Hadoop YARN Architecture: Comprehensive Guide to YARN Components and Functionality

Hadoop YARN (Yet Another Resource Negotiator) is the central resource management layer for the Hadoop ecosystem. It efficiently allocates system resources and manages workloads across a cluster, ensuring that different applications can run simultaneously without conflict. 

Understanding how YARN functions is crucial for optimizing resource utilization and improving the performance of distributed applications. 

This blog will explore the Hadoop YARN architecture, including its key components, such as the ResourceManager, NodeManager, and ApplicationMaster. We'll also break down its core functionalities and explain how YARN helps optimize the execution of distributed applications in a Hadoop environment! 

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Core Components of Hadoop YARN Architecture

The core components of Hadoop YARN architecture are integral to the system’s ability to manage resources and execute distributed tasks across large-scale clusters effectively. ResourceManager, NodeManager, ApplicationMaster, Containers, and the optional Timeline Server handle a specific aspect of job execution and resource management. 

Understanding these elements is essential for optimizing resource allocation and task execution in any Hadoop cluster environment.

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1. Resource Manager

The ResourceManager (RM) is the master daemon thread responsible for managing and allocating resources across the entire Hadoop cluster. It consists of two main components:

• Scheduler: Responsible for allocating resources to different applications based on defined policies and priorities. It uses resource requests (memory, CPU) and job scheduling constraints (e.g., fairness, capacity) to make resource decisions.
  • Capacity Scheduler and Fair Scheduler are two common types of schedulers, each with different approaches to job allocation based on cluster priorities.
• Application Manager: Responsible for managing the lifecycle of submitted applications. It interacts with the NodeManager to launch and monitor the Application Master, which handles the actual execution of the job on individual nodes.

Key Technical Functions:

• Manages cluster-wide resource allocation and load balancing.
• Tracks cluster health, freeing resources from failed or completed tasks.
• Supports different types of job scheduling, including queue-based and resource-based policies.

2. Node Manager

The NodeManager (NM) is a per-node daemon that works alongside the ResourceManager to monitor the status of resources on individual nodes. It is responsible for:

• Monitoring Node Health: Tracks the health and resource usage (memory, CPU, disk) on each node. Reports resource availability and utilization to the ResourceManager. 
• Container Management: Allocates and manages containers where tasks run. Ensures containers run within allocated resource limits (e.g., memory, CPU).
  • Each container can run one or more tasks, depending on resource requirements. 
• Log Aggregation: Collects logs from applications running inside containers and aggregates them for centralized access.

Key Technical Functions:

• Reports resource status (e.g., available memory, CPU usage) back to the ResourceManager at regular intervals.
• Handles monitoring and cleanup of container execution.
• Ensures task execution adheres to the allocated container’s resource constraints.

3. Application Master

The ApplicationMaster (AM) is a per-application component that runs on the cluster. It is responsible for managing the execution of a specific application from start to finish:

• Resource Negotiation: The AM interacts with the ResourceManager to request resources (containers) based on the application's needs.
• Application Execution: After receiving resources, the AM launches and monitors tasks in allocated containers, ensuring job execution continues smoothly.
  • In case of task failure, the AM handles retries and resource reallocation.
• Task Scheduling and Monitoring: The AM schedules tasks within containers and ensures they run efficiently. It tracks job progress and reports status back to the user.

Key Technical Functions:

• Tracks the execution progress of individual tasks within the application.
• Ensures fault tolerance by handling task failure and managing retries.
• Interacts with the ResourceManager to handle resource requests, job execution, and job completion status updates.

Also Read: Resource Management Projects: Examples, Terminologies, Factors & Elements

4. Containers in YARN

Containers are the fundamental units of resource allocation in YARN. Each container encapsulates the necessary resources (memory, CPU, etc.) required to run a specific task.

• Dynamic Allocation: Containers are dynamically allocated based on workload, allowing for flexible resource utilization across the cluster.
• Resource Constraints: Containers have fixed memory and CPU limits, defined at the time of their allocation. YARN ensures that no container exceeds its resource limits, enabling the effective sharing of resources.
• Isolation: Containers provide isolation for tasks, meaning each application or job runs independently, minimizing conflicts between them.

Key Technical Functions:

• Provides resource isolation and enables multiple tasks to run concurrently within the same physical node.
• Dynamically scales container resources based on workload requirements.
• Ensures that tasks within containers do not interfere with one another, maintaining a stable environment for job execution.

