本节主要内容：

MapReduce Helloworld实验：

运行wordcount单词计数案例，计算词语出现的次数

1.系统环境：

OS：CentOS Linux release 7.5.1804 (Core)

CPU：2核心

Memory：1GB

运行用户：root

JDK版本：1.8.0_252

Hadoop版本：cdh5.16.2

2.集群各节点角色规划为：

172.26.37.245 node1.hadoop.com---->namenode，zookeeper，journalnode，hadoop-hdfs-zkfc，resourcenode，historyserver

172.26.37.246 node2.hadoop.com---->datanode，zookeeper，journalnode，nodemanager，hadoop-client，mapreduce

172.26.37.247  node3.hadoop.com---->datanode，nodemanager，hadoop-client，mapreduce

172.26.37.248  node4.hadoop.com---->namenode，zookeeper，journalnode，hadoop-hdfs-zkfc

实验步骤

1.在HDFS文件系统上建立input文件夹(Node1节点)

       # sudo -u hdfs hadoop fs -mkdir -p /user/cloudera/wordcount/input

2.建立测试文本(Node1节点)

在resourcenode上创建一个空文件夹，并进入

       # cd /

       # echo "Hello World Bye World" > file0

       # echo "Hello Hadoop Goodbye Hadoop" > file1

将文件上传到hdfs中

       # sudo -u hdfs hadoop fs -put file* /user/cloudera/wordcount/input

3.编译WordCount.jave

       # vim WordCount.java

       插入以下内容：

package org.myorg;

import java.io.IOException;

import java.util.*;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.conf.*;

import org.apache.hadoop.io.*;

import org.apache.hadoop.mapred.*;

import org.apache.hadoop.util.*;

public class WordCount {

  public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {

    private final static IntWritable one = new IntWritable(1);

    private Text word = new Text();

    public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {

      String line = value.toString();

      StringTokenizer tokenizer = new StringTokenizer(line);

      while (tokenizer.hasMoreTokens()) {

        word.set(tokenizer.nextToken());

        output.collect(word, one);

      }

    }

  }

  public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {

    public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {

      int sum = 0;

      while (values.hasNext()) {

        sum += values.next().get();

      }

      output.collect(key, new IntWritable(sum));

    }

  }

  public static void main(String[] args) throws Exception {

    JobConf conf = new JobConf(WordCount.class);

    conf.setJobName("wordcount");

    conf.setOutputKeyClass(Text.class);

    conf.setOutputValueClass(IntWritable.class);

    conf.setMapperClass(Map.class);

    conf.setCombinerClass(Reduce.class);

    conf.setReducerClass(Reduce.class);

    conf.setInputFormat(TextInputFormat.class);

    conf.setOutputFormat(TextOutputFormat.class);

    FileInputFormat.setInputPaths(conf, new Path(args[0]));

    FileOutputFormat.setOutputPath(conf, new Path(args[1]));

    JobClient.runJob(conf);

  }

}

编译

       # mkdir wordcount_classes

       # javac -cp /usr/lib/hadoop/*:/usr/lib/hadoop/client-0.20/* -d wordcount_classes WordCount.java

成功的话没有任何回应，但是在 wordcount_classes 里面出现了org文件夹

       # ll wordcount_classes

total 0

drwxr-xr-x 3 root root 19 Jun 25 22:41 org

4.创建jar

       # jar -cvf wordcount.jar -C wordcount_classes/ .

added manifest

adding: org/(in = 0) (out= 0)(stored 0%)

adding: org/myorg/(in = 0) (out= 0)(stored 0%)

adding: org/myorg/WordCount$Map.class(in = 1938) (out= 797)(deflated 58%)

adding: org/myorg/WordCount$Reduce.class(in = 1611) (out= 647)(deflated 59%)

adding: org/myorg/WordCount.class(in = 1534) (out= 753)(deflated 50%)

把这个 wordcount.jar移动到 /data/

       # mv wordcount.jar /data/

因为hdfs用户的根目录是/var/lib/hadoop-hdfs，所以我们要cd到刚刚有jar文件的目录

       # cd /data

       # sudo -u hdfs  hadoop jar wordcount.jar org.myorg.WordCount /user/cloudera/wordcount/input /user/cloudera/wordcount/output

20/06/25 22:49:32 INFO client.RMProxy: Connecting to ResourceManager at node1.hadoop.com/172.26.37.245:8032

20/06/25 22:49:33 INFO client.RMProxy: Connecting to ResourceManager at node1.hadoop.com/172.26.37.245:8032

20/06/25 22:49:36 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.

