Apache YARN

Definition

Apache YARN (Yet Another Resource Negotiator) is a resource management framework for Hadoop that allows multiple data processing engines to handle data stored in a single platform. It separates the resource management from the data processing, enabling applications to run concurrently on a shared cluster while managing resources more efficiently. This capability makes YARN a crucial component in distributed computing environments, particularly in big data applications.

5 Must Know Facts For Your Next Test

1. YARN was introduced in Hadoop 2.0 to improve resource management and scalability compared to the original Hadoop architecture.
2. It allows various applications to share the same cluster, increasing resource utilization and operational efficiency.
3. YARN's architecture consists of a Resource Manager and Node Managers, which work together to manage resources across the cluster.
4. Applications can run in different frameworks on top of YARN, including Spark, Tez, and MapReduce, enabling flexibility in processing data.
5. YARN supports multiple programming languages beyond Java, making it accessible to a broader range of developers and data engineers.

Review Questions

• How does Apache YARN enhance resource management in distributed computing environments?
  • Apache YARN enhances resource management by decoupling the resource management layer from the data processing layer. This separation allows multiple applications to run concurrently on a single cluster, sharing resources more effectively. The Resource Manager monitors and allocates resources based on demand while Node Managers manage resources at the individual node level, leading to improved overall efficiency and scalability.
• Discuss the key components of YARN's architecture and their roles in managing resources within a cluster.
  • YARN's architecture primarily consists of two key components: the Resource Manager and Node Managers. The Resource Manager acts as the master server that manages resource allocation across all nodes in the cluster. Each Node Manager runs on individual nodes and is responsible for overseeing resource usage on that node, including monitoring containers that run application tasks. This architecture allows YARN to efficiently allocate resources based on the needs of various applications while ensuring optimal performance across the cluster.
• Evaluate the impact of Apache YARN on big data processing frameworks and how it influences application development.
  • Apache YARN significantly impacts big data processing by providing a versatile and efficient resource management system that enables various frameworks like Spark, Tez, and traditional MapReduce to coexist on a single cluster. This flexibility allows developers to choose the most suitable processing engine for their specific use cases without being limited to one framework. By supporting multiple programming languages and frameworks, YARN encourages innovation and accelerates application development in distributed computing environments.

Related terms

Hadoop: An open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.

MapReduce: A programming model for processing large data sets with a distributed algorithm on a cluster, commonly associated with Hadoop.

Resource Manager: A central authority in YARN responsible for allocating resources and managing the overall resource utilization in the cluster.