Feature-Based Database Rollback for Application-Specific Change Recovery
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Solution Overview
Problem
Existing transaction logs lack the ability to efficiently identify and reverse database changes associated with specific application features, leading to complex and time-consuming manual processes for data restoration, especially in intricate applications.
Innovation Solution
Implementing feature-based data rollback by associating rollback contexts with sets of database commands, enabling automated identification and reversal of logically related data changes, and generating data rollback scripts to restore data to a consistent state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional transaction logs are used to track database changes, then all database operations can be recorded, but it becomes difficult to efficiently identify and reverse changes associated with specific application features
Solution Approach 1:
The patent segments the monolithic transaction log into feature-specific transaction logs by introducing feature identifiers that categorize database changes according to their associated application features. This segmentation enables precise identification of changes related to specific features while reducing the complexity of manual tracking by automatically organizing transactions into manageable, feature-specific groups.
Solution Approach 2:
The patent introduces an intermediary component (transaction log analyzer with feature identifiers) that sits between the traditional transaction log and the user/recovery process. This intermediary automatically tags and categorizes transactions with feature identifiers, enabling efficient feature-specific change identification without requiring users to manually analyze the entire transaction log.
2Adaptability or versatility
If manual processes are used for data restoration, then flexibility in handling different scenarios is maintained, but the process becomes time-consuming and complex
Solution Approach 1:
The patent performs preliminary action by pre-tagging and categorizing database transactions with feature identifiers at the time the transactions occur. This advance organization enables rapid retrieval and restoration of specific feature-related changes when needed, eliminating the time-consuming manual analysis phase while preserving the ability to handle various restoration scenarios through the structured identifier system.
3Reliability
If brute force data restoration methods are used, then complete data recovery can be achieved, but data integrity and consistency may be compromised
Solution Approach 1:
The patent applies local quality by enabling selective restoration of only those database changes associated with specific feature identifiers rather than restoring all changes uniformly. This targeted approach maintains data integrity and consistency by allowing precise control over which transactions are reversed, avoiding the pitfalls of brute force methods that may disrupt unrelated data while ensuring complete recovery of the intended feature-specific changes.
Data Source
AI summary
Pre-update data is changed to post-update data based on a data modification request. A rollback context is associated with the pre-update data and the post-update data in a log that stores the pre-update data and the post-update data after a commit of transactions associated with the data modification request. The rollback context includes a unique tracking identifier that identifies a request handler or application feature. A rollback request associated with the rollback context is received after the commit of the transactions associated with the data modification request. Based on the rollback request, a data rollback script is generated. When executed, the data rollback script changes at least some of the post-update data to corresponding pre-update data.