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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2001.05667 |
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| _version_ | 1866913392673947648 |
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| author | Zhang, Shuhao Zhang, Feng Wu, Yingjun He, Bingsheng Johns, Paul |
| author_facet | Zhang, Shuhao Zhang, Feng Wu, Yingjun He, Bingsheng Johns, Paul |
| contents | Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve real-time data analytics, recent researches keep focusing on optimizing the system latency and throughput. Witnessing the recent great achievements in the computer architecture community, researchers and practitioners have investigated the potential of adoption hardware-conscious stream processing by better utilizing modern hardware capacity in DSPSs. In this paper, we conduct a systematic survey of recent work in the field, particularly along with the following three directions: 1) computation optimization, 2) stream I/O optimization, and 3) query deployment. Finally, we advise on potential future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2001_05667 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Hardware-Conscious Stream Processing: A Survey Zhang, Shuhao Zhang, Feng Wu, Yingjun He, Bingsheng Johns, Paul Databases Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve real-time data analytics, recent researches keep focusing on optimizing the system latency and throughput. Witnessing the recent great achievements in the computer architecture community, researchers and practitioners have investigated the potential of adoption hardware-conscious stream processing by better utilizing modern hardware capacity in DSPSs. In this paper, we conduct a systematic survey of recent work in the field, particularly along with the following three directions: 1) computation optimization, 2) stream I/O optimization, and 3) query deployment. Finally, we advise on potential future research directions. |
| title | Hardware-Conscious Stream Processing: A Survey |
| topic | Databases |
| url | https://arxiv.org/abs/2001.05667 |