Top Hadoop Interview Questions and Answers in 2023

Are you preparing for a Hadoop interview? If yes, then you are at the right place. So, Hadoop is one of the popular open-source software frameworks. It is used for distributed storage and for the processing of large data sets. It was initially developed by Doug Cutting and Mike Cafarella in 2005. It is maintained by the Apache Software Foundation.

In this article, we will discuss Hadoop interview questions. We will discuss interview questions in different levels, such as beginner, intermediate, and advance levels. Let us start with the beginner-level Hadoop interview questions. 

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Beginner Level Interview Questions for Hadoop

In this section we will discuss beginner level Hadoop interview questions.

1. What do you mean by Hadoop?

It is an open-source software framework. It is used for distributed storage. It is also used for processing of large data sets. 

It is designed to run on many commodity hardware machines in a cluster. It allows distributed processing of data.

2. What are the components of Hadoop?

There are two main components of Hadoop:

1. Hadoop Distributed File System (HDFS): This is a distributed file system. It provides high-throughput access to application data.
    
2. MapReduce: This is a programming model. It is used to process large data sets in parallel across a distributed cluster of machines.

3. Why do we need Hadoop?

We need Hadoop for several reasons:

• Handling Big Data
   
• Distributed Storage
   
• Distributed Processing
   
• Cost-Effective
   
• Flexibility

4. Explain big data and list its characteristics.

Big Data refers to exceptionally large and complex datasets that traditional data processing tools cannot handle effectively. Big Data is characterized by its volume, velocity, variety, and value.

• Volume: Big Data involves vast amounts of information, which can range from terabytes to petabytes or more, making it challenging to store and process with conventional databases.
   
• Velocity: Data is generated and collected at a very high speed. For example, social media updates, sensor data, and financial transactions occur rapidly.
   
• Variety: Big Data very diverse in terms of data types and sources. It includes structured data (like databases), semi-structured data (like XML and JSON), and unstructured data (like text, images, and videos).
   
• Value: The primary goal of big data analytics is to extract valuable information and knowledge from Big Data. It can help organizations make data-driven decisions.

5. What are the Limitations of Hadoop 1.0?

Hadoop 1.0 was an early version of the Apache Hadoop framework, and it had several limitations compared to the newer versions. Some of the key limitations of Hadoop 1.0 are:-

• Scalability: Hadoop 1.0 had limitations in terms of scalability. It could handle only a limited number of nodes in a cluster, which made it harder to process very large datasets.
   
• JobTracker and TaskTracker: In Hadoop 1.0, the JobTracker and TaskTracker components were responsible for managing and tracking jobs and tasks. However, these components acted as a single point of failure.
   
• Resource Management: Resource management in Hadoop 1.0 was based on a simple first-come, first-served model, which was inefficient in resource allocation.
   
• Batch Processing: Hadoop 1.0 was primarily designed for batch processing, making it less suitable for real-time or interactive data processing tasks.

6. What is Hadoop Streaming?

It is a utility that allows developers to create and run MapReduce programs. Those programs can be written in languages other than Java, such as Python, Ruby, and Perl. Using Hadoop Streaming, data can be passed between a MapReduce job's mapper and reducer functions through standard input and output streams instead of using Java-specific interfaces.

7. Can you give some examples of vendor-specific Hadoop distributions?

There are several vendor-specific distributions of Hadoop available in the market. Here are some examples:

• Cloudera Distribution for Hadoop (CDH)
   
• Hortonworks Data Platform (HDP)
   
• MapR Distribution for Apache Hadoop
   
• IBM Open Platform with Apache Hadoop (IOP)
   
• Amazon EMR (Elastic MapReduce)
   
• Microsoft Azure HDInsight
   
• Google Cloud Dataproc

8. What is the default block size in Hadoop?

Hadoop stores large files in blocks. Each block in Hadoop is stored in multiple DataNodes in the cluster. The default block size of 128 MB(Megabytes) is set to balance the disk space utilization and the processing overhead of managing the blocks.
 

