Database Data Compression Based on Access Frequency
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Solution Overview
Problem
Current database compression methods require significant CPU resources for query access and necessitate manual management of compressed and uncompressed database tables, failing to efficiently utilize storage capacity and resources.
Innovation Solution
A database system that determines usage frequency of data and selectively compresses subsets based on this frequency, using a processor to identify and manage compression of less frequently accessed data, thereby optimizing storage and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If wholesale compression of entire database tables is implemented, then storage capacity is improved, but CPU resources and query performance deteriorate
Solution Approach 1:
The patent divides the database table into multiple segments or partitions, allowing selective compression of only those segments that meet compression criteria (e.g., low query frequency, high compressibility). This avoids the need to compress entire tables, thereby reducing CPU overhead while still achieving storage savings on appropriate data portions.
Solution Approach 2:
The patent applies different compression strategies to different parts of the database based on local characteristics such as data access patterns, data type, and compressibility. Frequently accessed or non-compressible data segments are left uncompressed, while suitable segments are compressed, optimizing the balance between storage efficiency and query performance.
2Quantity of substance
If manual management of compressed and uncompressed database tables is implemented, then storage capacity is improved, but operational complexity deteriorates
Solution Approach 1:
The patent implements automated mechanisms that monitor database data characteristics and automatically determine which segments should be compressed or decompressed based on predefined criteria. The system self-manages the compression state of data segments without requiring manual intervention, thereby maintaining storage optimization while eliminating operational complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor data access patterns and compression effectiveness, automatically adjusting compression decisions based on observed performance metrics. This closed-loop approach ensures optimal storage utilization while adapting to changing data characteristics without manual management overhead.
Data Source
AI summary
A database system may include a storage array including a plurality of storage devices configured to store database data. The database system may further include a processor in communication with the memory device. The processor may be further configured to determine usage frequency of the database data. The processor may be configured to select a subset of the database data for compression based on the usage frequency. The processor may be further configured to perform the compression of the selected subset of the database data. A method and computer-readable medium may also be implemented.