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  • 7.Mapreduce实例——二次排序

    Mapreduce实例——二次排序

    实验步骤

    1.开启Hadoop

     

    2.新建mapreduce8目录

    在Linux本地新建/data/mapreduce8目录

     

    3. 上传文件到linux中

    (自行生成文本文件,放到个人指定文件夹下)

    goods_visit2

    1010037 100

    1010102 100

    1010152 97

    1010178 96

    1010280 104

    1010320 103

    1010510 104

    1010603 96

    1010637 97

    4.在HDFS中新建目录

    首先在HDFS上新建/mymapreduce8/in目录,然后将Linux本地/data/mapreduce8目录下的goods_visit2文件导入到HDFS的/mymapreduce8/in目录中。

    hadoop fs -mkdir -p /mymapreduce8/in

    hadoop fs -put /root/data/mapreduce8/goods_visit2 /mymapreduce8/in

     

    5.新建Java Project项目

    新建Java Project项目,项目名为mapreduce。

    在mapreduce项目下新建包,包名为mapreduce7。

    在mapreduce7包下新建类,类名为SecondarySort。

    6.添加项目所需依赖的jar包

    右键项目,新建一个文件夹,命名为:hadoop2lib,用于存放项目所需的jar包。

    将/data/mapreduce2目录下,hadoop2lib目录中的jar包,拷贝到eclipse中mapreduce2项目的hadoop2lib目录下。

    hadoop2lib为自己从网上下载的,并不是通过实验教程里的命令下载的

    选中所有项目hadoop2lib目录下所有jar包,并添加到Build Path中。

     

    7.编写程序代码

    SecondarySort.java

    package mapreduce7;
    
    import java.io.DataInput;
    import java.io.DataOutput;
    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.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.io.WritableComparable;
    import org.apache.hadoop.io.WritableComparator;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.Mapper;
    import org.apache.hadoop.mapreduce.Partitioner;
    import org.apache.hadoop.mapreduce.Reducer;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
    public class SecondarySort
    {
    
        public static class IntPair implements WritableComparable<IntPair>
        {
            int first;
            int second;
    
            public void set(int left, int right)
            {
                first = left;
                second = right;
            }
            public int getFirst()
            {
                return first;
            }
            public int getSecond()
            {
                return second;
            }
            @Override
    
            public void readFields(DataInput in) throws IOException
            {
                // TODO Auto-generated method stub
                first = in.readInt();
                second = in.readInt();
            }
            @Override
    
            public void write(DataOutput out) throws IOException
            {
                // TODO Auto-generated method stub
                out.writeInt(first);
                out.writeInt(second);
            }
            @Override
    
            public int compareTo(IntPair o)
            {
                // TODO Auto-generated method stub
                if (first != o.first)
                {
                    return first < o.first ? 1 : -1;
                }
                else if (second != o.second)
                {
                    return second < o.second ? -1 : 1;
                }
                else
                {
                    return 0;
                }
            }
            @Override
            public int hashCode()
            {
                return first * 157 + second;
            }
            @Override
            public boolean equals(Object right)
            {
                if (right == null)
                    return false;
                if (this == right)
                    return true;
                if (right instanceof IntPair)
                {
                    IntPair r = (IntPair) right;
                    return r.first == first && r.second == second;
                }
                else
                {
                    return false;
                }
            }
        }
    
        public static class FirstPartitioner extends Partitioner<IntPair, IntWritable>
        {
            @Override
            public int getPartition(IntPair key, IntWritable value,int numPartitions)
            {
                return Math.abs(key.getFirst() * 127) % numPartitions;
            }
        }
        public static class GroupingComparator extends WritableComparator
        {
            protected GroupingComparator()
            {
                super(IntPair.class, true);
            }
            @Override
            //Compare two WritableComparables.
            public int compare(WritableComparable w1, WritableComparable w2)
            {
                IntPair ip1 = (IntPair) w1;
                IntPair ip2 = (IntPair) w2;
                int l = ip1.getFirst();
                int r = ip2.getFirst();
                return l == r ? 0 : (l < r ? -1 : 1);
            }
        }
        public static class Map extends Mapper<LongWritable, Text, IntPair, IntWritable>
        {
            private final IntPair intkey = new IntPair();
            private final IntWritable intvalue = new IntWritable();
            public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException
            {
                String line = value.toString();
                StringTokenizer tokenizer = new StringTokenizer(line);
                int left = 0;
                int right = 0;
                if (tokenizer.hasMoreTokens())
                {
                    left = Integer.parseInt(tokenizer.nextToken());
                    if (tokenizer.hasMoreTokens())
                        right = Integer.parseInt(tokenizer.nextToken());
                    intkey.set(right, left);
                    intvalue.set(left);
                    context.write(intkey, intvalue);
                }
            }
        }
    
        public static class Reduce extends Reducer<IntPair, IntWritable, Text, IntWritable>
        {
            private final Text left = new Text();
            private static final Text SEPARATOR = new Text("------------------------------------------------");
    
            public void reduce(IntPair key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException
            {
                context.write(SEPARATOR, null);
                left.set(Integer.toString(key.getFirst()));
                System.out.println(left);
                for (IntWritable val : values)
                {
                    context.write(left, val);
                    //System.out.println(val);
                }
            }
        }
        public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException
        {
    
            Configuration conf = new Configuration();
            Job job = new Job(conf, "secondarysort");
            job.setJarByClass(SecondarySort.class);
            job.setMapperClass(Map.class);
            job.setReducerClass(Reduce.class);
            job.setPartitionerClass(FirstPartitioner.class);
    
            job.setGroupingComparatorClass(GroupingComparator.class);
            job.setMapOutputKeyClass(IntPair.class);
    
            job.setMapOutputValueClass(IntWritable.class);
    
            job.setOutputKeyClass(Text.class);
    
            job.setOutputValueClass(IntWritable.class);
    
            job.setInputFormatClass(TextInputFormat.class);
    
            job.setOutputFormatClass(TextOutputFormat.class);
            String[] otherArgs=new String[2];
            otherArgs[0]="hdfs://192.168.109.10:9000/mymapreduce8/in/goods_visit2";
            otherArgs[1]="hdfs://192.168.109.10:9000/mymapreduce8/out";
    
            FileInputFormat.setInputPaths(job, new Path(otherArgs[0]));
    
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        }
    }

    8.运行代码

    在SecondarySort类文件中,右键并点击=>Run As=>Run on Hadoop选项,将MapReduce任务提交到Hadoop中。

     

    9.查看实验结果

    待执行完毕后,进入命令模式下,在HDFS中/mymapreduce8/out查看实验结果。

    hadoop fs -ls /mymapreduce8/out  

    hadoop fs -cat /mymapreduce8/out/part-r-00000  

    图一为我的运行结果,图二为实验结果

    经过对比,发现结果一样

     

     

    此处为浏览器截图

     

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  • 原文地址:https://www.cnblogs.com/wangdayang/p/15582038.html
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