Instance-Optimized Data Layouts for Cloud Analytics Workloads
Summary: Introducing MTO, an instance-optimized data-layout framework that jointly blocks across all tables in multi-table cloud workloads (star/snowflake schemas) to maximize block skipping. Leveraging sideways information from joins, it beats single-table layouts, with up to 93% fewer blocks accessed and 75% faster end-to-end queries on a commercial service. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jialin Ding (Massachusetts Institute of Technology)
- 2. Umar Farooq Minhas (Microsoft)
- 3. Badrish Chandramouli (Microsoft)
- 4. Chi Wang (Microsoft)
- 5. Yinan Li (Microsoft)
- 6. Ying Li (Microsoft)
- 7. Donald Kossmann (Microsoft)
- 8. Johannes Gehrke (Microsoft)
- 9. Tim Kraska (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{ding_sigmod21,
title = {{Instance-Optimized Data Layouts for Cloud Analytics Workloads}},
author = {Ding, Jialin and Minhas, Umar Farooq and Chandramouli, Badrish and Wang, Chi and Li, Yinan and Li, Ying and Kossmann, Donald and Gehrke, Johannes and Kraska, Tim},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457270},
url = {https://dl.acm.org/doi/10.1145/3448016.3457270},
year = {2021}
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