|
前言
Hadoop的MapReduce环境是一个复杂的编程环境，所以我们要尽可能地简化构建MapReduce项目的过程。Maven是一个很不错的自动化项目构建工具，通过Maven来帮助我们从复杂的环境配置中解脱出来，从而标准化开发过程。所以，写MapReduce之前，让我们先花点时间把刀磨快！！当然，除了Maven还有其他的选择Gradle(推荐),
Ivy….
后面将会有介绍几篇MapReduce开发的文章，都要依赖于本文中Maven的构建的MapReduce环境。
1. Maven介绍
Apache Maven，是一个Java的项目管理及自动构建工具，由Apache软件基金会所提供。基于项目对象模型（缩写：POM）概念，Maven利用一个中央信息片断能管理一个项目的构建、报告和文档等步骤。曾是Jakarta项目的子项目，现为独立Apache项目。
maven的开发者在他们开发网站上指出，maven的目标是要使得项目的构建更加容易，它把编译、打包、测试、发布等开发过程中的不同环节有机的串联了起来，并产生一致的、高质量的项目信息，使得项目成员能够及时地得到反馈。maven有效地支持了测试优先、持续集成，体现了鼓励沟通，及时反馈的软件开发理念。如果说Ant的复用是建立在”拷贝–粘贴”的基础上的，那么Maven通过插件的机制实现了项目构建逻辑的真正复用。
2. Maven安装(win)
下载Maven：http://maven.apache.org/download.cgi
下载最新的xxx-bin.zip文件，在win上解压到 D:\toolkit\maven3
并把maven/bin目录设置在环境变量PATH：
然后，打开命令行输入mvn，我们会看到mvn命令的运行效果
~ C:\Users\Administrator>mvn
[INFO] Scanning for projects...
[INFO] ------------------------------------------------------------------------
[INFO] BUILD FAILURE
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 0.086s
[INFO] Finished at: Mon Sep 30 18:26:58 CST 2013
[INFO] Final Memory: 2M/179M
[INFO] ------------------------------------------------------------------------
[ERROR] No goals have been specified for this build. You must specify a valid lifecycle phase or a goal in the format : or :[:]:.
Available lifecycle phases are: validate, initialize, generate-sources, process-sources,
generate-resources, process-resources, compile, process-class
es, generate-test-sources, process-test-sources, generate-test-resources, process-test-resources, test-compile,
process-test-classes, test, prepare-package, package, pre-integration-test, integration-test, post-integration-test,
verify, install, deploy, pre-clean, clean, post-clean, pre-site, site, post-site, site-deploy. -> [Help 1]
[ERROR]
[ERROR] To see the full stack trace of the errors, re-run Maven with the -e switch.
[ERROR] Re-run Maven using the -X switch to enable full debug logging.
[ERROR]
[ERROR] For more information about the errors and possible solutions, please read the following articles:
[ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/NoGoalSpecifiedException |
安装Eclipse的Maven插件：Maven Integration for Eclipse
Maven的Eclipse插件配置
3. Hadoop开发环境介绍
如上图所示，我们可以选择在win中开发，也可以在linux中开发，本地启动Hadoop或者远程调用Hadoop，标配的工具都是Maven和Eclipse。
Hadoop集群系统环境：
Linux: Ubuntu 12.04.2 LTS 64bit Server
Java: 1.6.0_29
Hadoop: hadoop-1.0.3，单节点，IP:192.168.1.210
4. 用Maven构建Hadoop环境
1. 用Maven创建一个标准化的Java项目
2. 导入项目到eclipse
3. 增加hadoop依赖，修改pom.xml
4. 下载依赖
5. 从Hadoop集群环境下载hadoop配置文件
6. 配置本地host
1). 用Maven创建一个标准化的Java项目
~ D:\workspace\java>mvn archetype:generate -DarchetypeGroupId=
org.apache.maven.archetypes -DgroupId=org.conan.myhadoop.mr
-DartifactId=myHadoop -DpackageName=org.conan.myhadoop.mr -Dversion=
1.0-SNAPSHOT -DinteractiveMode=false
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------------------------
[INFO] Building Maven Stub Project (No POM) 1
[INFO] -----------------------------------------------
[INFO]
[INFO] >>> maven-archetype-plugin:2.2:generate (default-cli) @ standalone-pom >>>
[INFO]
[INFO] <<< maven-archetype-plugin:2.2:generate (default-cli) @ standalone-pom <<<
[INFO]
[INFO] --- maven-archetype-plugin:2.2:generate (default-cli) @ standalone-pom ---
[INFO] Generating project in Batch mode
[INFO] No archetype defined. Using maven-archetype-quickstart
(org.apache.maven.archetypes:maven-archetype-quickstart:1.0)
Downloading: http://repo.maven.apache.org/maven2/org/apache/maven/archetypes/
maven-archetype-quickstart/1.0/maven-archet
ype-quickstart-1.0.jar
Downloaded: http://repo.maven.apache.org/maven2/org/apache/maven/archetypes/
maven-archetype-quickstart/1.0/maven-archety
pe-quickstart-1.0.jar (5 KB at 4.3 KB/sec)
Downloading: http://repo.maven.apache.org/maven2/org/apache/maven/archetypes/
maven-archetype-quickstart/1.0/maven-archet
ype-quickstart-1.0.pom
