如何通过Java程序提交yarn的mapreduce计算任务

标签：程序提交hadoop任务   组合键   分组   分区   数据切分   

    由于项目需求，需要通过Java程序提交Yarn的MapReduce的计算任务。与一般的通过Jar包提交MapReduce任务不同，通过程序提交MapReduce任务需要有点小变动，详见以下代码。

    以下为MapReduce主程序，有几点需要提一下：

    1、在程序中，我将文件读入格式设定为WholeFileInputFormat，即不对文件进行切分。

    2、为了控制reduce的处理过程，map的输出键的格式为组合键格式。与常规的<key,value>不同，这里变为了<TextPair,Value>，TextPair的格式为<key1,key2>。

    3、为了适应组合键，重新设定了分组函数，即GroupComparator。分组规则为，只要TextPair中的key1相同（不要求key2相同），则数据被分配到一个reduce容器中。这样，当相同key1的数据进入reduce容器后，key2起到了一个数据标识的作用。

package web.hadoop;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.JobStatus;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.NullOutputFormat;

import util.Utils;

public class GEMIMain {
	
	public GEMIMain(){
		job = null;
	}
	
	public Job job;
	public static class NamePartitioner extends
			Partitioner<TextPair, BytesWritable> {
		@Override
		public int getPartition(TextPair key, BytesWritable value,
				int numPartitions) {
			return Math.abs(key.getFirst().hashCode() * 127) % numPartitions;
		}
	}

	/**
	 * 分组设置类，只要两个TextPair的第一个key相同，他们就属于同一组。他们的Value就放到一个Value迭代器中，
	 * 然后进入Reducer的reduce方法中。
	 * 
	 * @author hduser
	 * 
	 */
	public static class GroupComparator extends WritableComparator {
		public GroupComparator() {
			super(TextPair.class, true);
		}

		@Override
		public int compare(WritableComparable a, WritableComparable b) {
			TextPair t1 = (TextPair) a;
			TextPair t2 = (TextPair) b;
			// 比较相同则返回0，比较不同则返回-1
			return t1.getFirst().compareTo(t2.getFirst()); // 只要是第一个字段相同的就分成为同一组
		}
	}
	
	
	public  boolean runJob(String[] args) throws IOException,
			ClassNotFoundException, InterruptedException {
		
		Configuration conf = new Configuration();
		// 在conf中设置outputath变量，以在reduce函数中可以获取到该参数的值
		conf.set("outputPath", args[args.length - 1].toString());
		//设置HDFS中，每次任务生成产品的质量文件所在文件夹。args数组的倒数第二个原数为质量文件所在文件夹
		conf.set("qualityFolder", args[args.length - 2].toString());
		//如果在Server中运行，则需要获取web项目的根路径；如果以java应用方式调试，则读取/opt/hadoop-2.5.0/etc/hadoop/目录下的配置文件
		//MapReduceProgress mprogress = new MapReduceProgress();
		//String rootPath= mprogress.rootPath;
		String rootPath="/opt/hadoop-2.5.0/etc/hadoop/";
		conf.addResource(new Path(rootPath+"yarn-site.xml"));
		conf.addResource(new Path(rootPath+"core-site.xml"));
		conf.addResource(new Path(rootPath+"hdfs-site.xml"));
		conf.addResource(new Path(rootPath+"mapred-site.xml"));
		this.job = new Job(conf);
		
		job.setJobName("Job name:" + args[0]);
		job.setJarByClass(GEMIMain.class);

		job.setMapperClass(GEMIMapper.class);
		job.setMapOutputKeyClass(TextPair.class);
		job.setMapOutputValueClass(BytesWritable.class);
		// 设置partition
		job.setPartitionerClass(NamePartitioner.class);
		// 在分区之后按照指定的条件分组
		job.setGroupingComparatorClass(GroupComparator.class);

		job.setReducerClass(GEMIReducer.class);

		job.setInputFormatClass(WholeFileInputFormat.class);
		job.setOutputFormatClass(NullOutputFormat.class);
		// job.setOutputKeyClass(NullWritable.class);
		// job.setOutputValueClass(Text.class);
		job.setNumReduceTasks(8);
		
		
		// 设置计算输入数据的路径
		for (int i = 1; i < args.length - 2; i++) {
			FileInputFormat.addInputPath(job, new Path(args[i]));
		}
		// args数组的最后一个元素为输出路径
		FileOutputFormat.setOutputPath(job, new Path(args[args.length - 1]));
		boolean flag = job.waitForCompletion(true);
		return flag;
	}
	
	@SuppressWarnings("static-access")
	public static void main(String[] args) throws ClassNotFoundException,
			IOException, InterruptedException {	
		
