June 05, 2019

Top 20 Hadoop Technical Interview Questions & Answers

Ques: 1. What is the need of Hadoop?

Ans: A large amount of unstructured data is getting dumped into our machines in every single day. The major challenge is not to store large data sets in our systems but to retrieve and analyze the big data in the organizations, that too data present in different machines at different locations.
In this situation, a necessity for Hadoop arises. Hadoop has the ability to analyze the data present in different machines at different locations very quickly and in a very cost-effective way. It uses the concept of MapReduce which enables it to divide the query into small parts and process them in parallel. This is also known as parallel computing.

Ques: 2. What is the Hadoop Framework?

Ans: It is an open source framework, which is written in java by Apache software foundation. This framework is used to write software application which requires to process vast amount of data. Hadoop could handle multi tera bytes of data. It works in-parallel on large clusters which could have 1000 of computers (Nodes) on the clusters. It also processes data very reliably and fault-tolerant manner.

Ques: 3. What are the various basic characteristics of Hadoop?

Ans: Hadoop framework has the capability of solving issues involving Big Data analysis. It is written in Java. Its programming model is based on Google. MapReduce and infrastructure is based on Google’s Big Data and distributed file systems. Hadoop is scalable, and more nodes can be added to it.

Ques: 4. What are the core components of Hadoop?

Ans: HDFS and MapReduce are the core components of Hadoop. Hadoop Distributed File System (HDFS) is basically used to store large data sets and MapReduce is used to process such large data sets.

Ques: 5. What do you understand by streaming access?

Ans: HDFS works on the principle of ‘Write Once, Read Many’. This feature of streaming access is extremely important in HDFS. In HDFS, reading the complete data is more important than the time taken to fetch a single record from the data. HDFS focuses not so much on storing the data but how to retrieve it at the fastest possible speed, especially while analyzing logs.

Ques: 6. What do you understand by a task tracker?

Ans: Task Trackers manage the execution of individual tasks on slave node. Task tracker is also a daemon that runs on DataNodes. When a client submits a job, the job tracker will initialize the job and divide the work and assign them to different task trackers to perform MapReduce tasks.
While performing this action, the task tracker will be simultaneously communicating with job tracker by sending heartbeat. If the job tracker does not receive heartbeat from task tracker within specified time, then it will assume that task tracker has crashed and assign that task to another task tracker in the cluster.

Ques: 7. If a particular file is 40 MB, Will the HDFS block still consume 64 MB as the default size?

Ans: No, 64 mb is just a unit where the data will be stored. In this particular situation, only 40 MB will be consumed by an HDFS block and 24 MB will be free to store something else. It is the MasterNode that does data allocation in an efficient manner.

Ques: 8. What is a Rack in Hadoop?

Ans: Rack is a storage area with all the DataNodes put together. Rack is a physical collection of DataNodes which are stored at a single location. There can be multiple racks in a single location. These DataNodes can be physically located at different places.

Ques: 9. How will the data be stored on a Rack?

Ans: The content of the file will be divided into blocks whenever the client is ready to load a file into the cluster. Now the client consults the NameNode and gets 3 DataNodes for every block of the file which indicates where the block should be stored. While placing the DataNodes, the key rule followed is “for every block of data, two copies will exist in one rack, third copy in a different rack”. This rule is known as “Replica Placement Policy”.

Ques: 10. Can you explain the input and output data format of the Hadoop Framework?

Ans: The MapReduce framework operates exclusively on pairs, that is, the framework views the input to the job as a set of pairs and produces a set of pairs as the output of the job, conceivably of different types.
The flow can be like: [input] -> map -> -> combine/sorting -> -> reduce -> [output]

Ques: 11. How can you use the Reducer?

Ans: Reducer reduces a set of intermediate values which share a key to a (can be smaller one) set of values. The number of reduces for the job is set by the user via Job.setNumReduceTasks(int).

Ques: 12. How can you explain the core methods of the Reducer?

Ans: The API of Reducer is very similar to that of Mapper, there's a run() method that receives a Context containing the job's configuration as well as interfacing methods that return data from the reducer itself back to the framework. The run() method calls setup() once, reduce() once for each key associated with the reduce task, and cleanup() once at the end. Each of these methods can access the job's configuration data by using Context.getConfiguration().
Reduce() method is the heart of any Reducer. This is called once per key; the second argument is an iteratable which returns all the values associated with that key.

Ques: 13. How can you schedule a Task by a Jobtracker?

Ans: The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These messages also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.

Ques: 14. How many Daemon processes run on a Hadoop cluster?

Ans: There are five daemons run on a Hadoop cluster. Each of these daemons runs in its own JVM.
NameNode, secondary NameNode and JobTracker Daemons run on Master nodes. DataNode and TaskTracker run on each Slave nodes.
·         NameNode: This daemon stores and maintains the metadata for HDFS.
·         Secondary NameNode: Performs housekeeping functions for the NameNode.
·         JobTracker: Manages MapReduce jobs, distributes individual tasks to machines running the Task Tracker.
·         DataNode: Stores actual HDFS data blocks.
·         TaskTracker: It is Responsible for instantiating and monitoring individual Map and Reduce tasks.

Ques: 15. What is Hadoop Distributed File System (HDFS)? How it is different from Traditional File Systems?

Ans: The Hadoop Distributed File System (HDFS), is responsible for storing huge data on the cluster. This is a distributed file system designed to run on commodity hardware.
It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant.
  • HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware.
  • HDFS provides high throughput access to application data and is suitable for applications that have large data sets.
  • HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files.

Ques: 16. What are the IdentityMapper and IdentityReducer in Mapreduce?

  • org.apache.hadoop.mapred.lib.IdentityMapper: Implements the identity function, mapping inputs directly to outputs. If MapReduce programmer does not set the Mapper Class using JobConf.setMapperClass then IdentityMapper.class is used as a default value.
  • org.apache.hadoop.mapred.lib.IdentityReducer : Performs no reduction, writing all input values directly to the output. If MapReduce programmer does not set the Reducer Class using JobConf.setReducerClass then IdentityReducer.class is used as a default value.

Ques: 17. What do you mean by commodity hardware? How can Hadoop work on them?

Ans: Average and non-expensive systems are known as commodity hardware and Hadoop can be installed on any of them. Hadoop does not require high end hardware to function.

Ques: 18. Which one is the Master Node in HDFS? Can it be commodity?

Ans: Name node is the master node in HDFS and job tracker runs on it. The node contains metadata and works as high availability machine and single pint of failure in HDFS. It cannot be commodity as the entire HDFS works on it.

Ques: 19. What is the main difference between Mapper and Reducer?

Ans: Map method is called separately for each key/value have been processed. It processes input key/value pairs and emits intermediate key/value pairs.
  • Reduce method is called separately for each key/values list pair. It processes intermediate key/value pairs and emits final key/value pairs.
  • Both are initialize and called before any other method is called. Both don’t have any parameters and no output.

Ques: 20. What is difference between MapSide Join and ReduceSide Join?

Joining the multiple tables in mapper side, called map side join. Please note mapside join should has strict format and sorted properly. If data set is smaller tables, goes through reducer phrase. Data should be partitioned properly.
Join the multiple tables in reducer side called reduceside join. If you have large amount of data tables, planning to join both tables. One table is large amount of rows and columns, another one has few number of tables only, goes through Reduceside join. It’s the best way to join the multiple tables.

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