Top 50 Hadoop Interview Questions & Answers

07-Oct-2022

Big Data has become the "It" thing in the technological landscape, which has also resulted in the growing demand for Big data-related positions. Big Data Analytics is being used by one in five large businesses, so it's time to start looking for employment in this area. In order to assist you to succeed in the interview, we now present the Top 50 Hadoop Interview Questions & Answers.

Top Hadoop Interview Questions & Answers at Basic Level

We have formulated the most typical and probable big data Hadoop Interview questions and answers. You will be given questions related to these concepts. 

These few Hadoop Interview Questions & answers are aimed at helping you prepare well to put you ahead of the competition. We will start off the list by concentrating on the typical and fundamental Hadoop interview questions and answers that individuals encounter when applying for a Hadoop-related job, regardless of position.

1. What ideas are employed by the Hadoop Framework?

The Hadoop Framework is based on two fundamental ideas: 

• Hadoop Distributed File System: HDFS is a Java-based file system for the scalable and dependable storage of massive datasets. HDFS stores all of its data in the form of blocks and operates on Master-Slave architecture. 
• MapReduce: Large data processing and generation are made possible by the programming model and accompanying implementation known as MapReduce. Hadoop jobs are essentially split into two distinct task jobs. The data collection is divided into key-value pairs or tuples by the map job. The data tuples from the output of the map task are then combined into a smaller set of tuples by the reduced job.

2. Describe Hadoop. Please list the primary elements of a Hadoop application.

Hadoop has emerged as the solution to the problems of Big data. To store and process Big Data, Hadoop is a platform that provides a variety of tools and services. When utilizing the conventional approach is challenging, it is crucial for the analysis of massive data and for making effective business decisions. 

Data processing and storing are extremely simple thanks to Hadoop's extensive toolkit. Here are all of the key elements of Hadoop:

• Hadoop Common
• HDFS
• Hadoop MapReduce
• YARN
• PIG and HIVE – Component of Data Access
• HBase – For Data Storage
•  Sqoop, Apache Flume, Chukwa – Components of Data Integration
• ZooKeepe, Oozie, and Ambari – Components of  Data Management and Monitoring
• Thrift and Avro – Components of Data Serialization 
• Apache Mahout and Drill – This is a Data Intelligence Components

3. What are the various Input formats that Hadoop supports?

The following three input formats are supported by Hadoop:

• Text Input Format: Hadoop's standard input format is text.
• Sequence File Input Format: Files can be read in sequence using the sequence file input format.
• Key Value Input Format: Files in plain text are accepted in this input format.

4. How familiar are you with YARN?

The Hadoop processing framework, known as YARN also referred to as Yet Another Resource Negotiator. The task of managing the resources and creating an environment in which the processes can run falls to YARN. 

5. Why are nodes in a Hadoop cluster constantly added and removed?

An administrator of a Hadoop cluster can add commission and remove decommission data nodes using the functionalities of the Hadoop framework listed below:

• Commodity hardware utilization is one of the key characteristics of the Hadoop system. In a Hadoop cluster, it leads to frequent DataNode crashes. 
• Another crucial aspect of the Hadoop framework that responds to the exponential expansion in data volume is its simplicity of scale.

6. How would you define "Rack Awareness"?

The mechanism used by NameNode to decide how blocks and their replicas are stored in the Hadoop cluster is known as Rack Awareness in Hadoop. Rack definitions, which reduce traffic between DataNodes inside a single rack, are used to accomplish this. Let's use the replication factor's default value of three as an example. Two duplicate copies of each block of data are to be kept in a single rack in accordance with the "Replica Placement Policy," while the third replica is kept in a different rack.

7. Explain a Speculative execution?

In Hadoop, Speculative Execution is a procedure that happens while a task is being executed at a node more slowly. The master node begins another instance of that task on the other node throughout this process. The work that is completed first is accepted, and by killing that, the execution of the other is halted

8. Name a few of Hadoop's key characteristics. 

The key attributes of Hadoop include:

Google MapReduce, which is built on Google's Big Data File Systems, serves as the foundation of the Hadoop framework. 

For Big Data analysis, the Hadoop platform can effectively address a wide range of issues.

