Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917946619592704 |
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| author | Suri, Raunaq Gofman, Ilan Yu, Guangwei Cresswell, Jesse C. |
| author_facet | Suri, Raunaq Gofman, Ilan Yu, Guangwei Cresswell, Jesse C. |
| contents | Large-scale data processing is increasingly done using distributed computing frameworks like Apache Spark, which have a considerable number of configurable parameters that affect runtime performance. For optimal performance, these parameters must be tuned to the specific job being run. Tuning commonly requires multiple executions to collect runtime information for updating parameters. This is infeasible for ad hoc queries that are run once or infrequently. Zero-execution tuning, where parameters are automatically set before a job's first run, can provide significant savings for all types of applications, but is more challenging since runtime information is not available. In this work, we propose a novel method for zero-execution tuning of Spark configurations based on retrieval. Our method achieves 93.3% of the runtime improvement of state-of-the-art one-execution optimization, entirely avoiding the slow initial execution using default settings. The shift to zero-execution tuning results in a lower cumulative runtime over the first 140 runs, and provides the largest benefit for ad hoc and analytical queries which only need to be executed once. We release the largest and most comprehensive suite of Spark query datasets, optimal configurations, and runtime information, which will promote future development of zero-execution tuning methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03826 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications Suri, Raunaq Gofman, Ilan Yu, Guangwei Cresswell, Jesse C. Distributed, Parallel, and Cluster Computing Large-scale data processing is increasingly done using distributed computing frameworks like Apache Spark, which have a considerable number of configurable parameters that affect runtime performance. For optimal performance, these parameters must be tuned to the specific job being run. Tuning commonly requires multiple executions to collect runtime information for updating parameters. This is infeasible for ad hoc queries that are run once or infrequently. Zero-execution tuning, where parameters are automatically set before a job's first run, can provide significant savings for all types of applications, but is more challenging since runtime information is not available. In this work, we propose a novel method for zero-execution tuning of Spark configurations based on retrieval. Our method achieves 93.3% of the runtime improvement of state-of-the-art one-execution optimization, entirely avoiding the slow initial execution using default settings. The shift to zero-execution tuning results in a lower cumulative runtime over the first 140 runs, and provides the largest benefit for ad hoc and analytical queries which only need to be executed once. We release the largest and most comprehensive suite of Spark query datasets, optimal configurations, and runtime information, which will promote future development of zero-execution tuning methods. |
| title | Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.03826 |