DBScholar

Back to papers

Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment

Summary: Rockhopper introduces a noise-resilient Centroid Learning optimizer for Spark configuration tuning in production. It uses benchmark-informed workload embeddings for context-aware transfer learning and achieves ~20% gains by tuning only three query-level configurations. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7176
Venue
SIGMOD
Year
2025
Pagerank
5.2209769e-05
Overall Rank
9,771 | 32.97%
DOI
10.1145/3722212.3724451

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhu_sigmod25,
        title = {{Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment}},
        author = {Zhu, Yiwen and Sen, Rathijit and Kroth, Brian P and Matusevych, Sergiy and Mueller, Andreas and Huang, Tengfei and Challapalli, Rahul and Tang, Weihan and Kot, Estera and Kahraman, Sule and He, Xin and Liu, Mo and Sekhon, Arshdeep and Bernal, Dario and Lakra, Aditya and Fozdar, Shaily and Relwani, Dhruv and Fang, Rui and Tian, Long and Krishna, Karuna Sagar and Gosalia, Ashit and Curino, Carlo and Krishnan, Subru},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3722212.3724451},
        url = {https://dl.acm.org/doi/10.1145/3722212.3724451},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,565 LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration 2026 VLDB 5.093636e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

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

Rank Cited Paper Year Venue Pagerank
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
334 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00020875082
2,740 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.1855759e-05
3,343 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.4983591e-05
3,400 A Demonstration of the OtterTune Automatic Database Management System Tuning Service 2018 VLDB 7.4433294e-05
4,011 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.959982e-05
4,854 LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications 2022 SIGMOD 6.4779623e-05
5,573 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1682747e-05
6,102 AutoExecutor: Predictive Parallelism for Spark SQL Queries 2021 VLDB 5.976708e-05
6,344 Towards General and Efficient Online Tuning for Spark 2023 VLDB 5.9060457e-05
7,846 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.5331459e-05
8,175 SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft 2021 VLDB 5.4737932e-05
8,193 Towards Building Autonomous Data Services on Azure 2023 SIGMOD 5.4696038e-05
8,457 Towards Resource Efficiency: Practical Insights into Large-Scale Spark Workloads at ByteDance 2024 VLDB 5.4217837e-05
8,615 A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning 2024 VLDB 5.4005602e-05
9,224 Phoebe: A Learning-based Checkpoint Optimizer 2021 VLDB 5.3035811e-05
9,257 MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud 2024 VLDB 5.2972217e-05
Previous Page 1 / 1 Next

Semantically Similar Papers