Database Data Temperature Heat Map for Backup Reliability
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Solution Overview
Problem
Historical data information regarding database data temperature is often lost during data movement operations such as backups and replication, leading to a lack of information on how to optimally store data upon return, as existing database systems do not effectively maintain and utilize data usage metrics for reorganization.
Innovation Solution
A database system that includes a storage array with a processor to determine and store data frequency at a predetermined granularity, generating a heat map to track data usage and temperature, allowing for temperature-based data relocation and reorganization upon return to storage devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is moved for backups or replication, then data redundancy and system reliability are improved, but historical data temperature information is lost
Solution Approach 1:
The system performs preliminary action by capturing and storing data temperature information in a heat map before data is moved for backups or replication. This historical temperature data is preserved and can be used later to guide optimal data placement when data returns to storage, preventing information loss while maintaining reliability improvements.
2Productivity
If data is reorganized based on access frequency, then retrieval efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically monitoring data access patterns and updating the heat map with temperature information without requiring manual intervention. The database system autonomously uses this heat map data to determine optimal data placement decisions, reducing operational complexity while maintaining high retrieval efficiency through frequency-based reorganization.
3Productivity
If detailed data usage metrics are maintained, then data placement optimization is improved, but storage overhead increases
Solution Approach 1:
The system applies local quality by maintaining detailed temperature information at the granular level where it is most useful - specifically for data being moved or reorganized. The heat map stores usage metrics with appropriate granularity (e.g., per tablespace, per data set) rather than uniformly across all data, optimizing placement decisions where needed while minimizing unnecessary storage overhead elsewhere in the system.
Data Source
AI summary
A database system may include a storage array that includes a plurality of storage devices configured to store a database. The database system may further include a processor in communication with the memory device. The processor may determine frequency of data values of a first set of data from the database. The frequency of data values are determined at a predetermined data granularity. The processor may also generate a data object to include information indicative of the frequency of data values. The processor may also store the data object in the storage array. A method and computer-readable medium may also be implemented.