1) What is Hadoop Map Reduce ?
For processing large data sets in parallel across a hadoop cluster, Hadoop MapReduce
framework is used. Data analysis uses a two-step map and reduce process.

2) How Hadoop MapReduce works?
In MapReduce, during the map phase it counts the words in each document, while in the reduce
phase it aggregates the data as per the document spanning the entire collection. During the
map phase the input data is divided into splits for analysis by map tasks running in parallel
across Hadoop framework.

3) Explain what is shuffling in MapReduce ?
The process by which the system performs the sort and transfers the map outputs to the
reducer as inputs is known as the shuffle

4) Explain what is distributed Cache in MapReduce Framework ?
Distributed Cache is an important feature provided by map reduce framework. When you want
to share some files across all nodes in Hadoop Cluster, DistributedCache is used. The files
could be an executable jar files or simple properties file.

5) Explain what is NameNode in Hadoop?
NameNode in Hadoop is the node, where Hadoop stores all the file location information in
HDFS (Hadoop Distributed File System). In other words, NameNode is the centrepiece of an
HDFS file system. It keeps the record of all the files in the file system, and tracks the file data
across the cluster or multiple machines

6) Explain what is JobTracker in Hadoop? What are the actions followed by Hadoop?
In Hadoop for submitting and tracking MapReduce jobs, JobTracker is used. Job tracker run on
its own JVM process
Hadoop performs following actions in Hadoop
Client application submit jobs to the job tracker
JobTracker communicates to the Namemode to determine data location
Near the data or with available slots JobTracker locates TaskTracker nodes
On chosen TaskTracker Nodes, it submits the work
When a task fails, Job tracker notify and decides what to do then.
The TaskTracker nodes are monitored by JobTracker

7) Explain what is heartbeat in HDFS?
Heartbeat is referred to a signal used between a data node and Name node, and between task
tracker and job tracker, if the Name node or job tracker does not respond to the signal, then it is
considered there is some issues with data node or task tracker

8) Explain what combiners is and when you should use a combiner in a MapReduce Job?
To increase the efficiency of MapReduce Program, Combiners are used. The amount of data
can be reduced with the help of combiner’s that need to be transferred across to the reducers.
If the operation performed is commutative and associative you can use your reducer code as a
combiner. The execution of combiner is not guaranteed in Hadoop

9) What happens when a datanode fails ?
When a datanode fails
Jobtracker and namenode detect the failure
On the failed node all tasks are re-scheduled
Namenode replicates the users data to another node

10) Explain what is Speculative Execution?
In Hadoop during Speculative Execution a certain number of duplicate tasks are launched. On
different slave node, multiple copies of same map or reduce task can be executed using
Speculative Execution. In simple words, if a particular drive is taking long time to complete a
task, Hadoop will create a duplicate task on another disk. Disk that finish the task first are
retained and disks that do not finish first are killed.

11) Explain what are the basic parameters of a Mapper?
The basic parameters of a Mapper are
LongWritable and Text
Text and IntWritable

12) Explain what is the function of MapReducer partitioner?
The function of MapReducer partitioner is to make sure that all the value of a single key goes to
the same reducer, eventually which helps evenly distribution of the map output over the

13) Explain what is difference between an Input Split and HDFS Block?
Logical division of data is known as Split while physical division of data is known as HDFS Block

14) Explain what happens in textinformat ?
In textinputformat, each line in the text file is a record. Value is the content of the line while Key
is the byte offset of the line. For instance, Key: longWritable, Value: text

15) Mention what are the main configuration parameters that user need to specify to run
Mapreduce Job ?
The user of Mapreduce framework needs to specify
Job’s input locations in the distributed file system
Job’s output location in the distributed file system
Input format
Output format
Class containing the map function
Class containing the reduce function
JAR file containing the mapper, reducer and driver classes

16) Explain what is WebDAV in Hadoop?
To support editing and updating files WebDAV is a set of extensions to HTTP. On most
operating system WebDAV shares can be mounted as filesystems , so it is possible to access
HDFS as a standard filesystem by exposing HDFS over WebDAV.

17) Explain what is sqoop in Hadoop ?
To transfer the data between Relational database management (RDBMS) and Hadoop HDFS a
tool is used known as Sqoop. Using Sqoop data can be transferred from RDMS like MySQL or
Oracle into HDFS as well as exporting data from HDFS file to RDBMS

18) Explain how JobTracker schedules a task ?
The task tracker send out heartbeat messages to Jobtracker usually every few minutes to make
sure that JobTracker is active and functioning. The message also informs JobTracker about the
number of available slots, so the JobTracker can stay upto date with where in the cluster work
can be delegated

19) Explain what is Sequencefileinputformat?
Sequencefileinputformat is used for reading files in sequence. It is a specific compressed binary
file format which is optimized for passing data between the output of one MapReduce job to the
input of some other MapReduce job.

20) Explain what does the conf.setMapper Class do ?
Conf.setMapperclass sets the mapper class and all the stuff related to map job such as reading
data and generating a key-value pair out of the mapper