由於項目需求，需要通過Java程序提交Yarn的MapReduce的計算任務。與一般的通過Jar包提交MapReduce任務不同，通過程序提交MapReduce任務需要有點小變動，詳見以下代碼。
以下為MapReduce主程序，有幾點需要提一下：
1、在程序中，我將文件讀入格式設定為WholeFileInputFormat，即不對文件進行切分。
2、為了控制reduce的處理過程，map的輸出鍵的格式為組合鍵格式。與常規的<key,value>不同，這裡變為了<TextPair,Value>，TextPair的格式為<key1,key2>。
3、為了適應組合鍵，重新設定了分組函數，即GroupComparator。分組規則為，只要TextPair中的key1相同（不要求key2相同），則數據被分配到一個reduce容器中。這樣，當相同key1的數據進入reduce容器後，key2起到了一個數據標識的作用。
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;
}
}
/**
* 分組設置類，只要兩個TextPair的第一個key相同，他們就屬於同一組。他們的Value就放到一個Value迭代器中，
* 然後進入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;
// 比較相同則返回0，比較不同則返回-1
return t1.getFirst().compareTo(t2.getFirst()); // 只要是第一個字段相同的就分成為同一組
}
}
public boolean runJob(String[] args) throws IOException,
ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
// 在conf中設置outputath變量，以在reduce函數中可以獲取到該參數的值
conf.set("outputPath", args[args.length - 1].toString());
//設置HDFS中，每次任務生成產品的質量文件所在文件夾。args數組的倒數第二個原數為質量文件所在文件夾
conf.set("qualityFolder", args[args.length - 2].toString());
//如果在Server中運行，則需要獲取web項目的根路徑；如果以java應用方式調試，則讀取/opt/hadoop-2.5.0/etc/hadoop/目錄下的配置文件
//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);
// 設置partition
job.setPartitionerClass(NamePartitioner.class);
// 在分區之後按照指定的條件分組
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);
// 設置計算輸入數據的路徑
for (int i = 1; i < args.length - 2; i++) {
FileInputFormat.addInputPath(job, new Path(args[i]));
}
// args數組的最後一個元素為輸出路徑
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);
}
}
以下為TextPair類
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)
* 如果比較相同，則比較結果為0
* 如果A大於B，則比較結果為1
* 如果A小於B，則比較結果為-1
*
*/
public int compareTo(TextPair tp) {
int cmp = first.compareTo(tp.first);
if (cmp != 0) {
return cmp;
}
//此時實現的是升序排列
return second.compareTo(tp.second);
}
}
以下為WholeFileInputFormat，其控制數據在mapreduce過程中不被切分
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;
}
}
以下為WholeFileRecordReader類
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;
}
}
Spark 顛覆 MapReduce 保持的排序記錄 http://www.linuxidc.com/Linux/2014-10/107909.htm
在 Oracle 數據庫中實現 MapReduce http://www.linuxidc.com/Linux/2014-10/107602.htm
MapReduce實現矩陣乘法--實現代碼 http://www.linuxidc.com/Linux/2014-09/106958.htm
基於MapReduce的圖算法 PDF http://www.linuxidc.com/Linux/2014-08/105692.htm
Hadoop的HDFS和MapReduce http://www.linuxidc.com/Linux/2014-08/105661.htm
MapReduce 計數器簡介 http://www.linuxidc.com/Linux/2014-08/105649.htm
Hadoop技術內幕：深入解析MapReduce架構設計與實現原理 PDF高清掃描版 http://www.linuxidc.com/Linux/2014-06/103576.htm
