Deduplicated Storage Data Chunk Reordering for Compression
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Solution Overview
Problem
Conventional data compression techniques in deduplicated storage systems face inefficiencies due to limited window sizes for identifying redundancy, leading to suboptimal compression rates and increased computational overhead, while also compromising read performance.
Innovation Solution
The proposed solution involves reorganizing data chunks based on similarity by generating sketches and reordering them to group similar chunks together, allowing for more effective compression and balancing compression efficiency with read performance through efficient indexing schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression techniques with limited window sizes are used, then computational overhead is reduced, but compression rates deteriorate
Solution Approach 1:
The patent divides the data stream into fixed-size chunks and processes them independently. By segmenting the data into manageable units, the system can apply compression algorithms to each chunk without requiring excessive computational resources, while still achieving improved compression rates through selective reordering of these segments based on similarity metrics.
Solution Approach 2:
The patent performs preliminary analysis of data chunks to identify similarity relationships before compression. By pre-processing the data to determine which chunks are similar and should be reordered, the system enables better compression rates without incurring excessive computational overhead during the actual compression process, as the heavy lifting of similarity detection is done in advance.
2Productivity
If data chunks are reorganized based on similarity for better compression, then compression rates improve, but read performance deteriorates
Solution Approach 1:
The patent applies reordering selectively rather than uniformly across all data. By identifying specific chunks that benefit from reordering based on similarity metrics and only reordering those particular segments, the system improves compression rates for those specific regions while minimizing the impact on overall read performance, as most of the data structure remains intact.
Solution Approach 2:
The patent performs partial reordering of data chunks rather than complete reordering of the entire data set. By applying the reordering operation only to the extent necessary to achieve compression benefits (i.e., to similar chunks that will be accessed together), the system avoids excessive reorganization that would severely impact read performance, thus finding an optimal balance between compression and readability.
Data Source
AI summary
Techniques for improving data compression of a deduplicated storage system are described herein. According to one embodiment, the similarity of a plurality of data chunks stored in one or more first storage areas of the storage system is determined based on a plurality of sketches, each describing characteristics of one of the data chunks. The data chunks are grouped into a plurality of groups of similar data chunks based on the similarity of the data chunks. The groups of similar data chunks are compressed, such that similar data chunks are compressed close to each other.