DB2 Data Inconsistency Issues (Data Repair Strategies)

Data inconsistency refers to discrepancies in data across different systems or sources, a problem that can arise in most practical scenarios, particularly within large and decentralized distributed systems.

What is Data Inconsistency?

Data inconsistency typically occurs when changes generated between two or more systems are not synchronized or updated. This phenomenon can manifest in various aspects such as data updates on different machines, asynchronous processing, transaction logs, and others.

Why Does Data Inconsistency Cause Problems?

1. Incorrect Results: If an application attempts to use raw data without performing necessary repairs and validation, incorrect results may occur due to the lack of required data correction and verification.

2. System Crash: In certain cases, data inconsistency can lead to system crashes. For example, if a system relies on transaction logs that are incomplete or corrupted, it could result in erroneous data processing leading to instability.

3. User Experience Variations: If inconsistencies exist between different systems, users may encounter varying results depending on which system they interact with, negatively impacting their user experience.

How to Solve Data Inconsistency Issues?

1. Transaction Logs: In some systems, transaction logs are used to record all information regarding transactions. When an application executes a transaction, it writes this data into the log. Upon completion of processing, it reads from and applies these logs. This is a reliable and ordered approach ensuring data consistency.

2. Asynchronous Processing: In certain architectures, data processing and updates are handled asynchronously. To address such issues, multiple machines can be utilized to process transactions concurrently rather than waiting for a single machine to complete all tasks sequentially.

3. Periodic Reconciliation: There may be scenarios where avoiding data inconsistency is not feasible. A solution involves running system update procedures alongside the application regularly to ensure new data gets written back to its original location, maintaining overall consistency within the system.

Best Practices

1. Use Transaction Logs: Whenever possible, employ transaction logs for recording and processing transactions.

2. Implement Periodic Reconciliation: In some cases, running both applications and data update processes together can ensure that new data is correctly written back to its original location.

3. Utilize Asynchronous Processing: If parallel processing requirements exist, implementing asynchronous mechanisms will help maintain system consistency effectively.