Learn How Yarn Plays a Crucial Role in Big Data Processing at Javatpoint
The ever-increasing use of big data has had a significant impact on the way business is conducted today. Companies are continuously looking for ways to manage, store, and process vast amounts of data quickly and efficiently. Apache Hadoop is one of the most popular big data processing frameworks used to accomplish this task.
One of the components of Hadoop, yet often overlooked, is Yarn. Yarn (Yet Another Resource Negotiator) is a powerful tool that acts as a resource manager, allowing big data processing across multiple machines. Yarn’s primary function is to allocate resources to different applications running on the cluster and monitor them to ensure efficient processing.
So how exactly does Yarn enable big data processing? Let’s take a closer look.
The Architecture of Yarn
Understanding the architecture of Yarn is crucial to understand how it manages big data processing across multiple machines. Yarn consists of three main components:
1. Resource Manager
2. Application Master
3. Node Manager
The resource manager is responsible for allocating resources, monitoring them, and providing control over the entire system. The application master interacts with the resource manager to allocate resources for a specific job and monitors its progress. The node manager runs on every machine in the cluster and reports its resource utilization to the resource manager.
Advantages of Using Yarn
Using Yarn for big data processing has several advantages. Here are some of them:
1. Scalability – Yarn is designed to handle large workloads and scale dynamically as processing requirements change.
2. Resource Allocation – With Yarn, resources are allocated based on priority, ensuring maximum efficiency for critical applications.
3. Fault Tolerance – Yarn ensures that failed tasks are automatically restarted, thereby improving the overall reliability of the system.
4. Cluster Utilization – Yarn allows for better utilization of cluster resources by sharing resources across different applications.
Real-World Examples of Yarn in Action
Yarn has been employed by numerous organizations globally to improve their big data processing capabilities. One such company is Yahoo. Yahoo uses Yarn to process billions of user actions and provide data-driven insights to its customers. Yarn has helped Yahoo to scale up to thousands of machines in the cluster, ensuring that customer queries are processed efficiently and in real-time.
Conclusion
Yarn is indeed a crucial component of Hadoop, and its role in big data processing cannot be overstated. Its architecture, scalability, fault tolerance, and cluster utilization make it a game-changer for businesses looking to improve their big data processing capabilities. Its implementation by Yahoo and other companies highlights its efficiency and reliability in processing large data volumes. If you’re looking to improve your big data processing capabilities, Yarn is certainly worth considering.
