MapReduce Partitioner

Implementation of MapReduce Partitioner

Suppose we have a small table called Student that has the following information. This sample information will be used as our input dataset to show how the Partitioner works.

We have to develop an application to analyze the input dataset in order to determine the student with the highest marks by gender in several student_age categories (for example, under 18, between 19 and 30, and above 30).

The algorithmic explanation of the Partitioner is given below based on the above input.

Input Data

In the context of MapReduce, input data refers to the dataset that needs to be processed using the MapReduce paradigm.

When discussing the implementation of a MapReduce partitioner specifically, the input data typically refers to the key-value pairs generated by the mapper function. In a MapReduce job, the input data is divided into chunks, and each chunk is processed by a mapper function. The mapper function generates intermediate key-value pairs based on the input data it receives.

Map Task

The map task accepts key-value pairs as input, and we have text data in a text file. The following is the input for this map task:

Input

A pattern like "any special key + filename + line number" would be the key. For example, key = @input1. The data in that line would be the value. For example, 1 \t Harry \t 12 \t Male \t 85.
 

Method

The following is how this map task works: 

• In a string, read the value (record data) that comes as an input value from the argument list.
   
• Separate the student_gender and save it in a string variable using the split method.
   

String[] str_array = value.toString().split("\t", -3);
String gender=str_array[3];

Send the student_gender information and the record data value from the map job to the partition task as an output key-value pair. 

context.write(new Text(student_gender), new Text(value));

Repeat the previous steps for each record in the text file.
 

Output

The gender data and the record data value will be returned as key-value pairs.

Partitioner Task

The partitioner task takes as input the key-value pairs from the map task. Partitioning data means separating it into segments. The input key-value paired data can be separated into three sections based on the student_age criteria using the given conditional criteria of partitions.

Input

The entire dataset is represented as a set of key-value pairs.

           key = student_gender field value in the record.

           value = Whole record data value of that gender.
 

Method

The process of partition logic runs as follows. 

• Read the student_age field value from the key-value pair input.
   

String[] str = value.toString().split("\t");
int age = Integer.parseInt(str[2]);

• Check the student_age value under the following conditions.
  
  
  1. student_age less than or equal to 18
      
  2. student_age greater than 18 and less than or equal to 30.
      
  3. student_age greater than 30.
      

if (student_age <= 18) {
    return 0;
} else if (student_age > 18 && student_age <= 30) {
    return 1 % numReduceTasks;
} else {
    return 2 % numReduceTasks;
}

Output

The entire key-value pair data set is divided into three sets of key-value pairs. The Reducer operates on each collection separately.

Reduce Task

The number of partitioner tasks equals the number of reducer tasks. We have three partitioner tasks and three Reducer tasks to run.

Input

The Reducer will run three times with a different set of key-value pairs each time. 

           key: It stores the student_gender field value in the record.

           value: It stores the whole record data of that gender.
 

Method

On each collection, the following logic will be applied: 

• Read the student_marks field value of each record.
   

String [] str = val.toString().split("\t", -3);
Note: str[4] has the student_marks field value.

• Check the sudent_marks with the maximum variable. If str[4] is the maximum student_marks, set it to the max; otherwise, skip this step.
   

if (Integer.parseInt(str[4]) > max) {
    max = Integer.parseInt(str[4]);
}

• For each key collection (Male & Female are the key collections), repeat Steps 1 and 2. You will discover one maximum salary from the Male key collection and one maximum salary from the Female key collection after completing these three stages.
   

context.write(new Text(key), new IntWritable(max));

Output

Finally, you will receive a set of key-value pair data in three collections of varying ages. It includes the maximum salary from the Male collection and the maximum salary from the Female collection in each age group. After the completion of the Map, Partitioner, and Reduce processes, the three collections of key-value pair data are saved in three distinct files as the output.

Also check out - Phases of Compiler

Frequently Asked Questions

What is MapReduce used for?

MapReduce is used for parallel processing of large datasets, dividing tasks into map (data processing) and reduce (aggregation) phases for efficient analysis.

What is a partitioner?

A partitioner in MapReduce distributes intermediate key-value pairs generated by mappers to reducers, ensuring a balanced workload and efficient processing.

What is the difference between partitioner and combiner in MapReduce?

A partitioner distributes data to reducers, while a combiner is an optional optimization in MapReduce that aggregates intermediate data locally on mapper nodes.

Conclusion

This article explains the concepts of the MapReduce Partitioner, its needs, and its simplified implementation, along with some frequently asked questions related to the topic. Hope this article was beneficial and you learned something new. To have a better understanding of the topic, you can further refer to MapReduce Fundamentals and Hadoop MapReduce.

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