Ace Your Hadoop Interview: Top Questions and Answers

Prepare for your Hadoop interview with this comprehensive guide covering frequently asked questions. From basic concepts to advanced topics, these questions and answers will help you confidently tackle technical interviews related to big data and the Hadoop ecosystem.

Apache Hadoop Interview Questions and Answers

This section covers frequently asked Hadoop interview questions, ranging from basic concepts to more advanced topics. Understanding these questions will help you prepare for technical interviews related to big data and Hadoop.

Basic Hadoop Concepts

1. What is Hadoop?

Hadoop is an open-source framework for storing, processing, and analyzing large datasets across a cluster of machines.

2. System Requirements for Hadoop:

Java 1.8 or higher, Linux or Windows (though Linux is more common), and sufficient hardware resources (RAM, CPU, disk space).

3. Hardware requirements for Hadoop:

Hadoop can run on commodity hardware, but the exact requirements depend on the size of your data and the complexity of your processing tasks.

4. Common Hadoop Input Formats:

TextInputFormat, KeyValueInputFormat, SequenceFileInputFormat.

5. Key characteristics of big data (the 3 Vs):

Volume, Velocity, and Variety.

Hadoop Modules and Architecture

6. What is HDFS (Hadoop Distributed File System)?

HDFS stores data in blocks across a cluster of machines, making it highly scalable and fault-tolerant.

7. What is YARN (Yet Another Resource Negotiator)?

YARN manages cluster resources and schedules jobs (improving on the Hadoop 1 JobTracker).

8. What is MapReduce?

MapReduce is a programming model for processing large datasets in parallel. It works in two phases: map (transforming input data into key-value pairs) and reduce (aggregating values associated with the same key).

9. What is Hadoop Common?

Provides core utilities and libraries for other Hadoop modules.

10. Hadoop's Master/Slave architecture:

Hadoop uses a master/slave architecture, with a single master node (managing the cluster) and multiple slave nodes (performing processing).

11. What is InputSplit in Hadoop?

An InputSplit is a logical division of the input data; each mapper gets one. The InputFormat determines how data is broken into InputSplits.

12. What is TextInputFormat?

TextInputFormat treats each line in a text file as a record. The key is the byte offset; the value is the line content.

13. What is SequenceFileInputFormat?

SequenceFileInputFormat is a binary format for efficiently passing data between MapReduce jobs.

14. Typical number of InputSplits in Hadoop

The number of InputSplits depends on the input file size and the block size. The number of InputSplits can be adjusted based on configuration settings.

15. What is RecordReader in Hadoop?

RecordReader reads data from an InputSplit and converts it into key-value pairs for the mapper.

16. What is JobTracker (Hadoop 1)?

JobTracker manages MapReduce jobs. This has been replaced by YARN in Hadoop 2.

17. What is WebDAV in Hadoop?

WebDAV (Web Distributed Authoring and Versioning) allows accessing HDFS (Hadoop Distributed File System) as a network file system.

18. What is Sqoop in Hadoop?

Sqoop transfers data between relational databases and Hadoop HDFS.

19. JobTracker functionalities (Hadoop 1):

Accepts jobs, communicates with NameNode, finds TaskTrackers, and monitors task progress. These functionalities are handled by YARN in Hadoop 2.

20. What is a TaskTracker (Hadoop 1)?

A slave node executing tasks assigned by the JobTracker. These functionalities are handled by NodeManagers in YARN.

21. What is a MapReduce job?

A programming model for parallel processing; map transforms data; reduce aggregates results.

22. What are map and reducer tasks in Hadoop?

Mapper transforms data into key-value pairs. Reducer aggregates values for each key.

23. What is shuffling in MapReduce?

The process of sorting and distributing mapper outputs to reducers.

24. What is NameNode in Hadoop?

The master node that stores HDFS metadata (file locations, etc.).

25. What are heartbeats in HDFS?

Periodic signals between DataNodes and NameNode (and TaskTrackers and JobTracker); used for monitoring.

26. How is indexing done in HDFS?

HDFS uses a hierarchical structure; each data block points to the next in a file. There is no index in the traditional sense.

27. What happens when a DataNode fails?

The NameNode detects the failure, reschedules tasks, and replicates data to other nodes.

28. What is Hadoop Streaming?

Allows running MapReduce jobs using scripts written in languages other than Java.

29. What is a combiner in Hadoop?

A mini-reducer that processes mapper outputs locally before sending them to reducers (for performance optimization).

30. Hadoop configuration files:

core-site.xml, hdfs-site.xml, yarn-site.xml (Hadoop 2).

31. Network requirements for Hadoop:

Passwordless SSH and a stable network connection are necessary for communication among nodes.

32. Storage and Compute Nodes:

Storage nodes hold data; compute nodes perform processing.

33. Is Java required for Hadoop?

While Hadoop is written in Java, you don't necessarily need to be a Java expert. A basic understanding of Java is very beneficial. You can use other languages with tools like Hadoop Streaming.

34. How to debug Hadoop code?

Use logging, counters, and the Hadoop web UI (YARN).

35. Can Hadoop handle multiple inputs?

Yes, using appropriate InputFormat classes.

36. Relationship between Hadoop jobs and tasks:

A job is divided into multiple tasks (map and reduce).

37. InputSplit vs. HDFS Block:

InputSplit is a logical division; HDFS block is a physical division of data.