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
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:
[mysqld] server-id=1 log-bin=mysql-bin
[mysqld] server-id=2 relay-log=relay-bin log-slave-updates=1
CREATE USER 'replication'@'%' IDENTIFIED BY 'password'; GRANT REPLICATION SLAVE ON *.* TO 'replication'@'%'; FLUSH PRIVILEGES;
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;
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:
CREATE TABLE measurements ( id SERIAL PRIMARY KEY, sensor_id INTEGER, value NUMERIC, ts TIMESTAMP );
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');
INSERT INTO measurements (sensor_id, value, ts) VALUES (1, 20, '2021-02-15');
SELECT * FROM measurements WHERE ts BETWEEN '2021-01-01' AND '2021-06-30';
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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