Top 50 Hadoop Interview Questions for Freshers & Experienced

You’ve arrived at the appropriate location if you’re getting ready for a Hadoop interview. In this extensive article, we have created a list of 52 interview questions that span many Hadoop themes, ranging from fundamental ideas for new candidates to more complex subjects for more seasoned candidates. These questions will improve your comprehension of Hadoop’s fundamental parts, such as HDFS and MapReduce, and delve into the complexities of data processing, fault tolerance, scalability, and security, whether you are new to Hadoop or have past familiarity with it. So let’s get started and have a look at the Hadoop interview questions.

Hadoop Interview Questions for Freshers

Following are the Hadoop interview questions and answers for freshers.

1. What is Hadoop?

Hadoop is an open-supply framework used for processing and storing massive datasets in dispensed computing surroundings.

2. What are the core components of Hadoop?

The middle components of Hadoop are the Hadoop dispensed document system (HDFS) and MapReduce.

3. What is the reason for HDFS in Hadoop?

HDFS is a distributed document device that offers excessive-throughput access to software data across a Hadoop cluster.

4. Explain the concept of MapReduce in Hadoop.

MapReduce is a programming version used for processing massive datasets in parallel throughout a distributed cluster.

5. What is the position of the NameNode in Hadoop?

The NameNode is accountable for dealing with the report gadget namespace and controlling getting admission to files in HDFS.

6. What is the significance of the Secondary NameNode?

The Secondary NameNode periodically creates checkpoints for the metadata of the HDFS, assisting in faster recuperation in case of failure.

7. What are the advantages of using Hadoop?

Hadoop offers scalability, fault tolerance, and system capability and examines huge quantities of facts correctly.

8. What is the function of a JobTracker in Hadoop?

The JobTracker manages the scheduling and execution of MapReduce jobs on a Hadoop cluster.

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9. Explain the concept of DataNode in Hadoop.

Data nodes are responsible for storing and retrieving data blocks within HDFS.

10. What is the purpose of the TaskTracker in Hadoop?

TaskTrackers are responsible for executing tasks on the slave nodes in a Hadoop cluster.

11. What are the limitations of Hadoop 1.0?

Here are some of the limitations:

• It is only allowed to configure one NameNode.
• The secondary NameNode was supposed to copy the NameNode’s hourly metadata backup.
• Per Cluster, it supports up to 4000 Nodes.
• It includes one component, called JobTracker, which can handle various tasks, including resource management, job scheduling, job monitoring, and job rescheduling.
• Only one Name No. and one Namespace is supported per Cluster.
• It needs to improve NameNode’s capacity to scale horizontally.

12. Describe the speculative execution in Hadoop.

As a result of Hadoop’s ability to initiate redundant tasks and select the one that completes first, speculative execution helps jobs finish faster overall.

13. What distinguishes organized, semi-structured, and unstructured data from one another?

The three types of data are structured, semi-structured, and unstructured, with structured data adhering to a predetermined framework, respectively.

14. How does Hadoop handle data replication and guarantee the accuracy of the data?

To ensure fault tolerance and data dependability, Hadoop replicates data blocks among various data nodes.

15. What function does a combinator perform in Hadoop MapReduce?

Before data is delivered to the reducer, the result of the map phase is locally aggregated by the combiner, a step in the MapReduce process that is optional.

16. What are the three modes that Hadoop can Run?

• Local Mode or Standalone Mode: Hadoop, by default, is configured to run in a non-distributed mode. It runs as a single Java procedure. Instead of HDFS, this mode utilizes the nearby file device. 
• Pseudo-Distributed Model: In this mode, each daemon runs in a separate Java manner. This mode calls for custom configuration.  This mode of deployment is useful for checking out and debugging functions.
• Fully Distributed Mode: This is Hadoop’s production mode. Hadoop Daemons require the definition of environment and configuration settings. This mode offers scalability, fault tolerance, security, and completely distributed computing capabilities.

