Database Restoration Using Metadata Analysis
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Solution Overview
Problem
The process of performing a point-in-time restoration of a database is time-consuming and error-prone, requiring manual selection and provision of log files, which complicates the selection of appropriate log files and backup chains due to frequent changes between backups.
Innovation Solution
A method and system that analyze metadata to identify a reduced dataset required for restoring a database to a specific point in time, using a distributed storage system that includes primary and secondary storage systems, and cloud storage, to automate the selection and provision of necessary backups and log files, thereby reducing the need for manual user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection and provision of log files is performed for point-in-time restoration, then the restoration can be completed, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system automatically identifies and selects the required log files and backup datasets without requiring manual user intervention. The storage system analyzes metadata, determines the appropriate backup chain, and provisions the reduced dataset autonomously, eliminating human error and time consumption associated with manual selection.
Solution Approach 2:
The patent introduces an automated intermediary process that mediates between the restoration request and the backup storage. This intermediary automatically analyzes metadata, identifies the correct backup chain, and selects the necessary log files, serving as a bridge that eliminates the need for direct manual user involvement in the complex selection process.
2Reliability
If all log files between backups are provided for point-in-time restoration, then complete restoration is ensured, but the amount of data required increases significantly
Solution Approach 1:
The system extracts only the specific log files and backup segments that are necessary for restoring the database to the desired point in time. By analyzing metadata and determining the appropriate backup chain, the system isolates and provisions only the relevant reduced dataset, excluding unnecessary log files and data segments.
Solution Approach 2:
The patent segments the backup data into distinct components (full backups, incremental backups, and log files) and selectively provisions only the necessary segments required for the specific point-in-time restoration. This segmentation allows the system to provide a reduced dataset rather than all available data.
3Adaptability or versatility
If user intervention is required to select log files and determine backup chains, then flexibility is maintained, but the process complexity increases
Solution Approach 1:
The storage system performs self-service by automatically analyzing metadata, identifying the appropriate backup chain, and selecting the necessary log files based on the restoration point. This autonomous process maintains flexibility in handling different restoration scenarios while eliminating the complexity of manual user intervention.
Data Source
AI summary
A method and system for restoring a database are described. An identification of a restoration point of the database is received. Using a processor, metadata of a plurality of backups are analyzed to identify from the plurality of backups a reduced dataset required to restore the database to the restoration point. The reduced dataset is provided for use in restoring the database to the restoration point.