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Hope you are good! You might be here because you want to appear for Hadoop MapReduce Developer interview either as a fresher or experienced. Well, the good news is that you have reached the right place today. Here, we have compiled a list of most important questions that are frequently asked during interviews. All the questions are prepared by the Hadoop MapReduce experts and we try to compile precise answers for each of the questions to guide you the best way to success. Do comment your experience. -- Happy job hunting!
MapReduce also termed as Hadoop Core, is a programming framework that has the capability to process large data sets and big data files across thousands of servers in a Hadoop cluster. MapReduce is made up of two main elements i.e. Map() and Reduce() functions. Map () collects the data from multiple sources and map the similar data together. Further, Reduce () function divided the large data sets outcome into smaller chunks for further processing.In this article, we will discuss on Hadoop interview questions and answers for freshers and experience to assess your knowledge of Hadoop MapReduce tool.
When data is transferred from mapper to shuffler, it is termed as shuffling. Once data is transferred to the reducer, it needs to be filtered based on Company requirement that is termed as sorting.
The two major components of MapReduce are Map () and Reduce () functions. Map () collects the data from multiple sources and map the similar data together. Further, Reduce () function divided the large datasets outcome into smaller chunks for further processing.
MapReduce also termed as Hadoop Core, is a programming framework that has the capability to process large data sets and big data files across thousands of servers in a Hadoop cluster.
Identity Mapper is the default class in the MapReduce that executes automatically if no other class is defined in the scenario. At the same time, Chain Mapper class executes through chain operations through the output of one Mapper class becomes the input for the other class.
There are two job control options in MapReduce. These are-
Read: An Introduction to Apache Spark and Spark SQL
Job.Submit () – This control option submits the job to the cluster. Job.waitforCompletion () – Once the job is submitted to the cluster, you need to wait until it does not complete.
Input Format is another important feature in MapReduce that defines the Input specifications for a job. Let us see how it works actually –
Validates the Input specification for a job, Splits the Input into logical instances with InputSplit and each of the instances is mapped to the Mapper class further. Provides implementation to extract records from each of the instances.
HDFS (Hadoop Distributed File System) distributes data into physical divisions while InputSplit splits data into logical instances.
To manage the large datasets, you should always opt for MapReduce in Hadoop while data flow form Input source to Output source can be managed through Pig programming language.
This is the default format for text files where data into files is broken into lines and mapped with the key values.
MapReduce job tracker is used to process jobs in a Hadoop cluster. It is responsible to submit the job to various nodes and track their status as well. If job tracker goes down then all jobs may halt in mid only.
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Pig is a data flow language that manages the data flow when data is transferred from input source to output source. At the same time,MapReduce is a programming framework that has the capability to process large data sets and big data files across thousands of servers in a Hadoop cluster.
This function reads the records that are broken down into logical instances through Input Split function.
YARN stands for Yet Another Source Navigator and it is taken as the next generation MapReduce and works on flaws detected in the previous versions.The latest version is more scalable and robust to manage the jobs, resources or scheduler etc.
When data is transmitted over a network across various nodes in a Hadoop cluster, it has to be converted into byte stream data from object data that is named as Serialization in Hadoop.
Deserialization is the reverse process of Data serialization where bytes are converted to data objects at the receiver end. Basically, the process is same as encoding and decoding of data in wireless networks.
The Combiner is a mini reducer to perform to reduce jobs on the local network. It is generally used for network optimization when a number of outputs are generated from each mapped class.
A job can be divided into multiple tasks in Hadoop cluster.
Read: Apache Storm Interview Questions and Answers: Fresher & Experience
The three primary phases of the reducer are – Shuffle, Sort, and Reduce.
This is possible to search files in Hadoop MapReduce with wildcards
The storage node is the place where file system resides to store data for the further processing. And the compute node is the place where the actual logic of the business is executed.
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