Automated tuning of query degree of parallelism via machine learning
| Autor: | Aws Albarghouthi, Paraschos Koutris, Rathijit Sen, Zhiwei Fan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021103 operations research
business.industry Computer science 0211 other engineering and technologies Degree of parallelism InformationSystems_DATABASEMANAGEMENT Provisioning 02 engineering and technology Machine learning computer.software_genre Query optimization 01 natural sciences 010104 statistics & probability Task (computing) Resource (project management) Relational database management system Parallelism (grammar) Performance prediction Artificial intelligence 0101 mathematics business computer |
| Zdroj: | aiDM@SIGMOD |
| Popis: | Determining the degree of parallelism (DOP) for query execution is of great importance to both performance and resource provisioning. However, recent work that applies machine learning (ML) to query optimization and query performance prediction in relational database management systems (RDBMSs) has ignored the effect of intra-query parallelism. In this work, we argue that determining the optimal or near-optimal DOP for query execution is a fundamental and challenging task that benefits both query performance and cost-benefit tradeoffs. We then present promising preliminary results on how ML techniques can be applied to automate DOP tuning. We conclude with a list of challenges we encountered, as well as future directions for our work. |
| Databáze: | OpenAIRE |
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