MapReduce應用案例--簡單排序

Amei1314發表於2016-04-11
排序

1. 設計思路

　　在MapReduce過程中自帶有排序，可以使用這個預設的排序達到我們的目的。 MapReduce 是按照key值進行排序的，我們在Map過程中將讀入的資料轉化成IntWritable型別，然後作為Map的key值輸出。 Reduce 階段拿到的就是按照key值排序好的<key,value list>,將key值輸出，並根據value list 中元素的個數決定key的輸出次數。

2. 實現

　　2.1 程式程式碼

　　

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 測試結果

　　測試用例

　　file1

2
3
1
89
34
21
67
35

　　file2

　　

38
29
1
23
49
16

　　執行資訊

16/04/11 10:09:00 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/04/11 10:09:00 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
****hdfs://master:9000/user/hadoop_admin/sortin
16/04/11 10:09:00 INFO input.FileInputFormat: Total input paths to process : 2
16/04/11 10:09:00 WARN snappy.LoadSnappy: Snappy native library not loaded
16/04/11 10:09:00 INFO mapred.JobClient: Running job: job_local_0001
16/04/11 10:09:00 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
16/04/11 10:09:00 INFO mapred.MapTask: io.sort.mb = 100
16/04/11 10:09:00 INFO mapred.MapTask: data buffer = 79691776/99614720
16/04/11 10:09:00 INFO mapred.MapTask: record buffer = 262144/327680
16/04/11 10:09:00 INFO mapred.MapTask: Starting flush of map output
16/04/11 10:09:00 INFO mapred.MapTask: Finished spill 0
16/04/11 10:09:00 INFO mapred.Task: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
16/04/11 10:09:01 INFO mapred.JobClient:  map 0% reduce 0%
16/04/11 10:09:03 INFO mapred.LocalJobRunner: 
16/04/11 10:09:03 INFO mapred.Task: Task 'attempt_local_0001_m_000000_0' done.
16/04/11 10:09:03 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
16/04/11 10:09:03 INFO mapred.MapTask: io.sort.mb = 100
16/04/11 10:09:03 INFO mapred.MapTask: data buffer = 79691776/99614720
16/04/11 10:09:03 INFO mapred.MapTask: record buffer = 262144/327680
16/04/11 10:09:03 INFO mapred.MapTask: Starting flush of map output
16/04/11 10:09:03 INFO mapred.MapTask: Finished spill 0
16/04/11 10:09:03 INFO mapred.Task: Task:attempt_local_0001_m_000001_0 is done. And is in the process of commiting
16/04/11 10:09:04 INFO mapred.JobClient:  map 100% reduce 0%
16/04/11 10:09:06 INFO mapred.LocalJobRunner: 
16/04/11 10:09:06 INFO mapred.Task: Task 'attempt_local_0001_m_000001_0' done.
16/04/11 10:09:06 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
16/04/11 10:09:06 INFO mapred.LocalJobRunner: 
16/04/11 10:09:06 INFO mapred.Merger: Merging 2 sorted segments
16/04/11 10:09:06 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 144 bytes
16/04/11 10:09:06 INFO mapred.LocalJobRunner: 
16/04/11 10:09:06 INFO mapred.Task: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
16/04/11 10:09:06 INFO mapred.LocalJobRunner: 
16/04/11 10:09:06 INFO mapred.Task: Task attempt_local_0001_r_000000_0 is allowed to commit now
16/04/11 10:09:06 INFO output.FileOutputCommitter: Saved output of task 'attempt_local_0001_r_000000_0' to /user/hadoop_admin/sortout
16/04/11 10:09:09 INFO mapred.LocalJobRunner: reduce > reduce
16/04/11 10:09:09 INFO mapred.Task: Task 'attempt_local_0001_r_000000_0' done.
16/04/11 10:09:10 INFO mapred.JobClient:  map 100% reduce 100%
16/04/11 10:09:10 INFO mapred.JobClient: Job complete: job_local_0001
16/04/11 10:09:10 INFO mapred.JobClient: Counters: 19
16/04/11 10:09:10 INFO mapred.JobClient:   File Output Format Counters 
16/04/11 10:09:10 INFO mapred.JobClient:     Bytes Written=71
16/04/11 10:09:10 INFO mapred.JobClient:   FileSystemCounters
16/04/11 10:09:10 INFO mapred.JobClient:     FILE_BYTES_READ=85835
16/04/11 10:09:10 INFO mapred.JobClient:     HDFS_BYTES_READ=97
16/04/11 10:09:10 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=239842
16/04/11 10:09:10 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=71
16/04/11 10:09:10 INFO mapred.JobClient:   File Input Format Counters 
16/04/11 10:09:10 INFO mapred.JobClient:     Bytes Read=38
16/04/11 10:09:10 INFO mapred.JobClient:   Map-Reduce Framework
16/04/11 10:09:10 INFO mapred.JobClient:     Reduce input groups=13
16/04/11 10:09:10 INFO mapred.JobClient:     Map output materialized bytes=152
16/04/11 10:09:10 INFO mapred.JobClient:     Combine output records=0
16/04/11 10:09:10 INFO mapred.JobClient:     Map input records=14
16/04/11 10:09:10 INFO mapred.JobClient:     Reduce shuffle bytes=0
16/04/11 10:09:10 INFO mapred.JobClient:     Reduce output records=14
16/04/11 10:09:10 INFO mapred.JobClient:     Spilled Records=28
16/04/11 10:09:10 INFO mapred.JobClient:     Map output bytes=112
16/04/11 10:09:10 INFO mapred.JobClient:     Total committed heap usage (bytes)=877854720
16/04/11 10:09:10 INFO mapred.JobClient:     Combine input records=0
16/04/11 10:09:10 INFO mapred.JobClient:     Map output records=14
16/04/11 10:09:10 INFO mapred.JobClient:     SPLIT_RAW_BYTES=230
16/04/11 10:09:10 INFO mapred.JobClient:     Reduce input records=14

　　結果

　　

1    1
2    1
3    2
4    3
5    16
6    21
7    23
8    29
9    34
10    35
11    38
12    49
13    67
14    89

 

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