# Hadoop基本操作

sudo su
sbin/start-dfs.sh

開好hadoop

可用下面指令檢查:

jps

## YARN on a Single Node

編輯以下兩個檔案
etc/hadoop/mapred-site.xml:

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.application.classpath</name>
        <value>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*:$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*</value>
    </property>
</configuration>

etc/hadoop/yarn-site.xml:

<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.env-whitelist</name>
        <value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_HOME,PATH,LANG,TZ,HADOOP_MAPRED_HOME</value>
    </property>
</configuration>

Start ResourceManager daemon and NodeManager daemon:
(在hadoop那資料夾裡執行)

sbin/start-yarn.sh

成功就會在 http://localhost:8088/ 看到以下畫面

## Do MapReduce Jobs

建立WordCount.java

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 {
    Configuration conf = new Configuration();
    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);
  }
}

在hadoop-3.3.1/etc/hadoop/hadoop-env.sh 加入

export JAVA_HOME=/usr/java/default
export PATH=${JAVA_HOME}/bin:${PATH}
export HADOOP_CLASSPATH=${JAVA_HOME}/lib/tools.jar

CMD編譯java檔生成jar

bin/hadoop com.sun.tools.javac.Main WordCount.java
jar cf wc.jar WordCount*.class

執行

bin/hadoop fs -ls

看檔案
#### 若Permission Denied出現
如下圖

可以在前指令前都加上

sudo -u <supergroup前的名稱>

#### 若沒問題
應該不會有資料出現
先把系統結構創好(個人習慣)
|
└-user
*** └-<用戶名>
********** └-wordcount
*************** └-input(放input)
*************** └-ouput(不用創)

創資料夾指令

bin/hadoop fs -mkdir -p /user/<用戶名>/wordcount/input

接著在本機創兩個input檔案:
file01

Hello World Bye World

file02

Hello Hadoop Goodbye Hadoop

(vim file01 這樣就行，不用副檔名)

使用以下指令放到hdfs裡:

bin/hadoop fs -put <file01路徑>/file01 <剛建的系統路徑>/input
bin/hadoop fs -put <file02路徑>/file02 <剛建的系統路徑>/input

(後面的路徑以我為範例就是/user/<用戶名>/wordcount/input)
可以用:

bin/hadoop fs -ls /user/<用戶名>/wordcount/input

看有沒有檔案

有了的話就能執行了

bin/hadoop jar wc.jar WordCount <剛建的系統路徑>/input <剛建的系統路徑>output

然後就沒問題的話會看到map,reduce在跑
(對，只有0便100%)
如下圖

執行

bin/hadoop fs -ls /user/<用戶名>/wordcount/output

可以看到有_SUCCESS跟result

執行

bin/hadoop fs -cat /user/<用戶名>/wordcount/output/part-r-00000

就能看到結果了

順帶一提 http://localhost:8088/ 可看到JOB成功


用完關掉

sbin/stop-yarn.sh
sbin/stop-dfs.sh