9. Name any businesses that use Hadoop?

Several well-known companies are now employing Hadoop. A few of the instances are:

Hadoop is used by Yahoo

• Hive was created by Facebook for analysis.
• Other well-known and reputable businesses that use Hadoop include Netflix, Twitter, Amazon, eBay, Adobe, and Spotify,

10. How are RDBMS and Hadoop distinct from one another?

The main characteristics that set RDBMS and Hadoop apart are:

• Hadoop can store any type of data, be it unstructured, structured, or semi-structured, unlike RDBMS which is designed to store structured data.
• Hadoop is built on the "Schema on Read" policy, whereas RDBMS adheres to the "Schema on Write" approach.
• RDBMS reads are quick because the data's schema is already known, whereas HDFS writes are quick since there is no need to validate the data's schema before writing it.
• RDBMS requires a license, so using it costs money, but Hadoop is open source software, thus using it is free.
• Hadoop is utilized for data analytics, data discovery, and OLAP systems, whereas RDBMS, is used for online transaction processing (OLTP) systems.

Hadoop Interview Questions and Answers based on Hadoop Architecture

The following Hadoop Interview Questions and Answers are based on the Hadoop architecture. These questions and answers will help a great deal in your interview and preparation. It will enhance your knowledge and understanding of Hadoop architecture and help you answer any Hadoop Interview question with ease and confidence.

11. What variations do Hadoop 1 and Hadoop 2 have?

We can make the distinctions between Hadoop 1 and Hadoop 2 on the following grounds:

• Unlike Hadoop 2.x which has Active and Passive NameNodes, Hadoop 1.x only has one NameNode, making it the single point of failure. In the event of a failure of the active NameNode the passive NameNode steps in and assumes control. As a result, Hadoop 2.x has high availability.
• Data processing was a difficulty with Hadoop 1.x, but with Hadoop 2.x, YARN offers a central resource manager that shares a shared resource to run different applications in Hadoop.

12. Can you differentiate Active NameNode from Passive NameNodes?

Both Active NameNode and Passive NameNode are high-availability Hadoop designs. 

The Active NameNode runs in the Hadoop cluster 

The backup NameNode known as the "Passive NameNode" stores the same information as the "Active NameNode" does.

When an active NameNode fails, a passive NameNode steps in and assumes control. The cluster never fails since there is always a running NameNode in it.

13. What are the components present in Apache HBase?

Apache HBase consists of the following primary components:

• Region Server: A table can be partitioned into different regions using the region server. It offers a collection of these regions to clients.
• HMaster: The Region server is managed and coordinated by the HMaster.
• ZooKeeper: In the distributed HBase context, this serves as a coordinator. It works by preserving the server state inside the cluster through session-based communication.

14. How does NameNode handle the DataNode failure?

The correct function of each DataNode in the Hadoop cluster is specified in a signal that NameNode continuously gets from those DataNodes. A block report keeps track of every block that is present on a DataNode. After a predetermined amount of time, a DataNode is considered dead if it fails to relay the signal to the NameNode. The blocks of the deceased node are then replicated or copied by the NameNode to a different DataNode using the replicas that were previously generated.

15. Describe how NameNode recovery works.

The following steps can illustrate how NameNode recovery works and how it helps the Hadoop cluster remain operational.

Step 1: Use the file system metadata replica to launch a new NameNode (FsImage)

Step 2: Set up the DataNodes and clients to recognize the new NameNode.

Step 3: The new NameNode begins servicing the client after it has finished loading the last checkpoint FsImage and has received block reports from the DataNodes.

16. What are the various schedulers that Hadoop offers?

The various Hadoop schedulers that are available are:

• COSHH - It schedules choices by taking the workload, the cluster, and heterogeneity into account.
• FIFO Scheduler: It does not use heterogeneity; instead, it ranks the jobs according to the time of their arrival in a queue.
• Fair Sharing - It establishes a pool of maps and reduced slots on a resource for each user. Each user is free to use their own pool to carry out job execution.

