Pruning in Snowflake: Working Smarter, Not Harder
Summary: Extends pruning from predicates to LIMIT, top-k, and JOIN, broadening pruning across workloads. Using min/max metadata and Iceberg-style formats, it prunes up to 99.4% of micro-partitions in Snowflake workloads and reveals higher real-world selectivity. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Andreas Zimmerer (University of Technology Nuremberg)
- 2. Damien Dam (Snowflake)
- 3. Jan Kossmann (Snowflake)
- 4. Juliane Waack (Snowflake)
- 5. Ismail Oukid (Snowflake)
- 6. Andreas Kipf (University of Technology Nuremberg)
BibTeX Citation
@inproceedings{zimmerer_sigmod25,
title = {{Pruning in Snowflake: Working Smarter, Not Harder}},
author = {Zimmerer, Andreas and Dam, Damien and Kossmann, Jan and Waack, Juliane and Oukid, Ismail and Kipf, Andreas},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3724447},
url = {https://dl.acm.org/doi/10.1145/3722212.3724447},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,306 | Workload-Aware Incremental Reclustering in Cloud Data Warehouses | 2026 | SIGMOD | 5.093636e-05 |
| 10,485 | PTO: A Workload-driven Predictive Table Optimizer for Lakehouse Systems | 2026 | SIGMOD | 5.093636e-05 |
| 10,529 | Robust Predicate Transfer with Dynamic Execution | 2026 | VLDB | 5.093636e-05 |
| 10,985 | Scaling GPU-Accelerated Databases beyond GPU Memory Size | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 25 of 25 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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