编写简单的Mapreduce程序并部署在Hadoop2.2.0上运行-Mysql Tutorial-php.cn

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：

<dependencies></dependencies>

<dependency></dependency>

<groupid>org.apache.hadoop</groupid>

<artifactid>hadoop-mapreduce-client-core</artifactid>

<version></version>2.1.1-beta

<dependency></dependency>

<groupid>org.apache.hadoop</groupid>

<artifactid>hadoop-common</artifactid>

<version></version>2.1.1-beta

<dependency></dependency>

<groupid>org.apache.hadoop</groupid>

<artifactid>hadoop-mapreduce-client-common</artifactid>

<version></version>2.1.1-beta

<dependency></dependency>

<groupid>org.apache.hadoop</groupid>

<artifactid>hadoop-mapreduce-client-jobclient</artifactid>

<version></version>2.1.1-beta

好了，现在给出程序，代码如下：

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package com.wyp.hadoop;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapred.*;

import java.io.IOException;

/**

* User: wyp

* Date: 13-10-25

* Time: 下午3:26

* Email:wyphao.2007@163.com

*/

public class MaxTemperatureMapper extends MapReduceBase

implements Mapper<longwritable text></longwritable>

Text,IntWritable>{

private static final int MISSING = 9999;

@Override

public void map(LongWritable key, Text value,

OutputCollector<text intwritable> output, </text>

Reporter reporter) throws IOException {

String line = value.toString();

String year = line.substring(15, 19);

int airTemperature;

if(line.charAt(87) == '+'){

airTemperature = Integer.parseInt(line.substring(88, 92));

}else{

airTemperature = Integer.parseInt(line.substring(87, 92));

}

String quality = line.substring(92, 93);

if(airTemperature != MISSING && quality.matches("[01459]")){

output.collect(new Text(year), new IntWritable(airTemperature));

}

package com.wyp.hadoop;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapred.MapReduceBase;

import org.apache.hadoop.mapred.OutputCollector;

import org.apache.hadoop.mapred.Reducer;

import org.apache.hadoop.mapred.Reporter;

import java.io.IOException;

import java.util.Iterator;

/**

* User: wyp

* Date: 13-10-25

* Time: 下午3:36

* Email:wyphao.2007@163.com

*/

public class MaxTemperatureReducer extends MapReduceBase

implements Reducer<text intwritable></text>

Text, IntWritable> {

@Override

public void reduce(Text key, Iterator<intwritable> values, </intwritable>

OutputCollector<text intwritable> output, </text>

Reporter reporter) throws IOException {

int maxValue = Integer.MIN_VALUE;

while (values.hasNext()){

maxValue = Math.max(maxValue, values.next().get());

}

output.collect(key, new IntWritable(maxValue));

}

package com.wyp.hadoop;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapred.FileInputFormat;

import org.apache.hadoop.mapred.FileOutputFormat;

import org.apache.hadoop.mapred.JobClient;

import org.apache.hadoop.mapred.JobConf;

import java.io.IOException;

/**

* User: wyp

* Date: 13-10-25

* Time: 下午3:40

* Email:wyphao.2007@163.com

*/

public class MaxTemperature {

public static void main(String[] args) throws IOException {

if(args.length != 2){

System.err.println("Error!");

System.exit(1);

}

JobConf conf = new JobConf(MaxTemperature.class);

conf.setJobName("Max Temperature");

FileInputFormat.addInputPath(conf, new Path(args[0]));

FileOutputFormat.setOutputPath(conf, new Path(args[1]));

conf.setMapperClass(MaxTemperatureMapper.class);

conf.setReducerClass(MaxTemperatureReducer.class);

conf.setOutputKeyClass(Text.class);

conf.setOutputValueClass(IntWritable.class);

JobClient.runJob(conf);

}

　　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：

1 2	`[wyp@wyp` `hadoop]$ sbin/start-dfs.sh` `[wyp@wyp` `hadoop]$ sbin/start-yarn.sh`

　　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

[wyp@wyp hadoop]$ jps

9582 Main

9684 RemoteMavenServer

16082 Jps

7011 DataNode

7412 ResourceManager

7528 NodeManager

7222 SecondaryNameNode

6832 NameNode

　　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：

[wyp@wyp Hadoop_jar]$ /home/wyp/Downloads/hadoop/bin/hadoop jar \

/home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/Hadoop.jar \

com/wyp/hadoop/MaxTemperature \

/user/wyp/data.txt \

/user/wyp/result

　　（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

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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：

1 2	`[wyp@wyp` `Hadoop_jar]$ export \` `HADOOP_CLASSPATH=/home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/`

　　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

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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：