All right! It sounds like you're after the MapReduce interview questions. Then this is the right place for you! Our experts have gathered some of the frequently asked interview questions in this blog. These questions will be very helpful for you in your MapReduce Interview preparation. So, why late? Let's get started with the most frequently asked MapReduce Interview questions.
Let's get started!
MapReduce is a programming framework for processing large sets of data and big data over thousands of servers within a Hadoop cluster. The MapReduce concept is the same as the data processing systems across the cluster. The word MapReduce refers to two key processes of the Hadoop program operates. The first job is the map() that converts a set of data into another by breaking down a single element into key or value pairs (tuples). Then the reduce() job comes into the picture in which the map jobs output, i.e., the tuples are used as the input in reduce() job, and they are combined as a smaller set of tuples. Map job is performed before every reduce() job.
MapReduce is divided into two phases. The first one is the map and the second is reduce.
Map: This phase involves sorting the data or counting words.
Reduce: This phase involves reducing the data and then aggregating them.
As a result, the data is initially divided for analysis purposes.
The following are the key components of MapReduce:
Main class: It involves giving the main parameters to the job, like giving various data files for sorting.
Mapper Class: In this class, mainly mapping is done. The map method will be executed.
Reducer Class: Aggregate data is pushed into the reducer class. There is a reduction in data in this class.
MapReduce has three significant advantages:
Extremely scalable: It Stores and distributes large data sets on thousands of servers.
Cost-effective: It allows us to store and process data at affordable prices.
Secure: It enables only the authorized users for operating the data and includes HDFS and HBase security.
An important feature in the Mapreduce framework is Inputformat. Inputformat specifies the input requirements for a job. It does the following actions:
JobTracker contacts the NameNode to find out where the data is located and then submits the work to the TaskTracker node. TaskTracker plays an important role because it alerts the JobTracker of any work failure. It is, in fact, referred to as a heartbeat reporter reassuring job tracker that it is alive. The JobTracker will be responsible for the actions, as it can submit the job again or mark a particular record as unreliable or can blacklist it.
SequenceFileInputFormat is a binary output file format (that is compressed) for reading sequenced files and extending the FileInputFormat. The SequenceFileInputFormat transfers data between the output-input phases of the MapReduce tasks (i.e., in between the output from a MapReduce job and input from another MapReduce job).
Shuffling and Sorting are the two important processes that work parallelly with the Mapper and reducer.
Shuffling: The process in which data is transferred to the reducer from Mapper is called as Shuffling. The reducer must continue its job further as the process of shuffling is used as the input to the reduce task.
Sorting: In Mapreduce, the output key-value pairs across the mapper phase and reduce phase are sorted automatically before they are moved to the reducer. This function is useful for programs in which you have to sort at certain steps. It will save the overall time of the programmer.
This is the main interface for defining a map-reduce job within the Hadoop to perform the job. JobConf determines combiner, Mapper, Reducer, partitioner, InputFormat, OutputFormat implementations and more. They are described in the Mapreduce online reference guide and in the Mapreduce community.
MapReduce Combiner is also referred to as semi-reducer. It is an optional class for combining the map of records with the same key. The primary function of the combiner is to accept the inputs from the Map class and pass these key-value pairs to the reducer class.
RecordReader is used for reading key or value pairs from InputSplit converting the byte-oriented view and sending the record-oriented view to the Mapper.
Chain Mapper: Chain Mapper is a simple Mapper class implemented by chain operations on a set of Mapper classes in a single map task. Here the first Mapper's output will be the input to the second Mapper, and the second Mapper's output will become the input to the third Mapper, and this process continues till the last Mapper. org.apache.hadoop.mapreduce.lib.ChainMapper is the class name.
Identity Mapper: Identity Mapper is Hadoop's default mapper class. When another Mapper class is not set, Identity Mapper will be executed. It will only write the input data to output and will not perform any computations or calculations on the input data. org.apache.hadoop.mapreduce.lib.IdentityMapper is the class name.
Partitioning is the process of identifying the instance of reducer that will be used for providing mapper output. Mapper, before emitting the key-value pair for the reducer, Mapper identifies the reducer as the receiver of the output of the Mapper. Any key, regardless of which Mapper produced this, has to be with the same reducer.
The following parameters must be specified by the user:
The only default format of text files or input data is the text input format. Files are broken in the text input format. The line of the text refers to the value, while the key refers to the position. Both of these are the major components in the data files.
