Dynamic Speculative Optimizations for SQL Compilation in Apache Spark
Summary: Dynamic speculative optimizations for Spark SQL compilation via runtime profiling and adaptive codegen to reduce data access and deserialization overhead on textual formats. Achieves up to 4.4x speedups on TPC-H with CSV/JSON, illustrating a unique runtime-driven codegen approach for Spark. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Filippo Schiavio (Università della Svizzera italiana)
- 2. Daniele Bonetta (Oracle)
- 3. Walter Binder (Università della Svizzera italiana)
BibTeX Citation
@article{schiavio_vldb20,
title = {{Dynamic Speculative Optimizations for SQL Compilation in Apache Spark}},
author = {Schiavio, Filippo and Bonetta, Daniele and Binder, Walter},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {5},
pages = {754--767},
doi = {10.14778/3377369.3377382},
url = {https://doi.org/10.14778/3377369.3377382},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,000 | Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling | 2021 | VLDB | 6.4069917e-05 |
| 8,636 | Adaptive Code Generation for Data-Intensive Analytics | 2021 | VLDB | 5.3954959e-05 |
| 9,419 | Language-Agnostic Integrated Queries in a Managed Polyglot Runtime | 2021 | VLDB | 5.273161e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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