Hadoop Architecture – Explanation in Detail

Hadoop has become essential in managing large-scale data processing, making it a powerful solution in the big data landscape. Its open-source nature allows businesses to store, process, and analyse vast amounts of data across clusters of computers, efficiently managing workloads that would be challenging otherwise.

With Hadoop’s structured layers and robust framework, companies can handle data complexities and achieve scalability and resilience. Each layer plays a unique role, working together to support data storage, processing, and fault tolerance.

In this blog, we’ll explore the components, data flow, benefits, and practical applications of Hadoop architecture, giving you a complete view of how it supports big data needs today.

What is Hadoop?

Hadoop is an open-source framework for the distributed storage and processing of massive data sets across applied clusters. Developed by the Apache Software Foundation, it allows companies and organisations to analyse and manage data on a larger scale and more efficiently. Furthermore, it is important to note that Hadoop can manipulate structured, semi-structured, and unstructured data, which is a necessity for the varied data needs of modern times.

Hadoop, at its foundation, implements a distributed computing approach, which entails splitting a large dataset into smaller chunks of good sizes and then storing them across a number of computing nodes. This allows for parallelism, hence faster data and analysis and better fault tolerance. In cases where a single node fails, the workload automatically switches to other nodes, which indeed guarantees reliability.

The Hadoop framework comprises four basic building blocks: HDFS, YARN, MapReduce and a few libraries that ease data processing. These components are hierarchically combined for storage, resource management as well as efficient data processing across huge volumes of data.

Hadoop architecture refers to a structure composed of several integrated layers, each of which is concerned with the distributed storage and processing of large data sets. The design is fault-tolerant and scalable, which makes the architectural style ideal for large datasets which can be broken down into blocks and dispersed to different nodes. This design improves the performance of the system and makes it more reliable, which explains its popularity in the context of data-sensitive applications.

Some of the most important components forming the core of the Hadoop architecture include HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) and MapReduce. HDFS contributes to the storage of data by breaking up files and spreading them out among different non-local nodes and incorporating strategies to guarantee data availability. In a nutshell, YARN controls and allocates system resources and assigns tasks, while MapReduce allows for millions of processes to run on distributed nodes within the network that all have the same purpose of processing and analysing data.

Together, these components allow Hadoop to manage complex data operations efficiently. The architecture supports high-performance computing by balancing storage and processing, ensuring reliability even when hardware failures occur.

4 Components of Hadoop Architecture

To effectively manage and process big data, the Hadoop architecture is constituted of four sub-architectures that are crucial. These subdivisions – HDFS, YARN, MapReduce, and Hadoop Common – are distinct in their functions and enable Hadoop to perform as a distributed data processing framework. So, let us examine them one at a time.

• HDFS (Hadoop Distributed File System)
• YARN (Yet Another Resource Negotiator)
• MapReduce
• Hadoop Common

HDFS (Hadoop Distributed File System)

HDFS is a system that was created to accommodate large quantities of data in a distributed manner across multiple servers and even withstands hardware outages. It partitions big files into smaller units, called ‘blocks’ and stores them on several nodes across the architecture. This method improves the availability, dependability, and efficiency of the stored information.

Key Features of HDFS

• Block-Based Storage: For HDFS, block sizes typically are 128 MB or 256 MB, allowing it to efficiently store data and enable parallel processing.
• Data Replication: A block is stored on at least three individual nodes so that data can still be shared, even if one of the nodes goes offline or fails.
• Fault Tolerance: By replicating data, HDFS prevents data loss from hardware failures, automatically managing data recovery.

HDFS Components

• NameNode: Acts as the master node, storing metadata (file locations, access permissions) and managing the file system’s namespace.
• DataNode: Stores the actual data blocks and communicates with the NameNode to confirm block storage status.
• Secondary NameNode: Maintains a backup of NameNode metadata, providing fault tolerance in case the NameNode fails.

HDFS Operations

• Read Operation: When a client requests data, the NameNode provides the DataNode location where the required blocks are stored. The client reads the data directly from the DataNodes, ensuring fast data access.
• Write Operation: When writing data, the client first communicates with the NameNode, which assigns DataNodes for storage. The data is divided into blocks and written across multiple data nodes, with replicas created as part of the process.

