java大数据hadoop2.9.2 Java编写Hadoop分析平均成绩

1、准备文件，例如score.txt，内容如下：

zs k1 88
ls k1 98
ww k1 78
zs k2 88
ls k2 98
ww k2 78
zs k3 88
ls k3 98
ww k3 78

2、创建maven项目

<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-common</artifactId>
			<version>2.9.2</version>
		</dependency>
		<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-client</artifactId>
			<version>${hadoop.version}</version>
		</dependency>
		<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-hdfs -->
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-hdfs</artifactId>
			<version>${hadoop.version}</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-mapreduce-client-core</artifactId>
			<version>${hadoop.version}</version>
		</dependency>

3、编写代码

（1）读取文件数据

package cn.com.dfs.score;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.util.LineReader;

public class ScoreRecoderReader extends RecordReader<Text, Text> {
	// 起始位置(相对整个分片而言)
	private long start;
	// 结束位置(相对整个分片而言)
	private long end;
	// 当前位置
	private long pos;
	// 文件输入流
	private FSDataInputStream fin = null;
	// key、value
	private Text key = null;
	private Text value = null;
	// 定义行阅读器(hadoop.util包下的类)
	private LineReader reader = null;

	@Override
	public void close() throws IOException {
		if (this.fin != null) {
			this.fin.close();
		}
	}

	@Override
	public Text getCurrentKey() throws IOException, InterruptedException {
		return this.key;
	}

	@Override
	public Text getCurrentValue() throws IOException, InterruptedException {
		return this.value;
	}

	@Override
	public float getProgress() throws IOException, InterruptedException {
		if (start == end) {
			return 0.0f;
		} else {
			return Math.min(1.0f, (pos - start) / (float) (end - start));
		}
	}

	@Override
	public void initialize(InputSplit split, TaskAttemptContext context) throws IOException, InterruptedException {
		// 获取分片
		FileSplit fileSplit = (FileSplit) split;
		// 获取起始位置
		start = fileSplit.getStart();
		// 获取结束位置
		end = start + fileSplit.getLength();
		// 创建配置
		Configuration conf = context.getConfiguration();
		// 获取文件路径
		Path path = fileSplit.getPath();
		// 根据路径获取文件系统
		FileSystem fileSystem = path.getFileSystem(conf);
		// 打开文件输入流
		fin = fileSystem.open(path);
		// 找到开始位置开始读取
		fin.seek(start);
		// 创建阅读器
		reader = new LineReader(fin);
		// 将当期位置置为1
		pos = this.start;
	}

	@Override
	public boolean nextKeyValue() throws IOException, InterruptedException {
		boolean bool = false;
		Text lineText = new Text();
		// 读取一行数据
		int count = this.reader.readLine(lineText);
		if(count != 0) {
			String line = lineText.toString();
			String[] content = line.split(" ");
			this.key = new Text(content[0]);
			this.value = new Text(content[1]+":"+content[2]);
			bool = true;
		}
		return bool;
	}

}

（2）格式化

public class ScoreInputFormat extends FileInputFormat<Text, Text> {

	@Override
	public RecordReader<Text, Text> createRecordReader(InputSplit arg0, TaskAttemptContext arg1)
			throws IOException, InterruptedException {
		return new ScoreRecoderReader();
	}

}

（3）mapper操作

package cn.com.dfs.score;

import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class ScoreMapper extends Mapper<Text, Text, Text, DoubleWritable> {
	// 从输入的value中获取分数
	// 输出key-名称 value-》分数
	@Override
	protected void map(Text key, Text value, Mapper<Text, Text, Text, DoubleWritable>.Context context)
			throws IOException, InterruptedException {
		String kmScore = value.toString();
		String[] content = kmScore.split(":");
		int score = Integer.parseInt(content[1]);

		context.write(key, new DoubleWritable(score));
	}

}

（4）reduce操作

package cn.com.dfs.score;

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

import java.io.IOException;

public class ScoreReduce extends Reducer<Text, DoubleWritable, Text, DoubleWritable> {

	@Override
	protected void reduce(Text arg0, Iterable<DoubleWritable> arg1,
			Reducer<Text, DoubleWritable, Text, DoubleWritable>.Context arg2) throws IOException, InterruptedException {
		double sum = 0;
		int count = 0;
		for(DoubleWritable num:arg1) {
			sum += num.get();
			count++;
		}
		sum = sum/count;
		arg2.write(arg0, new DoubleWritable(sum));
	}

}

（5）执行操作

package cn.com.dfs.score;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
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 ScoreDriver {

	public static void main(String[] args) throws Exception{
		Configuration conf = new Configuration();
		Job job = Job.getInstance(conf, "My WordCount Demo Job");
		
		job.setJarByClass(ScoreDriver.class);
		job.setInputFormatClass(ScoreInputFormat.class);
		job.setMapperClass(ScoreMapper.class);
		job.setReducerClass(ScoreReduce.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(DoubleWritable.class);
		
		FileInputFormat.addInputPath(job, new Path("/demo/score/input"));
		FileOutputFormat.setOutputPath(job, new Path("/demo/score/output"));
		
		System.exit(job.waitForCompletion(true)?0:1);
	}

}

这几个类编写完成后，执行maven打包操作

mvn install

4、上传文件

例如把打包的maven项目的jar和score.txt文件上传到自己的目录/usr/local/jar

cd /usr/local/jar

hdfs dfs -mkdir /demo/score/input

hdfs dfs -put ./score.txt /demo/score/input

下面的命令是执行jar包的类中的main函数

hadoop jar ./HadoopDfs-0.0.1-SNAPSHOT.jar cn.com.dfs.score.ScoreDriver

任务开始执行，执行完成如下

hdfs dfs -cat /demo/score/output/part-r-00000

part-r-00000任务执行完成生成的，会分析出每个学生的平均成绩