分布式数据流系统中的迭代处理

G. Gévay, Juan Soto, V. Markl
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引用次数: 8

摘要

在过去的十年中,分布式数据流系统(DDS)已经成为一种标准技术。在这些系统中,用户使用受限的数据流编程模型(如MapReduce)编写程序,这使他们能够将程序执行扩展到无共享的机器集群。然而,对于如何扩展这些编程模型以支持迭代算法,目前还没有建立共识。在本调查中,我们回顾了研究文献,并从编程模型和执行层的角度确定了DDS如何处理控制流,例如迭代。这项调查对DDS的用户和设计者都很有意义。
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Handling Iterations in Distributed Dataflow Systems
Over the past decade, distributed dataflow systems (DDS) have become a standard technology. In these systems, users write programs in restricted dataflow programming models, such as MapReduce, which enable them to scale out program execution to a shared-nothing cluster of machines. Yet, there is no established consensus that prescribes how to extend these programming models to support iterative algorithms. In this survey, we review the research literature and identify how DDS handle control flow, such as iteration, from both the programming model and execution level perspectives. This survey will be of interest for both users and designers of DDS.
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