9. What are the different Hadoop configuration files?

There are several configuration files in Hadoop. These files are used to control the behavior of the Hadoop framework and its various components. 

Some of the important configuration files in Hadoop are:

10. Explain key Features of HDFS.

Hadoop Distributed File System (HDFS) is the storage system used by Hadoop and it is designed to store and manage vast amounts of data efficiently. Some of its key features are listed below:-

• Distributed Storage: HDFS stores data across multiple nodes in a cluster, offering distributed and fault-tolerant storage.
   
• High Fault Tolerance: HDFS replicates data across multiple nodes to ensure data availability even during hardware failures. If one node goes down, data can still be retrieved.
   
• Data Block Size: HDFS divides files into fixed-size blocks. This block size simplifies data distribution and helps manage large files more efficiently.
   
• Write-Once, Read-Many Model: HDFS follows a write-once, read-many model, suitable for batch processing. Once data is written, it is not typically modified, simplifying data management.

11. What do you mean by a block in HDFS?

A block in Hadoop Distributed File System (HDFS) is the basic unit of storage. It is used for storing a file. A block is a contiguous sequence of bytes. It has a fixed size, which is set by the Hadoop administrator when the cluster is set up.

12. Explain the  main differences between HDFS (Hadoop Distributed File System ) and Network Attached Storage(NAS).

The following are some main differences between HDFS and NAS:-

13. What are the different modes available for running Hadoop?

Hadoop can be run in three different modes. It depends on the requirements of the Hadoop cluster and the resources available in the environment:

• Local (Standalone) Mode
   
• Pseudo-Distributed Mode
   
• Fully-Distributed Mode

14. What is shuffling in MapReduce?

Shuffling refers to the process of redistributing and exchanging data between different nodes or reducers in a distributed storage environment. Shuffling is a crucial step in the MapReduce execution model and occurs between the Map phase and the Reduce phase.

15. What is the use of the 'Hadoop fsck' command?

The 'Hadoop fsck' command checks the health and status of files stored in the Hadoop Distributed File System (HDFS). It provides a report that includes information about the files. That information can be their replication factor, block size, file size, and the number of blocks they occupy in the HDFS.

Intermediate Level Interview Questions for Hadoop

In this section we will discuss intermediate level Hadoop interview questions.

16. What are the differences between MapReduce and Spark?

MapReduce and Spark are the two most popular big data processing frameworks. They are used for processing large volumes of data. Both frameworks are designed to handle big data. They can be used to build large-scale data processing applications. Here are some differences between MapReduce and Spark:

17. How does Hadoop handle data replication?

Hadoop handles data replication by making multiple copies of the data. It stores them on different nodes in a Hadoop cluster. Whenever a file is uploaded to HDFS, it is automatically divided into blocks. That each block is replicated a certain number of times, depending on the replication factor set for the cluster. 

The default replication factor is 3, which means that each block is replicated three times.
 

18. Define under-replicated blocks.

It is a block with fewer replicas than the replication factor set for the cluster. This might be possible due to node failures or network issues. When a block is under-replicated, it reduces the fault-tolerance. It also reduces the availability of the data.

The Hadoop NameNode periodically checks the status of blocks in the cluster. It triggers the replication of under-replicated blocks to restore the essential replication factor.
 

19. Define over-replicated blocks.

It is a block with more replicas than the replication factor set for the cluster. This might be possible when the replication factor is increased for the cluster. It might also be possible when the blocks are manually copied to additional nodes. When a block is over-replicated, it wastes storage space on the nodes and may lead to slower data access times due to increased network traffic. 

The Hadoop NameNode periodically checks the status of blocks in the cluster. It triggers the deletion of over-replicated blocks to optimize storage utilization.
 

