Explain Mapper code for Hadoop map reduce
user
the mapper code in a hadoop mapreduce program is responsible for processing input data and generating output key-value pairs. here's an example of a simple mapper code in java:
```java
import java.io.ioexception;
import java.util.stringtokenizer;

import org.apache.hadoop.io.intwritable;
import org.apache.hadoop.io.text;
import org.apache.hadoop.mapreduce.mapper;

public class simplemapper extends mapper<object, text, text, intwritable> {

    @override
    public void map(object key, text value, context context)
            throws ioexception, interruptedexception {

        string line = value.tostring();
        stringtokenizer tokenizer = new stringtokenizer(line, ",");

        while (tokenizer.hasmoretokens()) {
            string word = tokenizer.nexttoken();
            string country = tokenizer.nexttoken(); // in this example, we assume the country comes after the word

            text wordtext = new text(word);
            intwritable countryint = new intwritable(integer.parseint(country));
            context.write(wordtext, countryint);
        }
    }
}
```
this mapper code is designed to work with the following input format:

1. each line represents a word and its country (e.g., "word,country").
2. the word is the key, and the country is the value (in the form of an integer).

here's how the mapper code works:

1. the mapper takes an object (the input split) and a text (the line of text) as input.
2. it converts the input line into a string and breaks it down into words and countries using a `stringtokenizer`.
3. it creates a text object containing the word and an intwritable object containing the country's integer value.
4. finally, it writes the key-value pair (text, intwritable) to the output using the context's write() method.

this mapper code is a simple example and can be modified to handle more complex input data and operations, depending on the requirements of your hadoop mapreduce job.
assistant
The Mapper code in a Hadoop MapReduce program is responsible for processing input data and generating output key-value pairs. Here's an example of a simple Mapper code in Java:
```java
import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class SimpleMapper extends Mapper<Object, Text, Text, IntWritable> {

    @Override
    public void map(Object key, Text value, Context context)
            throws IOException, InterruptedException {

        String line = value.toString();
        StringTokenizer tokenizer = new StringTokenizer(line, ",");

        while (tokenizer.hasMoreTokens()) {
            String word = tokenizer.nextToken();
            String country = tokenizer.nextToken(); // In this example, we assume the country comes after the word

            Text wordText = new Text(word);
            IntWritable countryInt = new IntWritable(Integer.parseInt(country));
            context.write(wordText, countryInt);
        }
    }
}
```
This Mapper code is designed to work with the following input format:

1. Each line represents a word and its country (e.g., "word,country").
2. The word is the key, and the country is the value (in the form of an integer).

Here's how the Mapper code works:

1. The Mapper takes an Object (the input split) and a Text (the line of text) as input.
2. It converts the input line into a String and breaks it down into words and countries using a `StringTokenizer`.
3. It creates a Text object containing the word and an IntWritable object containing the country's integer value.
4. Finally, it writes the key-value pair (Text, IntWritable) to the output using the Context's write() method.

This Mapper code is a simple example and can be modified to handle more complex input data and operations, depending on the requirements of your Hadoop MapReduce job.
assistant