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.

中文翻译：

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处理分布式数据流系统中的迭代

在过去十年中，分布式数据流系统 (DDS) 已成为一种标准技术。在这些系统中，用户使用受限数据流编程模型（例如 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，还没有既定的共识。在本次调查中，我们回顾了研究文献并确定了 DDS 如何从编程模型和执行级别的角度处理控制流，例如迭代。DDS 的用户和设计者都会对这项调查感兴趣。