---
layout: post
title: 编写第一个MapReduce的wordcount程序
category: 技术
tags: Hadoop
keywords:
description: 该MapReduce是基于hadoop2.7环境开发运行
---
{:toc}
## 准备环境
- hadoop2.7.2 集群环境(三个节点,h2m1,h2s1,h2s2)
- jdk 1.7.0_75版本
- centos6.5系统
**该MR代码支持输入源为多个文件或多个目录,不可以文件和目录混合作为输入源**
## 搭建程序
使用eclipse新建maven程序,开发在window环境,运行在linux环境
在maven的pom.xml文件中配置
```
4.0.0
brief-hadoop-demo
2.7.2
org.apache.hadoop
hadoop-client
${hadoop.version}
```
新建三个类:`WordCount.java`,`WordCountMapper.java`,`WordCountReduce.java`
假定三个类所在的包为:`cn.followtry.hadoop.demo.mr`
三个类的内容:
`WordCount.java`
```java
package cn.followtry.hadoop.demo.mr;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.net.URI;
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.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import cn.followtry.hadoop.demo.HelloHadoop;
public class WordCount {
public static void main(String[] args) throws IOException {
if (args == null || args.length < 2) {
System.out.println("用法:至少需要两个参数,最后一个为输出目录,其他为输入文件路径");
System.exit(-1);
}
StringBuilder inputPaths = new StringBuilder();
String outpathDir;
int len = args.length - 1;
for (int i = 0; i < len; i++) {
inputPaths.append(args[i]);
if (i < len - 1) {
inputPaths.append(",");
}
}
outpathDir = args[len];
//检查输出目录是否存在,存在则直接删除目录
rmExistsOutputDir(outpathDir);
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("word count mapreduce demo");
conf.setMapperClass(WordCountMapper.class);
conf.setReducerClass(WordCountReduce.class);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
//在命令行mainclass后的第一个参数作为输入参数
FileInputFormat.setInputPaths(conf, inputPaths.toString());
//在命令行mainclass后的第二个参数作为输出参数
FileOutputFormat.setOutputPath(conf, new Path(outpathDir));
JobClient.runJob(conf);
}
private static void rmExistsOutputDir(String outpathDir) throws FileNotFoundException, IOException {
// 将本地文件上传到hdfs。
Configuration config = new Configuration();
FileSystem fs = FileSystem.get(URI.create("webhdfs://h2m1:50070"), config);
Path output = new Path(outpathDir);
if (fs.exists(output)) {
System.out.println("目录" + outpathDir + "已经存在,正在删除...");
if (fs.delete(output, true)) {
System.out.println("目录" + outpathDir + "已经删除");
}else {
System.out.println("目录" + outpathDir + "删除失败");
}
} else {
System.out.println("目录" + outpathDir + "不存在");
}
}
}
```
`WordCountMapper.java`文件
```java
package cn.followtry.hadoop.demo.mr;
import java.io.IOException;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
public class WordCountMapper extends MapReduceBase implements Mapper{
private static final int ONE = 1;
@Override
public void map(LongWritable key, Text value, OutputCollector output, Reporter reporter)
throws IOException {
String line = value.toString();
if (StringUtils.isNotEmpty(line)) {
String[] words = line.split(" ");
for (String word : words) {
output.collect(new Text(word), new IntWritable(ONE));
}
}
}
}
```
`WordCountReduce.java`文件
```java
package cn.followtry.hadoop.demo.mr;
import java.io.IOException;
import java.util.Iterator;
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 org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class WordCountReduce extends MapReduceBase implements Reducer {
private static final Logger LOGGER = LoggerFactory.getLogger(WordCountReduce.class);
@Override
public void reduce(Text key, Iterator values, OutputCollector output,
Reporter reporter) throws IOException {
int count = 0;
while (values.hasNext()) {
values.next();
count++;
}
LOGGER.info("统计{}的次数为{}", key, count);
output.collect(key, new IntWritable(count));
}
}
```
## 打包发布
### 打包
项目(右键) --> Export --> java(jar file) --> next --> jar file(browse,指定输出位置) --> finish。
### 上传到hadoop linux服务器
## 创建并将输入文件上传到hdfs
比如:
输入文件`file1.txt`内容如下:
```
hello world
hello world
hello world2
hello world2
hello world3
hello world4
hello world5
hello world5
hello world5
hello world6
hello world7
hello world8
hello world8
```
执行`hdfs dfs -put -f file1.txt /user/root/input/file1.txt`命令,上传输入文件
## 执行
`hadoop jar wordcount.jar cn.followtry.hadoop.demo.mr.WordCount /user/root/input/file1.txt /user/root/output/`
或者
`hadoop jar wordcount.jar cn.followtry.hadoop.demo.mr.WordCount viewfs://hadoop-cluster-jingzz/user/root/input/file1.txt /user/root/output/`
输入为全路径,`hadoop-cluster-jingzz`为RM的集群名称。
部分执行日志显示:
```
16/12/13 04:15:13 INFO demo.HelloHadoop: 目录/user/root/output/已经存在,正在删除...
