Snapshot Replication via Incremental Block Change Tracking
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Solution Overview
Problem
Current data management systems face challenges in efficiently performing snapshot backups and replications due to the increasing volume of data and the need for reliable, cost-effective solutions that minimize productivity impact, particularly in tracking incremental block changes across large datasets.
Innovation Solution
The implementation of improved snapshot replication operations based on incremental block change tracking, which facilitates the maintenance of mirror copies for data restoration and application recovery by tracking changes between snapshots, reducing the time and resources required for backups and replications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional snapshot backup methods are used to ensure data protection, then reliability is improved, but productivity deteriorates due to significant time consumption and resource usage
Solution Approach 1:
The patent segments the backup process by identifying and tracking only the incremental blocks that have changed since the last snapshot, rather than backing up the entire dataset. This segmentation allows the system to focus resources on only the necessary portions of data, significantly reducing backup time while maintaining complete data protection through systematic tracking of all changes.
Solution Approach 2:
The patent implements preliminary action by maintaining change tracking information between snapshots, so that when a backup is needed, the system already has identified which blocks have changed. This preliminary tracking eliminates the need for full scans during backup operations, reducing the time required while ensuring no data is missed.
2Reliability
If comprehensive data replication is performed to maintain mirror copies for recovery, then reliability is improved, but loss of time increases due to the extensive processing required
Solution Approach 1:
The patent extracts only the essential information needed for replication by identifying and tracking specifically the incremental blocks that have changed. Rather than replicating entire datasets or performing comprehensive analysis, the system extracts only the changed blocks and their metadata, dramatically reducing replication time while maintaining the ability to restore complete application states.
Solution Approach 2:
The patent changes the parameter being tracked from entire file or dataset level to block level, and further optimizes by tracking only the state changes of blocks. This parameter change from coarse-grained to fine-grained tracking allows the system to identify minimal changes, reducing replication time while ensuring complete recovery capability through precise tracking of all modifications.
3Measurement precision
If full data scanning is performed to track changes accurately, then measurement precision is improved, but productivity deteriorates due to increased computational resources required
Solution Approach 1:
The patent segments the data into blocks and tracks changes at the block level rather than scanning entire files or datasets. This segmentation allows the system to achieve precise tracking of all changes while processing only the relevant blocks, maintaining measurement precision without the computational overhead of full data scanning.
Solution Approach 2:
The patent implements self-service by using change tracking mechanisms that automatically identify modified blocks without requiring external scanning or analysis. The system maintains its own change state information, allowing it to precisely track changes while eliminating the need for resource-intensive scanning operations, thereby improving both accuracy and processing speed.
Data Source
AI summary
A system according to certain aspects improves the process of performing snapshot replication operations (e.g., maintaining a mirror copy of primary data at a secondary location by generating snapshots of the primary data). The system can collect and maintain cumulative block-level changes to the primary data after each sub-interval of a plurality of sub-intervals between the snapshots. When a snapshot is generated, any changes to the primary data not reflected in the cumulative block-level changes are identified based on the snapshot and transmitted to the secondary location along with the cumulative block-level changes. By the time the snapshot is generated, some or all of the changes to the primary data associated with the given snapshot have already been included in the cumulative block-level changes, thereby reducing the time and computing resources spent to identify and collect the changes for transmission to the secondary location.