What is Hadoop HDFS?

Hadoop HDFS, or Hadoop Distributed File System, is the primary storage system used by Hadoop, an open-source distributed computing framework. It is designed to store and manage vast amounts of data across a cluster of commodity hardware, providing high fault tolerance and scalability for big data processing.

HDFS Architecture

 HDFS architecture is a crucial component of Hadoop, ensuring that data is reliably stored and processed across distributed nodes. Its key components include

  • NameNode

The NameNode is the master server that manages the metadata and namespace of the file system. It keeps track of file structure, permissions, and the locations of data blocks but does not store the actual data.

  • Secondary NameNode

Despite its name, the Secondary NameNode is not a backup for the primary NameNode. Instead, it periodically merges the namespace image and edit logs to prevent the primary NameNode’s image from becoming too large. This process helps in maintaining a consistent and smaller image for faster recovery.

  • DataNode

DataNodes are worker nodes responsible for storing the actual data blocks. They periodically send heartbeat signals and block reports to the NameNode, allowing it to track the health and status of data blocks.

  • Checkpoint Node

The Checkpoint Node was introduced to reduce the load on the Secondary NameNode. It assists the NameNode in creating periodic checkpoints and retains the latest checkpoint for recovery purposes.

  • Backup Node

The Backup Node is another enhancement to reduce the load on the Secondary NameNode. It maintains an in-memory copy of the file system namespace and assists in creating checkpoints without interrupting the cluster’s operation.

  • Blocks

HDFS divides data into fixed-size blocks (typically 128MB or 256MB) and distributes these blocks across DataNodes in the cluster. This block-based storage and distribution enhance data locality and parallel processing.

Features of HDFS

  • Scalability

HDFS is highly scalable, allowing organizations to add more nodes to the cluster as data volumes increase.

  • Fault Tolerance

HDFS replicates data across multiple DataNodes, ensuring data availability even in the presence of node failures.

  • Data Locality

It promotes data locality by storing and processing data on the same node whenever possible, reducing network overhead.

  • High Throughput

HDFS provides high data throughput by allowing parallel data access across multiple nodes.

  • Write-Once, Read-Many Model

HDFS follows a write-once, read-many model, making it suitable for data warehousing and batch processing.

Replication Management in HDFS Architecture

HDFS maintains data reliability and availability through block replication. Key aspects of replication management include

  • Replication Factor

HDFS allows users to set the replication factor, determining how many copies of each data block should be maintained in the cluster.

  • Replica Placement

Replicas are strategically placed on different racks and DataNodes to ensure fault tolerance and reduce the risk of data loss.

Write Operation

When data is written to HDFS, it goes through the following steps

  • The client contacts the NameNode to create a new file and obtain block locations.
  • Data is divided into blocks, and the client communicates directly with the designated DataNodes to write these blocks.
  • Each DataNode acknowledges the successful write, and the client receives acknowledgments.
  • The client informs the NameNode of the completed write, updating the metadata.

Read Operation

When reading data from HDFS, the process is as follows

  • The client contacts the NameNode to retrieve the block locations of the requested file.
  • The client communicates directly with the DataNodes containing the desired blocks to retrieve the data.

Advantages of  HDFS Architecture

  • Scalability

HDFS can handle massive datasets by adding more nodes to the cluster.

  • Fault Tolerance

Data replication and distributed architecture ensure high availability.

  • High Throughput

Parallel processing and data locality enable high data throughput.

  • Cost-Effective

HDFS can be deployed on commodity hardware, reducing infrastructure costs.

Disadvantages of HDFS Architecture

  • Not Suitable for Small Files

HDFS is optimized for large files and may not perform well with small files due to high overhead.

  • Limited Real-Time Processing

While HDFS supports batch processing, it may not be ideal for real-time data processing.

  • Complexity

Setting up and configuring an HDFS cluster can be complex, requiring expertise.


Hadoop HDFS is a fundamental component of the Hadoop ecosystem, providing a distributed and fault-tolerant storage system for big data processing. Its architecture, featuring NameNodes, DataNodes, and replication, enables scalable and reliable data storage and retrieval, making it a vital technology for organizations dealing with massive datasets. However, HDFS is most effective when used in conjunction with other Hadoop components for data processing and analytics.


1.What is Hadoop HDFS?

Hadoop HDFS, or Hadoop Distributed File System, is a distributed storage system designed to store and manage large-scale data across a cluster of computers. It’s a key component of the Hadoop ecosystem.

2.What is the role of the NameNode in HDFS?

The NameNode is the master server in HDFS responsible for managing the file system’s metadata and namespace. It keeps track of file structure, permissions, and block locations.

3.What is the purpose of DataNodes in HDFS?

DataNodes are worker nodes that store the actual data blocks. They communicate with the NameNode, report block status, and replicate data for fault tolerance.

4.How does HDFS ensure data fault tolerance?

HDFS achieves fault tolerance by replicating data blocks across multiple DataNodes. If a DataNode fails, data can still be retrieved from replicas on other nodes.

5.What is the significance of block size in HDFS?

HDFS divides data into fixed-size blocks (e.g., 128MB or 256MB) to distribute them across DataNodes efficiently. It enhances data locality and parallel processing.

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