MapReduce Interview Questions and Answers, MapReduce Interview Questions and Answers Freshers, MapReduce Interview Questions and Answers, MapReduce Interview Questions
Before getting on to the MapReduce interview questions, the student must know that the MapReduce is a continuously varying field which needs the students as well as professionals to upgrade their skills with the new features and knowledge, to get fit for the jobs associated with MapReduce. This post related to MapReduce Interview Questions and Answers, MapReduce Interview Questions and Answers Freshers, MapReduce Interview Questions and Answers, MapReduce Interview Questions will help you let out find all the solutions that are frequently asked in you upcoming MapReduce interview.
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Hence, if you are looking for boosting up your profile and securing your future, MapReduce will help you in reaching the zenith of your career. Apart from this, you would also have a lot of opportunities as a fresher.
These questions alone are omnipotent. Read and re-read the questions and their solutions to get accustomed to what you will be asked in the interview. These MapReduce interview questions and answers will also help you on your way to mastering the skills and will take you to the giant world where worldwide and local businesses, huge or medium, are picking up the best and quality MapReduce professionals.
This ultimate list of best MapReduce interview questions will ride you through the quick knowledge of the subject and topics like Job Initialization, Algorithms, Setting up Eclipse Development Environment. This MapReduce interview questions and answers can be your next gateway to your next job as a MapReduce expert.
These are very Basic MapReduce Interview Questions and Answers for freshers and experienced both.
Q1: What is EAI ?
|Standalone mode||Needs Hadoop||Can work independently|
|Ease of use||Needs extensive Java program||APIs for Python, Java, & Scala|
|Versatility||Real-time & machine learning applications||Not optimized for real-time & machine learning applications|
Q1: Compare MapReduce and Spark
Criteria MapReduce Spark
Processing Speeds Good Exceptional
Standalone mode Needs Hadoop Can work independently
Ease of use Needs extensive Java program APIs for Python, Java, & Scala
Versatility Real-time & machine learning applications Not optimized for real-time & machine learning applications
Q2: What is MapReduce?
A2: Referred as the core of Hadoop, MapReduce is a programming framework to process large sets of data or big data across thousands of servers in a Hadoop Cluster. The concept of MapReduce is similar to the cluster scale-out data processing systems. The term MapReduce refers to two important processes of Hadoop program operates.
First is the map() job, which converts a set of data into another breaking down individual elements into key/value pairs (tuples). Then comes reduce() job into play, wherein the output from the map, i.e. the tuples serve as the input and are combined into smaller set of tuples. As the name suggests, the map job every time occurs before the reduce one.
Q3: What are the main components of MapReduce Job?
A3: Main Driver Class: providing job configuration parameters
Mapper Class: must extend org.apache.hadoop.mapreduce.Mapper class and performs execution of map() method
Reducer Class: must extend org.apache.hadoop.mapreduce.Reducer class
Q4: What is Shuffling and Sorting in MapReduce?
A4: Shuffling and Sorting are two major processes operating simultaneously during the working of mapper and reducer.
The process of transferring data from Mapper to reducer is Shuffling. It is a mandatory operation for reducers to proceed their jobs further as the shuffling process serves as input for the reduce tasks.
In MapReduce, the output key-value pairs between the map and reduce phases (after the mapper) are automatically sorted before moving to the Reducer. This feature is helpful in programs where you need sorting at some stages. It also saves the programmer’s overall time.
Q5: What is Partitioner and its usage?
A5: Partitioner is yet another important phase that controls the partitioning of the intermediate map-reduce output keys using a hash function. The process of partitioning determines in what reducer, a key-value pair (of the map output) is sent. The number of partitions is equal to the total number of reduce jobs for the process.
Hash Partitioner is the default class available in Hadoop , which implements the following function.int getPartition(K key, V value, int numReduceTasks)
The function returns the partition number using the numReduceTasks is the number of fixed reducers.
Q6: What is Identity Mapper and Chain Mapper?
A6: Identity Mapper is the default Mapper class provided by Hadoop. when no other Mapper class is defined, Identify will be executed. It only writes the input data into output and do not perform and computations and calculations on the input data.
The class name is org.apache.hadoop.mapred.lib.IdentityMapper.
Chain Mapper is the implementation of simple Mapper class through chain operations across a set of Mapper classes, within a single map task. In this, the output from the first mapper becomes the input for second mapper and second mapper’s output the input for third mapper and so on until the last mapper.
The class name is org.apache.hadoop.mapreduce.lib.ChainMapper.
Q7: What main configuration parameters are specified in MapReduce?
A7: The MapReduce programmers need to specify following configuration parameters to perform the map and reduce jobs:
• The input location of the job in HDFs.
• The output location of the job in HDFS.
• The input’s and output’s format.
• The classes containing map and reduce functions, respectively.
• The .jar file for mapper, reducer and driver classes
Q8: Name Job control options specified by MapReduce.
A8: Since this framework supports chained operations wherein an input of one map job serves as the output for other, there is a need for job controls to govern these complex operations.
The various job control options are:
Job.submit() : to submit the job to the cluster and immediately return
Job.waitforCompletion(boolean) : to submit the job to the cluster and wait for its completion
Q9: What is InputFormat in Hadoop?
A9: Another important feature in MapReduce programming, InputFormat defines the input specifications for a job. It performs the following functions:
- Validates the input-specification of job.
- Split the input file(s) into logical instances called InputSplit. Each of these split files are then assigned to individual Mapper.
- Provides implementation of RecordReader to extract input records from the above instances for further Mapper processing
Q10: What is the difference between HDFS block and InputSplit?
