Improving LZ4 for Effective Compression and Efficient Query
Summary: Extends LZ4 with LZV, using variable-length hashes to find longer matches, improving compression ratio while reducing match-dependency costs. Adds auxiliary-index and prediction-guided key search to query compressed data directly in TsFile, avoiding full decompression and cutting latency. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Zhiheng Liu (Tsinghua University)
- 2. Shaoxu Song (Tsinghua University)
BibTeX Citation
@inproceedings{liu_sigmod26,
title = {{Improving LZ4 for Effective Compression and Efficient Query}},
author = {Liu, Zhiheng and Song, Shaoxu},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786660},
url = {https://dl.acm.org/doi/10.1145/3786660},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
| 148 | Gorilla: A Fast, Scalable, In-Memory Time Series Database | 2015 | VLDB | 0.00029250767 |
| 345 | Column-Stores vs. Row-Stores: How Different Are They Really? | 2008 | SIGMOD | 0.00020656723 |
| 3,793 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 7.1217835e-05 |
| 3,877 | An Empirical Evaluation of Columnar Storage Formats | 2024 | VLDB | 7.0532293e-05 |
| 4,065 | LeCo: Lightweight Compression via Learning Serial Correlations | 2024 | SIGMOD | 6.930275e-05 |
| 9,156 | Apache TsFile: An IoT-native Time Series File Format | 2024 | VLDB | 5.3119028e-05 |
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