Also Read: HDFS Architecture – A Detailed Guide

Advantages of HDFS

• Scalability: HDFS offers a simple solution for scaling. It lets you add more nodes in the system and can accommodate several petabytes worth of information this way.
• Cost-Efficiency: HDFS allows enterprises to use commodity hardware to store data, vastly lowering their cost of storage.
• Dependability: The accessibility as well as protection of data files is guaranteed with the aid of mechanisms for data replication and fault tolerance.

YARN (Yet Another Resource Negotiator)

YARN represents the resource management layer of Hadoop. it aims to provide mechanisms for the effective distribution of resources and timing of tasks within the cluster. YARN is essential for Hadoop’s scalability by decoupling resources and job scheduling from the data processing component.

Key Functions of YARN

• Resource Management: To utilise resources efficiently, YARN allocates everything from CPU to memory to different tasks as and when required.
• Task Prioritization: In synchronising operation, it identifies the load level in the system and the availability of resources and dispatches tasks accordingly.
• Dynamic Resource Allocation: In case of an unexpected failure of the node, YARN has the ability to remove one or more of the assigned resources to the node and re-distribute the load to the rest of the viable nodes, largely maintaining the integrity of the system.

YARN Components

• ResourceManager: Serves as the leader node in YARN, providing resources for each application while controlling how these resources are used by other applications.
• NodeManager: At every node, the Node manager is responsible for controlling the utilisation of CPU, memory and other resources throughout the node level.
• ApplicationMaster: It oversees the execution of each application by breaking it down into specific tasks, monitoring and reporting the completion of the tasks, the resources consumed in the process and managing communication with the ResourceManager.

Resource Management Process

• Job Submission: When a client submits a job, the ResourceManager delegates the application to an ApplicationMaster, which requests resources from NodeManagers.
• Monitoring and Reporting: NodeManagers and ApplicationMasters send real-time updates to the ResourceManager, which monitors the health and status of each application.
• Dynamic Resource Allocation: YARN allocates resources based on load, using different scheduling techniques (e.g., FIFO, Capacity, Fair Scheduler) to balance cluster performance.

Scheduling Techniques in YARN

• FIFO (First In, First Out): The First In First Out (FIFO) mechanic provides simplicity of scheduling where jobs are scheduled in the order of their arrival, however, it lacks prioritisation.
• Capacity Scheduler: Within the confines of a Capacity Scheduler, resource allocation is best done based on set resource quotas, providing control over resource distribution.
• Fair Scheduler: Balances resource distribution to avoid prolonged job wait times, dynamically adjusting resources based on demand.

Advantages of YARN

• Flexibility: Different kinds of applications, such as MapReduce, Spark, and other big data processing frameworks, can be supported by YARN.
• Scalability: YARN can manage resources for thousands of nodes, supporting large-scale data processing.
• Improved Resource Utilisation: YARN reduces cluster congestion and operational delays by providing dynamic resource management that facilitates improved operational efficiency.

MapReduce

MapReduce is the data processing layer of Hadoop and is about processing large-scale data sets over a distributed network of machines. It fosters parallelism in data processing by breaking down tasks into smaller subtasks that can be executed concurrently, which increases efficiency and speed.

MapReduce Phases

• Map Phase: Divides input data into key-value pairs, which are then processed independently.
• Shuffle and Sort Phase: Groups and sorts the output from the Map phase, organising data for efficient reduction.
• Reduce Phase: Aggregates the data from the Map phase, producing the final output.

MapReduce Components

• JobTracker: Monitors MapReduce jobs and assigns tasks to TaskTrackers. It also handles job scheduling and fault tolerance.
• TaskTracker: Executes individual tasks assigned by the JobTracker, reporting progress and handling any errors that occur.

MapReduce Workflow

• Data Input: The input data is split and fed into the Map phase.
• Processing: Tasks are processed in parallel, sorted, and organised for the Reduce phase.
• Final Output: The Reduce phase produces a unified output that is saved back to HDFS.

