MapReduce当中自定义输出：多文件输出MultipleOutputs

自定义输出：多文件输出MultipleOutputs
马克- to-win：马克 java社区：防盗版实名手机尾号： 73203。

马克-to-win @ 马克java社区：对于刚才的单独订单topN的问题，如果需要把单独的订单id的记录放在自己的一个文件中，并以订单id命名。怎么办？multipleOutputs可以帮我们解决这个问题。注意：和我们本章开始讲的多文件输出不一样的是，这里的多文件输出还可以跟程序的业务逻辑绑定在一起，比如文件的名字和订单有关系且把此个订单的最大值放在文件当中。
 
multipleOutputs的用法可以见底下的程序。

package com;
import java.io.File;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
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.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;
public class MultipleOutputsTest {

    public static class TokenizerMapper extends Mapper<Object, Text, Text, DoubleWritable> {
        /*
o1abc,p2,250.0
o2kkk,p3,500.0
o2kkk,p4,100.0
o2kkk,p5,700.0
o3mmm,p1,150.0
o1abc,p1,200.0
         */
        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            System.out.println("key is " + key.toString() + " value is " + value.toString());
            String line = value.toString();
            String[] fields = line.split(",");
            String orderId = fields[0];
            double amount = Double.parseDouble(fields[2]);
            DoubleWritable amountDouble = new DoubleWritable(amount);
            context.write(new Text(orderId), amountDouble);
        }
    }

    public static class IntSumReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable> {
      
        DoubleWritable resultDouble = new DoubleWritable(0.0);

        private MultipleOutputs<Text, DoubleWritable> multipleOutputs;

        protected void setup(Context context) throws IOException, InterruptedException {
            multipleOutputs = new MultipleOutputs<Text, DoubleWritable>(context);
        }

        public void reduce(Text key, Iterable<DoubleWritable> values, Context context)
                throws IOException, InterruptedException {
            System.out.println("reduce key is " + key.toString());
            double max = Double.MIN_VALUE;
            for (DoubleWritable v2 : values) {
                if (v2.get() > max) {
                    max = v2.get();
                }
            }
            resultDouble.set(max);
            String fileName = key.toString().substring(0, 4);
            System.out.println("fileName is "+ fileName);
/* multipleOutputs用于map输出时采用：name-m-nnnnn 形式的文件名，用于reduce输出时采用name-r-nnnnn 形式的文件名，其中 name 是由程序设定的任意名字， nnnnn 是一个指明块号的整数（从 0 开始）。块号保证在相同名字name情况下不会冲突
void MultipleOutputs.write(Text key, DoubleWritable value, String baseOutputPath)


Write key 和 value to an output file name.
Parameters:key：the key。value： the value。 baseOutputPath： base-output path to write the record to. */          
            multipleOutputs.write(key, resultDouble, fileName);
        //    context.write(key, resultDouble);
        }
      
        protected void cleanup(Context context) throws IOException ,InterruptedException{
             multipleOutputs.close();
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = new Job(conf, "word count");
        job.setJarByClass(MultipleOutputsTest.class);
        job.setMapperClass(TokenizerMapper.class);
        // job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);
        File file = new File("e:/temp/output");
        if (file.exists() && file.isDirectory()) {
            deleteFile(file);
        }
        FileInputFormat.setInputPaths(job, new Path("e:/temp/input/multipleOutput.txt"));
        FileOutputFormat.setOutputPath(job, new Path("e:/temp/output"));
        System.out.println("mytest hadoop successful");
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

    public static boolean deleteFile(File dirFile) {
        if (!dirFile.exists()) {
            return false;
        }
        if (dirFile.isFile()) {
            return dirFile.delete();
        } else { /* 空目录就不进入for循环了， 进入到下一句dirFile.delete(); */
            for (File file : dirFile.listFiles()) {
                deleteFile(file);
            }
        }
        return dirFile.delete();
    }

}

输入文件是：

o1abc,p2,250.0
o2kkk,p3,500.0
o2kkk,p4,100.0
o2kkk,p5,700.0
o3mmm,p1,150.0
o1abc,p1,200.0

输出文件是：























console的输出是： reduce key is 马克-to-win @ 马克java社区：o1abc name is o1ab INFO - File Output Committer Algorithm version is 1 reduce key is 马克-to-win @ 马克java社区：o2kkk name is o2kk INFO - File Output Committer Algorithm version is 1 reduce key is 马克-to-win @ 马克java社区：o3mmm name is o3mm o1ab-r-00000这个文件的输出是： o1abc 250.0