HDFS Architecture – A Detailed Guide

Currently, we live in a world where we require tools that are able to manage and store huge amounts of data. HDFS is frequently used in the distribution of large datasets in order to provide data storage and processing capabilities. It supports applications that require quick access to massive amounts of information, making it a valuable resource for big data environments.

In this blog, we shall study HDFS in detail, exploring its architecture, characteristics, and functionality. Specific topics like replication management, rack awareness, and the input/output processes for reading and writing files will also be covered. The advantages and the disadvantages of the system will also be explored with the practical approaches to using the technology in the management of big data.

What is Hadoop HDFS?

The Hadoop Distributed File System (HDFS) is intended for use in a distributed network of systems with large data volumes. HDFS was developed as part of the Hadoop project and is appropriate for big data storage utilisation since it is designed for scalable and dependable storage needs. It takes a larger logical data file and splits it into smaller, manageable pieces called blocks and distributes them onto different machines. This arrangement allows HDFS to achieve high availability and resilience, as the data is always available despite the possibility of a single machine breaking down.

One of the defining features of HDFS is its ability to handle data redundancy through replication. Each data block is typically copied to multiple machines, ensuring that a backup is always available. This replication system enhances fault tolerance, making HDFS a dependable choice for critical applications. Additionally, HDFS is designed to work best with large files that are read sequentially, rather than small files needing frequent access.

Also Read: What is a Hadoop

The HDFS architecture provides a way to accommodate huge quantities of information by implementing a network of machines for both data storage and processing. However, this is achieved without compromising on fault tolerance and high availability of the system. This distributed structure also makes it possible for the HDFS to organise a much larger volume of data. Some primary components, such as the NameNode and DataNode, collaborate in order to form a comprehensive, dependable system for the storage and retrieval of data within the reasonable extent of being overloaded with large data collection.

NameNode

The NameNode is the core element of HDFS, responsible for managing the namespace of the file system and the access rights of clients. It plays a vital role in keeping track of where data is stored by maintaining essential metadata rather than the data itself. This metadata is stored at the hard disk of the NameNode as two main files:

• Fsimage: The File System image, or Fsimage, holds a complete snapshot of the file system namespace, capturing its state since the NameNode was first created.
• Edit Log: The Edit Log records all recent changes to the file system, such as file additions, deletions, or modifications, updating the Fsimage with the latest state.

Functions of the NameNode

The NameNode performs several key functions to ensure smooth HDFS operations:

• Namespace Operations: It handles operations on the file system’s namespace, including opening, closing, renaming, and deleting files and directories.
• DataNode Management: The NameNode supervises DataNodes, mapping blocks of data to these nodes and overseeing their storage.
• Block Mapping: Each file in HDFS is split into blocks. The NameNode keeps track of which blocks are stored on which DataNodes.
• Metadata Updates: Every change made to the file system, such as creating or deleting files, is recorded by the NameNode to maintain an accurate and current namespace.
• Replication Control: To ensure fault tolerance, the NameNode enforces the replication factor for data blocks, determining where replicas should be stored.
• Heartbeat Monitoring: The NameNode receives regular “heartbeat” signals and block reports from DataNodes. This keeps it informed about which DataNodes are active and available.

In case of DataNode failure, the NameNode quickly reallocates replicas to other DataNodes to maintain data redundancy. Before Hadoop 2, the NameNode posed a single point of failure. However, with the introduction of High Availability (HA) architecture in Hadoop 2, clusters can now include multiple NameNodes in a hot standby setup, significantly improving reliability.

DataNode

DataNodes are the storage nodes within HDFS, responsible for storing data blocks and serving client requests for read and write operations. Each DataNode operates independently across various machines, working under the coordination of the NameNode. Here’s an overview of how DataNodes function:

• Data Storage: DataNodes store data blocks assigned by the NameNode, dividing large files into smaller, manageable blocks distributed across the network.
• Heartbeat Signals: DataNodes send heartbeat signals to the NameNode at regular intervals, confirming they’re operational and available for tasks.
• Block Reports: In addition to heartbeat signals, DataNodes periodically send detailed block reports to the NameNode. These reports provide a status of each block stored on the DataNode, ensuring data integrity.
• Data Replication: DataNodes play a key role in maintaining data redundancy by replicating blocks to other nodes, as instructed by the NameNode. This replication enhances fault tolerance, making HDFS resilient to DataNode failures. If a DataNode fails or becomes unreachable, the NameNode assigns its blocks to other nodes, ensuring continuous access to data.

Secondary NameNode

The Secondary NameNode assists the primary NameNode by keeping backup copies of its metadata, although it doesn’t serve as a direct failover. Instead, it serves as a checkpoint, periodically merging the Fsimage and Edit Log to prevent the Edit Log from growing too large. Key aspects of the Secondary NameNode’s function include:

• Fsimage Checkpoints: The Secondary NameNode creates regular checkpoints of the Fsimage file, reducing the load on the primary NameNode.
• Edit Log Management: It merges the Edit Log with the Fsimage, preventing an overly large log that could slow down recovery.
• Disaster Recovery: Although it isn’t a true failover solution, the Secondary NameNode’s backups can assist in data recovery if the NameNode fails, improving data security.

