Selective Database Rollback via Transaction Journal Reverse-Application
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Solution Overview
Problem
Existing database rollback methods are all-or-nothing, making them ineffective for debugging non-deterministic issues, and they are computationally expensive and disruptive, especially in high-volume data environments. Additionally, legacy databases often lack primary and foreign keys, leading to data corruption and manual intensive conversion processes.
Innovation Solution
A method and system for selective database data rollback that analyzes database journals to revert specific data at a row/column level, identifies primary and foreign keys, and generates a copy of the data by reverse-applying transaction journal entries, allowing for granular control and data integrity improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a table-wide or database-wide rollback is performed, then data integrity is restored to an earlier point in time, but computational resources are excessively consumed and production data may be unnecessarily affected
Solution Approach 1:
The patent segments the rollback operation from database-wide to table-specific level. Instead of rolling back entire databases, the system identifies and rolls back only the specific tables containing problematic data, dramatically reducing computational resources while maintaining data integrity for affected tables.
Solution Approach 2:
The patent applies local quality by making rollback effects localized to specific tables rather than uniformly applied across the entire database. This allows different parts of the database to maintain different states, with only problematic tables being rolled back while production data in other tables remains intact.
2Reliability
If a table-wide or database-wide rollback is performed, then data integrity is restored to an earlier point in time, but production data that should not be affected is unnecessarily altered
Solution Approach 1:
The patent segments the rollback scope to affect only specific tables identified as containing issues, rather than applying rollback uniformly across the entire database. This prevents unintended alteration of production data in tables that are not problematic.
Solution Approach 2:
The patent extracts the rollback operation from a database-wide context and applies it selectively to only those tables that require it. This extraction allows the system to isolate and revert only the problematic portions while leaving the rest of the production database untouched.
3Reliability
If conventional rollback methods are used, then database crashes and data corruption are prevented, but debugging of non-deterministic issues is ineffective because entire databases must be rolled back
Solution Approach 1:
The patent segments the rollback capability to operate at the table level rather than database level, enabling developers to roll back specific tables for debugging purposes without affecting the entire database. This granular control makes debugging non-deterministic issues effective while maintaining data protection.
Solution Approach 2:
The patent applies partial action by performing rollback only on the extent necessary for debugging specific tables, rather than applying excessive database-wide rollback. This allows iterative debugging where only problematic tables are reverted, making the debugging process efficient and controlled.
4Adaptability or versatility
If legacy database schemas without key constraints are used, then system compatibility is maintained, but data corruption occurs due to lack of constraints on primary and foreign keys
Solution Approach 1:
The patent applies preliminary action by automatically discovering and establishing key constraints (primary keys and foreign keys) before data corruption can occur. This proactive approach adds data integrity safeguards to legacy schemas without requiring complete schema redesign, maintaining compatibility while preventing future corruption.
Solution Approach 2:
The patent changes the parameters of legacy schemas by automatically adding key constraints to tables that previously lacked them. This modifies the schema structure to enforce data integrity rules while maintaining the overall schema compatibility with existing applications.
Data Source
AI summary
A selective database rollback method includes identifying a table, identifying a root key, storing a target rollback date, retrieving schema information including keys, ordering the table, iterating over the table, storing the current table state, obtaining a transaction journal, and reverse-applying the transaction journal to generate an output file. A method or system for identifying database key includes iterating over a table's columns and rows, generating similarity metrics by comparing column-wise and/or row-wise data, and comparing the generated similarity metrics to a threshold value. A server includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the server to identify a table, identify a root key, store a target rollback date, retrieve schema information including keys, order the table, iterate over the table, store the current table state, obtain a transaction journal, and reverse-apply the transaction journal to generate an output file.