A10: An HDFS block splits data into physical divisions while InputSplit in MapReduce splits input files logically.
While InputSplit is used to control number of mappers, the size of splits is user defined. On the contrary, the HDFS block size is fixed to 64 MB, i.e. for 1GB data , it will be 1GB/64MB = 16 splits/blocks. However, if input split size is not defined by user, it takes the HDFS default block size.
Q11: Explain what is heartbeat in HDFS?
A11: 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
Q12: Explain what combiners is and when you should use a combiner in a MapReduce Job?
A12: 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
Q13: What happens when a datanode fails ?
A13: 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
Q14: Explain what is Speculative Execution?
A14: 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.
Q15: Explain what are the basic parameters of a Mapper?
A15: The basic parameters of a Mapper are
• LongWritable and Text
• Text and IntWritable
Q16: Explain what is the function of MapReducer partitioner?
A16: 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 reducers
Q17: Explain what is difference between an Input Split and HDFS Block?
A17: Logical division of data is known as Split while physical division of data is known as HDFS Block
Q18: Is it possible to rename the output file?
A18: Yes, this can be done by implementing the multiple format output class.
Q19: What do you understand by compute and storage nodes?
A19: Storage node is the system, where the file system resides to store the data for processing.
Compute node is the system where the actual business logic is executed.
Q20: When should you use a reducer?
A20: It is possible to process the data without a reducer but when there is a need to combine the output from multiple mappers – reducers are used. Reducers are generally used when shuffle and sort are required.
Q21: What is the role of a MapReduce partitioner?
A21: MapReduce is responsible for ensuring that the map output is evenly distributed over the reducers. By identifying the reducer for a particular key, mapper output is redirected accordingly to the respective reducer.
Q22: What is identity Mapper and identity reducer?
A22: IdentityMapper is the default Mapper class in Hadoop. This mapper is executed when no mapper class is defined in the MapReduce job.
IdentityReducer is the default Reducer class in Hadoop. This mapper is executed when no reducer class is defined in the MapReduce job. This class merely passes the input key value pairs into the output directory.
Q23: What do you understand by the term Straggler ?
A23: A map or reduce task that takes unsually long time to finish is referred to as straggler.
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Q24: Is it necessary to write a MapReduce job in Java?
A24: No, MapReduce framework supports multiple languages like Python, Ruby etc.
Q25: How do you stop a running job gracefully?
A25: One can gracefully stop a MapReduce job by using the command: hadoop job -kill JOBID
Q26: How will you submit extra files or data ( like jars, static files, etc. ) for a MapReduce job during runtime?
A26: 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.
Q27: How does inputsplit in MapReduce determines the record boundaries correctly?
A27: RecordReader is responsible for providing the information regarding record boundaries in an input split.
Q28: How do reducers communicate with each other?
A28: This is a tricky question. The “MapReduce” programming model does not allow “reducers” to communicate with each other. “Reducers” run in isolation.
Q29: What is heartbeat in HDFS? Explain.
A29: A heartbeat in HDFS is a signal mechanic used to signal if it is active or not. For example, a DataNode and NameNode use heartbeat to convey if they are active or not. Similarly, JobTracker and NameNode also use heartbeat to do the same.
Q30: What happens when a DataNode fails?
A30: As big data processing is data and time sensitive, there are backup processes if DataNode fails. Once a DataNode fails, a new replication pipeline is created. The pipeline takes over the write process and resumes from where it failed. The whole process is governed by NameNode which constantly observes if any of the blocks is under-replicated or not.
Q31: Can you tell us how many daemon processes run on a Hadoop system?
A31: There are five separate daemon processes on a Hadoop system. Each of the daemon processes has its JVM. Out of the five daemon processes, three runs on the master node whereas two runs on the slave nodes. They are as below.
- NameNode- maintains and store data in HDFS
- Secondary NameNode – Works for NameNode and performs housekeeping functions.
- JobTracker – Take care of the main MapReduce jobs. Also takes care of distributing tasks to machines listed under task tracker.
- DataNode – manages HDFS data blocks.
- TaskTracker – manages the individual Reduce and Map tasks.
These MapReduce interview questions will help you get started with the MapReduce interview preparation. Notice that you need to read more questions and answers to get truly prepared for the job interview as this article only covers the 10 most popular MapReduce interview questions. If you have any questions regarding MapReduce or MapReduce interview, you can easily ask us using the comments section below!
MapReduce Conclusion Interview FAQs
We know the list of MapReduce Interview Questions and Answers, MapReduce Interview Questions and Answers Freshers, MapReduce Interview Questions and Answers, MapReduce Interview Questions is overwhelming but the advantages of reading all the questions will maximize your potential and help you crack the interview. The surprising fact is that this MapReduce interview questions and answers post covers all the basic of the MapReduce technology and you have to check out the FAQs of different components of MapReduce too.
However, you will be asked with the questions in the interview related to the above mentioned questions. Preparing and understanding all the concept of MapReduce technology will help you strengthen the other little information around the topic.
After preparing these interview questions, we recommend you to go for a mock interview before facing the real one. You can take the help of your friend or a MapReduce expert to find the loop holes in your skills and knowledge. Moreover, this will also allow you in practicing and improving the communication skill which plays a vital role in getting placed and grabbing high salaries.
Remember, in the interview, the company or the business or you can say the examiner often checks your basic knowledge of the subject. If your basics is covered and strengthened, you can have the job of your dream. The industry experts understand that if the foundation of the student is already made up, it is easy for the company to educate the employ towards advance skills. If there are no basics, there is no meaning of having learnt the subject.
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