• MapReduce进行本地的单词统计


    1、处理输入文本为<k,v>对,继承Mapper方法

    package com.cr.hdfs;
    
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Mapper;
    
    import java.io.IOException;
    
    public class map extends Mapper<LongWritable,Text,Text,IntWritable> {
    
        /**
         * WordCountMapper 处理文本为<k,v>对
         * @param key 每一行字节数的偏移量
         * @param value 每一行的文本
         * @param context 上下文
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            Text keyOut = new Text();
            IntWritable valueout = new IntWritable();
            String[] arr = value.toString().split(" ");
            for(String s : arr){
                keyOut.set(s);
                valueout.set(1);
                context.write(keyOut,valueout);
            }
    
        }
    }
    

    2、对Mapper的输入进行reduce操作,继承reducer方法

    package com.cr.hdfs;
    
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Reducer;
    
    import java.io.IOException;
    
    public class reduce extends Reducer<Text,IntWritable,Text,IntWritable>{
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            int count = 0;
            for(IntWritable iw : values){
                count += iw.get();
            }
            context.write(key,new IntWritable(count));
    
        }
    }
    

    3、wordcountApp

    package com.cr.hdfs;
    
    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.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import java.io.IOException;
    
    /**
     * wordcount单词统计
     */
    public class wordcount {
        public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
            //单例作业
            Configuration conf = new Configuration();
    //        conf.set("fs.defaultFS","file:///");
            Job job = Job.getInstance(conf);
    
            //设置job的各种属性
            job.setJobName("wordcountAPP");                 //设置job名称
            job.setJarByClass(wordcount.class);              //设置搜索类
    //        job.setInputFormatClass(org.apache.hadoop.mapreduce.lib.input.TextInputFormat.class);  //设置输入格式
    
            FileInputFormat.addInputPath(job, new Path(args[0])); //添加输入路径
            FileOutputFormat.setOutputPath(job,new Path(args[1]));//设置输出路径
    
            job.setInputFormatClass(org.apache.hadoop.mapreduce.lib.input.TextInputFormat.class);
            job.setMapperClass(map.class);                   //设置mapper类
            job.setReducerClass(reduce.class);               //设置reduecer类
    
            job.setNumReduceTasks(1);                         //设置reduce个数
    
            job.setMapOutputKeyClass(Text.class);            //设置之map输出key
            job.setMapOutputValueClass(IntWritable.class);   //设置map输出value
            job.setOutputKeyClass(Text.class);               //设置mapreduce 输出key
            job.setOutputValueClass(IntWritable.class);      //设置mapreduce输出value
            job.waitForCompletion(true);
        }
    }
    

    4、设置参数输入路径和输出路径


    5、运行结果





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  • 原文地址:https://www.cnblogs.com/flyingcr/p/10326995.html
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