Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters
Summary: Quickr lazily injects samplers into optimized query plans, approximating complex ad-hoc queries without precomputed samples. Its universe sampler supports multi-input joins, while accuracy analysis preserves groups and bounds aggregates; TPC-DS achieves median 2× resource reduction at cluster scale. (summarized by gpt-5.6-luna on Jul 21 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Srikanth Kandula (Microsoft)
- 2. Anil Shanbhag (Microsoft)
- 3. Aleksandar Vitorovic (Microsoft)
- 4. Matthaios Olma (Microsoft)
- 5. Robert Grandl (Microsoft)
- 6. Surajit Chaudhuri (Microsoft)
- 7. Bolin Ding (Microsoft)
BibTeX Citation
@inproceedings{kandula_sigmod16,
title = {{Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters}},
author = {Kandula, Srikanth and Shanbhag, Anil and Vitorovic, Aleksandar and Olma, Matthaios and Grandl, Robert and Chaudhuri, Surajit and Ding, Bolin},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2882940},
url = {https://dl.acm.org/doi/10.1145/2882903.2882940},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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