Data Block Deduplication and Compression for Storage Reduction
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Solution Overview
Problem
Existing data storage and communication systems face challenges in reducing data size effectively, as deduplication is inefficient without duplicates, and compression can hinder deduplication processes, leading to substantial overhead.
Innovation Solution
A system combining deduplication and compression techniques, where a data block identifier identifies duplicate data blocks, and a deduplication component replaces them with key values, while a compression component compresses unique blocks, reducing data storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to reduce data size, then storage efficiency is improved, but deduplication effectiveness deteriorates because compressed data loses duplicate patterns
Solution Approach 1:
The patent applies deduplication before compression in the data processing pipeline. By identifying and replacing duplicate data blocks with references before the compression step, the system preserves duplicate patterns for effective deduplication while still achieving compression benefits on unique data, resolving the contradiction between compression efficiency and deduplication effectiveness
Solution Approach 2:
The patent segments the data processing into distinct stages: deduplication stage and compression stage. This segmentation allows each process to operate optimally on appropriately prepared data, with deduplication working on original data patterns and compression working on the deduplicated result, thereby maintaining both deduplication effectiveness and compression efficiency
2Measurement precision
If deduplication is performed on uncompressed data, then duplicate detection accuracy is improved, but processing overhead increases due to larger data volume
Solution Approach 1:
The system performs deduplication as a preliminary step before compression, using efficient hashing algorithms to quickly identify duplicates in the original data. This preliminary deduplication reduces the data volume that subsequently requires compression processing, thereby reducing overall processing overhead while maintaining high duplicate detection accuracy through hash-based identification
3Quantity of substance
If both deduplication and compression are applied, then storage efficiency is improved, but system complexity increases due to multiple processing steps
Solution Approach 1:
The patent combines deduplication and compression into a unified data processing system where both functions operate in sequence on the same data stream. The deduplication component identifies and replaces duplicates, and the compression component compresses the result, with both integrated into a single processing pipeline that manages complexity through coordinated operation rather than separate independent systems
Solution Approach 2:
The system implements a multi-functional data processing component that performs both deduplication and compression operations. This universal component handles different data types and processing requirements through a single integrated architecture, reducing system complexity compared to having separate specialized systems for each function
Data Source
AI summary
The described technology is generally directed towards reducing the amount of data stored in a sequence of data blocks by combining deduplication and compression. According to an embodiment, a system can comprise a memory that can store computer executable components, and a processor that can execute the components stored in the memory. The components can comprise a data block identifier that can identify, for a sequence of data blocks, a first data block that corresponds to a first data, resulting in a first identified data block, and a deduplication component that can identify a second data block that corresponds to the first data, resulting in a second identified data block, wherein the deduplication component can replace the second identified data block with a key value corresponding to the first identified data block. Further, a compression component can compress the first identified data block, resulting in a compressed data block.