目录/user/root/output/已经存在,正在删除...
目录/user/root/output/已经删除
16/12/13 04:15:13 INFO demo.HelloHadoop: 目录/user/root/output/已经删除
16/12/13 04:15:15 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
16/12/13 04:15:15 INFO mapred.FileInputFormat: Total input paths to process : 1
16/12/13 04:15:15 INFO mapreduce.JobSubmitter: number of splits:2
16/12/13 04:15:16 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1481615539888_0004
16/12/13 04:15:16 INFO impl.YarnClientImpl: Submitted application application_1481615539888_0004
16/12/13 04:15:16 INFO mapreduce.Job: The url to track the job: http://h2m1:8088/proxy/application_1481615539888_0004/
16/12/13 04:15:16 INFO mapreduce.Job: Running job: job_1481615539888_0004
16/12/13 04:15:25 INFO mapreduce.Job: Job job_1481615539888_0004 running in uber mode : false
16/12/13 04:15:25 INFO mapreduce.Job: map 0% reduce 0%
16/12/13 04:15:32 INFO mapreduce.Job: map 50% reduce 0%
16/12/13 04:16:00 INFO mapreduce.Job: map 100% reduce 17%
16/12/13 04:16:01 INFO mapreduce.Job: map 100% reduce 100%
16/12/13 04:16:02 INFO mapreduce.Job: Job job_1481615539888_0004 completed successfully
16/12/13 04:16:02 INFO mapreduce.Job: Counters: 55
File System Counters
FILE: Number of bytes read=329
FILE: Number of bytes written=355404
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=467
HDFS: Number of bytes written=80
HDFS: Number of read operations=9
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
VIEWFS: Number of bytes read=0
VIEWFS: Number of bytes written=0
VIEWFS: Number of read operations=0
VIEWFS: Number of large read operations=0
VIEWFS: Number of write operations=0
Job Counters
Killed map tasks=1
Launched map tasks=3
Launched reduce tasks=1
Data-local map tasks=3
Total time spent by all maps in occupied slots (ms)=53297
Total time spent by all reduces in occupied slots (ms)=25951
Total time spent by all map tasks (ms)=53297
Total time spent by all reduce tasks (ms)=25951
Total vcore-seconds taken by all map tasks=53297
Total vcore-seconds taken by all reduce tasks=25951
Total megabyte-seconds taken by all map tasks=54576128
Total megabyte-seconds taken by all reduce tasks=26573824
Map-Reduce Framework
Map input records=13
Map output records=26
Map output bytes=271
Map output materialized bytes=335
Input split bytes=216
Combine input records=0
Combine output records=0
Reduce input groups=9
Reduce shuffle bytes=335
Reduce input records=26
Reduce output records=9
Spilled Records=52
Shuffled Maps =2
Failed Shuffles=0
Merged Map outputs=2
GC time elapsed (ms)=1257
CPU time spent (ms)=4820
Physical memory (bytes) snapshot=515735552
Virtual memory (bytes) snapshot=2546524160
Total committed heap usage (bytes)=281157632
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=0
File Output Format Counters
Bytes Written=0
```
执行命令:`hdfs dfs -cat /user/root/output/part-00000`
显示执行结果:
```
hello 13
world 2
world2 2
world3 1
world4 1
world5 3
world6 1
world7 1
world8 2
```