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Table of contents
1.
Beginner Level Interview Questions for Hadoop
1.1.
1. What do you mean by Hadoop?
1.2.
2. What are the components of Hadoop?
1.3.
3. Why do we need Hadoop?
1.4.
4. Explain big data and list its characteristics.
1.5.
5. What are the Limitations of Hadoop 1.0?
1.6.
6. What is Hadoop Streaming?
1.7.
7. Can you give some examples of vendor-specific Hadoop distributions?
1.8.
8. What is the default block size in Hadoop?
1.9.
9. What are the different Hadoop configuration files?
1.10.
10. Explain key Features of HDFS.
1.11.
11. What do you mean by a block in HDFS?
1.12.
12. Explain the  main differences between HDFS (Hadoop Distributed File System ) and Network Attached Storage(NAS).
1.13.
13. What are the different modes available for running Hadoop?
1.14.
14. What is shuffling in MapReduce?
1.15.
15. What is the use of the 'Hadoop fsck' command?
2.
Intermediate Level Interview Questions for Hadoop
2.1.
16. What are the differences between MapReduce and Spark?
2.2.
17. How does Hadoop handle data replication?
2.3.
18. Define under-replicated blocks.
2.4.
19. Define over-replicated blocks.
2.5.
20. What do you mean by Combiner in Hadoop MapReduce? How does it help in reducing network traffic?
2.6.
21. What do you mean by YARN in Hadoop? How does it improve the performance of Hadoop clusters?
2.7.
22. What is Apache ZooKeeper?
2.8.
23. What do you understand by Hadoop's Fair Scheduler?
2.9.
24. What are the key configuration parameters that need to be specified in a MapReduce program?
2.10.
25. What are the JobTracker and TaskTracker in Hadoop?
2.11.
26. What do you mean by NameNode in Hadoop? What happens if NameNode goes down?
3.
Advance Level Interview Questions for Hadoop
3.1.
27. What is Hadoop Security? How is it implemented, and what are its components?
3.2.
28. What is an Apache Hive?
3.3.
29. Explain the differences between MapReduce and Apache Pig.
3.4.
30. List the components of Apache Spark.
3.5.
31. What are the main components of a Hive architecture?
3.6.
32. What do you understand by Hadoop Secondary NameNode and its role in Hadoop cluster management?
3.7.
33. What are the types of schedulers in YARN?
3.8.
34. What do you mean by Hadoop Distributed Cache in Hadoop MapReduce jobs?
3.9.
35. What are the various complex data types supported by Apache Pig?
3.10.
36. Explain the Apache Pig architecture
3.11.
37. What do you mean by Hadoop SequenceFile format and its advantages over plain text files?
3.12.
38. Describe the process of writing a custom partitioner in Hadoop MapReduce.
3.13.
39. Explain the purpose of the dfsadmin tool?
3.14.
40. What do you mean by Hadoop Rack Awareness?
4.
Conclusion
Last Updated: Jun 12, 2024

Top Hadoop Interview Questions and Answers in 2023

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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.

Hadoop Interview Questions

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. 

Click on the following link to read further: Javascript Interview Questions and Answers

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:

Hadoop configuration files

 

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:-

AspectHDFSNAS
ArchitectureHDFS provides a distributed file system for large-scale data storage.NAS allows you to share storage devices in a network.
ScalabilityHDFS is highly scalable and it supports horizontal scaling.NAS has limitations on scalability and it mainly depends on the device or vendor.
Data DistributionHDFS distributes data across clusters for fault tolerance.NAS has a single point of failure.
Use CasesHDFS is ideal for big data processing.NAS is suitable for traditional file sharing.

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:

MapReduceSpark
MapReduce is slower than Spark. It writes data to disk after each operation.Spark is faster than MapReduce. This is because Spark uses in-memory processing and keeps data in memory as long as possible.
MapReduce is mainly used with Java.Spark is easier to use than MapReduce because it has a simpler API and supports multiple programming languages, including Java, Python, and Scala.
MapReduce is better for batch processing.Spark is better suited for real-time processing.
MapReduce has fewer built-in libraries.Spark has a larger number of built-in libraries for machine learning, graph processing, and stream processing, making it a more versatile framework.
MapReduce is designed with fault tolerance in mind and provides robust error-handling mechanisms.Spark also provides fault tolerance but is not as robust as MapReduce.

 

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:

ways to improve performance using yarn

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. 
Also see, Power Apps Interview Questions

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.

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Advance Level Interview Questions for Hadoop

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

27. What is Hadoop Security? How is it implemented, and what are its components?

Hadoop Security means a set of mechanisms and protocols. They are used to ensure the confidentiality, integrity, and availability of data in a Hadoop cluster. Hadoop Security is very important for protecting sensitive data stored in a Hadoop cluster. It ensures that the cluster can operate securely.