17. Can DataNode and NameNode hardware be considered commodity hardware?

Only because it can store data like laptops and personal computers, which are needed in large quantities, are DataNodes considered commodity hardware. Instead, NameNode serves as the master node and houses information about all of the HDFS blocks. It operates as a high-end system with a lot of memory because it requires a lot of memory.

18. What do Hadoop daemons do? Describe your roles.

The NameNode, Secondary NameNode, DataNode, NodeManager, ResourceManager, and JobHistoryServer are the Hadoop daemons. Their functions can be categorized as below:

• NameNode: The NameNode is the master node that stores the metadata for all directories and files. Additionally, it contains metadata on each file block's placement in the Hadoop cluster.
• Secondary NameNode: This daemon is in charge of merging and archiving the updated Filesystem Image. It is utilized if the NameNode malfunctions.
• DataNode: The slave node that houses the actual data.
• Node Manager: Running on slave machines, NodeManager manages the start of application containers, keeps track of resource utilization, and reports data to ResourceManager.
• ResourceManager: This is the principal authority in charge of scheduling apps running on top of YARN and managing resources.
• JobHistoryServer - When the Application Master ceases functioning, it is responsible for keeping all information about the MapReduce jobs up to date (terminates).

19. Explain Checkpointing and its advantages. 

A FsImage and Edit log are compressed into a new FsImage during checkpointing. Instead of replaying an edit log, the NameNode handles the loading of the final in-memory state directly from the FsImage. The checkpointing operation must be carried out by the secondary NameNode.

An advantage of checkpointing is that it reduces the NameNode's starting time and is a very effective process.

Hadoop Interview Questions & Answers based on Administration

A  Hadoop Administration performs certain administrative tasks to ensure the smooth running of the Hadoop cluster. The following Hadoop Interview questions & answers are Administrative, cluster, and environment based and will be a comprehensive guide for prospective candidates. 

20. What Important Hardware factors should you take into account while deploying Hadoop in a production environment?

The following points are essential

• Memory Requirement: Depending on the application, this will differ between worker services and management services.
• Operating system: A 64-bit OS is desirable as it does not impose any limitations on how much RAM may be used on worker nodes.
• Storage: To achieve scalability and high performance, a Hadoop Platform should be constructed by shifting computing tasks to data.
• Capacity: Large Form Factor discs will be less expensive and offer more storage space.
• Network: To eliminate the possibility of redundancy, two TOR switches per rack are preferable.

21. What should you take into account while setting up a secondary NameNode?

An additional standalone system should always be used to deploy a secondary NameNode. This stops it from affecting how the principal node functions.

22. Can you list the execution modes for Hadoop Code?

The various ways to run Hadoop are:

• Fully-distributed Mode
• Pseudo-distributed Mode
• Standalone Mode

23. List the supported operating systems for the Hadoop Deployment. 

The most popular operating system for Hadoop is Linux. However, with the aid of some additional software, it may also be deployed on the windows operating system.

24. Why is HDFS used for applications that require massive data sets rather than for many individual files?

As opposed to small data chunks stored in multiple files, HDFS is more effective for maintaining a large number of data sets in a single file. The quantity of memory restricts the number of files in the HDFS file system since the NameNode stores the file system's information in RAM. Simply put, adding more files will result in the production of more metadata, which will increase the need for memory (RAM). A block, file, or directory's metadata should ideally take up 150 bytes.

25. What attributes of hdfs-site.xml are crucial?

The following three aspects of hdfs-site.xml are crucial:

• Find the location of the data storage with data.dr.
• Name.dr indicates where metadata storage is located and indicates whether DFS is on a local disc or a remote site.
• A Secondary NameNode's checkpoint.dir directory.

26. What are the key Hadoop tools that improve Big Data performance?

The following are some of the crucial Hadoop tools that improve Big Data performance.

Clouds, Flume, SolrSee/Lucene, Hive, HDFS, HBase, Avro, SQL, NoSQL, Oozie, and ZooKeeper.

Hadoop Interview questions & answers for Experienced Level

27. Define DistCp.

It is a tool used for parallel data copying to and from Hadoop file systems with very huge amounts of data. Its distribution, error handling, recovery, and reporting are all impacted by MapReduce. A list of files and directories is expanded into a series of inputs to map jobs, each of which copies a specific subset of the files listed in the source list.

