Top 20 Hadoop MapReduce Interview Questions To Boost Your Knowledge

access_time 2019-10-25T13:18:39.953Z face Learnoa.com Interview Questions

Don’t you want to ace the Hadoop MapReduce Interview? Who wouldn’t want to? To get the answers for all the questions that are asked by the employers, this is a must-read article. But before we all start, let’s understand the meaning of MapReduce -

It is a programming framework that helps the user to execute divided and parallel processing on big sets of data in a distributed environment. This framework even follows the Master or Slave Topology where the resource manager helps in managing and tracking different MapReduce jobs that are performed by the node managers. There are two primary component oof resource manager -Application Manager and Scheduler.

Now that the fundamentals have been discussed. Let’s talk about the Hadoop MapReduce Interview Questions -

Que 1 - What is the definition of data locality?

Data locality is related to the moving of the computation unit to data rather than data to the computation unit. By processing the data locally, the MapReduce framework achieves data locality.

Que 2 - Is it necessary to set output and input type/format in MapReduce?

No, it isn't compulsory to set the information and yield type/position in MapReduce. Of course, the bunch takes the information and the yield type as ‘text’.

Que 3 - Can the output file be renamed?

Yes, the output file can be renamed by implementing multiple format output class.

Que 4 - What is shuffling and sorting in MapReduce?

Shuffling and sorting happen after the finish of the guide task where the contribution to each reducer is arranged by the keys. Essentially, the procedure by which the framework sorts the key-esteem yield of the guide errands and move it to the reducer is called shuffle.

Que 5 - Can you tell us more about the process of spilling in MapReduce?

All the output from the map tasks is written into RAM. With a default size of 100 MB for the buffer, it can be tuned with the help of mapreduce.task.io.sort.mb property. Presently, spilling is a procedure of duplicating the information from memory buffer to disc. This is done when the buffer content has reached a certain threshold size. As a matter of course, a foundation string starts spilling the substance from memory to plate after 80% of the support size is filled. Along these lines, for a 100 MB size buffer, the spilling will begin after the content of the buffer arrive at a size of 80 MB.
Note: One can change this spilling edge utilizing MapReduce.map.sort.spill.percent which is set to 0.8 or 80 %.

Que 6 - Define combiner and where you should use it?

Combiner resembles a smaller than normal reducer work that enables us to perform a nearby total of guide yield before it is moved to the reducer stage. Fundamentally, it is utilized to improve the system transmission capacity utilization during a MapReduce task by chopping down the information that is moved from a mapper to the reducer.

Que 7 - Why the output of map tasks are stored (spilled ) into a local disc and not in HDFS?

The yields of map task are the halfway key-esteem sets which are then prepared by reducer to deliver the last amassed outcome. When a MapReduce employment is finished, there is no need of the middle of the road yield delivered by guide assignments. Hence, putting away these output into HDFS and duplicate it will make superfluous overhead.

Que 8 - What will happen if the node running the map task fails before the map output has been sent to the reducer?

In such a case, the map task is provided with a new node and the entire task will be executed again to create the map output again.

Que 9 - Define the role of a MapReduce Partitioner?

All the intermediate key-value pairs which are produced by the map tasks get divided by an into the partition. All the partitions equal total reducers. The processing of each partition is done by the corresponding reducer. Using the hash function, the partitioning is done which is based on a single key or group of keys. HashPartitioner is the default partitioner which is available in Hadoop.

Que 11 - How to make sure that the values regarding a particular key go to the same reducer?

With the help of partitioner, one can easily control that a particular key-value enters the same reducer for processing. 

Que 12 - Differentiate between Input Split and HDFS Block?

HDFS block tells about the way the data is physically divided in HDFS while input split tells about the logical boundary of the records that are needed to process it.

Que 13 - What is a map side join?

It is a process that joins two data sets by the mapper.

Que 14 – Tell the advantages of using map side join in MapReduce?

The advantages are as follows:

It minimizes the cost that comes at the time of sorting and merging during the shuffle and reduce stages.

It improves the performance of the task by minimizing the time to finish the task.

Que 15 - What is reduced side join in MapReduce?

As the name recommends, in the lessen side join, the reducer is in charge of playing out the join activity. It is relatively basic and simpler to execute than the guide side joins as the arranging and rearranging stage sends the qualities having indistinguishable keys to a similar reducer and subsequently, as a matter of course, the information is sorted out for us.

Que 16 – What do you mean by Speculative Execution?

In the event that a node gives off an impression of being executing an undertaking slower than anticipated, the ace hub can needlessly execute another example of a similar assignment on another hub. At that point, the errand which completes first will be acknowledged though different assignments will be murdered. This procedure is a speculative execution.