Downloaded: http://repo.maven.apache.org/maven2/org/apache/maven/archetypes
/maven-archetype-quickstart/1.0/maven-archety
pe-quickstart-1.0.pom (703 B at 1.6 KB/sec)
[INFO] --------------------------------------------------
[INFO] Using following parameters for creating project from Old (1.x) Archetype: maven-archetype-quickstart:1.0
[INFO] ----------------------------------------------------
[INFO] Parameter: groupId, Value: org.conan.myhadoop.mr
[INFO] Parameter: packageName, Value: org.conan.myhadoop.mr
[INFO] Parameter: package, Value: org.conan.myhadoop.mr
[INFO] Parameter: artifactId, Value: myHadoop
[INFO] Parameter: basedir, Value: D:\workspace\java
[INFO] Parameter: version, Value: 1.0-SNAPSHOT
[INFO] project created from Old (1.x) Archetype in dir: D:\workspace\java\myHadoop
[INFO] ----------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ----------------------------------------------------
[INFO] Total time: 8.896s
[INFO] Finished at: Sun Sep 29 20:57:07 CST 2013
[INFO] Final Memory: 9M/179M
[INFO] ---------------------------------------------------- |
进入项目，执行mvn命令
~ D:\workspace\java>cd myHadoop
~ D:\workspace\java\myHadoop>mvn clean install
[INFO]
[INFO] --- maven-jar-plugin:2.3.2:jar (default-jar) @ myHadoop ---
[INFO] Building jar: D:\workspace\java\myHadoop\target\myHadoop-1.0-SNAPSHOT.jar
[INFO]
[INFO] --- maven-install-plugin:2.3.1:install (default-install) @ myHadoop ---
[INFO] Installing D:\workspace\java\myHadoop\target\myHadoop-1.0-SNAPSHOT.jar to
C:\Users\Administrator\.m2\repository\o
rg\conan\myhadoop\mr\myHadoop\1.0-SNAPSHOT\myHadoop-1.0-SNAPSHOT.jar
[INFO] Installing D:\workspace\java\myHadoop\pom.xml to
C:\Users\Administrator\.m2\repository\org\conan\myhadoop\mr\myHa
doop\1.0-SNAPSHOT\myHadoop-1.0-SNAPSHOT.pom
[INFO] -------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] -------------------------------------------------------
[INFO] Total time: 4.348s
[INFO] Finished at: Sun Sep 29 20:58:43 CST 2013
[INFO] Final Memory: 11M/179M
[INFO] ------------------------------------------------------- |
2). 导入项目到eclipse
我们创建好了一个基本的maven项目，然后导入到eclipse中。 这里我们最好已安装好了Maven的插件。
3). 增加hadoop依赖
这里我使用hadoop-1.0.3版本，修改文件：pom.xml
~ vi pom.xml
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi=
"http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
http://maven.apache.org/maven-v4_0_0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.conan.myhadoop.mr</groupId>
<artifactId>myHadoop</artifactId>
<packaging>jar</packaging>
<version>1.0-SNAPSHOT</version>
<name>myHadoop</name>
<url>http://maven.apache.org</url>
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-core</artifactId>
<version>1.0.3</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.4</version>
<scope>test</scope>
</dependency>
</dependencies>
</project> |
4). 下载依赖
下载依赖：
在eclipse中刷新项目：
项目的依赖程序，被自动加载的库路径下面。
5). 从Hadoop集群环境下载hadoop配置文件
core-site.xml
hdfs-site.xml
mapred-site.xml
|
查看core-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://master:9000</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/home/conan/hadoop/tmp</value>
</property>
<property>
<name>io.sort.mb</name>
<value>256</value>
</property>
</configuration> |
查看hdfs-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>dfs.data.dir</name>
<value>/home/conan/hadoop/data</value>
</property>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<property>
<name>dfs.permissions</name>
<value>false</value>
</property>
</configuration> |
查看mapred-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>mapred.job.tracker</name>
<value>hdfs://master:9001</value>
</property>
</configuration> |
保存在src/main/resources/hadoop目录下面
删除原自动生成的文件：App.java和AppTest.java
6).配置本地host，增加master的域名指向
~ vi c:/Windows/System32/drivers/etc/hosts
192.168.1.210 master |
5. MapReduce程序开发
编写一个简单的MapReduce程序，实现wordcount功能。
新一个Java文件：WordCount.java
