Metadata-Based Recovery Classification for Data Sets
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Solution Overview
Problem
Current data recovery systems face inefficiencies when dealing with large numbers of data items, particularly when there are numerous versions, as manual recovery operations are time-consuming and resource-intensive, especially when dealing with millions of data items across multiple versions.
Innovation Solution
A recovery management system that automatically classifies data items based on metadata regarding versioning and storage operations, using a target recovery time to determine recovery actions, which can modify metadata rather than the underlying data, thereby reducing the need for extensive data copying and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recovery operations are performed on large numbers of data items with numerous versions, then recovery accuracy can be maintained, but time consumption and resource usage increase significantly
Solution Approach 1:
The system performs self-service by automatically classifying data items into recovery classes based on their metadata and versioning information. The recovery management system autonomously determines which data items require recovery and what actions to take, eliminating the need for manual assessment and classification of each data item while maintaining accurate recovery outcomes
Solution Approach 2:
The system changes the parameter of recovery operation from manual item-by-item processing to automated batch processing based on classification parameters. By analyzing metadata parameters such as version counts, deletion timestamps, and data item properties, the system automatically groups data items into recovery classes and applies appropriate recovery actions at scale
2Reliability
If manual recovery operations are performed on large numbers of data items with numerous versions, then recovery completeness can be maintained, but resource consumption increases significantly
Solution Approach 1:
The system segments the large set of data items into distinct recovery classes based on their metadata characteristics and versioning patterns. By dividing the recovery task into classified groups, the system can apply optimized recovery strategies to each segment, ensuring comprehensive recovery while reducing overall resource consumption through targeted processing
Solution Approach 2:
The system creates and processes metadata copies for classification purposes rather than manually processing the actual data items. By working with metadata representations and version information, the system can determine recovery actions without duplicating or transmitting large volumes of actual data, significantly reducing resource consumption
3Productivity
If automated classification based on metadata is implemented, then processing efficiency increases, but system complexity increases
Solution Approach 1:
The recovery management system performs multiple functions through a single automated classification mechanism. It simultaneously analyzes metadata, determines recovery classes, identifies appropriate recovery actions, and coordinates execution across multiple data items. This multi-functional approach consolidates what would otherwise require separate manual processes into one unified system
Data Source
AI summary
Systems and methods are provided for using an algorithm and data structure for efficient and accurate classification of data items into recovery classes. When a target recovery time (TRT) is specified for a data set, a system may obtain version metadata regarding data items in the data set. The metadata may be obtained in reverse chronological order such that the latest record representing a version or other storage operation is first, followed by the second latest record, and so on. The system may use a bidirectional doubly linked list to efficiently store version data for a particular data item in memory. As version metadata records are read and added to the data structure in reverse chronological order, classification determinations may be triggered when certain conditions are met.