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arXiv · 2311.13947

High-Ratio Compression for Machine-Generated Data

Jiujing Zhang·Zhitao Shen·Shiyu Yang·Lingkai Meng·Chuan Xiao·Wei Jia·Yue Li·Qinhui Sun·Wenjie Zhang·Xuemin Lin
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Abstract

Machine-generated data is rapidly growing and poses challenges for data-intensive systems, especially as the growth of data outpaces the growth of storage space. To cope with the storage issue, compression plays a critical role in storage engines, particularly for data-intensive applications, where high compression ratios and efficient random access are essential. However, existing compression techniques tend to focus on general-purpose and data block approaches, but overlook the inherent structure of machine-generated data and hence result in low-compression ratios or limited lookup efficiency. To address these limitations, we introduce the Pattern-Based Compression (PBC) algorithm, which specifically targets patterns in machine-generated data to achieve Pareto-optimality in most cases. Unlike traditional data block-based methods, PBC compresses data on a per-record basis, facilitating rapid random access. Our experimental evaluation demonstrates that PBC, on average, achieves a compression ratio twice as high as state-of-the-art techniques while maintaining competitive compression and decompression speeds.We also integrate PBC to a production database system and achieve improvement on both comparison ratio and throughput.

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Jiujing Zhang, Zhitao Shen, Shiyu Yang, Lingkai Meng, Chuan Xiao, Wei Jia, Yue Li, Qinhui Sun, Wenjie Zhang, Xuemin Lin. 2023-11-23. High-Ratio Compression for Machine-Generated Data. https://arxiv.org/abs/2311.13947

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