# 用eclipse建立java為基底的Apache Maven Project的MapReduce 應用程式 並在遠端Apache Hadoop上運行

***以下follow hadoop官網docs中的Map Reduce Tutorial
http://hadoop.apache.org/docs/r2.7.1/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html***

## 預備環境

JDK8
https://www.oracle.com/tw/java/technologies/javase/javase-jdk8-downloads.html

Eclipse IDE for Eclipse Committers
https://www.eclipse.org/downloads/packages/release/2020-06/r/eclipse-ide-eclipse-committers

Apache Maven 3.6.3
https://maven.apache.org/install.html

## 生成jar檔
Ecilpse > File > New > Other > Maven Project

選擇maven-archetype-quickstart > Next

填寫Group Id, Artifact Id > Finish

> **groupId**: uniquely identifies your project across all projects. A group ID should follow Java's package name rules. This means it starts with a reversed domain name you control. For example,
> org.apache.maven, org.apache.commons

> **artifactId:** is the name of the jar without version. If you created it, then you can choose whatever name you want with lowercase letters and no strange symbols. If it's a third party jar, you have to take the name of the jar as it's distributed.
> eg. maven, commons-math
>

建立一個新的class WordCount
在WordCount/pom.xml中配置以下dependency

	<dependencies>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-common</artifactId>
			<version>2.7.1</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-hdfs</artifactId>
			<version>2.7.1</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-mapreduce-client-core</artifactId>
			<version>2.7.1</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
			<version>2.7.1</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-mapreduce-client-common</artifactId>
			<version>2.7.1</version>
		</dependency>
	</dependencies>

> POM配置的版本內容等 可以以自己所需參考：
> https://mvnrepository.com/artifact/org.apache.hadoop

用maven update project後
eclipse自動load進上面那些dependency


確認有自動load進

創建一個class WordCount.java
引入官方文檔教學示範[Example: WordCount v1.0](http://hadoop.apache.org/docs/r2.7.1/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html)

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);
  }
}

Run As > Maven build

Goals: clean install > Run

console

cd到自己目錄的eclipse-workspace/WordCount/target中
可以看多到多了一個 **WordCount-0.0.1-SNAPSHOT.jar**
以上完成生成jar檔部分

## hadoop上執行jar檔

利用scp將local端jar檔案丟到remote端
> ***注意大寫-P***

scp -P _____ eclipse-workspace/WordCount/target/WordCount-0.0.1-SNAPSHOT.jar user@___.__.__.__:/user/___

透過ssh進入remote端

ssh -p _____ user@___.__.__.__
enter password

查看剛剛的路徑是否有成功傳入

hdfs dfs -ls /user/___

由於執行這隻WordCount需要兩個資料夾
先建立input directory

hdfs dfs -mkdir /input

加入測資

vim test1.txt(按下i進入vim編輯)
>Hello World Bye World(esc後:wq儲存)

vim test2.txt
>Hello Hadoop Goodbye Hadoop
:wq

將兩筆txt丟入input資料夾

hdfs dfs -put test1.txt /user/___/input
hdfs dfs -put test2.txt /user/___/input

檢查是否丟入

hdfs dfs -ls /user/___/input

result:

Found 2 items
-rw-rw-rw-   3 user user         22 2020-08-14 03:05 /user/___/input/test1.txt
-rw-rw-rw-   3 user user         28 2020-08-14 03:05 /user/___/input/test2.txt

最後步驟 執行jar檔 指定input output資料夾

hadoop jar WordCount-0.0.1-SNAPSHOT.jar com.WordCount.WordCount /user/___/input /user/___/output


結果輸出在output資料夾中 用cat去查看