Granular Data Object Recovery in Backup Storage Groups
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Solution Overview
Problem
Conventional data recovery methods require restoring entire databases or large blocks of data, wasting resources and losing changes made since the last backup, as users cannot selectively recover individual data objects without prior knowledge of database contents.
Innovation Solution
Systems and methods that identify and display individual data objects in a recovery storage group using APIs or MAPIs, allowing users to select and restore only necessary data objects to their original locations on a production server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire database is restored from backup, then data recovery is ensured, but resource waste increases and changes made since backup are lost
Solution Approach 1:
The database is segmented into individual data objects that can be independently identified, displayed, and selected for recovery. The system parses the backup database into discrete data objects with metadata, allowing users to select only the specific objects needing recovery rather than restoring the entire database, thus reducing resource waste while maintaining recovery reliability.
Solution Approach 2:
The system extracts and displays individual data objects from the backup database along with their metadata (such as creation time, modification time, size). This extraction allows users to identify and select only the specific data objects that need recovery, separating the necessary recovery function from the unnecessary restoration of entire databases.
2Loss of energy
If filter is used to restore portion of database, then resource waste is reduced, but user knowledge of database contents is required
Solution Approach 1:
The system provides self-service by automatically parsing the backup database into data objects and displaying them with metadata without requiring users to have prior knowledge of database contents. The system itself performs the complex task of identifying and presenting recoverable objects, eliminating the need for users to understand database structure while still enabling selective recovery.
Solution Approach 2:
The system introduces an intermediary layer between the backup database and the user. This intermediary automatically parses the database, extracts data objects, and presents them through a user-friendly interface with metadata. This mediator handles the complexity of database parsing and object identification, allowing users to simply select objects for recovery without needing to know database contents.
3Ease of operation
If individual data objects are identified and displayed, then selective recovery is enabled, but system complexity increases
Solution Approach 1:
The system performs preliminary action by automatically parsing the backup database into data objects and extracting metadata before the user initiates recovery. This preliminary processing breaks down the complex database structure into manageable data objects with associated information, simplifying the subsequent user selection process while managing system complexity through automated pre-processing.
Data Source
AI summary
A method and system for restoring data on a production server by recovering individual data objects from a recovery storage group to a database residing on the production server. In one example method, a recovery interface accesses a recovery storage group containing a recovered database. The recovered database has been recovered from a backup storage location and contains a copy of a database residing on the production server. The method then identifies each data object residing in the recovery storage group and displays each of the data objects on a graphical user interface configured to allow a user to select and individual data objects for recovery from the recovered database. The selected data objects are restored to the appropriate location on the production server.