HcBench:一个具有客户使用代表性的大数据/Hadoop基准的方法论、开发和特征

V. Saletore, Karthik Krishnan, Vish Viswanathan, Matthew E. Tolentino
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引用次数: 14

摘要

在Hadoop上使用Map-Reduce进行大数据分析已经成为在大型服务器集群上进行分布式编程的领先范例。Hadoop平台广泛用于电子商务、电信、媒体、零售、社交网络等领域的交互式和批处理分析,并正在积极评估其在其他领域的应用。然而,到目前为止,还没有行业标准或客户代表的基准来衡量和评估Hadoop集群的真实性能。当前的Hadoop微基准测试,如HiBench-2、GridMix-3、Terasort等,都是客户用来评估Hadoop集群的应用程序的狭窄功能切片。但是,这些基准测试无法捕捉数据中心环境中的实际用法和性能。考虑到Hadoop的典型数据中心部署在严格的服务水平协议(SLA)要求下处理各种分析交互和查询作业以及批处理转换作业,用于评估集群的性能基准必须捕获在生产环境中并发运行这种不同作业类型的影响。在本文中,我们介绍了客户数据中心使用代表性Hadoop基准“HcBench”的方法和开发,该基准包括大量客户代表交互,查询,机器学习和转换作业,各种数据大小,包括计算,存储和网络密集型作业,作业间到达时间与典型数据中心环境一样。我们介绍了这个基准测试的细节,并讨论了在英特尔Sandy Bridge Xeon处理器Hadoop集群上收集的应用程序级、服务器级和集群级性能特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HcBench: Methodology, development, and characterization of a customer usage representative big data/Hadoop benchmark
Big Data analytics using Map-Reduce over Hadoop has become a leading edge paradigm for distributed programming over large server clusters. The Hadoop platform is used extensively for interactive and batch analytics in ecommerce, telecom, media, retail, social networking, and being actively evaluated for use in other areas. However, to date no industry standard or customer representative benchmarks exist to measure and evaluate the true performance of a Hadoop cluster. Current Hadoop micro-benchmarks such as HiBench-2, GridMix-3, Terasort, etc. are narrow functional slices of applications that customers run to evaluate their Hadoop clusters. However, these benchmarks fail to capture the real usages and performance in a datacenter environment. Given that typical datacenter deployments of Hadoop process a wide variety of analytic interactive and query jobs in addition to batch transform jobs under strict Service Level Agreement (SLA) requirements, performance benchmarks used to evaluate clusters must capture the effects of concurrently running such diverse job types in production environments. In this paper, we present the methodology and the development of a customer datacenter usage representative Hadoop benchmark "HcBench" which includes a mix of large number of customer representative interactive, query, machine learning, and transform jobs, a variety of data sizes, and includes compute, storage 110, and network intensive jobs, with inter-job arrival times as in a typical datacenter environment. We present the details of this benchmark and discuss application level, server and cluster level performance characterization collected on an Intel Sandy Bridge Xeon Processor Hadoop cluster.
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