O'Reilly、Cloudera 主办
Make Data Work
2016年8月3-4日:培训
2016年8月4-6日:会议
北京,中国

Spark和YARN:最好一起工作

15:30–16:10 2016年8月06日
Spark及更多新发展
地点: 紫金大厅B(Grand Hall B)

必要预备知识

Spark和YARN的基本知识

描述

现在Spark已经获得了广泛的使用。由于它框架设计上的灵活性,Spark可以运行在公有云上、私有的或者共享的集群里面,并依托于不同的集群管理器模式:Standalone、Mesos和YARN。但是每种模式都有不同,而为大数据选择一个适合的集群管理模式是非常重要的。

在本讲话里中我们会聚焦于运行在YARN上的Spark,讲解如何在YARN上运行Spark,以及为什么Spark最好是运行在YARN上。我们还会介绍一些最佳实践的经验,尤其是在生产环境上的。最后介绍这个领域的未来,比如用容器实现、更长的在线时间支持以及ATS集成。

Photo of Jerry Shao

Jerry Shao

Hortonworks

Jerry Shao works as a member of the technical staff at Hortonworks focused mainly on Spark, especially Spark core, Spark on YARN, and Spark Streaming. Jerry is an active Apache Spark contributor and Apache Chukwa committer. Prior to Hortonworks, he was a software engineer at Intel working on performance tuning and optimization of Hadoop and Spark.

Photo of Jianfeng Zhang

Jianfeng Zhang

Hortonworks

Jeff Zhang has 9 years of experience in big data industry. He started to use hadoop since 2009 and is a member of apache software foundation, committer of multiple apache projects ( Pig/Tez/Zeppelin/Livy). His past experience is not only on big data infrastructure, but also on how to leverage these big data tools to get insight. He speaks several times in big data conferences like hadoop summit, strata data conference and apache big data conference. Now he works in hortonworks as member of technical staff.

Hortonworks is a leading innovator in the industry, creating, distributing and supporting enterprise-ready open data platforms and modern data applications.

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