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Robust Predicate Transfer with Dynamic Execution
Summary: RPT+ revisits Robust Predicate Transfer for join execution, cutting its overhead via asymmetric transfer plans, cascade filters (block skipping + tuple filtering), and runtime-adjustable dynamic pipelines. Implemented in DuckDB, it preserves RPT’s robustness while delivering consistent speedups and avoiding regressions across JOB/TPC-H/SQLStorm.
(summarized by gpt-5.4-mini on Apr 12 2026)
Paper ID
14465
Venue
VLDB
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,529 | 27.77%
DOI
10.14778/3797919.3797934
Incoming Non-self Citations Over Time
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BibTeX Citation
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@article{qiao_vldb26,
title = {{Robust Predicate Transfer with Dynamic Execution}},
author = {Qiao, Yiming and Boncz, Peter and Zhang, Huanchen},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {6},
pages = {1278--1290},
doi = {10.14778/3797919.3797934},
url = {https://doi.org/10.14778/3797919.3797934},
year = {2026}
}
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Citing Paper
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Venue
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Outgoing Citations (Sorted by Pagerank)
Showing 32 of 32 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Rank
Cited Paper
Year
Venue
Pagerank
18
How Good Are Query Optimizers, Really?
2016
VLDB
0.00059284255
53
Eddies: Continuously Adaptive Query Processing
2000
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0.00041071971
103
DuckDB: an Embeddable Analytical Database
2019
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0.00034161428
154
Neo: A Learned Query Optimizer
2019
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0.00028726181
941
Data Blocks: Hybrid OLTP and OLAP on Compressed Storage using both Vectorization and Compilation
2016
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2022
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8.1539867e-05
2,991
FactorJoin: A New Cardinality Estimation Framework for Join Queries
2023
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7.8880723e-05
3,510
Performance-Optimal Filtering: Bloom Overtakes Cuckoo at High Throughput
2019
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7.3561488e-05
3,622
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2024
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7.2465862e-05
3,653
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7.2241143e-05
3,877
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2024
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7.0532293e-05
4,470
Kepler: Robust Learning for Faster Parametric Query Optimization
2023
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6.6817353e-05
4,553
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2024
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6.6346951e-05
4,849
Bitvector-aware Query Optimization for Decision Support Queries
2020
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6.4803828e-05
5,399
Efficient Massively Parallel Join Optimization for Large Queries*
2022
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6.2319315e-05
5,529
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6.18591e-05
5,639
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2025
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6.1385102e-05
5,744
SQLStorm: Taking Database Benchmarking into the LLM Era
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6.1019672e-05
5,983
Adaptive and Robust Query Execution for Lakehouses at Scale
2024
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6.0206841e-05
6,138
Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics
2016
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5.9640712e-05
6,271
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective
2024
VLDB
5.9326197e-05
6,434
Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees
2025
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5.8799421e-05
6,444
Output-Optimal Algorithms for Join-Aggregate Queries
2025
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5.8774519e-05
6,593
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis
2023
VLDB
5.8297039e-05
7,660
Pruning in Snowflake: Working Smarter, Not Harder
2025
SIGMOD
5.5736132e-05
8,056
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2023
VLDB
5.4983582e-05
8,074
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5.4929961e-05
8,721
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5.093636e-05
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2024
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