课程笔记（厦门大学林子雨编著的《大数据技术原理与应用（第3版）》）_厦门大学林子雨jar包

第一步：配置环境

执行DateDistinct.java的时候，需要在在Eclipse中创建项目的基础上添加以下的JAR包

（1）“/usr/local/hadoop/share/yarn/sources”目录下的所有JAR包；
（2）“/usr/local/hadoop/share/hadoop/yarn/lib”目录下的所有JAR包；
（3）“/usr/local/hadoop/share/hadoop/yarn”目录下的所有JAR包，但是，不包括目录，具体如下图所示。

第二步：上传文件

在本地中建立input文本文件

然后在基础链接上配置eclipse中的jar包以及开启hadoop，使用以下代码将input.txt上传

import org.apache.hadoop.conf.Configuration;
 
public class MergeFile {
 
    public static void main(String[] args) throws IOException {
        String localFilePath = "/home/hadoop/haha.txt";
        String hdfsFilePath = "/user/hadoop/input/haha.txt";
 
        Configuration conf = new Configuration();
        conf.set("fs.defaultFS", "hdfs://localhost:9000");
        FileSystem fs = FileSystem.get(conf);
 
        Path localPath = new Path(localFilePath);
        Path hdfsPath = new Path(hdfsFilePath);
 
        fs.copyFromLocalFile(localPath, hdfsPath);
        System.out.println("File uploaded to HDFS successfully!");
    }
}

然后可以在新建一个.java文件

使用一下代码查看路径/user/hadoop/input/内有哪些hdfs文件，是否上传成功

import org.apache.hadoop.conf.Configuration;
 
public class MergeFile2 {
 
    public static void main(String[] args) throws IOException {
        String hdfsFolderPath = "/user/hadoop/input/";
 
        Configuration conf = new Configuration();
        conf.set("fs.defaultFS", "hdfs://localhost:9000");
        FileSystem fs = FileSystem.get(conf);
 
        Path folderPath = new Path(hdfsFolderPath);
 
        FileStatus[] fileStatuses = fs.listStatus(folderPath);
        for (FileStatus file : fileStatuses) {
            System.out.println(file.getPath().getName());
        }
    }
}

第三步：运行DateDistinct.java

运行以下代码，可以实现数据去重以及展示结果

import java.io.IOException;
import java.util.StringTokenizer;
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.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;
import java.io.BufferedReader;
import java.io.InputStreamReader;
 
public class DateDistinct {
 
	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 {		
		//1.设置HDFS配置信息
		String namenode_ip = "localhost";
		String hdfs = "hdfs://localhost:9000";			
		Configuration conf = new Configuration();	//Hadoop配置类
		conf.set("fs.defaultFS", "hdfs://localhost:9000");
		conf.set("mapreduce.app-submission.cross-platform", "true");	//集群交叉提交
/*		conf.set("hadoop.job.user", "hadoop");
		conf.set("mapreduce.framework.name", "yarn");
		conf.set("mapreduce.jobtracker.address", namenode_ip + ":9001");
		conf.set("yarn.resourcemanager.hostname", namenode_ip);	
		conf.set("yarn.resourcemanager.resource-tracker.address", namenode_ip + ":8031");
		conf.set("yarn.resourcemtanager.address", namenode_ip + ":8032");
		conf.set("yarn.resourcemanager.admin.address", namenode_ip + ":8033");
		conf.set("yarn.resourcemanager.scheduler.address", namenode_ip + ":8034");
		conf.set("mapreduce.jobhistory.address", namenode_ip + ":10020"); */
		
		//2.设置MapReduce作业配置信息
		String jobName = "WordCount";					//定义作业名称
		Job job = Job.getInstance(conf, jobName);
		job.setJarByClass(DateDistinct.class);			//指定作业类
		job.setJar("/usr/local/hadoop/myapp/WordCount.jar");			//指定本地jar包
		job.setMapperClass(TokenizerMapper.class);
		job.setCombinerClass(IntSumReducer.class);		//指定Combiner类
		job.setReducerClass(IntSumReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		System.out.println("Hello, World!");
		//3.设置作业输入和输出路径
		String dataDir = "/user/hadoop/input";		//实验数据目录	
		String outputDir = "/user/hadoop/output";	//实验输出目录
		Path inPath = new Path(hdfs + dataDir);
		Path outPath = new Path(hdfs + outputDir);
		FileInputFormat.addInputPath(job, inPath);
		FileOutputFormat.setOutputPath(job, outPath);
		//如果输出目录已存在则删除
		FileSystem fs = FileSystem.get(conf);
		if(fs.exists(outPath)) {
			fs.delete(outPath, true);
		}
		
		//4.运行作业
		System.out.println("Job: " + jobName + " is running...");
		if(job.waitForCompletion(true)) {
			System.out.println("success!");
			Path resultPath = new Path(hdfs + outputDir + "/part-r-00000"); // 输出结果文件路径
			BufferedReader br = new BufferedReader(new InputStreamReader(fs.open(resultPath)));
			String line;
			while ((line = br.readLine()) != null) {
			    System.out.println(line);
			}
			br.close();
			System.exit(0);
		} else {
			System.out.println("failed!");
			System.exit(1);
		}
	}
 
}

成功结果如下