What is Hadoop YARN? The Resource Manager of the Big Data Ecosystem

As organizations collect and analyze ever-growing volumes of data, they need robust platforms that can manage not just data, but the resources and workloads behind the scenes. This is where Hadoop YARN comes into play — a critical component of the Apache Hadoop ecosystem that enables efficient resource management across big data applications.

In this article, we’ll explore what Hadoop YARN is, how it works, its architecture, benefits, and where it fits into the world of big data processing.

📘 What is Hadoop YARN?

YARN stands for Yet Another Resource Negotiator. It is the resource management and job scheduling layer of Hadoop, introduced in Hadoop version 2.0 to address the limitations of the original MapReduce engine.

Before YARN, the resource management capabilities in Hadoop were tightly coupled with MapReduce. YARN decouples these functions, allowing multiple data processing engines like MapReduce, Apache Spark, Apache Tez, and more to run simultaneously on the same Hadoop cluster.

🧱 Key Components of Hadoop YARN

Hadoop YARN consists of the following main components:

1. ResourceManager (RM)

The master daemon that manages the global allocation of cluster resources. It has two main parts:

• Scheduler: Allocates resources based on availability and constraints (e.g., memory, CPU).
• ApplicationManager: Manages job submissions and monitors running applications.

2. NodeManager (NM)

Runs on each node in the cluster. It monitors resource usage (CPU, memory, disk) and reports to the ResourceManager. It also launches and manages containers.

3. ApplicationMaster (AM)

Each job submitted to the cluster has its own ApplicationMaster. It negotiates resources with the ResourceManager and coordinates the execution of tasks via containers.

4. Container

A lightweight process running on a NodeManager that executes a task. Containers are the fundamental units of resource allocation in YARN.

🔄 How Hadoop YARN Works

Here’s how the YARN architecture works step-by-step:

1. Job Submission: A client submits a job to the ResourceManager.
2. ApplicationMaster Launch: The ResourceManager allocates a container and launches the ApplicationMaster for that job.
3. Resource Negotiation: The ApplicationMaster requests containers to execute tasks.
4. Task Execution: NodeManagers launch containers to run the tasks.
5. Monitoring & Completion: The ApplicationMaster monitors task progress and reports status to the ResourceManager.

🌟 Benefits of Hadoop YARN

• ✅ Multi-engine Support: Allows different types of processing engines (e.g., Spark, Tez) to run on the same cluster.
• ✅ Better Resource Utilization: Dynamically allocates resources based on need, improving cluster efficiency.
• ✅ Scalability: Designed to support thousands of nodes and applications concurrently.
• ✅ Fault Tolerance: Automatically handles node failures and task retries.
• ✅ Decoupled Architecture: Separates resource management from job processing.

🛠️ Real-World Use Cases of YARN

• Running Spark and MapReduce workloads on the same cluster
• Deploying streaming, batch, and interactive jobs concurrently
• Managing complex workflows with tools like Apache Oozie and Apache Hive
• Dynamic scaling of jobs based on real-time demands

⚖️ YARN vs Traditional MapReduce Job Tracker

Feature	YARN	Original MapReduce Job Tracker
Multi-Engine Support	Yes	No
Scalability	Highly Scalable	Limited
Resource Isolation	Container-based	Basic
Fault Tolerance	Improved	Basic
Performance	Better Cluster Utilization	Less Efficient

🧠 Conclusion

Hadoop YARN revolutionized the way resources are managed in big data ecosystems. By supporting multiple processing engines and dynamically allocating cluster resources, YARN has made Hadoop more flexible, scalable, and efficient.

Whether you’re managing batch jobs with MapReduce or running real-time analytics with Spark, understanding YARN is essential for optimizing performance and maximizing your infrastructure investment.