Rover: An online Spark SQL tuning service via generalized transfer learning Article Swipe
Yu Shen
,
Xinyuyang Ren
,
Yu‐peng Lu
,
Huaijun Jiang
,
Huanyong Xu
,
Di Peng
,
Yang Li
,
Wentao Zhang
,
Bin Cui
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.04046
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.04046
Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configurations, where the optimal ones vary with the executed workloads. Among various alternatives for Spark SQL tuning, Bayesian optimization (BO) is a popular framework that finds near-optimal configurations given sufficient budget, but it suffers from the re-optimization issue and is not practical in real production. When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production. In this paper, we present Rover, a deployed online Spark SQL tuning service for efficient and safe search on industrial workloads. To address the challenges, we propose generalized transfer learning to boost the tuning performance based on external knowledge, including expert-assisted Bayesian optimization and controlled history transfer. Experiments on public benchmarks and real-world tasks show the superiority of Rover over competitive baselines. Notably, Rover saves an average of 50.1% of the memory cost on 12k real-world Spark SQL tasks in 20 iterations, among which 76.2% of the tasks achieve a significant memory reduction of over 60%.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.04046
- https://arxiv.org/pdf/2302.04046
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4319793413Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.04046Digital Object Identifier
- Title
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Rover: An online Spark SQL tuning service via generalized transfer learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-08Full publication date if available
- Authors
-
Yu Shen, Xinyuyang Ren, Yu‐peng Lu, Huaijun Jiang, Huanyong Xu, Di Peng, Yang Li, Wentao Zhang, Bin CuiList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.04046Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2302.04046Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2302.04046Direct OA link when available
- Concepts
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SPARK (programming language), Computer science, Bayesian optimization, SQL, Transfer of learning, Process (computing), Performance tuning, Transfer (computing), Database, Machine learning, Artificial intelligence, Distributed computing, Operating system, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.depends | 22 |
| abstract_inverted_index.engines | 3 |
| abstract_inverted_index.history | 118, 180 |
| abstract_inverted_index.improve | 106 |
| abstract_inverted_index.massive | 11 |
| abstract_inverted_index.optimal | 30 |
| abstract_inverted_index.popular | 49 |
| abstract_inverted_index.present | 139 |
| abstract_inverted_index.process | 10 |
| abstract_inverted_index.propose | 161 |
| abstract_inverted_index.service | 147 |
| abstract_inverted_index.studied | 114 |
| abstract_inverted_index.suffers | 60 |
| abstract_inverted_index.tuning, | 43 |
| abstract_inverted_index.various | 38 |
| abstract_inverted_index.Bayesian | 44, 176 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.Notably, | 197 |
| abstract_inverted_index.analytic | 2 |
| abstract_inverted_index.applying | 73 |
| abstract_inverted_index.deployed | 142 |
| abstract_inverted_index.executed | 35 |
| abstract_inverted_index.external | 172 |
| abstract_inverted_index.history, | 94 |
| abstract_inverted_index.learning | 75, 164 |
| abstract_inverted_index.previous | 88 |
| abstract_inverted_index.process, | 80 |
| abstract_inverted_index.transfer | 74, 163 |
| abstract_inverted_index.carefully | 122 |
| abstract_inverted_index.efficient | 149 |
| abstract_inverted_index.engineers | 100 |
| abstract_inverted_index.framework | 50 |
| abstract_inverted_index.including | 174 |
| abstract_inverted_index.industry. | 14 |
| abstract_inverted_index.knowledge | 97 |
| abstract_inverted_index.potential | 104 |
| abstract_inverted_index.practical | 68 |
| abstract_inverted_index.reduction | 227 |
| abstract_inverted_index.transfer. | 181 |
| abstract_inverted_index.utilized, | 123 |
| abstract_inverted_index.accelerate | 77 |
| abstract_inverted_index.baselines. | 196 |
| abstract_inverted_index.benchmarks | 185 |
| abstract_inverted_index.controlled | 179 |
| abstract_inverted_index.dissimilar | 126 |
| abstract_inverted_index.industrial | 154 |
| abstract_inverted_index.knowledge, | 173 |
| abstract_inverted_index.real-world | 187, 210 |
| abstract_inverted_index.sufficient | 56 |
| abstract_inverted_index.workloads. | 36, 155 |
| abstract_inverted_index.Distributed | 0 |
| abstract_inverted_index.Experiments | 182 |
| abstract_inverted_index.challenges, | 159 |
| abstract_inverted_index.challenges: | 85 |
| abstract_inverted_index.competitive | 195 |
| abstract_inverted_index.generalized | 162 |
| abstract_inverted_index.iterations, | 216 |
| abstract_inverted_index.performance | 17, 109, 132, 169 |
| abstract_inverted_index.production. | 71, 134 |
| abstract_inverted_index.significant | 225 |
| abstract_inverted_index.superiority | 191 |
| abstract_inverted_index.alternatives | 39 |
| abstract_inverted_index.deteriorated | 131 |
| abstract_inverted_index.near-optimal | 53 |
| abstract_inverted_index.optimization | 45, 177 |
| abstract_inverted_index.transferring | 92 |
| abstract_inverted_index.configurations | 54 |
| abstract_inverted_index.configurations, | 27 |
| abstract_inverted_index.domain-specific | 84 |
| abstract_inverted_index.expert-assisted | 175 |
| abstract_inverted_index.re-optimization | 63 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |