编写简单的Mapreduce程序并部署在Hadoop2.2.0上运行
经过几天的折腾,终于配置好了 Hadoop 2.2.0(如何配置在Linux平台部署 Hadoop 请参见本博客《在Fedora上部署Hadoop2.2.0伪分布式平台》),今天主要来说说怎么在Hadoop2.2.0伪分布式上面运行我们写好的 Mapreduce 程序。先给出这个程序所依赖的Maven包: 01 0
经过几天的折腾,终于配置好了Hadoop2.2.0(如何配置在Linux平台部署Hadoop请参见本博客《在Fedora上部署Hadoop2.2.0伪分布式平台》),今天主要来说说怎么在Hadoop2.2.0伪分布式上面运行我们写好的Mapreduce程序。先给出这个程序所依赖的Maven包:
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好了,现在给出程序,代码如下:
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将上面的程序编译和打包成jar文件,然后开始在Hadoop2.2.0(本文假定用户都部署好了Hadoop2.2.0)上面部署了。下面主要讲讲如何去部署:
首先,启动Hadoop2.2.0,命令如下:
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如果你想看看Hadoop2.2.0是否运行成功,运行下面的命令去查看
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其中jps是jdk自带的一个命令,在jdk/bin目录下。如果你电脑上面出现了以上的几个进程(NameNode、SecondaryNameNode、NodeManager、ResourceManager、DataNode这五个进程必须出现!)说明你的Hadoop服务器启动成功了!现在来运行上面打包好的jar文件(这里为Hadoop.jar,其中/home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/Hadoop.jar是它的绝对路径,不知道绝对路径是什么?那你好好去学学吧!),运行下面的命令:
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(上面是一条命令,由于太长了,所以我分行写,在实际情况中,请写一行!)其中,/home/wyp/Downloads/hadoop/bin/hadoop是hadoop的绝对路径,如果你在环境变量中配置好hadoop命令的路径就不需要这样写;com/wyp/hadoop/MaxTemperature是上面程序的main函数的入口;/user/wyp/data.txt是Hadoop文件系统(HDFS)中的绝对路径(注意:这里不是你Linux系统中的绝对路径!),为需要分析文件的路径(也就是input);/user/wyp/result是分析结果输出的绝对路径(注意:这里不是你Linux系统中的绝对路径!而是HDFS上面的路径!而且/user/wyp/result一定不能存在,否则会抛出异常!这是Hadoop的保护机制,你总不想你以前运行好几天的程序突然被你不小心给覆盖掉了吧?所以,如果/user/wyp/result存在,程序会抛出异常,很不错啊)。好了。输入上面的命令,应该会得到下面类似的输出:
13/10/28 15:20:44 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032 13/10/28 15:20:44 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032 13/10/28 15:20:45 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this. 13/10/28 15:20:45 WARN mapreduce.JobSubmitter: No job jar file set. User classes may not be found. See Job or Job#setJar(String). 13/10/28 15:20:45 INFO mapred.FileInputFormat: Total input paths to process : 1 13/10/28 15:20:46 INFO mapreduce.JobSubmitter: number of splits:2 13/10/28 15:20:46 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir 13/10/28 15:20:46 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1382942307976_0008 13/10/28 15:20:47 INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources. 13/10/28 15:20:49 INFO impl.YarnClientImpl: Submitted application application_1382942307976_0008 to ResourceManager at /0.0.0.0:8032 13/10/28 15:20:49 INFO mapreduce.Job: The url to track the job: http://wyp:8088/proxy/application_1382942307976_0008/ 13/10/28 15:20:49 INFO mapreduce.Job: Running job: job_1382942307976_0008 13/10/28 15:20:59 INFO mapreduce.Job: Job job_1382942307976_0008 running in uber mode : false 13/10/28 15:20:59 INFO mapreduce.Job: map 0% reduce 0% 13/10/28 15:21:35 INFO mapreduce.Job: map 100% reduce 0% 13/10/28 15:21:38 INFO mapreduce.Job: map 0% reduce 0% 13/10/28 15:21:38 INFO mapreduce.Job: Task Id : attempt_1382942307976_0008_m_000000_0, Status : FAILED Error: java.lang.RuntimeException: Error in configuring object at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:109) at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:75) at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:133) at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:425) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341) at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:162) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:415) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1491) at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:157) Caused by: java.lang.reflect.InvocationTargetException at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:106) ... 9 more Caused by: java.lang.RuntimeException: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.wyp.hadoop.MaxTemperatureMapper1 not found at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1752) at org.apache.hadoop.mapred.JobConf.getMapperClass(JobConf.java:1058) at org.apache.hadoop.mapred.MapRunner.configure(MapRunner.java:38) ... 14 more Caused by: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.wyp.hadoop.MaxTemperatureMapper1 not found at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1720) at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1744) ... 16 more Caused by: java.lang.ClassNotFoundException: Class com.wyp.hadoop.MaxTemperatureMapper1 not found at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:1626) at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1718) ... 17 more Container killed by the ApplicationMaster. Container killed on request. Exit code is 143
程序居然抛出异常(ClassNotFoundException)!这是什么回事?其实我也不太明白!!
在网上Google了一下,找到别人的观点:
经个人总结,这通常是由于以下几种原因造成的:
(1)你编写了一个java lib,封装成了jar,然后再写了一个Hadoop程序,调用这个jar完成mapper和reducer的编写
(2)你编写了一个Hadoop程序,期间调用了一个第三方java lib。
之后,你将自己的jar包或者第三方java包分发到各个TaskTracker的HADOOP_HOME目录下,运行你的JAVA程序,报了以上错误。
那怎么解决呢?一个笨重的方法是,在运行Hadoop作业的时候,先运行下面的命令:
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其中,/home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/是上面Hadoop.jar文件所在的目录。好了,现在再运行一下Hadoop作业命令:
[wyp@wyp Hadoop_jar]$ hadoop jar /home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/Hadoop.jar com/wyp/hadoop/MaxTemperature /user/wyp/data.txt /user/wyp/result 13/10/28 15:34:16 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032 13/10/28 15:34:16 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032 13/10/28 15:34:17 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this. 13/10/28 15:34:17 INFO mapred.FileInputFormat: Total input paths to process : 1 13/10/28 15:34:17 INFO mapreduce.JobSubmitter: number of splits:2 13/10/28 15:34:17 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir 13/10/28 15:34:18 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1382942307976_0009 13/10/28 15:34:18 INFO impl.YarnClientImpl: Submitted application application_1382942307976_0009 to ResourceManager at /0.0.0.0:8032 13/10/28 15:34:18 INFO mapreduce.Job: The url to track the job: http://wyp:8088/proxy/application_1382942307976_0009/ 13/10/28 15:34:18 INFO mapreduce.Job: Running job: job_1382942307976_0009 13/10/28 15:34:26 INFO mapreduce.Job: Job job_1382942307976_0009 running in uber mode : false 13/10/28 15:34:26 INFO mapreduce.Job: map 0% reduce 0% 13/10/28 15:34:41 INFO mapreduce.Job: map 50% reduce 0% 13/10/28 15:34:53 INFO mapreduce.Job: map 100% reduce 0% 13/10/28 15:35:17 INFO mapreduce.Job: map 100% reduce 100% 13/10/28 15:35:18 INFO mapreduce.Job: Job job_1382942307976_0009 completed successfully 13/10/28 15:35:18 INFO mapreduce.Job: Counters: 43 File System Counters FILE: Number of bytes read=144425 FILE: Number of bytes written=524725 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=1777598 HDFS: Number of bytes written=18 HDFS: Number of read operations=9 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=2 Launched reduce tasks=1 Data-local map tasks=2 Total time spent by all maps in occupied slots (ms)=38057 Total time spent by all reduces in occupied slots (ms)=24800 Map-Reduce Framework Map input records=13130 Map output records=13129 Map output bytes=118161 Map output materialized bytes=144431 Input split bytes=182 Combine input records=0 Combine output records=0 Reduce input groups=2 Reduce shuffle bytes=144431 Reduce input records=13129 Reduce output records=2 Spilled Records=26258 Shuffled Maps =2 Failed Shuffles=0 Merged Map outputs=2 GC time elapsed (ms)=321 CPU time spent (ms)=5110 Physical memory (bytes) snapshot=552824832 Virtual memory (bytes) snapshot=1228738560 Total committed heap usage (bytes)=459800576 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=1777416 File Output Format Counters Bytes Written=18
到这里,程序就成功运行了!很高兴吧?那么怎么查看刚刚程序运行的结果呢?很简单,运行下面命令:
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到此,你自己写好的一个Mapreduce程序终于成功运行了!
附程序测试的数据的下载地址:http://pan.baidu.com/s/1iSacM
过往记忆(http://www.iteblog.com/)
编写简单的Mapreduce程序并部署在Hadoop2.2.0上运行(http://www.iteblog.com/archives/789)
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