package duogemap;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class JoinJob {

public static final String DELIMITER = “\u0009”;

public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
//多路径判断
if (args.length < 2) {
System.out.println(“参数数量不对，至少两个以上参数：<数据文件输出路径>、<输入路径…>”);
System.exit(1);
}
//输出结果路径
String dataOutput = args[0];
//多个路输入径
String[] inputs = new String[args.length – 1];
System.arraycopy(args, 1, inputs, 0, inputs.length);

Configuration conf = new Configuration();
Job job = Job.getInstance(conf, “join 测试”);
job.setJarByClass(JoinJob.class);
job.setMapperClass(MyMapper.class);
job.setReducerClass(MyReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);

//将输出路径和输入路径放入Path中
Path[] inputPathes = new Path[inputs.length];
for (int i = 0; i < inputs.length; i++) {
inputPathes[i] = new Path(inputs[i]);
}
Path outputPath = new Path(dataOutput);
FileInputFormat.setInputPaths(job, inputPathes);
FileOutputFormat.setOutputPath(job, outputPath);
job.waitForCompletion(true);
}

static class MyMapper extends Mapper<LongWritable, Text, Text, Text> {
private String inputPath;
private String fileCode = “”;

protected void setup(Context context) throws IOException, InterruptedException {
// 每个文件传进来时获得文件中属性前缀
FileSplit input = (FileSplit) context.getInputSplit();
inputPath = input.getPath().getName();
try {
//获得文件名
fileCode = inputPath.split(“_”)[0];
} catch (Exception e) {
e.printStackTrace();
}
}

@Override
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] values = value.toString().split(DELIMITER);
StringBuffer sb = new StringBuffer();
//将文件名拼接到value中，做reduce的判断标识
sb.append(fileCode + “#”);
boolean first = true;
for (String v : values) {
if (!first) {
sb.append(v + DELIMITER);
}
first = false;
}
context.write(new Text(values[0]), new Text(sb.toString().substring(0, sb.toString().length() – 1)));

}
}

static class MyReducer extends Reducer<Text, Text, Text, Text> {
@Override
public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
List<String> left = new ArrayList<String>();
List<String> right = new ArrayList<String>();
for (Text value : values) {
String[] vv = value.toString().split(“#”);
String fileCode = vv[0];
if (fileCode.equals(“A.txt”)) {
// 左表数据
left.add(vv[1]);
} else {
// 右表数据
right.add(vv[1]);
}
}

//只有当left和right都有数据是才会遍历
for (String l : left) {
for (String r : right) {
context.write(new Text(key), new Text(l + DELIMITER + r));
}
}
}
}
}
//首先准备数据：
//
//假设我们有2张表：
//
//表A（左表）数据：
//
//1 a
//2 b
//3 c
//3 d
//4 e
//6 f
//表B（右表）数据：
//1 10
//2 20
//3 30
//4 40
//4 400
//5 50
//我们需要得到的结果是：
//1 a 10
//2 b 20
//3 c 30
//3 d 30
//4 e 40
//4 e 400