		String[] inputPaths = new String[] { "normalizeJob",
				"hdfs://192.168.168.101:9000/user/hduser/red1/",
				"hdfs://192.168.168.101:9000/user/hduser/nir1/","quality11111",
				"hdfs://192.168.168.101:9000/user/hduser/test" };
		GEMIMain test = new GEMIMain();
		boolean result = test.runJob(inputPaths);       	
	}
}

以下为TextPair类

public class TextPair implements WritableComparable<TextPair> {
	private Text first;
	private Text second;

	public TextPair() {
		set(new Text(), new Text());
	}

	public TextPair(String first, String second) {
		set(new Text(first), new Text(second));
	}

	public TextPair(Text first, Text second) {
		set(first, second);
	}

	public void set(Text first, Text second) {
		this.first = first;
		this.second = second;
	}

	public Text getFirst() {
		return first;
	}

	public Text getSecond() {
		return second;
	}

	@Override
	public void write(DataOutput out) throws IOException {
		first.write(out);
		second.write(out);
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		first.readFields(in);
		second.readFields(in);
	}

	@Override
	public int hashCode() {
		return first.hashCode() * 163 + second.hashCode();
	}

	@Override
	public boolean equals(Object o) {
		if (o instanceof TextPair) {
			TextPair tp = (TextPair) o;
			return first.equals(tp.first) && second.equals(tp.second);
		}
		return false;
	}

	@Override
	public String toString() {
		return first + "\t" + second;
	}
	
	@Override
	/**A.compareTo(B)
	 * 如果比较相同，则比较结果为0
	 * 如果A大于B，则比较结果为1
	 * 如果A小于B，则比较结果为-1
	 * 
	 */
	public int compareTo(TextPair tp) {
		int cmp = first.compareTo(tp.first);
		if (cmp != 0) {
			return cmp;
		}
		//此时实现的是升序排列
		return second.compareTo(tp.second);
	}
}

package web.hadoop;

import java.io.IOException;  

import org.apache.hadoop.fs.Path;  
import org.apache.hadoop.io.BytesWritable;  
import org.apache.hadoop.io.Text;  
import org.apache.hadoop.mapreduce.InputSplit;  
import org.apache.hadoop.mapreduce.JobContext;  
import org.apache.hadoop.mapreduce.RecordReader;  
import org.apache.hadoop.mapreduce.TaskAttemptContext;  
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

public class WholeFileInputFormat extends FileInputFormat<Text, BytesWritable> {  
	  
    @Override  
    public RecordReader<Text, BytesWritable> createRecordReader(  
            InputSplit arg0, TaskAttemptContext arg1) throws IOException,  
            InterruptedException {  
        // TODO Auto-generated method stub  
        return new WholeFileRecordReader();  
    }  
  
    @Override  
    protected boolean isSplitable(JobContext context, Path filename) {  
        // TODO Auto-generated method stub  
        return false;  
    }  
}  

package web.hadoop;

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.BytesWritable;
import org.apache.hadoop.io.IOUtils;
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;

public class WholeFileRecordReader extends RecordReader<Text, BytesWritable> {

	private FileSplit fileSplit;
	private FSDataInputStream fis;

	private Text key = null;
	private BytesWritable value = null;

	private boolean processed = false;

	@Override
	public void close() throws IOException {
		// TODO Auto-generated method stub
		// fis.close();
	}

	@Override
	public Text getCurrentKey() throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		return this.key;
	}

	@Override
	public BytesWritable getCurrentValue() throws IOException,
			InterruptedException {
		// TODO Auto-generated method stub
		return this.value;
	}

	@Override
	public void initialize(InputSplit inputSplit, TaskAttemptContext tacontext)
			throws IOException, InterruptedException {

		fileSplit = (FileSplit) inputSplit;
		Configuration job = tacontext.getConfiguration();
		Path file = fileSplit.getPath();
		FileSystem fs = file.getFileSystem(job);
		fis = fs.open(file);
	}

	@Override
	public boolean nextKeyValue() {

		if (key == null) {
			key = new Text();
		}

		if (value == null) {
			value = new BytesWritable();
		}

		if (!processed) {
			byte[] content = new byte[(int) fileSplit.getLength()];

			Path file = fileSplit.getPath();

			System.out.println(file.getName());
			key.set(file.getName());

			try {
				IOUtils.readFully(fis, content, 0, content.length);
				// value.set(content, 0, content.length);
				value.set(new BytesWritable(content));
			} catch (IOException e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
			} finally {
				IOUtils.closeStream(fis);
			}

			processed = true;
			return true;
		}

		return false;
	}

	@Override
	public float getProgress() throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		return processed ? fileSplit.getLength() : 0;
	}

}

如何通过Java程序提交yarn的mapreduce计算任务

标签：程序提交hadoop任务   组合键   分组   分区   数据切分   

原文地址：http://blog.csdn.net/until_v/article/details/40867973