Apache YARN

Definition

Apache YARN (Yet Another Resource Negotiator) is a resource management layer for Hadoop that allows multiple data processing engines to handle data stored in a single platform. It enhances the Hadoop ecosystem by enabling better resource utilization and scheduling for various applications, thus improving efficiency in processing big data workloads.

5 Must Know Facts For Your Next Test

1. YARN was introduced in Hadoop 2.0 to address scalability and performance issues present in earlier versions of Hadoop that relied solely on MapReduce.
2. YARN decouples resource management from data processing, allowing multiple frameworks to run on top of Hadoop, including Spark, Tez, and Flink.
3. YARN's architecture consists of a Resource Manager and Node Managers that manage resources across different nodes in the cluster, ensuring efficient allocation.
4. With YARN, different applications can share the same cluster resources dynamically, leading to better resource utilization and reduced idle time.
5. YARN supports containerization, allowing applications to run within isolated environments, which increases security and minimizes conflicts between different processes.

Review Questions

• How does Apache YARN improve resource management compared to earlier versions of Hadoop?
  • Apache YARN enhances resource management by separating the resource management functions from the data processing tasks that were tightly integrated in earlier versions of Hadoop. This separation allows for multiple processing engines to utilize the same underlying infrastructure simultaneously, resulting in better scalability and more efficient use of resources. With YARN, various applications can run concurrently without being limited by the MapReduce framework, providing flexibility in processing big data.
• Discuss the architectural components of YARN and their roles within the Hadoop ecosystem.
  • The architecture of Apache YARN consists primarily of two main components: the Resource Manager and Node Managers. The Resource Manager is the central authority that manages cluster resources and schedules application requests. Node Managers operate on individual nodes within the cluster, monitoring resource usage and reporting back to the Resource Manager. This architecture allows YARN to effectively allocate resources dynamically based on the needs of different applications running on the Hadoop ecosystem.
• Evaluate the impact of YARN's support for containerization on application deployment and resource allocation.
  • YARN's support for containerization significantly impacts application deployment by allowing multiple applications to run in isolated environments on shared infrastructure. This reduces conflicts between applications and enhances security by containing processes within their own containers. Furthermore, containerization allows for dynamic resource allocation, enabling YARN to allocate resources based on real-time demand rather than static configurations. This adaptability leads to improved efficiency in resource utilization and better performance of big data applications.

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 and processing engine used in Hadoop to perform distributed data processing through a sequence of map and reduce tasks.

Resource Manager: The master daemon in YARN responsible for managing resources and scheduling applications across the cluster.