hadoop sorted using MapReduce (linux cloud server)

hadoop achieve the most basic numerical order, and is always sort multiple files.
Configuration:
System: Ubuntu 16.04
the Java: 1.8.0_191
hadoop: 1.2.1
precondition is configured hadoop environment variables and start.
Input Terminal:

jps

If at first and then the following process hadoop start successfully .

A, MapReduce implementation process

Second, explain the sorting algorithm

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.IntWritable;

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.Partitioner;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

public class Sort {

	public static class Map extends
			Mapper<Object, Text, IntWritable, IntWritable> {

		private static IntWritable data = new IntWritable();

		public void map(Object key, Text value, Context context)
				throws IOException, InterruptedException {
			String line = value.toString();

			data.set(Integer.parseInt(line));

			context.write(data, new IntWritable(1));

		}

	}

	public static class Reduce extends
			Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {

		private static IntWritable linenum = new IntWritable(1);

		public void reduce(IntWritable key, Iterable<IntWritable> values,
				Context context) throws IOException, InterruptedException {

			for (IntWritable val : values) {

				context.write(linenum, key);

				linenum = new IntWritable(linenum.get() + 1);
			}

		}
	}

	public static class Partition extends Partitioner<IntWritable, IntWritable> {

		@Override
		public int getPartition(IntWritable key, IntWritable value,
				int numPartitions) {
			int MaxNumber = 65223;
			int bound = MaxNumber / numPartitions + 1;
			int keynumber = key.get();
			for (int i = 0; i < numPartitions; i++) {
				if (keynumber < bound * i && keynumber >= bound * (i - 1))
					return i - 1;
			}
			return 0;
		}
	}

	/**
	 * @param args
	 */

	public static void main(String[] args) throws Exception {
		// TODO Auto-generated method stub
		Configuration conf = new Configuration();
		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();
		if (otherArgs.length != 2) {
			System.err.println("Usage WordCount <int> <out>");
			System.exit(2);
		}
		Job job = new Job(conf, "Sort");
		job.setJarByClass(Sort.class);
		job.setMapperClass(Map.class);
		job.setPartitionerClass(Partition.class);
		job.setReducerClass(Reduce.class);
		job.setOutputKeyClass(IntWritable.class);
		job.setOutputValueClass(IntWritable.class);
		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
		FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}

}

Third, specific operation

1. Create a project directory

cd /usr/projects/hadoopExamples
mkdir numbersort

2, written Sort.java file and compile (javac)

The above java code written into the copy and save Sort.java;

vi Sort.java

Create a directory to store number_sort_class compiled .class files;

mkdir number_sort_class

Compile Sort.java file and compiled .class files stored in the number_sort_class folder;
/opt/hadoop-1.2.1 for my hadoop installation directory.

javac -classpath /opt/hadoop-1.2.1/hadoop-core-1.2.1.jar:/opt/hadoop-1.2.1/lib/commons-cli-1.2.jar -d number_sort_class/ Sort.java

3, the compiled .class file packaging

* On behalf of all the .class files,
packaged good jar named numberSort.jar;

cd number_sort_class
jar -cvf numberSort.jar *.class

result:

4. Create an input file

Back to the parent directory, create input directory, and create three files, which each store multiple numbers.
E.g:

cd ..
mkdir input
cd input
vi number1
vi number2
vi number3
cd ..

5, upload the input file to the hadoop

You must first create a folder to upload,
the next input all the files are uploaded to input_numersort under hadoop in;

hadoop fs -mkdir input_numbersort
hadoop fs -put input/* input_numersort/

View uploaded files;

hadoop fs  -ls

Results:

see specific file directory:

hadoop fs -ls input_numbersort

Results:

It was our number three input file folder!

View file details:

hadoop fs -cat input_numbersort/number2

result:

6, the package and run the jar synthesized output

Road King jar package is number_sort_class / numberSort.jar,
the Sort java file name,
input_numbersort input folder,
out_numbersort (automatically created) for the output file;

hadoop jar number_sort_class/numberSort.jar Sort input_numbersort out_numbersort

Result:

you can see the first map to reach 100%, reduce began execution.

hadoop file viewing at:

hadoop fs -ls

Structure, we found more than a out_numbersort, the output is just out of

view out_numbersort catalog:

hadoop fs -ls out_numbersort

Results:

Results in the part-r-00000.

7 View Results

View Order results.

hadoop fs -cat out_numbersort/part-r-00000

Results:

here the use of MapReduce hadoop sort has been completed, after learning of more complex applications hadoop helpful.
Uploaded to github https://github.com/NH4L/hadoopSort/tree/master