Inline Compression and Deduplication With Early Compressibility Prediction
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Solution Overview
Problem
Conventional data reduction systems perform data compression and deduplication as separate steps, leading to latency and bandwidth penalties, and require significant cycles and power, with limited ability to predict compressibility of data blocks.
Innovation Solution
A unified data path that combines data compression and deduplication in a single pass, using smaller data blocks, parallel hash table lookups, and early compressibility prediction to minimize wasted effort and optimize system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data compression and deduplication are performed as separate steps, then each function can be implemented independently, but system latency increases and bandwidth penalties occur
Solution Approach 1:
The patent combines data compression and deduplication into a single unified data path that processes data blocks simultaneously. The system performs both compression encoding and deduplication hashing in parallel within the same processing pipeline, eliminating the sequential execution overhead and reducing system latency while maintaining independent functional capabilities through modular design components.
Solution Approach 2:
The system performs preliminary compressibility detection and entropy analysis on data blocks before full compression processing. By predicting whether data blocks are compressible in advance, the system可以避免对不可压缩数据执行耗时的压缩操作，从而减少整体处理延迟，同时为后续的deduplication操作做好准备。
2Reliability
If conventional compression functions are performed on all data blocks, then compression opportunities are not missed, but significant cycles and power are consumed
Solution Approach 1:
The patent applies partial compression action by performing lightweight entropy detection and compressibility prediction on data blocks before committing to full compression. The system performs just enough preliminary analysis to identify compressible data, then applies full compression only to those blocks that meet the compressibility threshold, avoiding excessive processing on incompressible data while maintaining reliable compression coverage.
Solution Approach 2:
The system uses self-service mechanisms through automated entropy detection and compressibility prediction algorithms that analyze data block characteristics without external intervention. The compression engine autonomously identifies which data blocks are suitable for compression based on their entropy values and patterns, making intelligent decisions about resource allocation and processing intensity for each block.
3Productivity
If compression is performed before checking compressibility, then all data blocks are processed, but resources are wasted on incompressible data
Solution Approach 1:
The patent performs preliminary entropy detection and compressibility prediction on data blocks before initiating full compression processing. The system analyzes data patterns, calculates entropy values, and predicts compressibility in advance, then uses this information to decide whether to proceed with full compression. This preliminary action prevents resource waste on incompressible data while maintaining high productivity for compressible data blocks.
Solution Approach 2:
The system implements feedback mechanisms where entropy detection results and compressibility predictions continuously inform compression processing decisions. The compression engine monitors data block characteristics in real-time, adjusts processing based on predicted compressibility, and uses outcome feedback to refine future compression decisions, optimizing resource utilization and preventing waste on incompressible data.
Data Source
AI summary
An apparatus for inline compression and deduplication includes a memory unit and a processor coupled to the memory unit. The processor is configured to receive a subset of data from a data stream and select a reference data block corresponding to the subset of data, in which the reference data block is stored in a memory buffer resident in the memory unit. The processor is also configured to compare a first hash value computed for the subset of data to a second hash value computed for the reference data block, in which the first hash value and the second hash value are stored in separate hash tables and generate a compressed representation of the subset of data by modifying header data corresponding to the subset of data responsive to a detected match between the first hash value and the second hash value in one of the separate hash tables.