Hadoop的Windows服务器上 [英] Hadoop on windows server
问题描述
我在考虑使用Hadoop来处理我现有的Windows 2003服务器（约10四核机有16GB的RAM）
I'm thinking about using hadoop to process large text files on my existing windows 2003 servers (about 10 quad core machines with 16gb of RAM)
的问题是大的文本文件
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是否有关于如何在Windows配置Hadoop集群
Is there any good tutorial on how to configure an hadoop cluster on windows?
有什么要求？ java的cygwin的+ + sshd的？还有别的吗？
What are the requirements? java + cygwin + sshd ? Anything else?
HDFS，它在Windows上发挥好？
HDFS, does it play nice on windows?
我D类似于流媒体模式下使用Hadoop的。任何意见，工具或技巧来开发自己的映射器/减速器在C＃？
I'd like to use hadoop in streaming mode. Any advice, tool or trick to develop my own mapper / reducers in c#?
你用什么提交和监测工作？
What do you use for submitting and monitoring the jobs?
感谢
推荐答案
虽然不您可能希望听到的答案，我会强烈建议再利用机器的，比方说，Linux服务器，并运行Hadoop的出现。您将受益于教程和经验，并在该平台上的测试进行的，花时间解决业务问题，而不是业务问题。
While not the answer you may want to hear, I would highly recommend repurposing the machines as, say, Linux servers, and running Hadoop there. You will benefit from tutorials and experience and testing performed on that platform, and spend your time solving business problems rather than operational issues.
不过，你仍然可以写你的作业C＃。由于Hadoop的支持流的实施，你可以用任何语言编写工作。随着单声道框架，你应该能够采取非常写在Windows平台上的任何.NET代码，只是在Linux上运行相同的二进制代码。
However, you can still write your jobs in C#. Since Hadoop supports the "streaming" implementation, you can write your jobs in any language. With the Mono framework, you should be able to take pretty much any .NET code written on the Windows platform and just run the same binary on Linux.
您也可以访问HDFS从Windows相当容易 - 虽然我不建议在Windows上运行Hadoop的服务，你当然可以运行在Windows平台上的DFS客户端进出分布式文件系统复制文件
You can also access HDFS from Windows fairly easily -- while I don't recommend running the Hadoop services on Windows, you can certainly run the DFS client from the Windows platform to copy files in and out of the distributed file system.
有关提交和监视工作，我认为你主要是对自己的...我不认为有Hadoop的作业管理尚未制定任何良好的通用系统。
For submitting and monitoring jobs, I think that you're mainly on your own... I don't think that there are any good general-purpose systems developed for Hadoop job management yet.
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