28. Why are HDFS chunks so large?

The HDFS data block has a default size of 128 MB. The following suggestions are for large-sized blocks:

To cut down on seek costs: Due to the enormous size of the blocks, it may take longer to move the data from the disc than it does to start the block. The numerous blocks are therefore sent at the disc transfer rate.

If there are small blocks, there will be an excessive number of blocks in Hadoop HDFS and an excessive amount of metadata to store. Managing such a large number of blocks and information will increase network traffic and add overhead.

29. What is the replication factor by default?

The replication factor is 3 by default. 

On the same data node, there won't be any duplicate copies. The first two copies are typically found on the same rack, but the third copy is typically taken off the shelf. In order to ensure that one copy is always secure, regardless of what happens to the rack, it is recommended to set the replication factor to at least three. The file system's default replication factor, as well as the replication factor for each individual file and directory, can be customized. We can set a lower replication factor for non-critical files, and a higher replication factor for key files.

30. How can faulty records be skipped in Hadoop?

When processing map inputs, Hadoop offers the ability to skip a specific set of poor input records. This feature can be controlled by applications using the SkipBadRecords class. When map tasks fail deterministically on a certain input, this capability might be employed. Typically, map function errors are the blame for this. They would need to be resolved by the user.

31. What is the storage system of the two metadata that NameNode Server keeps?

The NameNode server keeps two different kinds of metadata on disc and in RAM.

Metadata is connected to the following two files:

• EditLogs: It contains all of the most recent file system modifications since the last FsImage.
• FsImage: It holds the complete state of the file system's namespace going back to the creation of the NameNode.

The NameNode will immediately record the file's deletion from HDFS in the EditLog.

The Secondary NameNode constantly reads all the file systems and metadata that are in the Namenode's RAM and records them into the file system or hard drive. FsImage and EditLogs are integrated in the NameNode. The EditLogs are periodically downloaded by Secondary NameNode from NameNode and then applied to FsImage. Once the NameNode has started the next time, the new FsImage is then copied back into it.

32. What command is used to determine the health of the File-system and Blocks?

The following command is used to determine the block's status: hdfs fsck path> -files -blocks

The program used to determine the health of the file system is hdfs fsck/ -files -blocks -locations > dfs-fsck.log.

33. Describe the procedure for copying data from the local system to HDFS.

Hadoop fs -copyFromLocal [source][destination] is the command used to copy data from the Local system to HDFS.

34. Describe the steps a Jobtracker in Hadoop takes.

The jobs are sent to Jobtracker via the client application.

To find the data location, the JobTracker connects to the NameNode.

JobTracker locates TaskTracker nodes using the slots that are open and the nearby data.

The work is submitted to the chosen TaskTracker Nodes.

JobTracker notifies the user and determines the next steps when a task fails.

TaskTracker nodes are watched over by JobTracker.

35. Describe how the MapReduce framework's distributed Cache works.

When you need to exchange files around all nodes in a Hadoop cluster, the MapReduce Framework's Distributed Cache is a crucial tool that you can use. These files can be simple properties files or jar files.

Text files, zip files, jar files, and other small to medium-sized read-only files can all be cached and distributed across all Datanodes (worker-nodes) where MapReduce jobs are running thanks to Hadoop's MapReduce architecture. A local copy of the file is sent via Distributed Cache to each Datanode.

36. Describe the steps that take place when a DataNode fails.

• Which blocks the DataNode failed on are detected by the Jobtracker and the name node, respectively.
• All jobs are rescheduled on the failing node by identifying other DataNodes that have copies of these blocks.
• For the purpose of preserving the configured replication factor, user data will be copied from one node to another.

37. What constitutes a mapper's fundamental parameters?

Text, LongWritable, text, and IntWritable are mappers' three main inputs. The first two are examples of input parameters, while the final two are examples of intermediate output parameters.

38. Describe Spark's resilient distributed datasets.

The fundamental data structure of Apache Spark is resilient distributed datasets. It has been put into the Spark Core. They are fault-tolerant and immutable. RDDs are created by transforming pre-existing RDDs or by storing an external dataset in a storage system with a solid foundation, such as HDFS or HBase.

