DeFiHap: Detecting and Fixing HiveQL Anti-Patterns
Summary: Empirical study identifies 38 HiveQL anti-patterns and their impact on performance. DeFiHap automates detection and repair via AST-based analysis and rule-based rewriting with perf-tuning; it detects 25 anti-patterns and suggests fixes for 17. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yuetian Mao (Shanghai Jiao Tong University)
- 2. Shuai Yuan (Shanghai Jiao Tong University)
- 3. Nan Cui (Shanghai Jiao Tong University)
- 4. Tianjiao Du (Shanghai Jiao Tong University)
- 5. Beijun Shen (Shanghai Jiao Tong University)
- 6. Yuting Chen (Shanghai Jiao Tong University)
BibTeX Citation
@article{mao_vldb21,
title = {{DeFiHap: Detecting and Fixing HiveQL Anti-Patterns}},
author = {Mao, Yuetian and Yuan, Shuai and Cui, Nan and Du, Tianjiao and Shen, Beijun and Chen, Yuting},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {2671--2674},
doi = {10.14778/3476311.3476316},
url = {https://doi.org/10.14778/3476311.3476316},
year = {2021}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,082 | SQLCheck: Automated Detection and Diagnosis of SQL Anti-Patterns | 2020 | SIGMOD | 6.9164719e-05 |
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