Hadoop词频统计（一）之集群模式运行

maven 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/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion>
<groupId>HadoopStu</groupId>
<artifactId>HadoopStu</artifactId>
<version>0.0.1-SNAPSHOT</version>
<build>
<sourceDirectory>src</sourceDirectory>
<resources>
<resource>
<directory>src</directory>
<excludes>
<exclude>**/*.java</exclude>
</excludes>
</resource>
</resources>
<plugins>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.3</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
</configuration>
</plugin>
</plugins>
</build>
<dependencies>
<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.6.0</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-core -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-core</artifactId>
<version>1.2.1</version>
</dependency>
<!-- https://mvnrepository.com/artifact/junit/junit -->
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.11</version>
</dependency>

</dependencies>
</project>

map:

package cn.hadoop.mr;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.util.StringUtils;

public class WCMapper extends Mapper<LongWritable, Text, Text, LongWritable>{

@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
String line = value.toString();

String[] words = StringUtils.split(line,' ');

for(String word : words) {
context.write(new Text(word), new LongWritable(1));
}
}
}

reduce:

package cn.hadoop.mr;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class WCReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
@Override
protected void reduce(Text key, Iterable<LongWritable> values,
Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {
long count = 0;
for(LongWritable value : values) {
count += value.get();
}
context.write(key, new LongWritable(count));
}
}

run:

package cn.hadoop.mr;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WCRunner {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

Configuration conf = new Configuration();
Job wcjob = Job.getInstance(conf);

wcjob.setJarByClass(WCRunner.class);

wcjob.setMapperClass(WCMapper.class);
wcjob.setReducerClass(WCReducer.class);

wcjob.setOutputKeyClass(Text.class);
wcjob.setOutputValueClass(LongWritable.class);

wcjob.setMapOutputKeyClass(Text.class);
wcjob.setMapOutputValueClass(LongWritable.class);

FileInputFormat.setInputPaths(wcjob, "/wc/inputdata/");
FileOutputFormat.setOutputPath(wcjob, new Path("/output/"));

wcjob.waitForCompletion(true);
}
}

生成输入数据：

[hadoop@hadoop01 ~]$ cat in.dat

haha lalala

hehe heiheihei

heiheihei lololo

lololo haha

haha haha

hehe lololo

在HDFS上创建相应路径：

[hadoop@hadoop01 ~]$ hadoop fs -mkdir -p /wc/inputdata

将in.dat文本文件上传到HDFS上的相应路径下：

[hadoop@hadoop01 ~]$ hadoop fs -put in.dat /wc/inputdata/

将上面的java程序打成jar包上传服务器，然后通过Hadoop调用：

hadoop jar mr.jar cn.hadoop.mr.WCRunner

[hadoop@hadoop01 ~]$ hadoop jar wc.jar cn.hadoop.mr.WCRunner
16/07/25 15:25:05 INFO client.RMProxy: Connecting to ResourceManager at hadoop01/192.168.56.200:8032
16/07/25 15:25:06 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
16/07/25 15:25:06 INFO input.FileInputFormat: Total input paths to process : 1
16/07/25 15:25:06 INFO mapreduce.JobSubmitter: number of splits:1
16/07/25 15:25:07 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1469431467769_0001
16/07/25 15:25:07 INFO impl.YarnClientImpl: Submitted application application_1469431467769_0001
16/07/25 15:25:07 INFO mapreduce.Job: The url to track the job: http://hadoop01:8088/proxy/application_1469431467769_0001/ 16/07/25 15:25:07 INFO mapreduce.Job: Running job: job_1469431467769_0001
16/07/25 15:25:16 INFO mapreduce.Job: Job job_1469431467769_0001 running in uber mode : false
16/07/25 15:25:16 INFO mapreduce.Job: map 0% reduce 0%
16/07/25 15:25:23 INFO mapreduce.Job: map 100% reduce 0%
16/07/25 15:25:30 INFO mapreduce.Job: map 100% reduce 100%
16/07/25 15:25:31 INFO mapreduce.Job: Job job_1469431467769_0001 completed successfully
16/07/25 15:25:31 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=204
FILE: Number of bytes written=211397
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=183
HDFS: Number of bytes written=44
HDFS: Number of read operations=6
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=4219
Total time spent by all reduces in occupied slots (ms)=4519
Total time spent by all map tasks (ms)=4219
Total time spent by all reduce tasks (ms)=4519
Total vcore-seconds taken by all map tasks=4219
Total vcore-seconds taken by all reduce tasks=4519
Total megabyte-seconds taken by all map tasks=4320256
Total megabyte-seconds taken by all reduce tasks=4627456
Map-Reduce Framework
Map input records=6
Map output records=12
Map output bytes=174
Map output materialized bytes=204
Input split bytes=105
Combine input records=0
Combine output records=0
Reduce input groups=5
Reduce shuffle bytes=204
Reduce input records=12
Reduce output records=5
Spilled Records=24
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=93
CPU time spent (ms)=1100
Physical memory (bytes) snapshot=348495872
Virtual memory (bytes) snapshot=1864597504
Total committed heap usage (bytes)=219480064
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=78
File Output Format Counters
Bytes Written=44


输出结果如下：

haha    4

hehe    2

heiheihei    2

lalala    1

lololo    3