1. What is Hadoop framework?

Ans: Hadoop is an open source framework which is written in java by apache software foundation. This framework is used to write software application which requires to process vast amount of data (It could handle multi tera bytes of data). It works in-parallel on large clusters which could have 1000 of Computers (Nodes) on the clusters. It also process data very reliably and fault-tolerant manner. See the below image how does it looks.

2. On What concept the Hadoop framework works?

Ans: It works on MapReduce, and it is devised by the Google.

3. What is MapReduce?

Ans: Map reduces is an algorithm or concept to process Huge amount of data in a faster way. As per its name you can divide it Map and Reduce.

• The main MapReduce job usually splits the input data-set into independent chunks. (Big data sets in the multiple small datasets)

• Reduce Task: And the above output will be the input for the reduce tasks, produces the final result. Your business logic would be written in the Mapped Task and Reduced Task. Typically both the input and the output of the job are stored in a file-system (Not database). The framework takes care of  scheduling tasks, monitoring them and re-executes the failed tasks.

4. What is compute and Storage nodes?


  • Compute Node: This is the computer or machine where your actual business logic will be executed.
  • Storage Node: This is the computer or machine where your file system resides to store the processing  data. In most of the cases compute node and storage node would be the same machine.

5. How does master slave architecture in the Hadoop?

Ans: the MapReduce framework consists of a single master Job Tracker and multiple slaves, each cluster-node will have one Task Tracker.

The master is responsible for scheduling the jobs’ component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves execute the tasks as directed by the master.

6. How does a Hadoop application look like or their basic components?

Ans: Minimally a Hadoop application would have following components.

  • Input location of data.
  • Output location of processed data.
  • A map task.
  • A reduced task.
  • Job configuration.

The Hadoop job client then submits the job (jar/executable etc.) and configuration to the Job Tracker which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

7. Explain how input and output data format of the Hadoop framework?

Ans: The MapReduce framework operates exclusively on pairs, that is, the framework views the input to the job as a set of pairs and produces a set of pairs as the output of the job, conceivably of different types. See the flow mentioned below (input) -> map -> -> combine/sorting -> -> reduce -> (output)

8. What are the restriction to the key and value class?

Ans: The key and value classes have to be serialized by the framework. To make them serializable Hadoop provides a Writable interface. As you know from the java itself that the key of the Map should be comparable, hence the key has to implement one more interface Writable Comparable.

9. Explain the Word Count implementation via Hadoop framework?

Ans: We will count the words in all the input file flow as below
• Input
Assume there are two files each having a sentence Hello World Hello World (In file 1) Hello World

Hello World (In file 2)

• Mapper: There would be each mapper for the a file

For the given sample input the first map output:

< Hello, 1>

< World, 1>
< Hello, 1>
< World, 1>

The second map output:
< Hello, 1>
< World, 1>
< Hello, 1>
< World, 1>

• Combiner/Sorting (This is done for each individual map)

So output looks like this The output of the first map:

< Hello, 2>
< World, 2>

The output of the second map:

< Hello, 2>
< World, 2>

• Reducer:
• Output

It sums up the above output and generates the output as below

< Hello, 4>
< World, 4>

Final output would look like

Hello 4 times
World 4 times

10. Which interface needs to be implemented to create Mapper and Reducer for the Hadoop?

Ans: org.apache.hadoop.mapreduce.Mapper org.apache.hadoop.mapreduce.Reducer