17. What is an Apache Hive?

Hive is an open-source solution that uses Hadoop to analyze structured data; it sits on top of Hadoop to aggregate Big Data and make analysis and queries easier. Additionally, hive enables SQL developers to create Hive Query Language statements for data analysis and querying that are identical to regular SQL statements. 

18. Describe Yarn.

Yarn is the Hadoop layer for resource management. In Hadoop 2.x, The Yarn was introduced to execute and process data saved in the Hadoop Distributed File System. Yarn offers a variety of data processing engines, including graph processing, batch processing, interactive processing, and stream processing. The Hadoop 2.x data operating technique is Apache Yarn. 

19. List the elements of YARN.

• Resource Manager: It manages resource allocation in the cluster and runs as a master daemon.
• Node Manager: This program runs on slave daemons and performs a task on each Data Node individually.
• Application Master: It manages the resource requirements and user job lifecycles for specific applications. It collaborates with the Node Manager and keeps track of task completion.
• Container: A container is a collection of resources on a single node, such as RAM, CPU, Network, HDD, etc.

Hadoop Interview Questions for Experienced Candidates

Following are the interview questions and answers for experienced candidates.

20. What is the difference between HDFS and a traditional document device?

HDFS is designed for massive-scale distributed garage and processing, at the same time as traditional report structures are generally used for single machines.

21. Explain the running precept of speculative execution in Hadoop.

Speculative execution allows Hadoop to launch redundant tasks and choose the only one that finishes first, decreasing normal job finishing touch time.

22. How will you enhance the overall performance of a Hadoop cluster?

Performance may be improved by tuning various parameters, inclusive of block size, replication aspect, reminiscence allocation, and network bandwidth.

23. What is the motive of the YARN (yet some other aid Negotiator) framework?

YARN is a resource management framework that permits strolling a couple of processing engines, together with MapReduce and Apache Spark, on a Hadoop cluster.

24. How can you configure a Hadoop cluster for excessive availability?

Excessive availability can be done by way of allowing Namenode HA (excessive Availability) and ensuring the right backup and restoration mechanisms.

25. Provide an explanation for the use cases of Apache Pig and Apache Hive in Hadoop.

Apache Pig is used for analyzing big datasets by the use of an excessive-degree scripting language, whilst Apache Hive affords a square-like interface for querying and dealing with statistics.

26. What are the security mechanisms available in Hadoop?

Hadoop presents diverse protection features, together with Kerberos authentication, access manage Lists (ACLs), and encryption.

27. Describe the idea of data locality in Hadoop.

Statistics locality refers back to the potential of executing duties near the facts they want, lowering network overhead and improving overall performance.

28. How can you handle massive documents that do not suit the default block length in HDFS?

Hadoop helps split large files into blocks of the default size, permitting efficient storage and processing across the cluster.

29. What are the different input/output codecs available in Hadoop?

Hadoop supports diverse enter/output codecs, including text, SequenceFile, Avro, Parquet, and RCFile.

30. How does Hadoop protect the accuracy of data in a distributed setting?

By storing several copies of data blocks and performing recurring integrity checks using checksums, Hadoop ensures data integrity.

31. What function does the Resource Manager in YARN perform?

Answer. In a Hadoop cluster, applications are scheduled and resources are managed by the resource manager.

32. How are data skew problems in MapReduce tasks handled by Hadoop?

Hadoop optimizes the distribution of data across nodes and uses bespoke partitioners and combiners to manage data skew issues.

Common Hadoop Admin Interview Questions

A candidate applying for an admin position can go through these additional questions.

33. What purpose do the Hadoop configuration files serve?

The settings in Hadoop configuration files regulate how different parts of the Hadoop ecosystem behave.

34. Describe the Hadoop notion of data replication.

By storing several copies of each data block on various data nodes, data replication improves data durability and fault tolerance.

35. What function does the ResourceManager perform in YARN?

The task of assigning resources to various apps operating on a Hadoop cluster falls under the purview of the Resource Manager.

