1. 介绍
本文主要参考资料为:IDEA 调试 Hadoop程序
2. win下安装hadoop
我们这里使用2.7.2版本。首先到hadoop官方网站下载hadoop。
PS: 本教程前提是你已经安装了JDK。
在windows下解压后配置如下的环境变量:
HADOOP_HOME:D:\soft\dev\hadoop-2.7.2
HADOOP_BIN_PATH:%HADOOP_HOME%\bin
HADOOP_PREFIX:%HADOOP_HOME%
在Path后面加上%HADOOP_HOME%\bin;%HADOOP_HOME%\sbin;
3. 新建hadoop maven工程
新建一个MAVEN项目,按照如下配置pom.xml
<?xml version="1.0" encoding="UTF-8"?>
<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/xsd/maven-4.0.0.xsd">
<parent>
<artifactId>kafka-learn</artifactId>
<groupId>com.best.kafka.test</groupId>
<version>1.0-SNAPSHOT</version>
</parent>
<modelVersion>4.0.0</modelVersion>
<artifactId>hadoop-test</artifactId>
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.7.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.7.2</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-common</artifactId>
<version>2.7.2</version>
</dependency>
<dependency>
<groupId>jdk.tools</groupId>
<artifactId>jdk.tools</artifactId>
<version>1.8</version>
<scope>system</scope>
<systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
</dependency>
</dependencies>
<!-- https://mvnrepository.com/artifact/junit/junit -->
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.12</version>
</dependency>
<!-- https://mvnrepository.com/artifact/log4j/log4j -->
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.17</version>
</dependency>
</project>
4. 编写wordcount代码
我们就拿官方的wordcount代码来当例子。
在该wordcount例子当中,额外添加hdfs和yarn的配置即可。
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
//设置访问HDFS的用户名
System.setProperty("HADOOP_USER_NAME", "root");
Configuration conf = new Configuration();
//设置hdfs和yarn地址
conf.set("fs.defaultFS", "hdfs://10.45.10.33:9000");
conf.set("yarn.resourcemanager.hostname","10.45.10.33");
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
5. 配置log4j
在resource目录下新建log4j的配置文件。也可以从HADOOP安装目录下拷贝
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.Target=System.out
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{ABSOLUTE} %5p %c{1}:%L - %m%n
log4j.rootLogger=INFO, console
6. 安装额外的win支持
在win下调试需要额外安装一些支持程序,否则会报错。
6.1 winutils.exe
下载后放到hadoop/bin下即可。
如果不进行该操作,会报如下错误:
java.io.IOException: Could not locate executable D:\soft\dev\hadoop-2.7.2\bin\winutils.exe in the Hadoop binaries.
6.2 nativeIO问题
当设置好运行参数(input的文件自己提前准备好),并且开始运行的时候,会产生如下报错
Exception in thread "main" java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z
根据文章解决Exception: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z 等一系列问题修改org.apache.hadoop.io.nativeio.NativeIO源码。
新建该包结构,并且下载该NativeIO类到该目录。
6.3 访问HDFS权限问题
完成以上操作再进行运行扔可能回有如下报错:
org.apache.hadoop.security.AccessControlException: Permission denied: ...
修改master上的hdfs-site.xml配置文件添加如下内容并且重启即可:
<property>
<name>dfs.permissions</name>
<value>false</value>
</property>
设置了关闭权限检查,但是在提交任务到yarn的时候仍然会报权限错误,这时候可以修改指定文件的权限:
hadoop fs -chmod -R 755 /tmp
6.4 /bin/bash: line 0: fg: no job control问题
在使用windows调用Hadoop yarn平台的时候,一般都会遇到如下的错误:
org.apache.hadoop.util.Shell$ExitCodeException: /bin/bash: line 0: fg: no job control
这时候需要在windows本地修改HADOOP配置文件mapred-site.xml
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapred.remote.os</name>
<value>Linux</value>
</property>
<property>
<name>mapreduce.app-submission.cross-platform</name>
<value>true</value>
</property>
</configuration>
7. 成功运行