Comparison of fault tolerance and data consistency between TiDB and MySQL

Comparison of fault tolerance and data consistency between TiDB and MySQL

Overview:
Fault tolerance and data consistency are very important features in relational database systems, especially in distributed database systems More critical. This article will compare the fault tolerance and data consistency of the distributed relational database system TiDB and the traditional relational database system MySQL, and provide corresponding code examples.

1. Fault tolerance comparison:
   Fault tolerance refers to the ability of the system to continue to work and provide correct services in the face of failure. TiDB and MySQL differ in terms of fault tolerance:

TiDB: As a distributed relational database system, TiDB has a high degree of fault tolerance. TiDB uses the Raft protocol to achieve data consistency and fault tolerance. By distributing data to multiple nodes, each node holds a complete copy of the data and the corresponding coordinator node, so that even if a node fails, other nodes will Nodes can take over the work of failed nodes to ensure the normal operation of the system.

MySQL: As a traditional relational database system, MySQL has low fault tolerance. MySQL mainly uses the master-slave replication mechanism to achieve data backup and fault tolerance. Although you can increase data redundancy by configuring multiple slave nodes, in the event of a master node failure, you need to manually switch from the slave node to the master node, and the switch Some data may be lost during the process. This kind of manual intervention is unacceptable for business systems.

2. Data consistency comparison:
   Data consistency refers to whether the data between multiple copies in the database system is consistent. TiDB and MySQL also have differences in data consistency:

TiDB: By using the Raft protocol, TiDB can ensure strong consistency of data. During a write operation, TiDB will synchronize data to multiple nodes and wait for confirmation from most nodes before returning success. This ensures data consistency on all nodes. At the same time, TiDB also supports the multi-version concurrency control (MVCC) mechanism to ensure the consistency of read operations.

MySQL: MySQL needs to rely on the master-slave replication mechanism in terms of data consistency. After receiving the write operation, the master node will synchronize the data to the slave node, but there is a certain delay in the data synchronization process, and the data from the slave node may not be real-time. Therefore, for read operations, if the latest data is required, the data of the master node needs to be read, and if slightly lagging data can be tolerated, the data of the slave node can be read.

Sample code:
The following is a simple example to demonstrate the comparison of fault tolerance and data consistency in TiDB and MySQL.

In TiDB, you can use the following code to create a table and insert data:

CREATE TABLE students (
id INT,
name VARCHAR(20),
PRIMARY KEY (id)
);

INSERT INTO students (id, name) VALUES (1, 'Alice'), (2, 'Bob');

In MySQL , you can use the following code to create a table and insert data:

CREATE TABLE students (
id INT,
name VARCHAR(20),
PRIMARY KEY (id)
) ;

INSERT INTO students (id, name) VALUES (1, 'Alice'), (2, 'Bob');

Through the above example code, we can see that in TiDB In MySQL, data will be automatically distributed to multiple nodes, ensuring data redundancy and fault tolerance; in MySQL, you need to manually configure the master and slave nodes and perform data synchronization, and the fault tolerance is relatively poor.

Conclusion:
In summary, TiDB, as a distributed relational database system, has a high degree of fault tolerance and data consistency. By using the Raft protocol and MVCC mechanism, TiDB can achieve strong consistency and high availability of data. In comparison, MySQL is slightly lacking in fault tolerance and data consistency. Therefore, TiDB is a better choice for distributed systems that require a high degree of fault tolerance and data consistency.

(Note: The above is a general introduction to the architecture and features, not an exhaustive technical document. Specific implementation details and application scenarios require detailed reference to relevant documents and materials.)

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