MapReduce Interview Questions

In this post we will look at MapReduce Interview questions. Examples are provided with explanation.

1. What is MapReduce?
2. What are the advantages of MapReduce?
3. What are the components of MapReduce?
4. What is Big Data?
5. What are Algorithm?
6. What is JobContext Interface in MapReduce?
7. What is JobTracker in MapReduce?
8. What is Identity Mapper?
9. What is a MapReduce Combiner?
10. What are the parameters of Mappers and Reducers?
11. What is Partition?
12. What are Master Nodes and Slave Nodes?

What is MapReduce?

MapReduce helps in processing larger sets of data through the servers of Hadoop Cluster.MapReduce consists of two tasks:
Map - used in taking and converting the set of data into another set of data.
Reduce - used in taking the output from one map as input and helps in combining data tuples in small sets of tuples.

What are the advantages of MapReduce?

• Highly Scalable - MapReduce is used in storing and distributing data sets through the servers.
• Cost Effective - MapReduce helps by allowing the data Storage and Processing at minimal prices.
• Security - MapReduce helps the users in operating the data and incorporating HDFS and HBase Security.

What are the components of MapReduce?

There are 2 main components of MapReduce:
Main Driver - helps by providing Job Configuration Parameters.
Mapper Class - helps by extending the commands and executing them.

What is Big Data?

Big Data contains a large number of datasets which cannot be processed by using Traditional Computing Techniques.It can help collecting and managing the volume of these datasets that can fall into Big Data.

What are Algorithm?

There are 2 types of Algorithms:



Mapper Class- inputs and maps, sorts and tokenizes the data.
Reducer Class- used in searching matching pairs and helps in reducing them.

What is JobContext Interface in MapReduce?

JobContext Interface is a super interface that gives us read-only view of the Jobs which are provided in the tasks.It contains 2 Sub-Interfaces such as:
MapContext - this context is given to the mapper.
ReduceContext - this context is passed to the reducer.

What is JobTracker in MapReduce?

JobTracker is used in processing of MapReduce Jobs in the Clusters.It is also helps in tracking and submitting the jobs in specifying the nodes which have data and runs it on single Hadoop Cluster on JVM Process.

What is Identity Mapper?

Identity Mapper is provided by Hadoop and helps in writing the input data into output data and cannot perform, computes and calculates the input data.

What is a MapReduce Combiner?

MapReduce Combiner is used in combining the map out records by using the similar key.Combiner is used in accepting the inputs from the Map Class and helps in passing the key value pairs to the reducer.

What are the parameters of Mappers and Reducers?

Parameters of Mappers are as follows:
LongWritable (Input)
Text (Input)
Text (Intermediate Output)
IntWritable (Intermediate Output)
Parameters of Reducers are as follows:
Text (Intermediate Output)
IntWritable (Intermediate Output)
Text (Final Output)
IntWritable (Final Output)

What is Partition?

Partitioner helps by controlling the keys after they have been sorted and shuffled.It is used in dividing the Intermediate Data according to the number if the reducers and can be executed by single reducer.Partition can be executed by a Single Reducer and can be automatically called by it.

What are Master Nodes and Slave Nodes?

Following are the Master Nodes:
NameNode used in maintaining and storing data in HDFS.
Secondary NameNode used in working and performing the functions.
JobTracker used in maintaining the MapReduce Jobs.
Following are the Slave Nodes:
DataNode helps in managing HDFS Data Block.
TaskTracker helps in managing Reduce and Map Tasks.

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