AutoExecutor: Predictive Parallelism for Spark SQL Queries
Summary: Predicts Spark SQL runtimes as a function of executor count to guide upfront resource sizing. AutoExecutor uses ML models to bound maximum parallelism, yielding cost-efficient performance on Azure Synapse. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Rathijit Sen
- 2. Abhishek Roy
- 3. Alekh Jindal
- 4. Rui Fang
- 5. Jeff Zheng
- 6. Xiaolei Liu
- 7. Ruiping Li
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,110 | Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud | 2022 | VLDB | 5.2056003e-05 |
| 7,778 | Runtime Variation in Big Data Analytics | 2023 | SIGMOD | 4.653651e-05 |
| 9,155 | Towards Resource Efficiency: Practical Insights into Large-Scale Spark Workloads at ByteDance | 2024 | VLDB | 4.3849295e-05 |
| 10,414 | Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment | 2025 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 22 | SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets | 2008 | VLDB | 0.0008456613 |
| 7,684 | AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft | 2020 | VLDB | 4.6796855e-05 |
| 8,197 | SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft | 2021 | VLDB | 4.5607121e-05 |
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