大数据 ›› 2015, Vol. 1 ›› Issue (3): 99-105.doi: 10.11959/j.issn.2096-0271.2015032

• 研究 • 上一篇    下一篇

大数据流式计算：应用特征和技术挑战

孙大为   

  1. 中国地质大学信息工程学院 北京 100083
  • 出版日期:2015-06-20 发布日期:2020-09-28
  • 作者简介:孙大为，男，博士后，中国地质大学（北京）信息工程学院讲师，目前主要从事大数据计算、云计算、可信计算等方面的研究工作。
  • 基金资助:
    中国博士后科学基金资助项目(2014M560976);中央高校基本科研业务费专项资金(2652015338)

Big Data Stream Computing:Features and Challenges

Dawei Sun   

  1. School of Information Engineering,China University of Geosciences,Beijing 100083,China
  • Online:2015-06-20 Published:2020-09-28
  • Supported by:
    The China Postdoctoral Science Foundation Under Grant(2014M560976);The Fundamental Research Funds for the Central Universities Under Grant(2652015338)

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摘要：

在大数据时代，数据的时效性日益突出，数据的流式特征更加明显，越来越多的应用场景需要部署在流式计算平台中。大数据流式计算作为大数据计算的一种形态，其重要性也不断提升。针对大数据环境中流式计算应用所呈现出的诸多鲜明特征进行了系统化的分析，并从系统架构的角度，给出了大数据流式计算系统构建的原则性策略。结合当前比较典型的流式计算平台，重点研究了当前大数据流式计算在在线环境下的资源调度和节点依赖环境下的容错策略等方面的技术挑战。

关键词: 大数据, 流式计算, 应用特征, 在线调度, 系统容错

Abstract:

In big data era,the timeliness of data has become one of the most important factors,and the streaming feature of data has become more obvious.More and more applications need to be deployed in stream computing platforms.Big data stream computing as a major form of big data computing has become more and more important.The features of big data stream computing application were systematically analyzed.The principle strategies to build a big data stream computing system were given from the perspective of system architecture.Combined with some typical big data stream computing systems,some technology challenges in big data stream computing environments were focused,such as resource scheduling in online environments,fault tolerance strategy in node-dependence environments.

Key words: big data, stream computing, application feature, online scheduling, system fault tolerance

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