Hadoop Common

Hadoop Common is a collection of libraries and utilities required by other components of Hadoop. It provides essential Java libraries, file system abstractions, and scripts needed for Hadoop to function.

Key Features of Hadoop Common

• Libraries and Utilities: Provides basic libraries (written in Java) required by all Hadoop modules.
• File System Abstractions: Offers support for various file systems and ensures compatibility with other components.
• Configuration Management: Manages configuration settings across the entire Hadoop ecosystem.

Hadoop Common Components

• Serialisation Libraries: Allows data to be converted into a format suitable for processing within Hadoop.
• Error Detection Tools: Provides tools to detect errors across the distributed environment, aiding in fault tolerance.
• Shell Scripts and Utilities: Includes utilities and scripts to manage Hadoop deployment and operations.

Hadoop Common’s Role in the Hadoop Ecosystem

• Compatibility: Ensures all Hadoop modules work together seamlessly.
• Maintenance and Support: Acts as the backbone of the Hadoop framework, providing consistent support for all other components.

Data Flow in Hadoop Architecture

The data flow in Hadoop architecture proceeds in an order that centres around the storage, processing, and retrieval of data from the distributed nodes as follows:

• Data Ingestion: The first stage is to load the data into the Hadoop Distributed File System (HDFS). The HDFS provides fault recovery strategies by creating many replicates of the blocks that the HDFS splits large files into blocks.
• Resource Allocation: YARN (Yet Another Resource Negotiator) includes resource allocation and scheduling of tasks across the cluster according to its pulling power. It ensures every application has the demanded resources for efficiency.
• MapReduce Processing:
  • Map Phase: Input data is divided into key-value pairs and processed in parallel.
  • Shuffle and Sort Phases: Intermediate data is organised for efficient processing.
  • Reduce Phase: The Reduce tasks aggregate data into the final output, completing the processing.
• Data Output: The processed results are stored back in HDFS, ready for analysis or export to other systems.

This structured data flow allows Hadoop to process large datasets efficiently, supporting scalability and fault tolerance.

Fault Tolerance in Hadoop Architecture

Hadoop’s architecture must be able to recover quickly and efficiently in the event of a failure of hardware, or software in a distributed system. This sophisticated system of Hadoop can tolerate queries through some of its core mechanisms, such as:

• Data Replication in HDFS:
  • HDFS (Hadoop Distributed File System) divides data into blocks and stores each data block on many nodes, the usual number being three.
  • In the event of a node failure, HDFS simply gets the data from another replica so that there is no interruption in the availability of data.
  • The NameNode monitors these replicas, redistributing copies as needed if a node becomes unavailable, effectively minimising data loss risk.

• Heartbeat and Health Checks:
  • DataNodes send periodic “heartbeat” signals to the NameNode, which monitors the status of each DataNode.
  • When the heartbeat is lost, the NodeMaster marks the DataNode as potentially failed and begins data re-replication, transferring its information of replicas to other nodes.
• Secondary NameNode:
  • Although not a true backup, the Secondary NameNode regularly captures snapshots of the NameNode’s metadata.
  • These snapshots serve as a fallback, aiding in metadata recovery in case of a NameNode failure.
• Resource Reallocation by YARN:
  • YARN (Yet Another Resource Negotiator) plays a critical role in managing faults during job processing.
  • In the event that a node that is working on a job fails, YARN will simply relocate that job to another active node in the job queue and avoid all interruptions in the processing.
• MapReduce Fault Tolerance:
  • In MapReduce architecture, in case of a task failure during its execution, this task is reassigned to another TaskTracker node automatically.
  • This redundancy enables recovery mechanisms which prevent entire job failure due to a few task failures, hence, enhancing reliability.
• Automatic Load Balancing:
  • HDFS dynamically adjusts data storage across nodes based on available space and resource health.
  • This load balancing enhances data accessibility and prevents nodes from being overloaded, which reduces failure risks.

What is Hadoop Architecture Used For?

