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MapReduce Use Case: YouTube Data Analysis

 July 14  | 0 Comments

YouTube Data Analysis

This blog is about, how to perform YouTube data analysis in Hadoop MapReduce.
This YouTube data is publicly available and the YouTube data set is described below under the heading Data Set Description.
Using that dataset we will perform some Analysis and will draw out some insights like what are the top 10 rated videos on YouTube, who uploaded the most number of videos.
By reading this blog you will understand how to handle data sets that do not have proper structure and how to sort the output of reducer.


Column 1: Video id of 11 characters.
Column 2: uploader of the video

Column 3: Interval between the day of establishment of Youtube and the date of uploading of the video.

Column 4: Category of the video.

Column 5: Length of the video.

Column 6: Number of views for the video.

Column 7: Rating on the video.

Column 8: Number of ratings given for the video
Column 9: Number of comments done on the videos.

Column 10: Related video ids with the uploaded video.
You can download the data set from the below link.


Youtube Data set


Here we will find out what are the top 5 categories with maximum number of videos uploaded.


Now from the mapper, we want to get the video category as key and final int value ‘1’ as values which will be passed to the shuffle and sort phase and are further sent to the reducer phase where the aggregation of the values is performed.


public class Top5_categories {
   public static class Map extends Mapper<LongWritable, Text, Text, IntWritable>{
      private Text category = new Text();
      private final static IntWritable one = new IntWritable(1);
      public void map(LongWritable key, Text value, Context context )
        throws IOException, InterruptedException {
           String line = value.toString();
           String str[]=line.split("\t");
          if(str.length > 5){
      context.write(category, one);

Explanation of the above Mapper code:
In line 1 we are taking a class by name Top5_categories,
In line 2 we are extending the Mapper default class having the arguments keyIn as LongWritable and ValueIn as Text and KeyOut as Text and ValueOut as IntWritable.

In line 3 we are declaring a private Text variable ‘category’ which will store the category of videos in youtube

In line 4 we are declaring a private final static IntWritable variable ‘one’ which will be constant for every value. MapReduce deals with Key and Value pairs.Here we can set the key as gender and value as age.

In line 5 we are overriding the map method which will run one time for every line.

In line 7 we are storing the line in a string variable ‘line’

In line 8 we are splitting the line by using tab “\t” delimiter and storing the values in a String Array so that all the columns in a row are stored in the string array.

In line 9 we are taking a condition if we have the string array of length greater than 6 which means if the line or row has at least 7 columns then it will enter into the if condition and execute the code to eliminate the ArrayIndexOutOfBoundsException. 

In line 10 we are storing the category which is in the 4th column.

In line 12 we are writing the key and value into the context which will be the output of the map method.


public static class Reduce extends Reducer<Text, IntWritable,Text,IntWritable>{
   public void reduce(Text key, Iterable<IntWritable> values,Context context throws IOException, InterruptedException {
           int sum = 0;
           for (IntWritable val : values) {
               sum += val.get();
           context.write(key, new IntWritable(sum));

While coming to the Reducer code

line 1 extends the default Reducer class with arguments KeyIn as Text and ValueIn as IntWritable which are same as the outputs of the mapper class and KeyOut as Text and ValueOut as IntWritbale which will be final outputs of our MapReduce program.

In line 2 we are overriding the Reduce method which will run each time for every key.

In line 3 we are declaring an integer variable sum which will store the sum of all the values for each key.

In line 4 a for each loop is taken which will run each time for the values inside the “Iterable values” which are coming from the shuffle and sort phase after the mapper phase.

In line 5 we are storing and calculating the sum of the values.

In line 7 writes the respected key and the obtained sum as value to the context.



This two configuration classes are included in the main class whereas to clarify the Output key type of mapper and the output value type of the Mapper.
You can download the whole source code from the below link.


GitHub link for Problem statement 1


hadoop jar top5.jar /youtubedata.txt /top5_out

Here ‘hadoop’ specifies we are running a Hadoop command and jar specifies which type of application we are running and top5.jar is the jar file which we have created consisting of the above source code.

The path of the Input file in our case is root directory of hdfs denoted by /youtubedata.txt  and the output file location to store the output has been given as top5_out.

How to view output

hadoop fs -cat /top5_out/part-r-00000 | sort –n –k2 –r | head  –n5
Here ‘hadoop’ specifies that we are running a Hadoop command and dfs specifies that we are performing an operation related to Hadoop Distributed File System and ‘- cat’ is used to view the contents of a file and top5_out/part-r-00000 is the file where output is stored.
Part file containing the actual output is created by default by the TextInputFormat class of Hadoop.
Here sort –n –k2 –r | head –n5 brings you the top 5 categories with maximum number of videos uploaded.
Instead of writing a secondary sort after reducer we can simply use this command to get the required output.
Sort will sort the data, –n means sorting numerically, –k2 means second column,  –r is for recursive operation and head –n5 means to bring the first 5 values after sorting.


[acadgild@localhost -]hadoop jar top5.jar /youtubedata.txt /top5_out
15/10/22 11:06:45 WARN util.NativeCodeLoader: Unable to load native-hadoop libra ry for your platform... using builtin-java classes where applicable
15/10/22 11:06:48 INFO client.RMProxy: Connecting to ResourceManager at / :8032
15/10/22 11:06:49 WARN mapreduce.JobSubmitter: Hadoop command-line option parsin g not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
15/10/22 11:06:50 INFO input.FileInputFormat: Total input paths to process : 1
15/10/22 11:06:50 INFO mapreduce.JobSubmitter: number of splits:1
15/10/22 11:06:51 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_14 45504384269 0002
15/10/22 11:06:51 INFO impl.YarnClientlmpl: Submitted application application 14 45504384269_0002
15/10/22 11:06:52 INFO mapreduce.Job: The url to track the job: http://localhost .localdomain:8088/proxy/application 1445504384269_0002/
15/10/22 11:06:52 INFO mapreduce.Job: Running job: job 1445504384269_0002
15/10/22 11:07:05 INFO mapreduce.Job: Job job_1445504384269_0002 running in uber mode : false
15/10/22 11:07:05 INFO mapreduce.Job: map 0% reduce 0%
15/10/22 11:07:15 INFO mapreduce.Job: map 100% reduce 0%
15/10/22 11:07:27 INFO mapreduce.Job: map 100% reduce 100%
[acadgild@localhost —]$ hadoop fs -cat /top5_out/part-r-88000 1 sort -n -k2 -r 1 head -n5
15/18/22 13:22:06 WARN util.NativeCodeLoader: Unable to load native-hadoop Libra ry for your platform... using builtin-java classes where applicable
Entertainment 911
Music 870
Comedy 420
Sports 253
Education 65