Distributed Backup Recovery Selective Data Object Restoration
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Solution Overview
Problem
Current distributed architectures fail to effectively address data persistence and selective recovery of user-specific data subsets in emerging data platforms, requiring the backup and restoration of entire datasets rather than targeted data objects, which is inefficient in terms of storage and compute resources.
Innovation Solution
The Distributed Computing Backup and Recovery (DCBR) system employs a processor, cluster of computing nodes, and a hash function to identify and manage data object storage locations, allowing for targeted backup and recovery of specific data objects by determining the most recent and correct replicas across multiple nodes, thereby enabling selective data restoration without backing up the entire dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire datasets are backed up and restored, then data availability and persistence are ensured, but storage and compute resources are consumed inefficiently
Solution Approach 1:
The patent segments the backup process by identifying and backing up only specific data objects rather than entire datasets. The system uses hash functions to locate specific data objects in distributed storage and creates targeted backup copies of only those objects, reducing the quantity of data that needs to be stored and processed while maintaining reliability for the selected data.
Solution Approach 2:
The patent extracts specific data objects from the larger dataset for backup purposes. By using hash function mapping and data object identification mechanisms, the system isolates and extracts only the necessary data objects that need to be backed up, rather than copying the entire dataset, thus reducing storage and compute resource consumption.
2Reliability
If full dataset restoration is performed, then complete data recovery is achieved, but recovery time and resource usage increase
Solution Approach 1:
The patent segments the recovery process by restoring only specific data objects rather than the entire dataset. The system maintains a mapping between data objects and their backup locations, allowing selective restoration of only the affected or needed data objects, thereby reducing recovery time and resource usage while ensuring complete recovery for the targeted data.
Solution Approach 2:
The patent extracts and restores only the necessary data objects from backup storage rather than reconstructing the entire dataset. This selective extraction approach significantly reduces the time and resources required for recovery operations while maintaining data recovery completeness for the extracted objects.
3Quantity of substance
If selective data object backup is implemented, then storage and compute resources are optimized, but system complexity increases
Solution Approach 1:
The patent introduces hash functions as intermediary components that map data objects to their storage locations and enable selective identification. The hash function serves as a mediator between the backup system and the distributed storage, allowing the system to efficiently locate and select specific data objects for backup without requiring complex search or metadata management mechanisms.
Solution Approach 2:
The patent uses copying mechanisms to create backup copies of specific data objects rather than the entire dataset. By copying only the necessary data objects and maintaining references through hash mappings, the system achieves resource optimization while managing complexity through straightforward copy-and-restore operations rather than complex data manipulation processes.
Data Source
AI summary
The distributed computing backup and recovery (DCBR) system and method provide backup and recovery for distributed computing models (e.g., NoSQL). The DCBR system extends the protections from server node-level failure and introduces persistence in time so that the evolving data set may be stored and recovered to a past point in time. The DCBR system, instead of performing backup and recovery for an entire dataset, may be configured to apply to a subset of data. Instead of keeping or recovering snapshots of the entire dataset which requires the entire cluster, the DCBR system identifies the particular nodes and/or archive files where the dataset resides so that backup or recovery may be done with a much smaller number of nodes.