Hadoop Security is implemented using a combination of technologies and protocols that work together to provide a comprehensive security solution. Some of the components of Hadoop Security are as follows:

  • Authentication
     
  • Authorization
     
  • Encryption
     
  • Auditing
     
  • Data Masking

28. What is an Apache Hive?

Apache Hive is an open-source data warehousing and SQL-like query language tool built on top of Hadoop. It provides a high-level abstraction to query and analyze large datasets stored in distributed storage systems, such as Hadoop Distributed File System (HDFS). It uses a language called Hive Query Language (HQL), which is similar to SQL, to perform data retrieval, transformation, and analysis tasks.

29. Explain the differences between MapReduce and Apache Pig.

Apache Pig and MapReduce are both components of the Hadoop ecosystem used for processing large datasets. However, there are significant differences between the two.

MapReduceApache Pig
It requires developers to write code in Java to process data.It provides a high-level scripting language called Pig Latin. This language is easier to learn and use than Java.
It is designed for low-level data processing. It requires developers to write custom code to handle each data processing stage.It simplifies data processing by providing built-in operators for common data transformations like filtering, sorting, and grouping.
Its code can be complex and requires significant development effort.It reduces code complexity by providing a simplified programming model and high-level abstractions.
It provides low-level control over data processing, leading to optimal performance when properly optimized.It may introduce additional overhead due to its higher-level abstractions.
It is well-suited for complex data processing tasks where low-level control over the processing pipeline is required.It is better suited for ad hoc data processing and iterative data analysis. In this, ease of use and faster development times are more important than performance.

 

 

 

 

 

 

 

 

 

 

 

 

30. List the components of Apache Spark.

The key components of Apache Spark are:-

  • Spark Core: This is the core component that provides the basic functionality of Spark. It includes the core data processing engine, memory management, and fault tolerance mechanisms.
     
  • Spark SQL: Spark SQL is used for structured data processing, allowing users to run SQL-like queries on structured data within Spark.
     
  • Spark Streaming: Spark Streaming enables real-time data processing and analytics by processing data in mini-batches or micro-batches. It can input data from various sources like Kafka, Flume, and HDFS.
     
  • Spark MLlib: MLlib is Spark's machine learning library, offering a wide range of machine learning algorithms and tools for tasks like classification, regression, etc.
     
  • Spark GraphX: Spark GraphX is a graph processing library that provides tools for graph computation and analysis such as social network analysis and recommendations.
     
  • SparkR: SparkR is an R package that allows R users to interact with Spark, allowing you to perform data analysis and machine learning using R within the Spark ecosystem.

31. What are the main components of a Hive architecture?

The main components of a Hive architecture are:

  • Metastore: A metadata repository that stores information about the structure of data stored in Hive tables, including column names, data types, and locations.
     
  • Hive Query Language (HQL): It is a SQL-like language. It is used to query data stored in Hive tables. HQL is compiled into MapReduce jobs by the Hive engine.
     
  • Hive Driver: A component that receives HQL queries from users. It converts them into a series of MapReduce jobs to be executed on the Hadoop cluster.
     
  • Hive Execution Engine: A component that executes MapReduce jobs generated by the Hive Driver. There are several execution engines available for Hive, including the default Hadoop MapReduce engine and Apache Tez.
     
  • Hadoop Distributed File System (HDFS): The underlying file system stores data in Hive tables. Hive tables are represented as directories and files in HDFS.
     
  • User Interface: Hive provides a command-line interface and a web-based graphical user interface (GUI) to interact with the system. Users can execute queries, view metadata, and monitor job progress using these interfaces.

32. What do you understand by Hadoop Secondary NameNode and its role in Hadoop cluster management?

The NameNode in Hadoop is primarily responsible for managing the file system namespace. It maintains the metadata information about the HDFS file system. As the amount of data stored in the Hadoop cluster grows, the metadata information also increases, which can strain the NameNode's resources and affect its performance.

Hadoop provides a Secondary NameNode to address this issue. It is a helper node that assists the NameNode in managing the metadata information. The Secondary NameNode periodically merges the edits log. It is a log of changes to the HDFS file system namespace with the current FsImage. FsImage is a snapshot of the file system metadata. This process creates a new FsImage, which is then copied back to the NameNode and replaces the old one.

33. What are the types of schedulers in YARN?

There are three types of schedulers in YARN:

  1. Capacity Scheduler: This is a pluggable scheduler. It allows multiple organizations or users to share a single Hadoop cluster. The Capacity Scheduler divides the cluster resources into queues. Each queue has a configurable capacity. This capacity determines the maximum percentage of cluster resources that can be used by the jobs submitted to that queue.
     
  2. Fair Scheduler: This is another pluggable scheduler. It allows fair sharing of cluster resources among multiple users and jobs. The Fair Scheduler allocates resources to jobs based on the job's resource requirements and the current resource availability in the cluster.
     