package org.conan.myhadoop.mr;
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
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 org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
public class WordCount {
public static class WordCountMapper extends
MapReduceBase implements Mapper<Object, Text,
Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
@Override
public void map(Object key, Text value, OutputCollector<Text,
IntWritable> output, Reporter reporter) throws
IOException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
output.collect(word, one);
}
}
}
public static class WordCountReducer extends
MapReduceBase implements Reducer<Text, IntWritable,
Text, IntWritable> {
private IntWritable result = new IntWritable();
@Override
public void reduce(Text key, Iterator values,
OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
result.set(sum);
output.collect(key, result);
}
}
public static void main(String[] args) throws
Exception {
String input = "hdfs://192.168.1.210:9000/user/hdfs/o_t_account";
String output = "hdfs://192.168.1.210:9000/user/hdfs/o_t_account/result";
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("WordCount");
conf.addResource("classpath:/hadoop/core-site.xml");
conf.addResource("classpath:/hadoop/hdfs-site.xml");
conf.addResource("classpath:/hadoop/mapred-site.xml");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(WordCountMapper.class);
conf.setCombinerClass(WordCountReducer.class);
conf.setReducerClass(WordCountReducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(input));
FileOutputFormat.setOutputPath(conf, new Path(output));
JobClient.runJob(conf);
System.exit(0);
}
} |
启动Java APP.
控制台错误
2013-9-30 19:25:02 org.apache.hadoop.util.NativeCodeLoader
警告: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2013-9-30 19:25:02 org.apache.hadoop.security.UserGroupInformation doAs
严重: PriviledgedActionException as:Administrator cause:java.io.IOException:
Failed to set permissions of path: \tmp\hadoop-Administrator\mapred\staging\Administrator1702422322\.staging to 0700
Exception in thread "main" java.io.IOException: Failed to set permissions of path:
\tmp\hadoop-Administrator\mapred\staging\Administrator1702422322\.staging to 0700
at org.apache.hadoop.fs.FileUtil.checkReturnValue(FileUtil.java:689)
at org.apache.hadoop.fs.FileUtil.setPermission(FileUtil.java:662)
at org.apache.hadoop.fs.RawLocalFileSystem.setPermission(RawLocalFileSystem.java:509)
at org.apache.hadoop.fs.RawLocalFileSystem.mkdirs(RawLocalFileSystem.java:344)
at org.apache.hadoop.fs.FilterFileSystem.mkdirs(FilterFileSystem.java:189)
at org.apache.hadoop.mapreduce.JobSubmissionFiles.getStagingDir(JobSubmissionFiles.java:116)
at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:856)
at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:850)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:396)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1121)
at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:850)
at org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:824)
at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:1261)
at org.conan.myhadoop.mr.WordCount.main(WordCount.java:78) |
这个错误是win中开发特有的错误，文件权限问题，在Linux下可以正常运行。
解决方法是，修改/hadoop-1.0.3/src/core/org/apache/hadoop/fs/FileUtil.java文件
688-692行注释，然后重新编译源代码，重新打一个hadoop.jar的包。
685 private static void checkReturnValue(boolean rv, File p,
686 FsPermission permission
687 ) throws IOException {
688 /*if (!rv) {
689 throw new IOException("Failed to set permissions of path: " + p +
690 " to " +
691 String.format("%04o", permission.toShort()));
692 }*/
693 } |
我这里自己打了一个hadoop-core-1.0.3.jar包，放到了lib下面。
我们还要替换maven中的hadoop类库。
~ cp lib/hadoop-core-1.0.3.jar C:\Users\Administrator
\.m2\repository\org\apache\hadoop\hadoop-core\1.0.3\hadoop-core-1.0.3.jar |
再次启动Java APP，控制台输出：