Also Read: What is the Future of Hadoop? Top Trends to Watch

5. Timeline Server (Optional Advanced Component)

The Timeline Server is an optional component of YARN that stores and serves historical information about application execution. It is typically used in larger, more complex YARN deployments where detailed tracking and analysis are necessary.

• Application History Tracking: The Timeline Server logs execution data, including resource usage, task status, and job completion details, providing a comprehensive view of past executions.
• Metrics and Logs: It aggregates performance metrics such as task duration, memory usage, and CPU utilization, helping administrators and developers track application performance over time.
• Advanced Monitoring: It enables sophisticated monitoring of job performance and resource allocation, which can be crucial for debugging and optimizing applications.

Key Technical Functions:

• Stores and serves historical data for debugging and performance tuning.
• Provides a REST API for accessing timeline data, facilitating integration with monitoring tools.
• Collects application-specific metrics, which can be analyzed to optimize resource management strategies.

Also Read: Understanding Hadoop Ecosystem: Architecture, Components & Tools

With a clear understanding of these core components, you can explore how Hadoop YARN architecture works in practice. 

Let’s examine the Application Workflow, where we’ll examine how YARN components interact during job execution, from the initial job submission to task completion.

How Does the Hadoop YARN Architecture Work? Application Workflow

The application workflow in YARN follows a precise sequence where different components collaborate to ensure efficient resource management and job execution. This process includes job submission, resource allocation, task execution, monitoring, and completion. 

Each step in the workflow plays a critical role in maintaining efficiency and minimizing resource contention across a distributed cluster.

1. Job Submission

When a user submits a job to the Hadoop cluster, the ApplicationMaster (AM) is created specifically for that application. The ResourceManager (RM) receives the job request and decides which nodes should handle the task based on the available resources. 

The AM interacts with the RM to request the necessary containers to run the job. During this negotiation, the job’s requirements, such as memory, CPU, and other resource constraints, are considered.

• Scheduler Decision: The Scheduler within the RM assigns the job to the appropriate nodes based on various policies, such as Capacity Scheduler or Fair Scheduler, ensuring efficient load balancing across the cluster.
• ApplicationMaster Launch: The AM is then launched on the cluster to handle job execution. It starts by negotiating with the RM for resources and managing the application's lifecycle. Each application gets its own AM, ensuring dedicated control over resource requests and task execution.

Example: If a data processing job requests 16GB of memory and 4 CPUs, the AM will request these resources, and the RM will check if such resources are available across the cluster before approving the allocation.

Also Read: 27 Big Data Projects to Try in 2025 For all Levels [With Source Code]

2. Resource Allocation

Once the job is submitted, the ResourceManager allocates resources based on cluster capacity, job priority, and the available resources across nodes. The NodeManagers periodically send resource utilization reports to the RM, providing an updated view of node health and capacity.

• Container Allocation: The RM allocates containers on available nodes. A container encapsulates the resources (memory, CPU) required to execute a task. Once resources are allocated, the AM ensures the containers are properly provisioned to run tasks.
• Node Selection: The Scheduler plays a key role in determining which nodes should be selected for container placement. This decision is based on various factors, such as resource availability, data locality, and job priority.

Example: If a job needs data located on a specific node, the RM will prioritize scheduling the task on that node, ensuring optimal data locality and reducing data transfer times.

3. Task Execution

After resource allocation, NodeManagers on the selected nodes run the containers where the application’s tasks are executed. Each container runs one or more tasks, which can be dynamically adjusted based on the workload.

• Container Launch: The NodeManager starts the container with the assigned resource allocation (memory, CPU) and executes the task. The AM is responsible for scheduling and launching tasks within these containers and making necessary adjustments (e.g., for task retries or reallocation).
• Fault Tolerance: If a task within a container fails, the AM is responsible for restarting the task in another available container on a different node. This ensures that tasks are completed even in the face of node failures.

Example: If a node fails during task execution, the AM will detect the failure, reallocate the task to a healthy node, and resume the process without manual intervention.

Also Read: Top 10 Hadoop Tools to Make Your Big Data Journey Easy

4. Monitoring and Reporting

While the tasks run, NodeManagers continuously monitor resource usage (e.g., memory, CPU) and report this information to the ResourceManager and ApplicationMaster. 

If enabled, the Timeline Server collects and stores this data for later analysis, offering more profound insights into task execution and resource usage patterns.

