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
- 2. Damien Dam
- 3. Jan Kossmann
- 4. Juliane Waack
- 5. Ismail Oukid
- 6. Andreas Kipf
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,196 | PTO: A Workload-driven Predictive Table Optimizer for Lakehouse Systems | 2026 | SIGMOD | 4.1945683e-05 |
| 10,241 | Robust Predicate Transfer with Dynamic Execution | 2026 | VLDB | 4.1945683e-05 |
| 10,749 | Scaling GPU-Accelerated Databases beyond GPU Memory Size | 2025 | VLDB | 4.1945683e-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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