Blitzcrank: Fast Semantic Compression for In-memory Online Transaction Processing
Summary: Blitzcrank combines fast semantic models for dynamic discrete/continuous values with delayed entropy coding, enabling fine-grained compression of in-memory OLTP row stores. It delivers sub-μs random-tuple decompression, 85% memory reduction, and only 19% TPC-C throughput loss. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yiming Qiao (Tsinghua University)
- 2. Yihan Gao
- 3. Huanchen Zhang (Shanghai Qi Zhi Institute; Tsinghua University)
BibTeX Citation
@article{qiao_vldb24,
title = {{Blitzcrank: Fast Semantic Compression for In-memory Online Transaction Processing}},
author = {Qiao, Yiming and Gao, Yihan and Zhang, Huanchen},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {10},
pages = {2528--2540},
doi = {10.14778/3675034.3675044},
url = {https://doi.org/10.14778/3675034.3675044},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,249 | Femur: A Flexible Framework for Fast and Secure Querying from Public Key-Value Store | 2025 | SIGMOD | 5.4574671e-05 |
| 10,529 | Robust Predicate Transfer with Dynamic Execution | 2026 | VLDB | 5.093636e-05 |
| 10,662 | Data Chunk Compaction in Vectorized Execution | 2025 | SIGMOD | 5.093636e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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