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();
}