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Looking out for Hadoop MapReduce Interview Questions that are frequently asked by employers?
I hope you have not missed the previous blog in this interview questions blog series that contains the most frequesntly asked Top 50 Hadoop Interview Questions by the employers. This will definitely help you kickstart you career as a Big Data Engineer and become a certified Big Data professional.Now, before moving ahead in this Hadoop MapReduce Interview Questions blog, let us have a brief understanding of MapReduce framework and its working:
MapReduce is a programming framework that allows us to perform distributed and parallel processing on large data sets in a distributed environment.
“A mind troubled by doubt cannot focus on the course to victory.”
– Arthur Golden
The above quote reflects the importance of having your fundamentals clear before appearing for an interview as well as while going through this Hadoop MapReduce Interview Question blog. Therefore, I would suggest you to go through MapReduce Tutorial blog to brush up your basics.
From this Big Data Course designed by a Big Data professional, you will get 100% real-time project experience in Hadoop tools such as Hive, and MapReduce commands and concepts. Here, is the list of Hadoop MapReduce Interview Questions that will help you to stand up to the expectation of the employers.
|Flexible||Hadoop MapReduce programming can access and operate on different types of structured and unstructured|
|Parallel Processing||MapReduce programming divides tasks for execution in parallel|
|Resilient||Is fault tolerant that quickly recognizes the faults & then apply a quick recovery solution implicitly|
|Scalable||Hadoop is a highly scalable platform that can store as well as distribute large data sets across plenty of servers|
|Cost-effective||High scalability of Hadoop also makes it a cost-effective solution for ever-growing data storage needs|
|Simple||It is based on a simple programming model|
|Secure||Hadoop MapReduce aligns with HDFS and HBase security for security measures|
|Speed||It uses the distributed file system for storage that processes even the large sets of unstructured data in minutes|
No, it is not mandatory to set the input and output type/format in MapReduce. By default, the cluster takes the input and the output type as ‘text’.
Yes, we can rename the output file by implementing multiple format output class.
Shuffling and sorting takes place after the completion of map task where the input to the every reducer is sorted according to the keys. Basically, the process by which the system sorts the key-value output of the map tasks and transfer it to the reducer is called shuffle.
The output of a map task is written into a circular memory buffer (RAM). The default size of buffer is set to 100 MB which can be tuned by using mapreduce.task.io.sort.mb property. Now, spilling is a process of copying the data from memory buffer to disc when the content of the buffer reaches a certain threshold size. By default, a background thread starts spilling the contents from memory to disc after 80% of the buffer size is filled. Therefore, for a 100 MB size buffer the spilling will start after the content of the buffer reach a size of 80 MB.
Note: One can change this spilling threshold using mapreduce.map.sort.spill.percent which is set to 0.8 or 80% by default.
Distributed Cache can be explained as, a facility provided by the MapReduce framework to cache files needed by applications. Once you have cached a file for your job, Hadoop framework will make it available on each and every data nodes where you map/reduce tasks are running. Therefore, one can access the cache file as a local file in your Mapper or Reducer job.
Combiner is like a mini reducer function that allow us to perform a local aggregation of map output before it is transferred to reducer phase. Basically, it is used to optimize the network bandwidth usage during a MapReduce task by cutting down the amount of data that is transferred from a mapper to the reducer.
The outputs of map task are the intermediate key-value pairs which is then processed by reducer to produce the final aggregated result. Once a MapReduce job is completed, there is no need of the intermediate output produced by map tasks. Therefore, storing these intermediate output into HDFS and replicate it will create unnecessary overhead.
In this case, map task will be assigned a new node and whole task will be run again to re-create the map output.
A partitioner divides the intermediate key-value pairs produced by map tasks into partition. The total number of partition is equal to the number of reducers where each partition is processed by the corresponding reducer. The partitioning is done using the hash function based on a single key or group of keys. The default partitioner available in Hadoop is HashPartitioner.
By using a partitioner we can control that a particular key – value goes to the same reducer for processing.
HDFS block defines how the data is physically divided in HDFS whereas input split defines the logical boundary of the records required for processing it.
InputFormat describes the input-specification for a MapReduce job.The MapReduce framework relies on the InputFormat of the job to:
TextInputFormat is the default input format present in the MapReduce framework. In TextInputFormat, an input file is produced as keys of type LongWritable (byte offset of the beginning of the line in the file) and values of type Text (content of the line).
InputSplit defines a slice of work, but does not describe how to access it. The “RecordReader” class loads the data from its source and converts it into (key, value) pairs suitable for reading by the “Mapper” task. The “RecordReader” instance is defined by the “Input Format”.
The main configuration parameters which users need to specify in “MapReduce” framework are:
SequenceFileInputFormat is an input format for reading within sequence files. It is a specific compressed binary file format which is optimized for passing the data between the outputs of one “MapReduce” job to the input of some other “MapReduce” job.
Sequence files can be generated as the output of other MapReduce tasks and are an efficient intermediate representation for data that is passing from one MapReduce job to another.
Identity mapper is the default mapper provided by the Hadoop framework. It runs when no mapper class has been defined in the MapReduce program where it simply passes the input key – value pair for the reducer phase.
Like Identity Mapper, Identity Reducer is also the default reducer class provided by the Hadoop, which is automatically executed if no reducer class has been defined. It also performs no computation or process, rather it just simply write the input key – value pair into the specified output directory.
Map side join is a process where two data sets are joined by the mapper.
The advantages of using map side join in MapReduce are as follows:
As the name suggests, in the reduce side join, the reducer is responsible for performing the join operation. It is comparatively simple and easier to implement than the map side join as the sorting and shuffling phase sends the values having identical keys to the same reducer and therefore, by default, the data is organized for us.
♣Tip: I would suggest you to go through a dedicated blog on reduce side join in MapReduce where the whole process of reduce side join is explained in detail with an example.
NLineInputFormat splits ‘n’ lines of input as one split.
Yes, It is legal to set the number of reduce-tasks to zero if there is no need for a reducer. In this case the outputs of the map task is directly stored into the HDFS which is specified in the setOutputPath(Path).
No, MapReduce framework supports multiple languages like Python, Ruby etc.
One can gracefully stop a MapReduce job by using the command: hadoop job -kill JOBID
The distributed cache is used to distribute large read-only files that are needed by map/reduce jobs to the cluster. The framework will copy the necessary files from a URL on to the slave node before any tasks for the job are executed on that node. The files are only copied once per job and so should not be modified by the application.
RecordReader is responsible for providing the information regarding record boundaries in an input split.
This is a tricky question. The “MapReduce” programming model does not allow “reducers” to communicate with each other. “Reducers” run in isolation.
If a node appears to be executing a task slower than expected, the master node can redundantly execute another instance of the same task on another node. Then, the task which finishes first will be accepted whereas other tasks will be killed. This process is called speculative execution.
I hope you find this blog on Hadoop MapReduce Interview Questions to be informative and helpful. You are welcome to mention your doubts and feedback in the comment section given below. In this blog, I have covered the interview questions for MapReduce only. To save your time in visiting several sites for interview questions related to each Hadoop component, we have prepared a series of interview question blogs that covers all the components present in Hadoop framework. Kindly, refer to the links given below to explore all the Hadoop related interview question and strengthen your fundamentals:
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