20/06/25 22:49:37 INFO mapred.FileInputFormat: Total input paths to process : 2

20/06/25 22:49:37 INFO mapreduce.JobSubmitter: number of splits:3

20/06/25 22:49:38 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1593100696092_0001

20/06/25 22:49:41 INFO impl.YarnClientImpl: Submitted application application_1593100696092_0001

20/06/25 22:49:41 INFO mapreduce.Job: The url to track the job: http://node1.hadoop.com:8088/proxy/application_1593100696092_0001/

20/06/25 22:49:41 INFO mapreduce.Job: Running job: job_1593100696092_0001

20/06/25 22:50:42 INFO mapreduce.Job: Job job_1593100696092_0001 running in uber mode : false

20/06/25 22:50:42 INFO mapreduce.Job:  map 0% reduce 0%

20/06/25 22:52:28 INFO mapreduce.Job:  map 33% reduce 0%

20/06/25 22:52:50 INFO mapreduce.Job:  map 33% reduce 11%

20/06/25 22:53:19 INFO mapreduce.Job:  map 67% reduce 11%

20/06/25 22:53:22 INFO mapreduce.Job:  map 67% reduce 22%

20/06/25 22:53:46 INFO mapreduce.Job:  map 100% reduce 22%

20/06/25 22:53:48 INFO mapreduce.Job:  map 100% reduce 100%

20/06/25 22:53:50 INFO mapreduce.Job: Job job_1593100696092_0001 completed successfully

20/06/25 22:53:50 INFO mapreduce.Job: Counters: 50

        File System Counters

                FILE: Number of bytes read=79

                FILE: Number of bytes written=585283

                FILE: Number of read operations=0

                FILE: Number of large read operations=0

                FILE: Number of write operations=0

                HDFS: Number of bytes read=362

                HDFS: Number of bytes written=41

                HDFS: Number of read operations=12

                HDFS: Number of large read operations=0

                HDFS: Number of write operations=2

        Job Counters

                Killed map tasks=2

                Launched map tasks=5

                Launched reduce tasks=1

                Data-local map tasks=5

                Total time spent by all maps in occupied slots (ms)=448613

                Total time spent by all reduces in occupied slots (ms)=77135

                Total time spent by all map tasks (ms)=448613

                Total time spent by all reduce tasks (ms)=77135

                Total vcore-milliseconds taken by all map tasks=448613

                Total vcore-milliseconds taken by all reduce tasks=77135

                Total megabyte-milliseconds taken by all map tasks=459379712

                Total megabyte-milliseconds taken by all reduce tasks=78986240

        Map-Reduce Framework

                Map input records=2

                Map output records=8

                Map output bytes=82

                Map output materialized bytes=91

                Input split bytes=309

                Combine input records=8

                Combine output records=6

                Reduce input groups=5

                Reduce shuffle bytes=91

                Reduce input records=6

                Reduce output records=5

                Spilled Records=12

                Shuffled Maps =3

                Failed Shuffles=0

                Merged Map outputs=3

                GC time elapsed (ms)=2101

                CPU time spent (ms)=47590

                Physical memory (bytes) snapshot=672563200

                Virtual memory (bytes) snapshot=10217422848

                Total committed heap usage (bytes)=379858944

        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=53

        File Output Format Counters

                Bytes Written=41

5.查看结果

       # sudo -u hdfs hdfs dfs -cat /user/cloudera/wordcount/output/part-00000

Bye    1

Goodbye 1

Hadoop  2

Hello  2

World  2

6.删除结果

如果你想再运行一次教程就要先删除掉结果

       # sudo -u hdfs dfs -rm -r /user/cloudera/wordcount/output

7.JobHistory

http://172.26.37.245:19888/jobhistory/

可以看到执行过的任务

2020.06.25 22:49:39 EDT 2020.06.25 22:50:33 EDT 2020.06.25 22:53:47 EDT job_1593100696092_0001 wordcount hdfs root.hdfs SUCCEEDED 3 3 1 1