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QCon北京2018-《京东如何基于容器打造高性能及效率的大数据平台》范振陈韦廷1
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2022-08-03
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京东如何基于容器打造高性能及效率的大数据平台*Other names and brands may be claimed as the property of
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INTEL NOTICE & DISCLAIMER
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by
this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of
merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from
course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All
information provided here is subject to change without notice. Contact your Intel representative to obtain
the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors known as errata which may cause
deviations from published specifications. Current characterized errata are available on request.
Copies of documents which have an order number and are referenced in this document may be obtained
by calling 1-800-548-4725 or by visiting www.intel.com/design/literature.htm.
Intel, the Intel logo, Intel® are trademarks of Intel Corporation in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others
Copyright © 2018 Intel Corporation.
*Other names and brands may be claimed as the property of others.
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AGENDA
BACKGROUND
SPARK ON KUBERNETES
- Why use Spark-on-K8s
- How it works
- Current Status & Issues
JD.COM CASE STUDY
- JD.com’s MoonShot
- Experience Sharing
SUMMARY
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*Other names and brands may be claimed as the property of others.
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ABOUT SPARK-ON-KUBERNETES
• https://github.com/apache-spark-on-k8s/spark
• Spark* on Kubernetes*(K8s) is a new project proposed by the
companies including Bloomberg, Google, Intel, Palantir, Pepperdata,
and Red Hat.
• The goal is to bring native support for Spark to use Kubernetes as a
cluster manager like Spark Standalone, YARN*, or Mesos*.
• The feature is planning to be put into Spark 2.3.0 release(SPARK-
18278).
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