怎样通过Java程序提交yarn的mapreduce计算任务
因为项目需求，须要通过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任务须要有点小变动。详见下面代码。
下面为MapReduce主程序，有几点须要提一下：
1、在程序中，我将文件读入格式设定为WholeFileInputFormat，即不正确文件进行切分。
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，又一次设定了分组函数。即GroupComparator。分组规则为，仅仅要TextPair中的key1同样（不要求key2同样），则数据被分配到一个reduce容器中。这样，当同样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。仅仅要两个TextPair的第一个key同样。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;
// 比較同样则返回0，比較不同则返回-1
return t1.getFirst().compareTo(t2.getFirst()); // 仅仅要是第一个字段同样的就分成为同一组
}
}
public boolean runJob(String[] args) throws IOException,
ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
// 在conf中设置outputath变量，以在reduce函数中能够获取到该參数的值
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目录。args数组的倒数第二个原数为质量文件所在目录
conf.set("qualityFolder", args[args.length - 2].toString());
//假设在Server中执行。则须要获取web项目的根路径；假设以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);
// 在分区之后依照指定的条件分组
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);
}
}
下面为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)
* 假设比較同样，则比較结果为0
* 假设A大于B，则比較结果为1
* 假设A小于B。则比較结果为-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);
}
}
下面为WholeFileInputFormat，其控制数据在mapreduce过程中不被切分
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;
}
}
下面为WholeFileRecordReader类
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;
}
}
以上是 怎样通过Java程序提交yarn的mapreduce计算任务 的全部内容， 来源链接： utcz.com/z/392087.html