2013-9-30 19:50:49 org.apache.hadoop.util.NativeCodeLoader
警告: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2013-9-30 19:50:49 org.apache.hadoop.mapred.JobClient copyAndConfigureFiles
警告: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
2013-9-30 19:50:49 org.apache.hadoop.mapred.JobClient copyAndConfigureFiles
警告: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
2013-9-30 19:50:49 org.apache.hadoop.io.compress.snappy.LoadSnappy
警告: Snappy native library not loaded
2013-9-30 19:50:49 org.apache.hadoop.mapred.FileInputFormat listStatus
信息: Total input paths to process : 4
2013-9-30 19:50:50 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Running job: job_local_0001
2013-9-30 19:50:50 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2013-9-30 19:50:50 org.apache.hadoop.mapred.MapTask runOldMapper
信息: numReduceTasks: 1
2013-9-30 19:50:50 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: io.sort.mb = 100
2013-9-30 19:50:50 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: data buffer = 79691776/99614720
2013-9-30 19:50:50 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: record buffer = 262144/327680
2013-9-30 19:50:50 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
2013-9-30 19:50:50 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2013-9-30 19:50:50 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
2013-9-30 19:50:51 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 0% reduce 0%
2013-9-30 19:50:53 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: hdfs://192.168.1.210:9000/user/hdfs/o_t_account/part-m-00003:0+119
2013-9-30 19:50:53 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000000_0' done.
2013-9-30 19:50:53 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2013-9-30 19:50:53 org.apache.hadoop.mapred.MapTask runOldMapper
信息: numReduceTasks: 1
2013-9-30 19:50:53 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: io.sort.mb = 100
2013-9-30 19:50:53 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: data buffer = 79691776/99614720
2013-9-30 19:50:53 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: record buffer = 262144/327680
2013-9-30 19:50:53 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
2013-9-30 19:50:53 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2013-9-30 19:50:53 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000001_0 is done. And is in the process of commiting
2013-9-30 19:50:54 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 100% reduce 0%
2013-9-30 19:50:56 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: hdfs://192.168.1.210:9000/user/hdfs/o_t_account/part-m-00000:0+113
2013-9-30 19:50:56 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000001_0' done.
2013-9-30 19:50:56 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2013-9-30 19:50:56 org.apache.hadoop.mapred.MapTask runOldMapper
信息: numReduceTasks: 1
2013-9-30 19:50:56 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: io.sort.mb = 100
2013-9-30 19:50:56 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: data buffer = 79691776/99614720
2013-9-30 19:50:56 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: record buffer = 262144/327680
2013-9-30 19:50:56 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
2013-9-30 19:50:56 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2013-9-30 19:50:56 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000002_0 is done. And is in the process of commiting
2013-9-30 19:50:59 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: hdfs://192.168.1.210:9000/user/hdfs/o_t_account/part-m-00001:0+110
2013-9-30 19:50:59 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: hdfs://192.168.1.210:9000/user/hdfs/o_t_account/part-m-00001:0+110
2013-9-30 19:50:59 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000002_0' done.