20. What do you mean by Combiner in Hadoop MapReduce? How does it help in reducing network traffic?

A Combiner is a feature in Hadoop MapReduce. It allows the intermediate data output from the Map phase to be combined on the Map task node before being sent to the Reduce phase. A Combiner function is similar to the Reducer function. It takes as input a key-value pair. It returns output in the same format.

A Combiner function helps to reduce the amount of data. That data needs to be transferred across the network from the Map phase to the Reduce phase. The Combiner function reduces the number of unique key-value pairs by aggregating or summarizing the values associated with each key. As a result, the amount of data that needs to be transferred across the network is reduced. It also helps to make data processing faster and lower network congestion.
 

21. What do you mean by YARN in Hadoop? How does it improve the performance of Hadoop clusters?

YARN stands for Yet Another Resource Negotiator. It is a component of Hadoop that manages the resources. It schedules the tasks for large-scale data processing. It was introduced in Hadoop 2.x to separate the responsibilities of job scheduling and resource management from the MapReduce engine. YARN improves the performance of Hadoop clusters in many ways:

22. What is Apache ZooKeeper?

Apache ZooKeeper is an open-source distributed coordination service that plays an important role in managing and maintaining configuration information, distributed synchronization, and group services in large-scale distributed systems. It provides a reliable infrastructure for coordinating distributed applications by offering features like distributed locks, leader election, and centralized configuration management.

23. What do you understand by Hadoop's Fair Scheduler?

The Hadoop’s Fair Scheduler is a pluggable scheduler for Hadoop. It enables fair sharing of cluster resources between multiple users and applications. Fair Scheduler dynamically schedules the resources on a per-job basis. It tries to allocate resources to jobs in a way that makes sure that each job receives a fair share of the cluster resources. 

The Fair Scheduler works by maintaining a pool of jobs and also their related resources. It assigns jobs to available resources based on their current utilization. It prioritizes jobs based on a configurable set of rules, such as job size or job priority. 
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24. What are the key configuration parameters that need to be specified in a MapReduce program?

In a MapReduce program, there are several key configuration parameters that need to be specified in order to control the behavior of the job. 

Some of the major configuration parameters include:

• Input and output paths
   
• Mapper and reducer classes
   
• Input and output formats
   
• Number of the map and reduce tasks
   
• Partitioner class
   
• Combiner class
   
• Job name and description

25. What are the JobTracker and TaskTracker in Hadoop?

JobTracker and TaskTracker in Hadoop are two essential components of the MapReduce framework. These are used for distributed processing of large datasets. 

The JobTracker is primarily responsible for managing the MapReduce jobs. These jobs are submitted to the cluster. It tracks the progress of each job. It schedules tasks to run on TaskTrackers and monitors the health of the TaskTrackers. The JobTracker handles job submission, task scheduling, task re-execution in case of failures, and task progress monitoring.

On the other hand, TaskTracker runs on each node in the Hadoop cluster. It is primarily responsible for executing tasks as directed by the JobTracker. Each TaskTracker is assigned a certain number of maps and reduced tasks by the JobTracker. It runs these tasks on the node where it is located. The TaskTracker reports task progress. It gives the status to the JobTracker and handles task retries in case of failures.

26. What do you mean by NameNode in Hadoop? What happens if NameNode goes down?

NameNode in Hadoop is a critical component of the HDFS(Hadoop Distributed File System). It serves as the centralized metadata repository for the entire HDFS cluster and stores information about the location of each block of data in the cluster. Here's what happens if the NameNode goes down:

• Filesystem becomes unavailable
   
• No new jobs can be submitted
   
• Data loss
   

Hadoop provides several mechanisms to prevent data loss due to NameNode failure. These mechanism includes taking regular backups of the NameNode metadata and maintaining a standby NameNode to take over in case of failure. You can also prevent data loss using techniques like Hadoop Federation and Hadoop High Availability. These mechanisms help to ensure the high availability and reliability of the Hadoop cluster.