MapReduce应用案例--简单排序

1. 设计思路

　　在MapReduce过程中自带有排序，可以使用这个默认的排序达到我们的目的。 MapReduce 是按照key值进行排序的，我们在Map过程中将读入的数据转化成IntWritable类型，然后作为Map的key值输出。 Reduce 阶段拿到的就是按照key值排序好的<key,value list>,将key值输出，并根据value list 中元素的个数决定key的输出次数。

2. 实现

　　2.1 程序代码

　　

package sort;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.output.FileOutputFormat;

public class SimpleSort {
    public static class Map extends
            Mapper<LongWritable, Text, IntWritable, IntWritable> {
        private IntWritable data;

        protected void map(LongWritable key, Text value, Context context)
                throws java.io.IOException, InterruptedException {
            data = new IntWritable();
            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 num = new IntWritable(1);
        protected void reduce(IntWritable key,
                java.lang.Iterable<IntWritable> values, Context output)
                throws java.io.IOException, InterruptedException {
            for ( IntWritable val : values){
                output.write(num, key);
                num = new IntWritable(num.get() + 1);
            }
        };
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf =  new Configuration();
        Job job = new Job(conf,"simple sort");

        job.setJarByClass(SimpleSort.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(IntWritable.class);

        FileInputFormat.addInputPath(job, new Path("/user/hadoop_admin/sortin"));
        FileOutputFormat.setOutputPath(job, new Path("/user/hadoop_admin/sortout"));

        System.exit((job.waitForCompletion(true) ? 0 : 1));
    }

}

　　2.2 测试结果

　　测试用例

　　file1

　　file2

　　

　　运行信息

// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
// :: WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
****hdfs://master:9000/user/hadoop_admin/sortin
// :: INFO input.FileInputFormat: Total input paths to process :
// :: WARN snappy.LoadSnappy: Snappy native library not loaded
// :: INFO mapred.JobClient: Running job: job_local_0001
// :: INFO mapred.Task:  Using ResourceCalculatorPlugin : null
// :: INFO mapred.MapTask: io.sort.mb =
// :: INFO mapred.MapTask: data buffer = /
// :: INFO mapred.MapTask: record buffer = /
// :: INFO mapred.MapTask: Starting flush of map output
// :: INFO mapred.MapTask: Finished spill
// :: INFO mapred.Task: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
// :: INFO mapred.JobClient:  map % reduce %
// :: INFO mapred.LocalJobRunner:
// :: INFO mapred.Task: Task 'attempt_local_0001_m_000000_0' done.
// :: INFO mapred.Task:  Using ResourceCalculatorPlugin : null
// :: INFO mapred.MapTask: io.sort.mb =
// :: INFO mapred.MapTask: data buffer = /
// :: INFO mapred.MapTask: record buffer = /
// :: INFO mapred.MapTask: Starting flush of map output
// :: INFO mapred.MapTask: Finished spill
// :: INFO mapred.Task: Task:attempt_local_0001_m_000001_0 is done. And is in the process of commiting
// :: INFO mapred.JobClient:  map % reduce %
// :: INFO mapred.LocalJobRunner:
// :: INFO mapred.Task: Task 'attempt_local_0001_m_000001_0' done.
// :: INFO mapred.Task:  Using ResourceCalculatorPlugin : null
// :: INFO mapred.LocalJobRunner:
// :: INFO mapred.Merger: Merging  sorted segments
// :: INFO mapred.Merger: Down to the last merge-pass, with  segments left of total size:  bytes
// :: INFO mapred.LocalJobRunner:
// :: INFO mapred.Task: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
// :: INFO mapred.LocalJobRunner:
// :: INFO mapred.Task: Task attempt_local_0001_r_000000_0 is allowed to commit now
// :: INFO output.FileOutputCommitter: Saved output of task 'attempt_local_0001_r_000000_0' to /user/hadoop_admin/sortout
// :: INFO mapred.LocalJobRunner: reduce > reduce
// :: INFO mapred.Task: Task 'attempt_local_0001_r_000000_0' done.
// :: INFO mapred.JobClient:  map % reduce %
// :: INFO mapred.JobClient: Job complete: job_local_0001
// :: INFO mapred.JobClient: Counters:
// :: INFO mapred.JobClient:   File Output Format Counters
// :: INFO mapred.JobClient:     Bytes Written=
// :: INFO mapred.JobClient:   FileSystemCounters
// :: INFO mapred.JobClient:     FILE_BYTES_READ=
// :: INFO mapred.JobClient:     HDFS_BYTES_READ=
// :: INFO mapred.JobClient:     FILE_BYTES_WRITTEN=
// :: INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=
// :: INFO mapred.JobClient:   File Input Format Counters
// :: INFO mapred.JobClient:     Bytes Read=
// :: INFO mapred.JobClient:   Map-Reduce Framework
// :: INFO mapred.JobClient:     Reduce input groups=
// :: INFO mapred.JobClient:     Map output materialized bytes=
// :: INFO mapred.JobClient:     Combine output records=
// :: INFO mapred.JobClient:     Map input records=
// :: INFO mapred.JobClient:     Reduce shuffle bytes=
// :: INFO mapred.JobClient:     Reduce output records=
// :: INFO mapred.JobClient:     Spilled Records=
// :: INFO mapred.JobClient:     Map output bytes=
// :: INFO mapred.JobClient:     Total committed heap usage (bytes)=
// :: INFO mapred.JobClient:     Combine input records=
// :: INFO mapred.JobClient:     Map output records=
// :: INFO mapred.JobClient:     SPLIT_RAW_BYTES=
// :: INFO mapred.JobClient:     Reduce input records=

　　结果