5. Implement MapReduce program on window side to complete wordcount function

Test text data used by the program ：

Dear River
Dear River Bear Spark 
Car Dear Car Bear Car
Dear Car River Car 
Spark Spark Dear Spark 

1 Write the main class

（1）Maper class

First of all, it's custom Maper Class code

public class WordCountMap extends Mapper<LongWritable, Text, Text, IntWritable> {
    public void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        //fields: Data representing a line of text : dear bear river
        String[] words = value.toString().split("\t");
        for (String word : words) {
            //  Every word appears １ Time , Output as intermediate result 
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

      This Map Class is a generic type , It has four parameter types , Assign separately map() The input key of the function 、 The input values 、 Type of output key and output value .LongWritable： Input key type ,Text： Input value type ,Text: Output key type ,IntWritable： Output value type .
     String[] words = value.toString().split("\t");,words The value of is Dear River Bear River
      Input key key Is a long integer offset , Used to find the data of the first row and the data of the next row , The input value is a line of text Dear River Bear River, The output key is the word Bear , The output value is an integer 1.
     Hadoop It provides a set of basic types that can optimize network serialization transmission , Instead of using Java Embedded type . These types are all in org.apache.hadoop.io In bag . Use here LongWritable type ( amount to Java Of Long type )、Text type ( amount to Java Medium String type ) and IntWritable type ( amount to Java Of Integer type ).
     map() The parameters of the method are the input key and the input value . Take this procedure for example , Input key LongWritable key It's an offset , The input values Text value yes Dear Car Bear Car , We'll first include the... With one line of input Text Value to Java Of String type , Then use substring() Method to extract the columns we are interested in .map() The method also provides Context Instance is used for writing output content .

（2）Reducer class

public class WordCountReduce extends Reducer<Text, IntWritable, Text, IntWritable> {
    /*
        (River, 1)
        (River, 1)
        (River, 1)
        (Spark , 1)
        (Spark , 1)
        (Spark , 1)
        (Spark , 1)
        
        key: River
        value: List(1, 1, 1)
        key: Spark
        value: List(1, 1, 1,1)
        
    */
    public void reduce(Text key, Iterable<IntWritable> values,
                          Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable count : values) {
            sum += count.get();
        }
        context.write(key, new IntWritable(sum));//  Output final results 
    };
}

Reduce The task starts with the partition number Map The end grabs data as ：
(River, 1)
(River, 1)
(River, 1)
(spark, 1)
(Spark , 1)
(Spark , 1)
(Spark , 1)
The result after treatment is ：
key: hello value: List(1, 1, 1)
key: spark value: List(1, 1, 1,1)
therefore reduce() The formal parameter of the function Iterable<IntWritable> values The received value is List(1, 1, 1) and List(1, 1, 1,1)

（3）Main function

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;

public class WordCountMain {
    // If in IDEA Middle local execution MR Program , Need to put mapred-site.xml Medium mapreduce.framework.name Value changed to local
    public static void main(String[] args) throws IOException,
            ClassNotFoundException, InterruptedException {
        if (args.length != 2 || args == null) {
            System.out.println("please input Path!");
            System.exit(0);
        }
        //System.setProperty("HADOOP_USER_NAME","hadoop2.7");
        Configuration configuration = new Configuration();
        //configuration.set("mapreduce.job.jar","/home/bruce/project/kkbhdp01/target/com.kaikeba.hadoop-1.0-SNAPSHOT.jar");
        // call getInstance Method , Generate job example 
        Job job = Job.getInstance(configuration, WordCountMain.class.getSimpleName());
        //  hit jar package 
        job.setJarByClass(WordCountMain.class);

        //  adopt job Set input / Output format 
        // MR The default input format for is TextInputFormat, So the next two lines can be commented out 
        // job.setInputFormatClass(TextInputFormat.class);
        // job.setOutputFormatClass(TextOutputFormat.class);
        //  Set input / The output path 
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        //  Set up processing Map/Reduce Stage class 
        job.setMapperClass(WordCountMap.class);
        //map combine Reduce the amount of Internet traffic 
        job.setCombinerClass(WordCountReduce.class);
        job.setReducerClass(WordCountReduce.class);

        // If map、reduce Output. kv Same for type , Set up directly reduce Output. kv That's right ; If it's not the same , It needs to be set separately map, reduce Of          Output kv type 
        //job.setMapOutputKeyClass(.class)
        // job.setMapOutputKeyClass(Text.class);
        // job.setMapOutputValueClass(IntWritable.class);

        //  Set up reduce task Final output key/value The type of 
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        //  Submit the assignment 
        job.waitForCompletion(true);

    }
}

2 Run locally

First change mapred-site.xml File configuration
take mapreduce.framework.name Is set to local

Then run locally ：

View results ：

3 Cluster operation

Mode one ：

Pack first

Changing configuration files , Change to yarn Pattern

Add local jar Bag location ：

 Configuration configuration = new Configuration();
 configuration.set("mapreduce.job.jar","C:\\Users\\tanglei1\\IdeaProjects\\Hadooptang\\target");

Set to allow cross platform remote calls ：

configuration.set("mapreduce.app-submission.cross-platform","true");

Modify input parameters ：

Running results ：

Mode two ：

take maven Project package , Run on the server side with commands mr Program

hadoop jar com.kaikeba.hadoop-1.0-SNAPSHOT.jar
com.kaikeba.hadoop.wordcount.WordCountMain /tttt.txt  /wordcount11