1、數據準備：

Mike,35

Steven,40

Ken,28

Cindy,32

2、預期結果

Max　　40

Min　　 28

Avg 33

3、MapReduce代碼如下

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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.util.GenericOptionsParser;

public class AgeMapReduce {

    public
 
 static class WordCountMapper extends
            Mapper<Object, Text, Text, Text> {
        private Text nameKey = new Text();
        private Text ageValue = new Text();

        @Override
        public void map(Object key, Text value, Context context)
                throws IOException, InterruptedException {
            StringTokenizer itr 
 
= new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                String content = itr.nextToken();
                String[] nameAndAge = content.split(",");
                //String name = nameAndAge[0];
                String age = nameAndAge[1];
                nameKey.set("only you");
                ageValue.set(age);
                context.write(nameKey, ageValue);
            }
        }
    }

    public static class WordCountReduce extends Reducer<Text, Text, Text, Text> {
        private int min = Integer.MAX_VALUE;
        private int max = 0;
        private int sum = 0;
        private int count = 0;

        @Override
        public void reduce(Text key, Iterable<Text> values, Context context)
                throws IOException, InterruptedException {
            for (Text tmpAge : values) {
                int age = Integer.valueOf(tmpAge.toString());
                if (age < min) {
                    min = age;
                }
                if (age > max) {
                    max = age;
                }
                sum += age;
                count++;
            }
            //String resultStr = min + "\t" + max + "\t" + (sum / count);
            //result.set(resultStr);
            context.write(new Text("Max"), new Text(String.valueOf(min)));
            context.write(new Text("Min"), new Text(String.valueOf(max)));
            context.write(new Text("Avg"), new Text(String.valueOf(sum/count)));
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args)
                .getRemainingArgs();
        if (otherArgs.length != 2) {
            System.err.println("Usage: MinMaxCountDriver <in> <out>");
            System.exit(2);
        }
        Job job = new Job(conf, "StackOverflow Comment Date Min Max Count");
        job.setJarByClass(AgeMapReduce.class);
        job.setMapperClass(WordCountMapper.class);
        // job.setCombinerClass(MusicReduce.class);
        job.setReducerClass(WordCountReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        // user/joe/wordcount/input
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

        // user/joe/wordcount/output
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

4、註意事項

因為輸出的結果和Key沒有關系，所以在map階段要固定一個Key即可。

Hadoop實戰-MapReduce之max、min、avg統計(六)