Encodermappreduce notes

1、MapReduce Definition

 Cut the data into three pieces , Then calculate and process these data separately （Map）,
 After processing, it is sent to a machine for merging （merge）,
 Then calculate the merged data , inductive （reduce） And the output .

Java Contained in the 3 A function ：
map Split the dataset
reduce Processing data
job Object to run MapReduce Homework ,

2、MapReduce Statistics in two text files , The number of times each word appears

First, we create two files in the current directory ：

establish file01 Input content ：
Hello World Bye World
establish file02 Input content ：
Hello Hadoop Goodbye Hadoop
Upload files to HDFS Of /usr/input/ Under the table of contents ：
Don't forget to start DFS：
start-dfs.sh

public class WordCount {
      
//Mapper class  
/* Because the file has the number of lines by default ,LongWritable Is used to accept the number of lines in the file ,  first Text It is used to accept the contents of the file ,  the second Text Is used to output to Reduce Class key, IntWritable Is used to output to Reduce Class value*/  
 public static class TokenizerMapper   
       extends Mapper<LongWritable, Text, Text, IntWritable>{
      
    private final static IntWritable one = new IntWritable(1);  
    private Text word = new Text();  
    public void map(LongWritable key, Text value, Context context  
                    ) throws IOException, InterruptedException {
      
      StringTokenizer itr = new StringTokenizer(value.toString());  
      while (itr.hasMoreTokens()) {
      
        word.set(itr.nextToken());  
        context.write(word, one);  
      }  
    }  
  }  
  public static class IntSumReducer   
       extends Reducer<Text,IntWritable,Text,IntWritable> {
      
    private IntWritable result = new IntWritable();  
    public void reduce(Text key, Iterable<IntWritable> values,   
                       Context context  
                       ) throws IOException, InterruptedException {
      
      int sum = 0;  
      for (IntWritable val : values) {
      
        sum += val.get();  
      }  
      result.set(sum);  
      context.write(key, result);  
    }  
  }  
  public static void main(String[] args) throws Exception {
      
    // Creating configuration objects  
    Configuration conf = new Configuration();  
    // establish job object  
    Job job = new Job(conf, "word count");  
    // Set up run job Class  
    job.setJarByClass(WordCount.class);  
    // Set up Mapper Class  
    job.setMapperClass(TokenizerMapper.class);  
    // Set up Reduce Class  
    job.setReducerClass(IntSumReducer.class);  
    // Set output key value Format  
    job.setOutputKeyClass(Text.class);  
    job.setOutputValueClass(IntWritable.class);  
    // Set the input path  
    String inputfile = "/usr/input";  
    // Set output path  
    String outputFile = "/usr/output";  
    // Perform input  
    FileInputFormat.addInputPath(job, new Path(inputfile));  
    // Execution output  
    FileOutputFormat.setOutputPath(job, new Path(outputFile));  
    // Whether it runs successfully or not ,true Output 0,false Output 1 
    System.exit(job.waitForCompletion(true) ? 0 : 1);  
  }  
}

hadoop Of MapReduce And hdfs Be sure to start it first start-dfs.sh

3、 use MapReduce Calculate the best grades of each student in the class

import java.io.IOException;
import java.util.StringTokenizer;
 
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.*;
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;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {
      
    /********** Begin **********/  
    public static class TokenizerMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
      
        private final static IntWritable one = new IntWritable(1);  
        private Text word = new Text();  
        private int maxValue = 0;  
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
      
            StringTokenizer itr = new StringTokenizer(value.toString(),"\n");  
            while (itr.hasMoreTokens()) {
      
                String[] str = itr.nextToken().split(" ");  
                String name = str[0];  
                one.set(Integer.parseInt(str[1]));  
                word.set(name);  
                context.write(word,one);  
            }  
            //context.write(word,one); 
        }  
    }
    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
      
        private IntWritable result = new IntWritable();
        public void reduce(Text key, Iterable<IntWritable> values, Context context)  
                throws IOException, InterruptedException {
      
            **int maxAge = 0;  
            int age = 0;  
            for (IntWritable intWritable : values) {
      
                maxAge = Math.max(maxAge, intWritable.get());  
            }  
            result.set(maxAge);**  
            context.write(key, result);  
        }  
    }
    public static void main(String[] args) throws Exception {
      
        Configuration conf = new Configuration();  
        Job job = new Job(conf, "word count");  
        job.setJarByClass(WordCount.class);  
        job.setMapperClass(TokenizerMapper.class);  
        job.setCombinerClass(IntSumReducer.class);  
        job.setReducerClass(IntSumReducer.class);  
        job.setOutputKeyClass(Text.class);  
        job.setOutputValueClass(IntWritable.class);  
        String inputfile = "/user/test/input";  
        String outputFile = "/user/test/output/";  
        FileInputFormat.addInputPath(job, new Path(inputfile));  
        FileOutputFormat.setOutputPath(job, new Path(outputFile));  
        job.waitForCompletion(true);  
    /********** End **********/  
    }  
}

4、 MapReduce The contents of the document are merged and duplicated

import java.io.IOException;
import java.util.*;  
import org.apache.hadoop.conf.Configuration;  
import org.apache.hadoop.fs.Path;  
import org.apache.hadoop.io.*;  
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;  
import org.apache.hadoop.util.GenericOptionsParser;  
public class Merge {
      
    /** * @param args *  Yes A,B Merge two files , And get rid of the repetition , Get a new output file C */  
    // Reload here map function , Directly input the value Copy to output data key On   Pay attention to map Method to throw an exception ：throws IOException,InterruptedException 
    /********** Begin **********/  
    public static class Map extends Mapper<LongWritable, Text, Text, Text >  
    {
      
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)  
                throws IOException, InterruptedException {
      
            String str = value.toString();  
            String[] data = str.split(" ");  
            Text t1= new Text(data[0]);  
            Text t2 = new Text(data[1]);  
            context.write(t1,t2);  
        }  
    }   
    /********** End **********/  
    // Reload here reduce function , Directly input the key Copy to output data key On   Pay attention to reduce Throw an exception on the method ：throws IOException,InterruptedException 
    /********** Begin **********/  
    public static class Reduce  extends Reducer<Text, Text, Text, Text>  
    {
      
        protected void reduce(Text key, Iterable<Text> values, Reducer<Text, Text, Text, Text>.Context context)  
                throws IOException, InterruptedException {
      
            List<String> list = new ArrayList<>();  
            for (Text text : values) {
      
                String str = text.toString();  
                if(!list.contains(str)){
      
                    list.add(str);  
                }  
            }  
            Collections.sort(list);  
            for (String text : list) {
      
                context.write(key, new Text(text));  
            }  
        }  
    /********** End **********/  
    }  
    public static void main(String[] args) throws Exception{
      
        Configuration conf = new Configuration();  
         Job job = new Job(conf, "word count");  
        job.setJarByClass(Merge.class);  
        job.setMapperClass(Map.class);  
        job.setCombinerClass(Reduce.class);  
        job.setReducerClass(Reduce.class);  
        job.setOutputKeyClass(Text.class);  
        job.setOutputValueClass(Text.class);  
        String inputPath = "/user/tmp/input/";  // Set the input path here  
        String outputPath = "/user/tmp/output/";  // Set the output path here  
        FileInputFormat.addInputPath(job, new Path(inputPath));  
        FileOutputFormat.setOutputPath(job, new Path(outputPath));  
        System.exit(job.waitForCompletion(true) ? 0 : 1);  
    }  
}