Storage Controller Data Grouping for Random Write Compression
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Solution Overview
Problem
Existing storage systems face challenges in improving compression rates and access performance, particularly when handling randomly written data, as they often rely on sequential data patterns and lack efficient methods to leverage data similarity for compression.
Innovation Solution
A storage system with a controller that groups data based on similarity degree, selects groups for compression, and stores them collectively, using techniques like deduplication and reversible compression to enhance compression rates and access performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If data is compressed using traditional methods without grouping, then the compression process is simple, but the compression rate is low for randomly written data
Solution Approach 1:
The data written to the cache area is divided into multiple groups based on similarity degree. This segmentation allows the system to identify and compress only the groups with high similarity, thereby improving the overall compression rate for randomly written data while maintaining efficient processing.
Solution Approach 2:
The system performs preliminary grouping of data by similarity degree before compression. This preliminary action enables the identification of compressible data patterns in advance, allowing the compression algorithm to focus on high-similarity groups and achieve better compression rates without processing all data uniformly.
2Loss of substance
If data is grouped and compressed based on similarity, then the compression rate increases, but the processing complexity increases
Solution Approach 1:
The system performs preliminary grouping of data by similarity degree before compression. This preliminary action enables the identification of compressible data patterns in advance, allowing the compression algorithm to focus on high-similarity groups and achieve better compression rates without processing all data uniformly.
Solution Approach 2:
Different processing strategies are applied to different groups of data based on their similarity characteristics. High-similarity groups undergo compression while low-similarity groups are handled differently, optimizing the balance between compression rate and processing complexity for each specific data group.
3Speed
If random data is compressed without preprocessing, then the access performance is maintained, but the compression rate remains low
Solution Approach 1:
The system performs preliminary grouping of data by similarity degree before compression. This preliminary action enables the identification of compressible data patterns in advance, allowing the compression algorithm to focus on high-similarity groups and achieve better compression rates without processing all data uniformly.
Solution Approach 2:
The data written to the cache area is divided into multiple groups based on similarity degree. This segmentation allows the system to identify and compress only the groups with high similarity, thereby improving the overall compression rate for randomly written data while maintaining efficient processing.
Data Source
AI summary
Provided is a storage system in which a compression rate of randomly written data can be increased and access performance can be improved. A storage controller 22A includes a cache area 203A configured to store data to be read out of or written into a drive 29. The controller 22A groups a plurality of pieces of data stored in the cache area 203A and input into the drive 29 based on a similarity degree among the pieces of data, selects a group, compresses data of the selected group in group units, and stores the compressed data in the drive 29.