• Resource Usage Reporting: The NodeManager sends periodic reports about the resource usage of containers. This helps the RM understand the cluster's health and allocate resources more efficiently for future tasks.
• Progress Updates: The AM also reports the job's progress to the user and ResourceManager. For example, if an application consists of multiple stages (e.g., MapReduce jobs), the AM provides updates on the completion of each stage, helping administrators monitor the progress in real time.

Example: The AM might report that 70% of a job’s map tasks are complete, which helps the system gauge whether additional resources are needed or if the job is progressing as expected.

Also Read: Top Hadoop Project Ideas to Start a Career in Big Data 2025

5. Job Completion

Once all tasks within the containers are completed, the ApplicationMaster signals job completion. The NodeManagers then release the resources allocated to the containers, making them available for other applications. The ResourceManager also removes the job's status from the cluster registry and updates its resource pool accordingly.

• Resource Cleanup: After the job is completed, the containers are decommissioned, and the allocated resources (memory, CPU) are freed up for other applications. The RM ensures that all the resources are released and available for new tasks.
• Job Status: The AM sends the final job status (success, failure) to the RM, which then updates its internal records. In case of failures, the RM tracks these for future analysis and helps maintain overall cluster health.

Example: If a job completes successfully, the RM updates the job registry and frees up the resources. If a failure occurs, the RM records the failure details, which can be used for further debugging or retries.

Also Read: Yarn vs NPM: Key Differences and How to Choose the Right

Next, let’s explore the advantages and key features of Hadoop YARN architecture to understand how it excels at managing distributed applications at scale.

Advantages and Key Features of Hadoop YARN Architecture

Hadoop YARN architecture has emerged as a powerful solution for managing resources in large-scale distributed environments. Its architecture offers several advantages over traditional Hadoop MapReduce, such as improved scalability, better resource utilization, and enhanced fault tolerance. 

In fact, YARN has been shown to manage clusters with up to 40,000 nodes, whereas traditional MapReduce struggles with performance at clusters larger than 1,000 nodes. By decoupling resource management from job execution, YARN enables a more flexible, efficient, and scalable environment, making it ideal for many big data applications.

Key Features and Advantages

Below is a summary of Hadoop YARN's primary features and advantages, making it a preferred choice for resource management in large-scale distributed computing environments.

Summary of Key Benefits:

• Enhanced Efficiency: YARN optimizes resource utilization by adjusting to job requirements and workload changes dynamically.
• Scalability: YARN enables clusters to scale efficiently without performance degradation, accommodating increasing data volumes.
• Fault Tolerance: YARN's ability to recover from node failures ensures that applications continue to run smoothly.
• Flexibility: The decoupling of resource management from application execution allows YARN to support a variety of big data frameworks and job types.

Also Read: How to Become a Hadoop Administrator: Everything You Need to Know

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As you can see, YARN significantly enhances resource management in Hadoop clusters compared to its predecessors. But how does it truly measure up to the traditional MapReduce framework? 

In the next section, we’ll compare YARN and traditional MapReduce, showcasing each approach's strengths and limitations.

YARN vs. Traditional MapReduce: A Comparative Analysis

Hadoop YARN and traditional MapReduce are key components of the Hadoop ecosystem, but YARN offers significant improvements. 

The table below highlights the key differences between YARN and traditional MapReduce, emphasizing resource management, scalability, fault tolerance, and flexibility.

Also Read: Hadoop vs MongoDB: Which is More Secure for Big Data?

This comparison highlights how YARN effectively addresses the key limitations of MapReduce’s architecture. YARN's components are crucial for enabling modern, scalable applications in the Hadoop ecosystem.

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How Can upGrad Help You Learn Hadoop YARN Architecture?

YARN separates resource management and job scheduling, boosting scalability and flexibility in Hadoop. Its components, like the ResourceManager, NodeManager, and ApplicationMaster, optimize resource allocation and task execution, making it ideal for unpredictable big data workloads. 

While real-world YARN implementations can present challenges like optimizing resource utilization and managing multi-tenant systems at scale, upGrad's programs offer comprehensive curricula to bridge the gap between theory and practical application.

If you're looking to expand your expertise further, here are some additional courses to consider:

• Master’s Degree in Artificial Intelligence and Data Science 
• Postgraduate Diploma in Software Development

upGrad offers hands-on experience with Hadoop, YARN, and big data technologies, along with personalized career counseling, to help you understand the industry implications. With offline centers in multiple cities, we also offers flexible learning and expert guidance!

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Reference Link:
https://medium.com/expedia-group-tech/herding-the-elephants-3501cb64eb3