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Predicate Pushdown for Data Science Pipelines

Summary: MagicPush uses a search-verification approach to predicate pushdown in data science pipelines, discovering input-space predicates and proving pushdown preserves outputs, even with non-relational operators and UDFs. Evaluations on TPC-H and 200 real-world GitHub Notebook pipelines show it beats a strong rule-based baseline, discovers new pushdown opportunities, and yields up to 99% running-time reduction in 42 pipelines while matching baseline opportunities elsewhere. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hb4a7fd26bb386237
Venue
SIGMOD
Year
2023
Pagerank
5.3866275e-05
Overall Rank
8,159 | 45.17%
DOI
10.1145/3589281

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yan_sigmod23,
        title = {{Predicate Pushdown for Data Science Pipelines}},
        author = {Yan, Cong and Lin, Yin and He, Yeye},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589281},
        url = {https://dl.acm.org/doi/10.1145/3589281},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
9,700 The UDFBench Benchmark for General-purpose UDF Queries 2025 VLDB 5.1372261e-05
10,085 LiquidCache: Efficient Pushdown Caching for Cloud-Native Data Analytics 2025 VLDB 5.0806786e-05
10,641 Data-Semantics-Aware Recommendation of Diverse Pivot Tables 2026 SIGMOD 4.9769913e-05
10,853 I-Rex: An Interactive Debugger for SQL 2026 VLDB 4.9769913e-05
11,135 Dynamic Pruning for Recursive Joins 2025 SIGMOD 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 27 of 27 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
23 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00055384955
125 Predicate Migration: Optimizing Queries with Expensive Predicates 1993 SIGMOD 0.00030462671
310 Optimization of Real Conjunctive Queries 1993 PODS 0.00021364898
896 Froid: Optimization of Imperative Programs in a Relational Database 2018 VLDB 0.00013203085
1,119 Query Optimization by Predicate Move-Around 1994 VLDB 0.00011946162
1,128 Qd-tree: Learning Data Layouts for Big Data Analytics 2020 SIGMOD 0.00011901941
1,697 PIVOT and UNPIVOT: Optimization and Execution Strategies in an RDBMS 2004 VLDB 9.8499097e-05
1,803 Tuplex: Data Science in Python at Native Code Speed 2021 SIGMOD 9.602292e-05
1,885 SQL-on-Hadoop: Full Circle Back to Shared-Nothing Database Architectures 2014 VLDB 9.4345374e-05
1,890 WeTune: Automatic Discovery and Verification of Query Rewrite Rules 2022 SIGMOD 9.4234723e-05
1,949 Quantifying TPC-H Choke Points and Their Optimizations 2020 VLDB 9.3172855e-05
2,208 Magpie: Python at Speed and Scale using Cloud Backends 2021 CIDR 8.8445332e-05
2,437 Mison: A Fast JSON Parser for Data Analytics 2017 VLDB 8.4668658e-05
2,483 AnalyticDB: Real-time OLAP Database System at Alibaba Cloud 2019 VLDB 8.3973995e-05
2,642 Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks 2020 SIGMOD 8.1751637e-05
3,075 Pushing Data-Induced Predicates Through Joins in Big-Data Clusters 2020 VLDB 7.6742518e-05
3,204 Demonstration of the Cosette Automated SQL Prover 2017 SIGMOD 7.5379936e-05
3,297 Automated Verification of Query Equivalence Using Satisfiability Modulo Theories 2019 VLDB 7.4421962e-05
3,520 FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS 2021 VLDB 7.2326541e-05
4,062 Aggify: Lifting the Curse of Cursor Loops using Custom Aggregates 2020 SIGMOD 6.8215782e-05
4,656 Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications 2018 SIGMOD 6.4819462e-05
5,451 Crystal: A Unified Cache Storage System for Analytical Databases 2021 VLDB 6.1238308e-05
6,149 Sia: Optimizing Queries using Learned Predicates 2021 SIGMOD 5.8683464e-05
6,215 YeSQL: "You extend SQL" with Rich and Highly Performant User-Defined Functions in Relational Databases 2022 VLDB 5.8453061e-05
6,293 Incorporating Super-Operators in Big-Data Query Optimizers 2020 VLDB 5.8195888e-05
7,141 Optimizing Recursive Queries with Program Synthesis 2022 SIGMOD 5.5979985e-05
10,160 Generating Application-Specific Data Layouts for In-memory Databases 2019 VLDB 5.0683727e-05
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