Database Table Compression Scheduling from Query Usage Patterns
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Solution Overview
Problem
Existing database compression methods do not efficiently manage storage space and query response time, as they fail to consider the usage patterns and resource availability of database tables, leading to suboptimal compression and decompression processes.
Innovation Solution
A compression management system that analyzes query logs to determine usage patterns and evaluates various compression strategies based on access frequency, processing resources, storage savings, and timing to selectively apply compression algorithms to database tables, optimizing storage efficiency and query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to database tables, then storage space is reduced, but query response time increases due to decompression requirements
Solution Approach 1:
The patent applies dynamics by making compression status changeable over time based on workload conditions. Tables are dynamically compressed when workload is low and decompressed when workload is high, allowing the system to adapt its storage optimization strategy to current operational demands rather than maintaining a static compression state
Solution Approach 2:
The patent implements preliminary action by proactively compressing tables during periods of low workload before query-intensive operations begin. This advance compression prepares storage optimization in advance, and the system later decompresses tables in anticipation of upcoming query workloads based on predicted usage patterns
2Quantity of substance
If compression is applied to frequently accessed tables, then storage efficiency improves, but processing resource consumption increases during compression and decompression operations
Solution Approach 1:
The patent applies periodic action by scheduling compression operations during specific time periods when workload is low, rather than attempting to compress tables continuously or during peak usage. This periodic approach concentrates resource consumption into off-peak periods, reducing the impact on overall system performance while still achieving storage efficiency gains
3Quantity of substance
If comprehensive compression evaluation is performed considering multiple strategies and algorithms, then compression effectiveness improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex compression evaluation process into distinct, manageable components: evaluating compression strategies, evaluating compression algorithms, determining workload conditions, and selecting candidate tables. This segmented approach allows each component to be independently analyzed and optimized, reducing overall system complexity while maintaining comprehensive evaluation capabilities
Data Source
AI summary
A database system may implement compression management of tables in the database system. The compression management may include determination of a pattern of usage of various database tables in the database system. Based on this pattern of usage, the database tables may be selected as candidates for compression or decompression at the appropriate time. In one example, the pattern of usage may be based on the contents of a query log of the database system. The compression management may also include evaluation of various compression strategies to apply to a candidate database table. Each compression strategy may be evaluated to determine if application to a database table or a portion of the database table would be beneficial based on various conditions. The compression management may also include consideration of each available compression strategy to be applied solely or in combination with one another.