Related Article: MapReduce In Big Data
Any local tasks to reduce local data files are carried out using a combiner. It works primarily on map output. Similar to the reducer, it also generates the output for the input of the reducer. Combiner also has other uses, such as it is frequently used as the network optimization job, particularly when the number of outputs increases by the Map generator. Combiners differ from the reducer in a number of ways. A reducer is restricted; however, a combiner has limitations such as input or output data, and the values should be the same as the mapper output data. A combiner may work with the commutative function. For example, it may operate on the subsets of the keys and values of the data. A combiner will get the input from a single mapper while the reducer gets the input from a number of mappers.
HDFS refers to Hadoop Distributed File System. It is the most critical component in the Hadoop Architecture and will be responsible for the data storage. The signal that is used in HDFS is referred to as the heartbeat. The signal is primarily transmitted between two kinds of nodes, which are data and name nodes. It occurs in between task tracker and job tracker. If the signal does not work properly and if there are problems with both nodes or trackers, it is regarded as having a poor heartbeat.
Consequences of a data node failure include:
Speculative execution is a kind of feature that makes it possible to launch multiple tasks on different types of nodes. Usually, duplicate copies of the task are created with the help of the function if a task takes more time to complete. Sometimes even certain multiple copies as well as done through speculative execution.
The identity mapper is related to the default mapper class, and the identity reducer is related to the default reducer class. If the mapper number is not defined during the work process, it is referred to as the identity mapper. When a reducer class is not defined, it is referred to as an identity reducer. As a result, this class transfers key values into the output directory.
PIG is essentially the dataflow language which handles the data flow from source to source. It manages and contributes to the compression of the data storage system. Pig reorganizes the steps to quicker and more efficient processing. PIG primarily handles MapReduce output data. Certain features of the MapReduce processing are included in the PIG processing. Functions include data grouping, ordering and counting. MapReduce is essentially the framework to write code for developers. It is a paradigm of data processing which separates the application of two kinds of developers, the people who write it and the people who scale it.
Mapreduce is not advisable for iterative processing. This means repeating the output over and over again. For processing the Mapreduce job series, MapReduce is not suitable. Every job persists the data into the local drive and then loads again to the other job. It will be a costly operation. So it is not recommended.
OutPutCommitter depicts the commit of the MapReduce task. FileOutputCommitter is the current default class of OutputCommitter in MapReduce. Following are the operations performed by OutPutCommitter.
InputFormat is a MapReduce feature that sets input specifications to a job. There are 8 different types of InputFormat in MapReduce. They are:
HDFS block: It is responsible for dividing the data into specific physical divisions.
Inputsplit: It is responsible for the logical splitting of Input files.
The InputSplit will also be able to control the number of mappers, while the size of the splits is user-specified. In HDFS, the HDFS block size is fixed as 64MB for 1GB data, 1GB/64MB = 16 blocks/splits. However, if the user does not set the size of the input split, then it will assume the size of the default HDFS block.
A NameNode in Hadoop is called the node, where Hadoop is able to store all information about the location of the file in the Hadoop Distributed File System. Simply put, a NameNode is a central element or feature of the HDFS filesystem. It is responsible for retaining the record of all files into the file system and also monitoring file data across the cluster or several machines.
MapReduce framework supports chained operations. So the output of one map job is used as the input of another map job. That is why there is a need for job controls to govern and work with these complex jobs. Various job control options are as follows:
Job.submit(): It is used for submitting the job to a cluster and return immediately.
Job.waitforCompletion(boolean): It is responsible for submitting the work to the cluster and wait until it is finished.
Hadoop includes five different daemons. All of these daemons will run in their own JVM. Here are the three Daemons which will execute on the Master nodes.
WebDAV is a set of HTTP extensions that offer extended support to edit and update files. On the majority of operating systems, WebDAV shares may be mounted as file systems. HDFS can thus be accessed as a standard filesystem by exhibiting HDFS on WebDAV.
We hope you will find this blog helpful in your preparation for your interview. We attempted to cover the basic, intermediate and advanced frequently asked interview questions of MapReduce. Do not hesitate to put your questions in the comments section below. We'll try to respond as best we can.
Batch starts on 20th Oct 2021, Weekday batch
Batch starts on 24th Oct 2021, Weekend batch
Batch starts on 28th Oct 2021, Weekday batch