36. How does Hadoop manage cluster failures?

Using replication and redundant job execution, Hadoop automatically detects problems and recovers from them.

37. What function does the MapReduce framework serve in Hadoop 2.x and subsequent releases?

The MapReduce framework largely focuses on data processing in Hadoop 2.x and later, whereas YARN is in charge of resource management.

38. Describe how combiners work in Hadoop MapReduce.

In order to minimize the quantity of data sent over the network, combiner mini-reduces execute local aggregation on the output of mappers.

39. Which input types are used most commonly in MapReduce jobs?

Input types that are frequently used include TextInputFormat, KeyValueTextInputFormat, and SequenceFileInputFormat.

40. How does Hadoop deal with data skew issues in MapReduce tasks?

Data skew can be resolved by using special partitioners, combiners, and data pre-processing techniques.

41. What various data compression methods does Hadoop support?

Various compression codecs, including Gzip, Snappy, LZO, and Bzip2, are supported by Hadoop.

42. Describe speculative execution and the role it plays in Hadoop.

To manage sluggish tasks or failures, redundant tasks are launched as part of the process known as “speculative execution,” which reduces the time it takes for a job to complete overall.

43. What function does the YARN application master perform?

The Resource Manager and the Application Master negotiate resources, and the Application Master coordinates the completion of tasks for a particular application.

44. How is data security handled by Hadoop?

To guarantee data security, Hadoop offers security features including Kerberos authentication, Access Control Lists (ACLs), and data encryption.

45. What function does the distributed Hadoop cache serve?

Read-only files, archives, and other resources needed by MapReduce jobs are distributed across a Hadoop cluster using the Hadoop Distributed Cache.

46. What is Hadoop’s speculative execution, and why is it significant?

In Hadoop, running many instances of a task on various nodes and selecting the one that completes first is known as “speculative execution.” It’s crucial to deal with errors or processes that run slowly. Hadoop creates a second instance on a different node when a task takes longer than anticipated, speeding up the overall job completion time.

47. What function do the TaskTracker and JobTracker provide in Hadoop 1.x?

In a Hadoop cluster, task assignment, resource management, and job scheduling are handled by the JobTracker in Hadoop 1.x. Each slave node has a TaskTracker process that runs, which is in charge of carrying out tasks and updating the JobTracker on their status.

48. What functions do the ResourceManager and NodeManager perform in Hadoop versions 2.x and later?

The Resource Manager in Hadoop 2.x and beyond is in charge of scheduling jobs, allocating resources, and overseeing cluster performance. Each node has a NodeManager, which controls resources, keeps track of container execution, and reports to the ResourceManager.

49. What advantages does using Zookeeper offer?

• Simple distributed coordination process: In Zookeeper, the coordination between all nodes is simple. Mutual exclusion and cooperation between server processes are examples of synchronization. 
• Ordered Messages: Zookeeper tracks messages with a number and indicates their order by stamping each update; as a result, communications are ordered here.
• Serialization: Follow predetermined procedures to encode the data. Make sure your program functions consistently.
• Atomicity: No transaction is incomplete; data transmission either succeeds or fails.

50. List the various Znode kinds.

• Persistent Znodes: The persistent znode is the standard znode in ZooKeeper. It remains on the Zookeeper server indefinitely until another client removes it.
• Ephemeral Znodes: These are short-lived Znodes. Every time the creator client leaves the ZooKeeper server, it is destroyed.
• Sequential Znodes: Each sequential Znode has a 10-digit number appended to its name in numerical order. 

Conclusion

The essential elements of Hadoop, data processing, fault tolerance, scalability, security, and the Hadoop ecosystem are just a few of the many subjects we’ve addressed in this blog. These Hadoop interview questions will test your comprehension and make sure you have a firm grasp of Hadoop’s essential principles and features, whether you are a novice entering the world of Hadoop or an experienced professional trying to improve your skills. You will be well-prepared to face any Hadoop interview and show your proficiency in this potent big data framework by carefully learning and practicing these questions.