The Secondary NameNode is crucial in maintaining an updated, stable state of the metadata, making NameNode restart faster and smoother when necessary.

Also Read: What is Big Data Architecture?

HDFS Client

The HDFS Client acts as the user interface for accessing HDFS. It interacts with both the NameNode and DataNodes to facilitate file storage and retrieval. The client’s primary roles include:

• Metadata Requests: When users need to access or write data, the HDFS Client first requests metadata from the NameNode, receiving information on the location of required blocks.
• DataNode Interaction: After receiving block information from the NameNode, the client connects directly with the relevant DataNodes to read or write data.
• Task Coordination: For efficient performance, the HDFS Client manages data flow between the client machine and DataNodes, ensuring optimal data transfer and load distribution.

The HDFS Client simplifies access to HDFS by managing data flow, translating user requests into HDFS operations, and coordinating data transactions between clients and storage nodes.

Block Structure

In HDFS, large files are split into smaller, fixed-size blocks, with each block typically 128 MB in size (configurable as needed). As a central component of HDFS, the block structure allows it to function as a disaggregated storage model that allows for the slicing of large files over a number of nodes. Some of the characteristics of the block structure include:

• Storage infrastructure: Each file is split into smaller blocks, and these blocks are distributed across different data nodes so as to provide parallel storage and, hence, enhance scalability while increasing load balancing.
• Fault Tolerance: Replicas of blocks are maintained on several DataNodes, hence data is guaranteed to be lost with the help of a single node.
• Efficient Access: Since files are divided into several blocks, HDFS provides efficient access and processing of files in a distributed manner because operations can be performed on several blocks independently while working in parallel.
• Replication and Redundancy: HDFS’s block replication policy establishes a maximum limit for the number of copies for each block across DataNodes. It enhances the capacity of HDFS to retrieve data if a block becomes unavailable through replication. Even so, the combination of the block layout and replication is important for HDFS archival of large datasets and meeting the high availability requirements of the system.

What is Replication Management?

Replication management in HDFS ensures data reliability and availability by creating multiple copies, or replicas, of each data block across different DataNodes. This replication process safeguards data against hardware failures, providing a built-in level of fault tolerance. The typical replication factor that is present in HDFS is usually set at a three, which denotes that three copies of each block are found in separate nodes. However, this should not be taken in its fixed sense as this factor can be changed depending on how much storage space is available and how much is required.

Key Aspects of Replication Management

• Fault Tolerance: HDFS guarantees data access if one or several nodes fail by saving multiple copies of the same data.
• Replication Factor: The replication factor determines how many replicas exist and can be changed according to the significance of the pertinent data or the resources available.
• Automatic Replication: The NameNode supervises DataNodes and, if DataNodes go offline or one of the replicas is missing, allocates another of the replicas automatically.
• Load Balancing: Primarily, replication in HDFS is used for workload sharing, which helps in overcoming bottlenecks and provides quick access to the required data.

Replication Process

When a file is stored in HDFS, the NameNode assigns the replication factor and designates the nodes that will store the replicas. If a node fails or goes offline, the NameNode immediately recognizes the missing replica through heartbeat signals and arranges for new copies to be created on available nodes. This continuous monitoring and management ensure data redundancy, making HDFS a robust and reliable system for handling large datasets.

What is Rack Awareness in HDFS Architecture?

As a component of the HDFS technology, rack awareness is the technique which optimises data placement by considering the physical location of nodes within a network. In most extensive data centres, there are several racks wherein the nodes are positioned, which are all connected to a central networking switch. Rack awareness helps HDFS understand this physical setup to improve data reliability and efficiency.

Purpose of Rack Awareness

• Fault tolerance: HDFS maintains fault tolerance through data backup mechanisms by making replicas of the data on nodes in diverse racks. This way, if a rack containing some replicas fails, HDFS still possesses copies available in other racks and eliminates the risk of losing data.
• Network optimization: Since data is distributed across multiple racks, large network traffic within a rack is decreased, facilitating quick access to required data.

How Rack Awareness Works

When storing data, the NameNode uses a rack-aware policy to place replicas:

• First Replica: Stored on the same rack as the client requesting the data (when possible).
• Second Replica: Placed on a node in a different rack to ensure redundancy if the first rack fails.
• Third Replica: Stored on the same rack as the second but on a different node, balancing network load and storage distribution.

Benefits of Rack Awareness

• Increased Containment: Since the block replicas are placed on different racks, HDFS is able to salvage the data even if one entire rack has been taken down.
• Reduced Retrieval Time: HDFS is able to enhance read and write speeds by minimising the distance between the block and its replicas.
• Increased Efficiency: The architectural framework ensures economic usage of space as well as resources for data management, allowing no one rack to be burdened more than others.

Rack awareness is essential in large HDFS deployments, where data is spread over numerous machines. By managing data placement based on rack structure, HDFS ensures high availability, optimised data access, and network efficiency.

