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MapReduce Program – Finding The Average Age of Male and Female Died in Titanic Disaster

  • Difficulty Level : Medium
  • Last Updated : 08 Oct, 2021

All of us are familiar with the disaster that happened on April 14, 1912. The big giant ship of 46000-ton in weight got sink-down to the depth of 13,000 feet in the North Atlantic Ocean. Our aim is to analyze the data obtained after this disaster. Hadoop MapReduce can be utilized to deal with this large datasets efficiently to find any solution for a particular problem.

Problem Statement: Analyzing the Titanic Disaster dataset, for finding the average age of male and female persons died in this disaster with MapReduce Hadoop. 

Step 1:

We can download the Titanic Dataset from this Link. Below is the column structure of our Titanic dataset. It consists of 12 columns where each row describes the information of a particular person. 

dataset-discription-of-titanic-dataset

Step 2:

The first 10 records of the dataset is shown below.



titanic-dataset-first-10-records

Step 3:

Make the project in Eclipse with below steps:

  • First Open Eclipse -> then select File -> New -> Java Project ->Name it Titanic_Data_Analysis -> then select use an execution environment -> choose JavaSE-1.8 then next -> Finish.

creating-titanic-data-analysis-project

  • In this Project Create Java class with name Average_age -> then click Finish

creating-average-age-java-class

  • Copy the below source code to this Average_age java class

Java




// import libraries
import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
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;
  
// Making a class with name Average_age
public class Average_age {
  
    public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
  
        // private text gender variable which
        // stores the gender of the person
        // who died in the Titanic Disaster
        private Text gender = new Text();
  
        // private IntWritable variable age will store
        // the age of the person for MapReduce. where
        // key is gender and value is age
        private IntWritable age = new IntWritable();
  
        // overriding map method(run for one time for each record in dataset)
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException
        {
  
            // storing the complete record
            // in a variable name line
            String line = value.toString();
  
            // splitting the line with ', ' as the
            // values are separated with this
            // delimiter
            String str[] = line.split(", ");
  
            /* checking for the condition where the
               number of columns in our dataset
               has to be more than 6. This helps in
               eliminating the ArrayIndexOutOfBoundsException
               when the data sometimes is incorrect
               in our dataset*/
            if (str.length > 6) {
  
                // storing the gender
                // which is in 5th column
                gender.set(str[4]);
  
                // checking the 2nd column value in
                // our dataset, if the person is
                // died then proceed.
                if ((str[1].equals("0"))) {
  
                    // checking for numeric data with
                    // the regular expression in this column
                    if (str[5].matches("\\d+")) {
  
                        // converting the numeric
                        // data to INT by typecasting
                        int i = Integer.parseInt(str[5]);
  
                        // storing the person of age
                        age.set(i);
                    }
                }
            }
            // writing key and value to the context
            // which will be output of our map phase
            context.write(gender, age);
        }
    }
  
    public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
  
        // overriding reduce method(runs each time for every key )
        public void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException
        {
  
            // declaring the variable sum which
            // will store the sum of ages of people
            int sum = 0;
  
            // Variable l keeps incrementing for
            // all the value of that key.
            int l = 0;
  
            // foreach loop
            for (IntWritable val : values) {
                l += 1;
                // storing and calculating
                // sum of values
                sum += val.get();
            }
            sum = sum / l;
            context.write(key, new IntWritable(sum));
        }
    }
  
    public static void main(String[] args) throws Exception
    {
        Configuration conf = new Configuration();
  
        @SuppressWarnings("deprecation")
        Job job = new Job(conf, "Averageage_survived");
        job.setJarByClass(Average_age.class);
  
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
         
        // job.setNumReduceTasks(0);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
  
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
  
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
  
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        Path out = new Path(args[1]);
        out.getFileSystem(conf).delete(out);
        job.waitForCompletion(true);
    }
}
  • Now we need to add external jar for the packages that we have import. Download the jar package Hadoop Common and Hadoop MapReduce Core according to your Hadoop version.
    Check Hadoop Version :
hadoop version

check-hadoop-version

  • Now we add these external jars to our Titanic_Data_Analysis project. Right Click on Titanic_Data_Analysis -> then select Build Path-> Click on Configure Build Path and select Add External jars…. and add jars from it’s download location then click -> Apply and Close.

adding-external-jar-files-to-our-project

  • Now export the project as jar file. Right-click on Titanic_Data_Analysis choose Export.. and go to Java -> JAR file click -> Next and choose your export destination then click -> Next. Choose Main Class as Average_age by clicking -> Browse and then click -> Finish -> Ok.

export-java-Titanic_Data_Analysis-projectselecting-main-class



Step 4:

Start Hadoop Daemons 

start-dfs.sh 
start-yarn.sh

Then, Check Running Hadoop Daemons. 

jps 

check-running-hadoop-daemons

Step 5:

Move your dataset to the Hadoop HDFS.

Syntax:  

hdfs dfs -put /file_path /destination

In below command / shows the root directory of our HDFS. 

hdfs dfs -put /home/dikshant/Documents/titanic_data.txt /

Check the file sent to our HDFS. 

hdfs dfs -ls /

putting-titanic-dataset-to-HDFS

Step 6:

Now Run your Jar File with below command and produce the output in Titanic_Output File.

Syntax: 

hadoop jar /jar_file_location /dataset_location_in_HDFS /output-file_name

Command:

hadoop jar /home/dikshant/Documents/Average_age.jar /titanic_data.txt /Titanic_Output 

running-the-average-age-jar-file

Step 7:

Now Move to localhost:50070/, under utilities select Browse the file system and download part-r-00000 in /MyOutput directory to see result.

Note: We can also view the result with below command 

hdfs dfs -cat /Titanic_Output/part-r-00000 

output

In the above image, we can see that the average age of the female is 28 and male is 30 according to our dataset who died in the Titanic Disaster.
 




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