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- Tomasz Nykiel
- University of Toronto
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- Michalis Potamias
- Boston University
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- Chaitanya Mishra
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- George Kollios
- Boston University
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- Nick Koudas
- University of Toronto
書誌事項
- タイトル別名
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- sharing across multiple queries in MapReduce
- 公開日
- 2010-09
- DOI
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- 10.14778/1920841.1920906
- 公開者
- Association for Computing Machinery (ACM)
この論文をさがす
説明
<jats:p>Large-scale data analysis lies in the core of modern enterprises and scientific research. With the emergence of cloud computing, the use of an analytical query processing infrastructure (e.g., Amazon EC2) can be directly mapped to monetary value. MapReduce has been a popular framework in the context of cloud computing, designed to serve long running queries (jobs) which can be processed in batch mode. Taking into account that different jobs often perform similar work, there are many opportunities for sharing. In principle, sharing similar work reduces the overall amount of work, which can lead to reducing monetary charges incurred while utilizing the processing infrastructure. In this paper we propose a sharing framework tailored to MapReduce.</jats:p> <jats:p>Our framework, MRShare, transforms a batch of queries into a new batch that will be executed more efficiently, by merging jobs into groups and evaluating each group as a single query. Based on our cost model for MapReduce, we define an optimization problem and we provide a solution that derives the optimal grouping of queries. Experiments in our prototype, built on top of Hadoop, demonstrate the overall effectiveness of our approach and substantial savings.</jats:p>
収録刊行物
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- Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment 3 (1-2), 494-505, 2010-09
Association for Computing Machinery (ACM)