Also Read: Complete Guide to Becoming a Data Engineer

Understanding HDFS Read and Write Operation

HDFS read and write operations are central to how data is stored and accessed within the distributed system. These operations involve communication between the client, NameNode, and DataNodes, ensuring data is written efficiently and read reliably. Here’s a breakdown of how each operation works:

HDFS Write Operation

The write operation allows clients to store data in HDFS. It follows a specific sequence to ensure data is safely stored across multiple DataNodes:

1. Client Request: The client requests to write a file, sending metadata information to the NameNode.
2. Block Allocation: The NameNode assigns DataNodes for each block, considering the replication factor and rack awareness for optimal placement.
3. Data Transfer: The client transfers the data to the first assigned DataNode, which then forwards the block to the next DataNode, forming a pipeline.
4. Acknowledgement: After each DataNode receives and stores the block, it sends an acknowledgement back through the pipeline to the client, confirming successful storage.

HDFS Read Operation

The read operation enables clients to access data stored in HDFS, retrieving blocks from the appropriate DataNodes.

1. Client Request: The client requests to read a file, querying the NameNode for block locations.
2. Block Location Retrieval: The NameNode provides the client with a list of DataNodes that hold replicas of the requested blocks.
3. Direct DataNode Access: The client directly contacts the nearest DataNode (based on rack awareness) to retrieve the data block.
4. Sequential Block Retrieval: The client reads each block sequentially from the DataNodes, reassembling them into the complete file.

Key Aspects of HDFS Operations

• Fault Tolerance: HDFS automatically manages replicas during both read and write operations to ensure high availability.
• Network Efficiency: Rack awareness optimises network usage, reducing latency by directing read and write requests to nearby nodes.
• Data Consistency: HDFS maintains data consistency by ensuring blocks are replicated correctly and verifying each write through acknowledgement.

These HDFS operations are designed to ensure data is reliably stored and quickly accessible, balancing fault tolerance, network efficiency, and ease of use.

Advantages of HDFS Architecture

• Fault Tolerance: HDFS automatically replicates data across multiple nodes, ensuring data availability even if nodes fail.
• Management: Its design makes it straightforward to integrate extra nodes in order to meet more storage requirements.
• High Throughput: HDFS has been optimised to offer and maintain large data transfers, useful in datasets and batch jobs.
• Low Cost: Due to its utility on low-spec machines, the expenses associated with employing HDFS are smaller compared to more expensive storage solutions.
• Data Integrity: Data integrity is ensured through the verification of numerous checksum data blocks.
• Ideal for substantial data sets: HDFS has been simplified for the use of bigger data to prevent the waste of resources in accessing and processing data.
• Simple Implementation: Overall, HDFS can combine effectively with its Hadoop components like MapReduce to give the end user a smooth data generation analysis process.

Disadvantages of HDFS Architecture

• Single Point of Failure: Although Hadoop 2 introduced High Availability, the NameNode can still be a bottleneck if not configured with redundancy.
• Not Efficient when Dealing with Small Files: HDFS is not ideal for managing numerous small files, which can impact performance and increase overhead.
• Complex Configuration: It is necessary to have technical experience to configure and monitor an HDFS cluster due to its complexity and management practices.
• High Latency: HDFS has been designed to suit batch processing, not real-time applications so latency for quick and small data operations is expected to be higher.
• Cost Overheads associated with Data replication: Additional replication leads to an increase in storage requirements, which can be expensive in the case of giant datasets.
• Lack of Modification Support: HDFS supports appending data but lacks efficient options for modifying existing files, limiting its flexibility.
• Hardware Dependence: Although HDFS uses commodity hardware, node failures can still lead to maintenance and replacement costs.

HDFS Use Cases

• Data Warehousing: In data warehouses, HDFS is massively used for the purposes of managing and storing huge chunks of data, which can be either structured or semi-structured.
• Big Data Analytics: HDFS supports high-throughput analytics, making it a preferred choice for big data applications like Hadoop MapReduce and Spark.
• Machine Learning: HDFS is employed in numerous machine learning workflows to store enormous data sets for the purposes of training and model evaluation.
• Content Management: HDFS is suitable for content repositories, especially where large multimedia files need reliable storage.
• Data Archiving: Businesses use HDFS to archive massive datasets, preserving historical data without compromising access.
• E-commerce: HDFS stores vast amounts of data generated from user interactions, product data, and customer behaviour for analytics purposes.

Conclusion

The architecture of the HDFS is a robust solution for handling large amounts of data in a distributed environment. Owing to its fault tolerance, scalability and economical storage costs, HDFS has, in fact, been a prerequisite in big data platforms. The characteristics of its structure design and replication management provide efficient data access and protection of data from loss of nodes, which is appropriate for data-oriented applications.

While HDFS has limitations, particularly with small files and real-time processing, it excels in batch processing and large-scale data storage. To get a better view into Hadoop and Data Analysis, the Accelerator Program in Business Analytics and Data Science with EdX, aligned with NASSCOM and FutureSkills Prime by Hero Vired, is an ideal choice. When the users learn the structure of HDFS and its foundational components, they can apply it in a way beneficial for data management tasks.