去掉重复数据的原理：相同的Key会被同一个Reducer处理，因此在Reducer中直接输出这个Key即可。因为没有用到Value，所以Value的类型是NullWritable。

1.程序源码

//DistinctMapper.java
package demo.distinct;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class DistinctMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
  @Override
  protected void map(LongWritable key1, Text value1, Context context)
    throws IOException, InterruptedException {
    // 数据:Hadoop
    String data = value1.toString().trim();
    //直接将这个字符串作为Key2输出
    context.write(new Text(data), NullWritable.get());
  }
}

//DistinctReducer.java
package demo.distinct;
import java.io.IOException;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class DistinctReducer extends Reducer<Text, NullWritable, Text, NullWritable> {
  @Override
  protected void reduce(Text key3, Iterable<NullWritable> value3, Context context) 
    throws IOException, InterruptedException {
    //直接将key3作为key4输出
    context.write(key3, NullWritable.get());
  }
}

//DistinctMain.java
package demo.distinct;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class DistinctMain {
  public static void main(String[] args) throws Exception {
    //创建job
    Job job = Job.getInstance(new Configuration());
    //指定任务入口
    job.setJarByClass(DistinctMain.class);
    //指定任务的Mapper和输出类型
    job.setMapperClass(DistinctMapper.class);
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(NullWritable.class);
    //指定任务的Reducer和输出类型
    job.setReducerClass(DistinctReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(NullWritable.class);
    //指定输入和输出目录：HDFS路径
    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    //执行任务
    job.waitForCompletion(true);
  }
}

2.打包执行

待处理的重复数据：

# hdfs dfs -cat /input/strings.txt
HBase
Hive
Hadoop
Spark
Storm
Pig
ZooKeeper
HUE
MapReduce
HDFS
Kafka
Flume
HBase
HDFS

将程序打包成Distinct.jar，上传到服务器上执行：

# hadoop jar Distinct.jar /input/strings.txt /output/distinct
……
18/11/18 10:04:16 INFO mapreduce.Job: map 0% reduce 0%
18/11/18 10:04:23 INFO mapreduce.Job: map 100% reduce 0%
18/11/18 10:04:27 INFO mapreduce.Job: map 100% reduce 100%
18/11/18 10:04:29 INFO mapreduce.Job: Job job_1542506318955_0001 completed successfully
……

查看处理结果：

# hdfs dfs -ls /output/distinct
Found 2 items
-rw-r--r-- 1 root supergroup 0 2018-11-18 10:04 /output/distinct/_SUCCESS
-rw-r--r-- 1 root supergroup 75 2018-11-18 10:04 /output/distinct/part-r-00000

# hdfs dfs -cat /output/distinct/part-r-00000
Flume
HBase
HDFS
HUE
Hadoop
Hive
Kafka
MapReduce
Pig
Spark
Storm
ZooKeeper