Database Table Valuation for Data Restoration Prioritization
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Solution Overview
Problem
In multi-environment systems, data restoration failures occur due to insufficient memory space in target computing environments, as existing mechanisms do not prioritize copying critical data tables before memory exhaustion.
Innovation Solution
The system automatically prioritizes data tables based on pre-defined metrics, assigning higher priority to critical tables, ensuring they are copied first to prevent memory exhaustion during data restoration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data copying is performed without prioritization, then all data files are copied in sequence, but critical data tables may not be copied before memory space is exhausted
Solution Approach 1:
The system performs preliminary actions by calculating a priority index for each data table before the data copying process begins. This priority index is based on multiple factors including table size, data age, and access frequency. By pre-establishing this prioritization scheme, the system ensures that critical data tables are identified and copied first before target memory space is exhausted, thereby resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system changes the parameter of data file selection by introducing a priority index that dynamically determines which data tables are copied next. Instead of using a fixed copying sequence, the system adjusts the copying order based on calculated priority values that reflect the criticality of each data table. This parameter change enables the system to adaptively optimize data restoration success while maintaining efficient use of available memory space.
2Reliability
If target memory devices have sufficient space for all critical data files, then data restoration can succeed, but no mechanism exists to selectively copy critical data files first when space is insufficient
Solution Approach 1:
The system implements self-service by automatically calculating priority indices and determining the optimal copying sequence without requiring manual intervention. The copying mechanism autonomously evaluates multiple parameters including table size, data age, and access frequency to generate a prioritized copying list. This self-service capability resolves the contradiction by providing an intelligent, automatic solution that enhances reliability without proportionally increasing device complexity.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring available target memory space during the data copying process. When memory space becomes limited, the system references the pre-calculated priority indices to determine which data tables to copy next, ensuring critical data is prioritized. This feedback loop allows the system to dynamically adjust the copying process based on real-time memory conditions, improving reliability while maintaining manageable complexity through automated decision-making.
3Reliability
If data tables are prioritized based on multiple parameters, then critical data is copied first, but the prioritization process requires calculating value coefficients based on table metadata and user-defined metrics
Solution Approach 1:
The system performs the time-consuming prioritization calculations in advance, before the actual data copying process begins. By calculating value coefficients based on table metadata and user-defined metrics during a preliminary phase, the system establishes a ready-to-use priority sequence that guides the subsequent copying operation. This preliminary action resolves the contradiction by separating the computational overhead from the time-critical copying process, ensuring critical data is copied first without delaying the restoration operation.
Data Source
AI summary
A system includes one or more source memory devices of a source computing environment that store a database comprising data files grouped in a plurality of file groups, wherein each of a plurality of data tables of the source computing environment includes data from one or more of the data files grouped into one or more of the file groups, one or more target memory devices of a target computing environment and at least one processor configured to calculate a number of read operations per megabyte (MB) for a data table, a number of write operations per MB, obtain an importance index of the table, weight the reads per MB, writes per MB and the importance index by respective user-defined weights, determine a criticality index of the table, calculate a value coefficient of the table, and assign a data file based on the value coefficient of the table.