Hadoop architecture is commonly applied to support data-intensive applications spanning across various distributed systems in a consistent and scalable manner, such as these:

• Big Data Storage and Processing: Stores and processes large volumes of structured, semi-structured and unstructured data.
• Data Analysis and Insights: This makes it possible for businesses to check on customer actions, forecast movements as well as make data-driven decisions.
• Log and Event Analysis: Takes in logs of web servers, web applications, and network instances for better system monitoring and enhanced security.
• Fraud Detection: Focusing on suspicious activities in real-time, its employment is mostly in finance and cyber security.
• Recommendation Engines: Works with recommendation systems for online shops and video streaming services based on users’ interests.
• Research and Development: Helps academic and scientific research by processing massive datasets for projects like genomics and climate analysis.
• Backup and Archiving: Stores large-scale backups cost-effectively with fault tolerance.

These applications make Hadoop worth its value for enterprises dealing in big data.

Who Uses Hadoop Architecture?

Hadoop architecture is applied in an assortment of industries to process their considerable amounts of intricate data. Here are some of the notable users:

• Tech Companies: Companies like Facebook, LinkedIn, and Twitter use Hadoop for storage purposes and analysis of users’ data and tailor content according to their behaviour.
• Financial Services: To meet regulatory obligations, detect fraudulent activity, or manage associated risks, financial institutions and banks utilise the intelligence generated from analysing large datasets. Hadoop is one of several technologies that allow for real-time analysis of extensive datasets gathered from diverse sources.
• Retail and E-commerce: Large retailers like Amazon, Walmart, etc. deploy Hadoop solutions to understand buying behaviour, manage supply and optimise personalised marketing and the buying experience for customers.
• Healthcare: Patient records, genomic information, and medical studies are stored in Hadoop databases to enhance healthcare and conduct advanced studies in healthcare organisations.
• Telecommunications: Service providers can utilise Hadoop to process telecommunication data in network optimization, and improve the quality of service by performing predictive analysis to avoid service delivery hampering events.
• Government and Public Sector: Law enforcement, as well as other forms of government, use Hadoop to manage data analysis tasks associated with crime prevention, terrorism, and other policy-related issues.
• Energy and Utilities: In the energy sector as well as the services sector, Hadoop is used for predictive analytics to enhance equipment operational reliability and minimise downtime.

Organisations that leverage data to enhance decision-making and efficiency management favour Hadoop because of its scalability and flexibility up to enterprise-level size.

Pros and Cons of Hadoop Architecture

Pros of Hadoop Architecture

• Scalability: The design of Hadoop encourages growth. Its distributed nature enables it to be scaled out to include additional nodes as data accumulates without compromising functionality.
• Cost-Effective: Hadoop employs commodity hardware, thus reducing storage expenditure and accordingly making big data processing available to a good number of organisations.
• Fault Tolerance: System reliability is achieved since data replication across nodes guarantees data availability even when a node fails.
• Flexibility in Data Storage: Hadoop is robust enough to store and process any class of data, which is structured, semi-structured, and unstructured, thus applicable in different spheres.
• Parallel Processing: Due to the MapReduce approach, Hadoop can also parallelize the processing of data over nodes, thus achieving faster computation and processing even of large datasets.

Cons of Hadoop Architecture

• High Latency: Since Hadoop is designed for batch processing, it would take time to respond when real-time data is processed, which makes it suitable for systems that do not require an immediate response.
• Complex Setup and Maintenance: Establishing and maintaining a Hadoop cluster requires specialised expertise and considerable resources, making it difficult for smaller organisations.
• Data Security and Privacy Issues: The security features of Hadoop are limited, making it challenging to handle sensitive data, especially in regulated sectors.
• Intensive Resource Usage: Hadoop deployment may involve high resource consumption, including high memory and storage, to allow for optimal performance, hence increasing hardware costs in large clusters.

Also Read: Data Analytics – An Ultimate Guide for Beginners

Conclusion

Hadoop architecture is a powerful framework for handling large-scale data processing across distributed systems. The use of HDFS, YARN, and MapReduce makes it a suitable solution for managing large datasets. This architecture is resistant to failure and easily expands, which increases its significance in many industries.

From analytics and research to recommendation systems, Hadoop makes it possible to deliver high performance regardless of load requirements. As the consumption of data continues to increase, mastering Hadoop presents new opportunities in the management and analysis of data. In order to learn more about 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 the perfect course for you.