Feature-Based Data Rollback With Application-Linked Context
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Solution Overview
Problem
Existing data rollback methods struggle with identifying and reversing database changes due to the lack of traceability from transaction logs to application logic, making it difficult to restore data to a consistent state, especially in complex applications.
Innovation Solution
Implementing feature-based data rollback by associating rollback contexts with sets of database commands, generating data rollback scripts, and executing them to revert changes, while ensuring data privacy and user consent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transaction logs are used to restore database data, then data can be recovered from hardware failures or application crashes, but it becomes difficult to identify and reverse specific application feature changes due to lack of traceability
Solution Approach 1:
The system performs preliminary actions by associating rollback contexts with sets of database commands before the commands are executed. This includes generating rollback context data that identifies the application feature, user, and timestamp, and storing this context in a searchable data structure. When a rollback is needed, the pre-prepared context enables immediate identification of which data changes resulted from which application feature, eliminating the need to sift through voluminous transaction logs.
Solution Approach 2:
The patent introduces an intermediary component (the rollback context association mechanism) that bridges the gap between application logic and transaction logs. This intermediary captures and stores metadata linking database changes to their originating application features, users, and timestamps. The searchable data structure acts as a mediator that enables efficient querying of which application features caused which data changes, without requiring direct analysis of the raw transaction logs.
2Reliability
If all data changes are logged for potential rollback, then complete data restoration is possible, but the complexity of identifying and managing data changes increases significantly
Solution Approach 1:
The system segments the monolithic transaction log into organized units by associating rollback contexts with specific sets of database commands. Each segment is labeled with metadata including application feature identifier, user identifier, and timestamp. This segmentation transforms the undifferentiated mass of transaction logs into discrete, searchable units that can be independently identified and managed, reducing the complexity of tracking which changes resulted from which application features.
Solution Approach 2:
Rollback context data is generated and stored in advance, organizing data changes by application feature, user, and timestamp before any rollback operation is needed. This preliminary organization creates a searchable index that maps application features to their corresponding data changes, eliminating the need for complex real-time analysis during rollback operations and significantly simplifying data change management.
3Manufacturing precision
If manual tracking of data changes is performed to ensure semantic consistency, then data integrity can be maintained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system implements self-service by automatically generating rollback context data that includes application feature identifiers, user identifiers, and timestamps. The searchable data structure automatically indexes and organizes this context data, enabling the system to autonomously identify which data changes resulted from which application features without requiring manual tracking or analysis. This automated self-service approach maintains data consistency while eliminating manual intervention.
Solution Approach 2:
The system establishes a feedback mechanism where rollback context data is continuously generated and stored alongside database changes. This feedback loop provides real-time information about which application features caused which data changes, enabling rapid identification and rollback of specific features. The searchable data structure provides immediate feedback when querying for changes related to a particular application feature, user, or time period, dramatically reducing restoration time while maintaining data consistency.
Data Source
AI summary
Pre-update data are changed to post-update data via a set of data update commands executed to process a data modification request. A rollback context that is based on the data modification request is associated with the pre-update data and the post-update data. A rollback request associated with the rollback context is received. In response to receiving the rollback request, a data rollback script is generated. The data rollback script, when executed, changes at least some of the post-update data to corresponding pre-update data. The data rollback script is then executed.