添加依赖
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.6.0</version>
</dependency>
一、jar方式
package Hadoop;
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;
import org.apache.hadoop.util.GenericOptionsParser;
public class WordCount {
//四个参数,前两个为输入<key,value>对,后两个为输出<key,value>对;
//LongWritable、IntWritable、Text可视为Java 的long、int、String替代品;
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{
//一个标记单词个数的常量,值为1,这个常量也可以不定义,在后面程序直接用整数1代替,private final static定义的是常量;
private final static IntWritable one = new IntWritable(1);
//充当中间变量,存储词;
private Text word = new Text();
//map方法,key为偏移量,对value进行拆分,<span style="font-family: Arial, Helvetica, sans-serif;">context为上下文机制;</span>
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
//对转换的字符串进行分隔;
StringTokenizer itr = new StringTokenizer(value.toString());
//利用循环函数进行依次处理;
while (itr.hasMoreTokens()) {
//返回从当前位置到下一个分隔符的字符串;
word.set(itr.nextToken());
//如 context.write("hello",1);
context.write(word, one);
}
}
}
//四个参数,前两个为输入<key,value>对,后两个为输出<key,value>对;
public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
//定义一个变量;
private IntWritable result = new IntWritable();
//reduce方法,key为如 "hello",Iterable遍历所有key的个数;
public void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
// 用于记录key个数的变量;
int sum = 0;
//求key的个数;
for (IntWritable val : values) {
sum += val.get();
}
//把sum个数存到result中去;
result.set(sum);
//如 context.write("hello",7);
context.write(key, result);
}
}
//主方法;
public static void main(String[] args) throws Exception {
//指定作业执行规范;
Configuration conf = new Configuration();
//这里需要配置参数即输入和输出的HDFS的文件路径
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length < 2) {
System.err.println("Usage: wordcount <in> [<in>...] <out>");
System.exit(2);
}
//设置Job名称、运行对象;
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
//为job设置map类;
job.setMapperClass(TokenizerMapper.class);
//为job设置Combiner类;
job.setCombinerClass(IntSumReducer.class);
//为job设置 reduce类;
job.setReducerClass(IntSumReducer.class);
//设置输出key类型;
job.setOutputKeyClass(Text.class);
//设置输出value类型;
job.setOutputValueClass(IntWritable.class);
//设置输入路径;
for (int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
}
//设置输出路径;
FileOutputFormat.setOutputPath(job,new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
生成jar,并将其拷贝到/usr/local/hadoop 目录下,执行以下命令
hadoop jar /usr/local/hadoop/MavenMapReduceHelloWorld-1.0-SNAPSHOT.jar
Hadoop.WordCount /input /output
结果
二、IDEA 远程执行
package Hadoop;
import java.io.IOException;
import java.net.URI;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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 WordCount2 {
//四个参数,前两个为输入<key,value>对,后两个为输出<key,value>对;
//LongWritable、IntWritable、Text可视为Java 的long、int、String替代品;
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{
//一个标记单词个数的常量,值为1,这个常量也可以不定义,在后面程序直接用整数1代替,private final static定义的是常量;
private final static IntWritable one = new IntWritable(1);
//充当中间变量,存储词;
private Text word = new Text();
//map方法,key为偏移量,对value进行拆分,<span style="font-family: Arial, Helvetica, sans-serif;">context为上下文机制;</span>
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
System.out.println("Map key:" + key + ",value:" + value);
//对转换的字符串进行分隔;
StringTokenizer itr = new StringTokenizer(value.toString());
//利用循环函数进行依次处理;
while (itr.hasMoreTokens()) {
//返回从当前位置到下一个分隔符的字符串;
word.set(itr.nextToken());
//如 context.write("hello",1);
context.write(word, one);
}
}
}
//四个参数,前两个为输入<key,value>对,后两个为输出<key,value>对;
public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
//定义一个变量;
private IntWritable result = new IntWritable();
//reduce方法,key为如 "hello",Iterable遍历所有key的个数;
public void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
StringBuffer sb = new StringBuffer();
sb.append("Reduce key:" + key + ",value:");
// 用于记录key个数的变量;
int sum = 0;
//求key的个数;
for (IntWritable val : values) {
sb.append(val.get()+" ");
sum += val.get();
}
System.out.println(sb.toString());
//把sum个数存到result中去;
result.set(sum);
//如 context.write("hello",7);
context.write(key, result);
}
}
//主方法;
public static void main(String[] args) throws Exception {
//指定作业执行规范;
Configuration conf = new Configuration();
conf.set("fs.defaultFS", "hdfs://192.168.255.128:9000");
System.setProperty("HADOOP_USER_NAME", "root");
//hadoop2.6 文件夹放在 hadoop2.6.rar 中
System.setProperty("hadoop.home.dir", "E:/hadoop2.6");
final String OUTPUT_PATH="hdfs://192.168.255.128:9000/output";
Path outpath = new Path(OUTPUT_PATH);
//清空原先的数据
FileSystem fs = FileSystem.get(new URI(OUTPUT_PATH),conf);
if(fs.exists(outpath)){
fs.delete(outpath,true);
}
//设置Job名称、运行对象;
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
//为job设置map类;
job.setMapperClass(TokenizerMapper.class);
//为job设置Combiner类;
job.setCombinerClass(IntSumReducer.class);
//为job设置 reduce类;
job.setReducerClass(IntSumReducer.class);
//设置输出key类型;
job.setOutputKeyClass(Text.class);
//设置输出value类型;
job.setOutputValueClass(IntWritable.class);
//设置输入路径
FileInputFormat.addInputPath(job, new Path("hdfs://192.168.255.128:9000/input"));
//设置输出路径;
FileOutputFormat.setOutputPath(job,new Path("hdfs://192.168.255.128:9000/output"));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
将 hadoop2.6/bin 文件夹下面 的hadoop.dll拷贝到C:\Windows\ System32