  3. FIFO(First In First Out) Scheduler: This is a simple scheduler. It processes jobs in the order in which they are submitted to the system. The FIFO Scheduler does not consider the priority or resource requirements of the jobs. It can result in inefficient use of cluster resources. It is mainly used for testing or debugging purposes.

34. What do you mean by Hadoop Distributed Cache in Hadoop MapReduce jobs?

The Hadoop Distributed Cache is a feature of Hadoop MapReduce. It allows users to distribute read-only files and archives (such as jars) to the compute nodes in a Hadoop cluster so that these files can be easily accessed by the Map and Reduce tasks. The Distributed Cache is used to cache data. It is required by many MapReduce tasks in a job, such as lookup tables or other auxiliary data.

When a MapReduce job is submitted, the files to be cached are specified in the configuration object of the job. The files are then distributed to the nodes in the cluster using HDFS. Once the files are copied to the nodes, they are made available to the Map and Reduce tasks via the local file system.

35. What are the various complex data types supported by Apache Pig?

Apache Pig supports various complex data types. These data types enable users to work with structured and semi-structured data in Hadoop. 

Some of the complex data types supported by Pig include:

  • Maps: These are key-value pairs. They can be used to store structured data. Maps can be created using the '[]' operator and accessed using the ‘.’ operator.
     
  • Tuples: These are ordered sets of fields in Pig. They can be used to represent structured data. Tuples can be created using the '()' operator and accessed using the '.' operator.
     
  • Bags: These are unordered collections of tuples. They can be used to represent semi-structured data. Bags can be created using '{}' operator and accessed using the '.' operator.
     
  • Byte Arrays: These are used to represent unstructured data in Pig. They are represented as a sequence of bytes and can be manipulated using Pig's built-in functions.

36. Explain the Apache Pig architecture

Apache Pig is a high-level platform for processing large datasets in Hadoop using a simple scripting language called Pig Latin. Some of its key components are:-

  • Pig Latin Scripts: Users can write scripts in Pig Latin and these scripts describe the data transformations and operations to be performed on the input data.
     
  • Parser: Pig Latin scripts are passed to the Pig Parser, which checks their syntax and generates an abstract syntax tree (AST).
     
  • Logical Plan: The logical plan is a sequence of operations represented by the AST. It defines the data flow but not the execution order.
     
  • Optimizer: The logical plan is optimized to improve the performance.
     
  • Physical Plan: The optimized logical plan is converted into a physical plan that specifies the execution steps and order.
     
  • Compiler: The physical plan is compiled into a series of MapReduce jobs that are submitted to the Hadoop cluster for execution.

37. What do you mean by Hadoop SequenceFile format and its advantages over plain text files?

Hadoop SequenceFile is a binary file format which is designed for storing large amounts of data efficiently. It is a flat file. It consists of binary key-value pairs, where both the key and value can be of any data type supported by Hadoop. The SequenceFile format is highly efficient for storing large datasets. Because it compresses the data and allows for parallel processing.

The advantages of using Hadoop SequenceFile over plain text files are:

  • Efficient storage
     
  • Optimized for MapReduce
     
  • Binary format
     
  • Support for compression
     
  • Random access

38. Describe the process of writing a custom partitioner in Hadoop MapReduce.

Partitioner in Hadoop MapReduce is responsible for dividing the intermediate key-value pairs. These key-value pairs are produced by the mapper into different partitions based on the keys. The number of partitions equals the number of reduced tasks in the job. Each partition is processed by a single reduced task. Hadoop uses a hash-based partitioner by default. It assigns partitions based on the hash value of the key.

However, in some cases, the default partitioner may not be sufficient for specific use cases. In such cases, a custom partitioner can be written to implement a specific partitioning logic. Here are the steps to write a custom partitioner in Hadoop MapReduce:

 the steps to write a custom partitioner in Hadoop MapReduce

39. Explain the purpose of the dfsadmin tool?

The dfsadmin tool is a command-line utility in the Hadoop Distributed File System (HDFS) that serves administrative purposes and it allows administrators to perform various management tasks on an HDFS cluster. Its primary purpose is to manage clusters and monitor their health using the various command dfsadmin provides.

40. What do you mean by Hadoop Rack Awareness?

It is a feature that allows Hadoop to be aware of the network topology of the cluster on which it is running. A rack is a group of computers. This group is located close to each other and is connected through a switch. Hadoop Rack Awareness is very essential. It helps to optimize the data transfer between nodes in the cluster.

Data is divided into blocks in a Hadoop cluster. It is distributed across multiple nodes. When a node needs to read data from another node, it is best if it is located on a node that is physically close to it in the network topology. This reduces network latency and improves data transfer speeds.

Conclusion

In this article, we have discussed Hadoop interview questions. We have discussed interview questions in three categories: beginner, intermediate, and advance. You can check out our other interview questions blogs:

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