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	<title>Reduce - 四号程序员</title>
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		<title>定制Hadoop的MapReduce任务的FileOutputFormat</title>
		<link>https://www.coder4.com/archives/7121</link>
					<comments>https://www.coder4.com/archives/7121#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Thu, 12 Nov 2020 09:33:09 +0000</pubDate>
				<category><![CDATA[大数据技术]]></category>
		<category><![CDATA[FileOutputFormat]]></category>
		<category><![CDATA[Hadoop]]></category>
		<category><![CDATA[Map]]></category>
		<category><![CDATA[Reduce]]></category>
		<category><![CDATA[定制]]></category>
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					<description><![CDATA[需求：Reduce输出特殊的格式结果 例如：如Reducer的结果，压到Guava的BloomFilter中 import com.google.common.hash.BloomFilter; import com.google.common.hash.Funnels; import org.apache.hadoop.fs.FSDataOutputStream; import org.apache.hadoop.fs.FileSystem; import org.apache.h[......] 继续阅读]]></description>
		
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		<title>[转 ]Hadoop - How to do a secondary sort on values ?</title>
		<link>https://www.coder4.com/archives/3946</link>
					<comments>https://www.coder4.com/archives/3946#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Sun, 28 Jul 2013 11:33:30 +0000</pubDate>
				<category><![CDATA[Linux]]></category>
		<category><![CDATA[大数据技术]]></category>
		<category><![CDATA[Hadoop]]></category>
		<category><![CDATA[Reduce]]></category>
		<category><![CDATA[value 排序]]></category>
		<category><![CDATA[二次排序]]></category>
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					<description><![CDATA[关于在hadoop中，如何让reduce阶段同一个key下的values有序，一篇很好的文章，写的比《Hadoop权威指南》清楚！ 转载自： http://www.bigdataspeak.com/2013/02/hadoop-how-to-do-secondary-sort-on_25.html The problem at hand here is that you need to work upon a sorted values set in your reducer.[......] 继续阅读]]></description>
		
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		<title>Hadoop小集群(5结点)测试</title>
		<link>https://www.coder4.com/archives/2021</link>
					<comments>https://www.coder4.com/archives/2021#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Sun, 07 Aug 2011 05:59:09 +0000</pubDate>
				<category><![CDATA[Java]]></category>
		<category><![CDATA[Python]]></category>
		<category><![CDATA[大数据技术]]></category>
		<category><![CDATA[Hadoop]]></category>
		<category><![CDATA[Map]]></category>
		<category><![CDATA[Reduce]]></category>
		<category><![CDATA[实例]]></category>
		<category><![CDATA[集群]]></category>
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					<description><![CDATA[1、Map/Reduce任务 输入： 文件格式 id value 其中id是1~100之间的随机整数，value为1~100之间的随机浮点数。 输出： 每个id的最大value 生成这类文件，可以用python搞定，见本文末尾的附录。 2、Map/Reduce程序 这里就直接使用新(0.20.2)的API了，即org.apache.hadoop.mapreduce.*下的接口。 特别注意： job.setNumReduceTasks(5) 指定了本Job的Redu[......] 继续阅读]]></description>
		
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