MySQL Cluster Data Inconsistency Solutions
MySQL Cluster is a shared-nothing clustered database system, which works on a mixture of replicating and sharding technology to improve the performance and scalability of distributed databases. However, data inconsistency will also occur in a distributed cluster. According to a mainstream survey, every 19 minutes of an investigation for errors caused by inconsistent data was taken place by an organisation.
In this article, I’m going to discuss solutions to data inconsistency problems in MySQL Cluster. What solutions can be taken to prevent inconsistency, how to detect inconsistency, and how to repair inconsistency when it happened will be answered.
II. Solutions to Data Inconsistency in MySQL Cluster
0.1 Common Best Practices
It is always better to take preventive measures to avoid data inconsistency. Following best practices can help to maintain the data integrity in MySQL Cluster:
• Standardize data entry processes. By implementing standard entries, redundant data can be removed.
• Require validations checks. By properly validating the data before entry, errors can be detected.
• Perform peer review on modifications. This helps to ensure that the data modification is handled correctly.
• Set up procedures for data backup and replication. Backups can provide recovery options and replications can improve data consistency.
0.2 Consistency Detection
Consistency detection can be carried out during runtime. First, a snapshot of the database schemas should be taken, with all the latest changes which have been executed. This would include determinations about what should have been done and what got done. Then, regular consistency check should be carried out without impeding production. It should be considered either manually or automated to find the inconsistencies between data points.
0.3 Repair the Inconsistency
When an inconsistency is detected, a repair strategy needs to implement to remove the inconsistent data points. This strategy needs to include:
• Direction identification: Identify the data points which need to go back to their correct state.
• Dependency analysis: Check the data points’ dependencies. If the inconsistent data points are related to dependent data points or a chain of dependencies, the corrective procedure and the effects of the corrective measure need to be known.
• Documentation: Document the inconsistencies and their remedial procedures.
• Corrective procedure: Start the corrective process. The corrective procedure must be carried out in an efficient fashion.
• Testing: After the corrective action, a testing should be conducted to ensure the correction have been made.
• Recovery plan: This can be either manual or automated, aimed to reduce the impact of an inconsistency occurrence.
In conclusion, data inconsistency in MySQL Cluster can be minimised by implementing standard entries and validations, as well as with peer review and backups/replications. The occurrence of inconsistencies can be detected by taking a snapshot of the database schemas. A specific strategy needs to be taken to repair the inconsistency, involving direction indentification, dependency analysis, documentation, corrective procedure, testing, and recovery plan.