Database Sub-Table Storage Partitioning Based on Access History
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Solution Overview
Problem
Database partitioning struggles to optimally distribute sub-tables across storage areas due to the difficulty in determining the most suitable locations based on access frequency and hit rates, leading to inefficient data management and increased maintenance costs.
Innovation Solution
A method that acquires access history for each sub-table, prioritizes storage areas based on access frequency and hit rates, and dynamically decides the storage location for sub-tables, allowing for efficient data placement by relocating high-access-frequency sub-tables to high-priority storage areas and low-access-frequency sub-tables to lower-priority areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If table partitioning is implemented to manage large amounts of data, then data management efficiency is improved, but the difficulty in determining optimal storage locations for sub-tables increases
Solution Approach 1:
The system acquires access history information about sub-tables and uses this feedback to dynamically determine optimal storage locations. By continuously monitoring access patterns and adjusting storage assignments based on this information, the system resolves the difficulty of determining optimal locations through empirical data rather than static rules
Solution Approach 2:
The system prioritizes table storage areas in advance based on access frequency and other criteria before assigning sub-tables to specific locations. This preliminary ordering of storage areas provides a structured framework that simplifies the location determination process while maintaining optimization capabilities
2Speed
If data is stored in high-performance storage devices with excellent access performance, then data retrieval efficiency is improved, but maintenance cost increases
Solution Approach 1:
The system assigns different storage locations to different sub-tables based on their specific access characteristics. High-access-frequency sub-tables are placed in high-performance storage devices where they can utilize the excellent access performance, while low-access-frequency sub-tables are placed in lower-cost storage devices, thereby optimizing the balance between retrieval efficiency and maintenance cost
Solution Approach 2:
The system changes the storage location parameter for sub-tables based on their access frequency and hit rate. By dynamically adjusting which sub-tables reside in which storage devices according to these parameters, the system achieves cost-effective data retrieval optimization
Data Source
AI summary
A method, system and computer program product for storing a sub-table by deciding one of a plurality of table storage areas, the sub-tables resulting from partitioning of a table in a database. To provide efficient table partitioning, a database is described for distributively locating a plurality of sub-tables resulting from partitioning of a table in a database. This includes an acquiring section that monitors access occurrences to each of the sub-tables to acquire a history of access occurrences. Also a deciding section decides a table storage area in which a sub-table is to be located, based on the history of access occurrences to each of the sub-tables.