They can be used in parallel operations since they have distributed collections of things. Parts of resilient distributed datasets are separated so they can be run on different cluster nodes.

39. Give a quick overview of the low latency tasks that Spark excels in, such as graph processing and machine learning.

For quicker processing and the building of machine learning models, Apache Spark stores the data in memory. To create an optimized model, machine learning algorithms may be repeated several times and move through different conceptual steps. Graph algorithms move along all the nodes and edges to construct a graph. Performance is improved by these low latency workloads, which demand a lot of repetitions.

40. Describe a meta store in Hive.

The metadata data is kept in a meta store; alternatively, a relational database management system (RDBMS) and an open-source ORM layer can be used to transform object representation into a relational schema. It houses all of the metadata for Apache Hive. In a relational database, it maintains metadata for Hive partitions and tables (including information about their location and schema). Utilizing the meta store service API, it provides the client with access to this data. HDFS storage is separate from disc storage for the Hive metadata.

41. Compare the variations between local and remote meta stores.

The variation between Local and Remote Met stores can be made in the following points:

• A local meta store operates within the same JVM as the Hive service. While the Remote meta store has a unique JVM that runs in a separate JVM process. 
• A local Meta store has the facility of accessing a separate database running in a different JVM either on a separate or the same computer. 
• However, the primary benefit of a remote meta store is that it forgoes requiring the administrator to disclose the JDBC login credentials for the meta store database.

42. Does Hive support Multiline comments? Give reasons either yes or no. 

No, only single-line comments are enabled at this time in Hive. Multiline comments are not yet supported.

43. Why is partitioning in Hive necessary?

Tables are divided into partitions using Apache Hive. Depending on the values of pertinent columns like date, city, and department, a table is partitioned into related components.

44. What is the task of the -compress-codec parameter?

To receive your output file of a sqoop importation in a format other than.gz, use the -compress-codec argument.

45. What is Hadoop Apache Flume?

For gathering, aggregating, and transporting massive amounts of streaming data, such as record files and events from various references, to a centralized data repository, Apache Flume is a tool, service, or data intake method. 

Flume is a distributed utility that is very customizable. Typically, it is made to copy log data from various web servers that are streaming data to HDFS.

46. Can you describe Apache Flume's architecture?

The following components make up the architecture of  Apache Flume

• Flume Source
• Flume Channel
• Flume Sink
• Flume Agent
• Flume Event

47. Describe the effects of distributed applications.

• Heterogeneity: Taking into account Hardware devices, OS, networks, and Programming languages, the design of applications should enable users to access services and deploy programs over a heterogeneous collection of networks and computers.
• Transparency: Distributed system architects must do everything in their power to disguise the complexity of the system. Location, access, relocation, relocation, and other terms related to transparency are only a few.
• Openness: The ability of a system to be extended and refactored in different ways depends on its openness.
• Security: Confidentiality, integrity, and availability must be considered by distributed system designers.
• Scalability: A system is said to be scalable if it can accommodate the increase of users and resources without experiencing discernible performance degradation.

48. Define Flume Channel?

The events are buffered until they are transmitted into the sink by an Intermediate Store, which receives the data from the flume source. The Flume Channel is the name of the Intermediate Store. An intermediary source is a channel. It connects a Sink Flume channel to a Source Flume channel. Both the Memory channel and the File channel are supported. Since the file channel is non-volatile, any data that is entered into it cannot be lost until you choose to delete it. The Memory Channel, in contrast, is very quick since events are kept in memory, making it volatile and subject to data loss.

49. What are the File formats by default for importing data by employing that Apache Sqoop?

The following file formats are the defaults that allow importing of Data:

• Delimited Text File Format
• Sequence File Format

50. Define the Execution Engine of a Hive Architecture.

Execution Engine processes the query by serving as a link between Hive and Hadoop. To carry out actions like creating or deleting tables, the Execution Engine engages in bidirectional communication with the Metastore.

That is the end to our list of the top Hadoop interview questions & answers. We hope this set of questions gives you a basic overview and helps you in your interview.

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Hadoop interview questions & answers top Hadoop interview questions & answers