2013-9-30 19:50:59 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2013-9-30 19:50:59 org.apache.hadoop.mapred.MapTask runOldMapper
信息: numReduceTasks: 1
2013-9-30 19:50:59 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: io.sort.mb = 100
2013-9-30 19:50:59 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: data buffer = 79691776/99614720
2013-9-30 19:50:59 org.apache.hadoop.mapred.MapTask$MapOutputBuffer
信息: record buffer = 262144/327680
2013-9-30 19:50:59 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
2013-9-30 19:50:59 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2013-9-30 19:50:59 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000003_0 is done. And is in the process of commiting
2013-9-30 19:51:02 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: hdfs://192.168.1.210:9000/user/hdfs/o_t_account/part-m-00002:0+79
2013-9-30 19:51:02 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000003_0' done.
2013-9-30 19:51:02 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2013-9-30 19:51:02 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2013-9-30 19:51:02 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Merging 4 sorted segments
2013-9-30 19:51:02 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Down to the last merge-pass, with 4 segments left of total size: 442 bytes
2013-9-30 19:51:02 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2013-9-30 19:51:02 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
2013-9-30 19:51:02 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2013-9-30 19:51:02 org.apache.hadoop.mapred.Task commit
信息: Task attempt_local_0001_r_000000_0 is allowed to commit now
2013-9-30 19:51:02 org.apache.hadoop.mapred.FileOutputCommitter commitTask
信息: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://192.168.1.210:9000/user/hdfs/o_t_account/result
2013-9-30 19:51:05 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: reduce > reduce
2013-9-30 19:51:05 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_r_000000_0' done.
2013-9-30 19:51:06 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 100% reduce 100%
2013-9-30 19:51:06 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Job complete: job_local_0001
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Counters: 20
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: File Input Format Counters
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Bytes Read=421
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: File Output Format Counters
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Bytes Written=348
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: FileSystemCounters
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: FILE_BYTES_READ=7377
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: HDFS_BYTES_READ=1535
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: FILE_BYTES_WRITTEN=209510
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: HDFS_BYTES_WRITTEN=348
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Map-Reduce Framework
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Map output materialized bytes=458
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Map input records=11
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Reduce shuffle bytes=0
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Spilled Records=30
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Map output bytes=509
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Total committed heap usage (bytes)=1838546944
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Map input bytes=421
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: SPLIT_RAW_BYTES=452
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Combine input records=22
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Reduce input records=15
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Reduce input groups=13
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Combine output records=15
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Reduce output records=13
2013-9-30 19:51:06 org.apache.hadoop.mapred.Counters log
信息: Map output records=22
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成功运行了wordcount程序，通过命令我们查看输出结果
~ hadoop fs -ls hdfs://192.168.1.210:9000/user/hdfs/o_t_account/result
Found 2 items
-rw-r--r-- 3 Administrator supergroup 0 2013-09-30 19:51 /user/hdfs/o_t_account/result/_SUCCESS
-rw-r--r-- 3 Administrator supergroup 348 2013-09-30 19:51 /user/hdfs/o_t_account/result/part-00000
~ hadoop fs -cat hdfs://192.168.1.210:9000/user/hdfs/o_t_account/result/part-00000
1,abc@163.com,2013-04-22 1
10,ade121@sohu.com,2013-04-23 1
11,addde@sohu.com,2013-04-23 1
17:21:24.0 5
2,dedac@163.com,2013-04-22 1
20:21:39.0 6
3,qq8fed@163.com,2013-04-22 1
4,qw1@163.com,2013-04-22 1
5,af3d@163.com,2013-04-22 1
6,ab34@163.com,2013-04-22 1
7,q8d1@gmail.com,2013-04-23 1
8,conan@gmail.com,2013-04-23 1
9,adeg@sohu.com,2013-04-23 1
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这样，我们就实现了在win7中的开发，通过Maven构建Hadoop依赖环境，在Eclipse中开发MapReduce的程序，然后运行JavaAPP。Hadoop应用会自动把我们的MR程序打成jar包，再上传的远程的hadoop环境中运行，返回日志在Eclipse控制台输出。
6. 模板项目上传github
https://github.com/bsspirit/maven_hadoop_template
大家可以下载这个项目，做为开发的起点。
~ git clone https://github.com/bsspirit/maven_hadoop_template.git |
我们完成第一步，下面就将正式进入MapReduce开发实践。
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