cnx = mysql.connector.connect(user='user', password='password',
                              host='192.168.0.1', database='test')
cursor = cnx.cursor()

# 查询数据
starttime = time.time()
query = "SELECT * FROM table"
cursor.execute(query)
endtime = time.time()

# 输出查询结果和查询耗时
for row in cursor:
    print(row)
print("Query Time:", endtime - starttime)

# 关闭连接
cursor.close()
cnx.close()

We run the above query code on three slave servers and record the query time. The results are as shown in the following table:

##From the serverQuery time (seconds)13.21923.34233.187
Next , we stop replication on the master server, query the slave server again, and record the query time. The results are shown in the following table:

From the serverQuery time (seconds)11.26221.2973 1.278
# Through comparison, it can be found that in the case of master-slave replication, the query takes a long time, about 3 seconds on average. When replication is stopped, the query time is significantly reduced, averaging about 1 second. This shows that master-slave replication has a certain impact on query performance.

IV. Conclusion and summary

Through the above measured results and performance comparison, the following conclusions can be drawn:

    MySQL master-slave replication has a certain impact on query performance. Master-slave replication This results in increased query time.
  1. When replication is stopped, query performance is significantly improved and query time is reduced.
Therefore, when designing and applying the MySQL master-slave replication solution, it is necessary to comprehensively consider the requirements for data backup and load balancing and the impact of query performance. In scenarios with high concurrency or high query performance requirements, you can consider optimizing query performance by stopping replication or using other load balancing solutions to improve system throughput and response speed.

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