MapReduce的map端join

用的都是旧的API,不知道怎么使用新的API

import java.io.IOException;

import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;


import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.KeyValueTextInputFormat;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.hadoop.mapred.join.CompositeInputFormat;
import org.apache.hadoop.mapred.join.TupleWritable;
import org.apache.hadoop.mapreduce.lib.input.KeyValueLineRecordReader;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


/**
 * map端合并数据
 * 
 * @author magina_lil
 * 
 *  * 1.准备文件
join1目录下放入文件内容
key0 value1_
key1 value1_a
key2 value1_b
key3 value1_c
key4 value1_d
join2目录下放入文件内容
key1 value2_a
key2 value2_b
key3 value2_c
key5 value2_e
 *
 *2.执行命令携带参数  hdfs://cdh1:8020/user/hdfs/join1 hdfs://cdh1:8020/user/hdfs/join2 hdfs://cdh1:8020/user/hdfs/joinoutput    前两个是输入目录，最后一个是输入目录
 *String dst = "hdfs://10.0.3.49:9000/mobile/data/20160621/join2.txt";
 *
 *3.输出结果：
  key0 value1_
key1 value1_a value2_a
key2 value1_b value2_b
key3 value1_c value2_c
key4 value1_d
key5 value2_e
 *
 * 
 */
public class TestJoin extends Configured implements Tool {


static int printUsage() {
System.out.println("join" + "[input]*

");
ToolRunner.printGenericCommandUsage(System.out);
return -1;
}


public static class MapClass extends MapReduceBase implements Mapper {


public void map(Text key, TupleWritable value, OutputCollector output, Reporter reporter)
throws IOException {
// key1    [value1_a,value2_a]
output.collect(key, value);
}
}


public static class ReduceClass extends MapReduceBase implements Reducer {
@Override
public void reduce(Text key, Iterator values, OutputCollector output,
Reporter reporter) throws IOException {
System.out.println(key.toString());
while (values.hasNext()) {
TupleWritable val = values.next();
Writable a = val.get(0);
System.out.println("val===0====" + a);
String av = "-";
if (a != null) {
av = a.toString();
}
Writable b = val.get(1);
System.out.println("val===1====" + b);
String bv = "-";
if (b != null) {
bv = b.toString();
}


output.collect(key, new Text(av + " " + bv));
}
}
}

public int run(String[] args) throws Exception {
JobConf jobConf = new JobConf(getConf(), TestJoin.class);
jobConf.setJobName("join");
jobConf.set(KeyValueLineRecordReader.KEY_VALUE_SEPERATOR, " "); // 使用空格分割


jobConf.setMapperClass(MapClass.class);
jobConf.setReducerClass(ReduceClass.class);


String op = "outer"; // inner outer override tbl 使用outer
List otherArgs = new ArrayList();
for (int i = 0; i < args.length; ++i) {
otherArgs.add(args[i]);
}


FileOutputFormat.setOutputPath(jobConf, new Path(otherArgs.remove(otherArgs.size() - 1)));
List plist = new ArrayList(otherArgs.size());
for (String s : otherArgs) {
plist.add(new Path(s));
}


jobConf.setInputFormat(CompositeInputFormat.class);
jobConf.set("mapred.join.expr",
CompositeInputFormat.compose(op, KeyValueTextInputFormat.class, plist.toArray(new Path[0]))); // 使用key value 读入
jobConf.setOutputFormat(TextOutputFormat.class);


jobConf.setOutputKeyClass(Text.class);
jobConf.setOutputValueClass(TupleWritable.class);


JobClient.runJob(jobConf);
return 0;
}


public static void main(String[] args) throws Exception {
String[] args0 = { "hdfs://10.0.3.49:9000/mobile/data/20160621/",
"hdfs://10.0.3.49:9000/mobile/data/20160622/",
"hdfs://10.0.3.49:9000/mobile/data/out" };
int res = ToolRunner.run(new Configuration(), new TestJoin(), args0);
System.exit(res);
}
}