Mapreduce Interview Questions

Mapreduce Interview Questions

Hadoop MapReduce is one of the software structured for effectively writing an application for preparing a large amount of information in parallel or on a vast cluster of a commodity. As it deals with preparing data, it is probably going to be asked in Hadoop Map Reduce Interview Questions and Answers. There is an enormous demand for the Map-Reduce experts in the market.

It doesn't make a difference, if you are a beginner or experienced one or the one who has re-applied for another job position, experiencing the most prevalent Hadoop Map Reduce questions and answers can assist you to get prepared for the Map-Reduce interview. This blog contains usually asked Hadoop Map-reduce questions and answers, which will make you more confident while going through an interview. Hope these Hadoop Map Reduce questions will assist you to get selected in Hadoop interview.

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Below are the list of Best Mapreduce Interview Questions and Answers

Map Reduce is the core of Hadoop. It is one of the programming paradigms that acknowledge into consideration enormous adaptability across a thousand’s of the server in a Hadoop cluster. It is a processing layer of Hadoop. Map Reduce is a programming model intended for preparing large volumes of information in parallel by isolating the work into the arrangement of chunks. We need to compose the business logic; at that point rest work will be taken care of by the system.
Map Reduce is information handling paradigms in itself. This was one of its kind information handlings and has been transformative. While utilizing Map Reduce, we are moving the calculation to information, which is less expensive when compared with information, is moved to the calculation.
The procedure by which the framework lays out the sort and transfers the map outputs to the reducer as sources of information is known as the shuffling.
Mapper is the client characterize program, which controls the info split in (key, value) combines according to the code design. Regularly Mapper is the base class, which needs to reach out by a software engineer to compose their logic according to the requirement. While broadening mapper, the programmer needs to specify information and output type under mapper class arguments.
In Hadoop for submitting and following Map Reduce occupations, Job Tracker is utilized. Job Tracker is a basic service which cultivates out all MapReduce tasks to the different nodes in the group, preferably to those nodes which as of now contain the information, or at very least are situated in the same rack from nodes containing the information.

Job Tracker performs following activities in Hadoop:

  • Client application presents jobs to the job tracker
  • Job Tracker imparts to the Name mode to decide data area
  • Near the data or with accessible openings Job Tracker finds Task Tracker nodes
  • On choosing Task Tracker Nodes, it submits the work
  • When a task fails, Job tracker notifies and chooses what to do then.
  • Job Tracker observes the Task Tracker nodes
To enhance the effectiveness of Map Reduce Program, Combiners are utilized. The amount of information can be lessened with the assistance of combiner’s that should be exchanged across to the reducers. If the task performed is commutative and affiliated you can utilize your reducer code as a combiner.
In Hadoop, Map Reduce breaks jobs into various tasks, and these tasks run parallel rather than going for consecutive, in this manner decreases overall execution time. This model of execution is delicate to moderate tasks as they slow down the general execution of a job. There might be different explanations behind the slowdown of tasks, including hardware debasement or programming misconfiguration, yet it might be difficult to identify causes since the tasks still complete effectively, although additional time is taken than the normal time. Hadoop doesn’t attempt to analyze and settle moderate running tasks; rather, it endeavors to recognize them and runs reinforcement tasks for them. This is called speculative execution in Hadoop.
The four essential parameters of a mapper are Long, Writable, text, text and Int-Writable. The initial two represents the input parameters and the second two speak about intermediate output parameters.
Web Distributed Authoring and Versioning (WebDAV) is an expansion of the Hypertext Transfer Protocol (HTTP) that enables customers to perform remote Web content composing tasks. The WebDAV protocol gives a structure to clients to make, change and move reports on a server, normally a web server or web share. On most working framework WebDAV shares can be mounted as file systems, so it is conceivable to get to HDFS as a standard file system by uncovering HDFS over WebDAV.
Sqoop is a device intended to exchange information between Hadoop and social database servers. It is utilized to import information from social databases, for example, MySQL, Oracle to Hadoop HDFS, and export from the Hadoop file framework to social databases.
The task tracker conveys heart messages to Job tracker generally like clockwork to ensure that Job Tracker is active and working. The message also informs Job Tracker about the number of accessible slots, so the Job Tracker can stay updated with wherein the cluster work can be appointed.
Various data components, which are utilized by Hadoop are as follows:
  • Spark
  • Hive
  • Pig
  • Hbase
  • Oozie
  • Sqoop
Hadoop rose as an answer to the “Enormous Data” issues. It is an open source programming structure for distributed storage and circulated preparing of large data sets. Apache Hadoop has a unique method for Indexing. As, Hadoop structure store the information according to the Data Bock size, HDFS will continue storing the last piece of the information which will state where the following part of the information will be.
In a large cluster of Hadoop, keeping in mind the end goal to enhance the network traffic while perusing/composing HDFS file, name-node picks the data node which is nearer to a similar rack or close-by rack to Read/Write ask. Name node accomplishes rack data by keeping up the rack id’s of each data node. This idea that picks nearer data nodes, which are based on rack data is called Rack Awareness in Hadoop. Rack awareness consists of the knowledge of Cluster topology or more specifically how the different information nodes are conveyed over the racks of a Hadoop cluster. Default Hadoop installation expects that all data nodes belong to with a similar rack.
If we set the quantity of Reducer to 0 at that point, no reducer will execute, and no accumulation will occur. In such a case, we must go for “Map only job” in Hadoop.
A Task Tracker in Hadoop is a slave node daemon in the cluster that acknowledges tasks from a Job Tracker. It also conveys the heartbeat messages to the Job Tracker, at regular intervals, to confirm that the Job Tracker is yet alive.
In Hadoop, Input records store the information for a Map-Reduce work. Input files, which store information regularly reside in HDFS. Hence, in Map-Reduce, Input Format characterizes how these information files split and read. Input Format does Input split.
Most common Input Format is as follows:
  • FileInputFormat
  • TextInputFormat
  • KeyValueTextInputFormat
Hadoop Sequence documents are one of the Apache Hadoop specific file formats, which store information in the serialized key-value combine. Hadoop Sequence File is utilized as a part of Map Reduce as input/output formats. By default Mapper output is stored on local document framework, which is in Mapper node. Outputs of Maps are put away utilizing Sequence File. Inside Hadoop utilizes Sequence File organize for the Mapper which is stored in the local document system. In general, Apache Hadoop supports text records which are normally utilized for keeping and storing the information, other than the text documents it additionally supports binary documents and one of these binary formats are called Sequence records.
To work appropriately, Map Reduce needs some design parameters to be set accurately. Without them set accurately, the map and reduce jobs won’t work appropriately. The configuration parameters that should be set effectively are as per the following:
  • Job’s input area in HDFS.
  • Job’s output area in HDFS.
  • Input and Output format.
  • Classes that contain the map and decrease capacities.
  • Lastly jar file for reducer, mapper and driver classes.
As large information processing is data and time delicate, there are backup processes if DataNode fails. Once a DataNode fails, another replication pipeline is made. The pipeline assumes control over the compose procedure and resumes from where it fizzled. Name Node, which continually watches if any of the blocks is under-repeated, administers the entire procedure or not.