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Build a distributed database architecture using MySQL and PostgreSQL

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Release: 2023-07-13 09:00:18
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Building a distributed database architecture using MySQL and PostgreSQL

With the advent of the Internet and big data era, the demand for data processing and storage continues to increase. Traditional stand-alone databases often cannot meet the needs of high concurrency and large data volume, so distributed database architecture has gradually become an important solution. This article will introduce how to use MySQL and PostgreSQL to build a distributed database architecture and demonstrate it through code examples.

1. Background knowledge

  1. MySQL
    MySQL is an open source relational database management system that is easy to use, high-performance and reliable. MySQL supports multiple storage engines and has good scalability.
  2. PostgreSQL
    PostgreSQL is another open source relational database management system that is widely used in highly concurrency and large data volume scenarios. PostgreSQL is known for its powerful scalability and advanced features.
  3. Distributed database architecture
    Distributed database architecture distributes data across multiple nodes to achieve high data availability, load balancing and parallel data processing. Common distributed database architectures include master-slave replication, sharding, and partitioning.

2. MySQL master-slave replication example
MySQL provides a master-slave replication function, which can synchronize the operations of the master database to multiple slave databases. The following is an example of MySQL master-slave replication:

  1. Configuring the master database
    On the master database, you need to enable the binary log function (binlog) in the my.cnf configuration file:
[mysqld]
server-id=1
log-bin=mysql-bin
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  1. Configuring the slave database
    On the slave database, you need to specify the IP address and connection information of the master database in the my.cnf configuration file:
[mysqld]
server-id=2
relay-log=relay-bin
log-slave-updates=1
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  1. Start master-slave replication
    On the master database, execute the following SQL statement to create a user for connecting to the slave database and grant replication permissions:
CREATE USER 'replication'@'%' IDENTIFIED BY 'password';
GRANT REPLICATION SLAVE ON *.* TO 'replication'@'%';
FLUSH PRIVILEGES;
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On the slave database, execute the following SQL Statement to connect to the main database and start replication:

CHANGE MASTER TO MASTER_HOST='主数据库IP地址', MASTER_USER='replication', MASTER_PASSWORD='password', MASTER_LOG_FILE='mysql-bin.000001', MASTER_LOG_POS=0;
START SLAVE;
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3. PostgreSQL partition example
PostgreSQL supports storing data in different partitions according to certain rules to improve query performance and data management efficiency. The following is an example of PostgreSQL partitioning:

  1. Create a partitioned table
    First, we create a main table and two subtables to partition the data according to date range:
CREATE TABLE measurements (
    id SERIAL PRIMARY KEY,
    sensor_id INTEGER,
    value NUMERIC,
    ts TIMESTAMP
);
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CREATE TABLE measurements_2021q1 PARTITION OF measurements FOR VALUES FROM ('2021-01-01') TO ('2021-03-31');
CREATE TABLE measurements_2021q2 PARTITION OF measurements FOR VALUES FROM ('2021-04-01') TO ('2021-06-30');
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  1. Insert data into the partition table
    Next, we insert data into the partition table:
INSERT INTO measurements (sensor_id, value, ts)
VALUES (1, 20, '2021-02-15');
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  1. Query the partition table
    Finally, we can query the entire Partitioned table for query:
SELECT *
FROM measurements
WHERE ts BETWEEN '2021-01-01' AND '2021-06-30';
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The above is a sample code for building a distributed database architecture using MySQL and PostgreSQL. It is worth mentioning that the implementation of a distributed database architecture also involves considerations such as data sharding, load balancing, and fault recovery, which are beyond the scope of this article. I hope this article can provide some reference and inspiration for readers to help them build a distributed database architecture suitable for their own application scenarios.

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