Adaptive Data Compression for Storage Efficiency and Access Speed
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Solution Overview
Problem
Existing data compression algorithms often fail to optimize storage efficiency and performance due to assumptions about uniform data access patterns, leading to suboptimal selection and inefficiencies when access patterns change.
Innovation Solution
Implementing a system for transparent, fine-grained, and adaptive data compression that dynamically determines the optimal compression algorithm for each data chunk based on its access patterns, using a data compression optimizer to track and adjust compression status according to read and write counts, and employing algorithms like LZ4 and Zlib for different data access scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is implemented in persistent storage, then storage efficiency is improved, but data-accessing system performance deteriorates
Solution Approach 1:
The patent divides the storage space into multiple data chunks and applies different compression algorithms to different chunks based on their access patterns. This segmentation allows the system to optimize storage efficiency for cold data while maintaining fast access for hot data, thereby resolving the contradiction between storage efficiency and access performance.
Solution Approach 2:
The patent implements dynamic adjustment of compression status for data chunks based on monitored access patterns (read count and write count). When a data chunk transitions from cold to hot or vice versa, the system dynamically changes its compression status, allowing the system to adapt to changing performance requirements while maintaining overall storage efficiency.
2Device complexity
If a single compression algorithm is used for all data, then device complexity is reduced, but adaptability to different data access patterns deteriorates
Solution Approach 1:
The patent creates a universal compression management system that can handle multiple compression algorithms (Zlib, LZ4, LZ4HC) and multiple access patterns through a unified interface. The data compression optimizer provides multi-functional capabilities to automatically select and manage different algorithms based on data characteristics, reducing the complexity burden on users while maintaining high adaptability.
Solution Approach 2:
The patent changes the parameter of compression algorithm selection based on data access patterns. By monitoring read count and write count, the system dynamically adjusts which compression algorithm is applied to each data chunk, enabling adaptability to different access patterns without requiring complex user configuration.
3Quantity of substance
If data is compressed using aggressive algorithms, then storage efficiency is improved, but data decompression speed deteriorates
Solution Approach 1:
The patent applies different compression qualities to different data chunks based on their local characteristics (access patterns). Cold data chunks use aggressive compression algorithms for maximum storage efficiency, while hot data chunks use lighter compression or no compression to maintain fast decompression speed. This local quality differentiation resolves the contradiction between compression ratio and decompression speed.
Solution Approach 2:
The system automatically monitors data access patterns and self-adjusts the compression status of data chunks without user intervention. When data becomes frequently accessed, the system automatically reduces its compression level to improve decompression speed, and vice versa, allowing the system to serve itself in optimizing the trade-off between storage efficiency and access performance.
Data Source
AI summary
Transparent, fine-grained, and adaptive data compression is described. A system determines a first data chunk and a second data chunk in a persistent storage. The system determines a first data read count and/or a first data write count for the first data chunk, and a second data read count and/or a second data write count for the second data chunk. The system determines then a first data compression status for the first data chunk and a second data compression status for the second data chunk. Based on the first data compression status and second data compression status, the system stores data in the first data chunk and data in the second data chunk to the persistent storage.