Data Restoration via Local Base and Remote Incremental Recovery
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Solution Overview
Problem
Current data restoration techniques are inefficient in restoring data to a precise desired state, particularly when dealing with hardware or software failures, as they often rely on outdated backup methods and lack effective incremental difference identification.
Innovation Solution
A method that restores data by first identifying and restoring point-in-time data from a local copy and then recovering incremental differences from a remote mirror, using techniques such as markers, checksums, or log maintenance to ensure accurate and efficient data recovery, potentially performed in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional backup methods are used to restore data, then data can be restored to a previous state, but the restoration process is slow and cannot efficiently recover incremental changes
Solution Approach 1:
The data restoration process is segmented into two independent parallel operations: restoring base point-in-time data from local backups and restoring incremental differences from remote mirrors. This segmentation allows each operation to use the most appropriate data source, improving both speed and accuracy of restoration.
Solution Approach 2:
The system performs preliminary actions by maintaining pre-computed incremental difference data at remote mirrors and establishing marker-based synchronization points in advance. When restoration is needed, the incremental data is already prepared and can be applied immediately after the base restoration, significantly reducing total restoration time.
2Reliability
If all data is restored from remote mirrors, then complete data recovery is achieved, but network bandwidth is consumed and restoration efficiency decreases
Solution Approach 1:
The invention extracts only the necessary incremental difference data from remote mirrors rather than transferring all data. By identifying and restoring only the changes that occurred after the point-in-time marker, the system achieves complete data recovery while minimizing network bandwidth consumption and improving restoration efficiency.
Solution Approach 2:
The system performs partial action by restoring only the incremental portion of data from remote mirrors rather than the complete dataset. This partial restoration approach, combined with local base restoration, achieves full data recovery with reduced network overhead and improved overall productivity.
3Measurement precision
If incremental differences are identified and restored from remote mirrors, then restoration accuracy to desired state is improved, but system complexity increases
Solution Approach 1:
The invention introduces marker-based intermediaries that serve as synchronization points between local backups and remote mirrors. These markers simplify the identification of incremental differences by providing clear delimiters, reducing the complexity of tracking changes while improving restoration precision to the desired state.
4Productivity
If parallel restoration operations are performed, then restoration speed is improved, but coordination complexity increases
Solution Approach 1:
The restoration process is divided into two independent parallel segments: local base restoration and remote incremental restoration. This segmentation enables simultaneous execution of both operations without complex coordination, as each segment operates independently on different data sources, improving restoration speed while keeping coordination manageable.
Data Source
AI summary
An embodiment of a method of restoring data begins with a step of restoring point-in-time data from a local copy. The method concludes with a step of restoring at least a portion of an incremental difference between the point-in-time data and a desired state of the data from a remote mirror.