Painless and Efficient Scaling of Pandas Programs: Demo
Summary: LaFP lets unmodified Pandas programs scale across multiple execution backends via minor configuration changes. It combines JIT static analysis with lazy runtime optimization to improve performance and avoid memory failures. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Bhushan Pal Singh (Indian Institute of Technology Mumbai; NewSpace India Limited)
- 2. Priyesh Kumar (Dream11; Indian Institute of Technology Mumbai)
- 3. Chiranmoy Bhattacharya (Fujitsu Research India; Indian Institute of Technology Mumbai)
- 4. Utkarsh Shetye (Indian Institute of Technology Mumbai)
- 5. Manish Kumar (Indian Institute of Technology Mumbai)
- 6. S. Sudarshan (Indian Institute of Technology Mumbai)
BibTeX Citation
@article{singh_vldb26,
title = {{Painless and Efficient Scaling of Pandas Programs: Demo}},
author = {Singh, Bhushan Pal and Kumar, Priyesh and Bhattacharya, Chiranmoy and Shetye, Utkarsh and Kumar, Manish and Sudarshan, S.},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4710--4713},
doi = {10.14778/3827998.3828103},
url = {https://doi.org/10.14778/3827998.3828103},
year = {2026}
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Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,207 | Magpie: Python at Speed and Scale using Cloud Backends | 2021 | CIDR | 8.8487039e-05 |
| 3,384 | Putting Pandas in a Box | 2021 | CIDR | 7.3550055e-05 |
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