YARN

Definition

YARN, which stands for Yet Another Resource Negotiator, is a resource management platform in Hadoop that enables the management and scheduling of resources across various applications. It allows multiple data processing engines to run on a Hadoop cluster, facilitating more efficient resource utilization and enabling better scalability. YARN separates resource management from the programming model, allowing different frameworks like MapReduce, Spark, and others to operate on the same cluster seamlessly.

5 Must Know Facts For Your Next Test

1. YARN was introduced in Hadoop 2.0 to improve resource management and support multiple processing frameworks beyond just MapReduce.
2. The architecture of YARN consists of a Resource Manager and Application Master, which work together to allocate resources and manage job execution.
3. YARN improves the overall efficiency of Hadoop clusters by allowing different applications to share resources dynamically based on demand.
4. One of the significant advantages of YARN is its ability to run diverse workloads, including batch processing, interactive processing, and stream processing on a single cluster.
5. YARN's design supports scalability, enabling organizations to easily expand their clusters as data volumes grow without needing to redesign their processing frameworks.

Review Questions

• How does YARN enhance resource management compared to earlier versions of Hadoop?
  • YARN enhances resource management by decoupling resource scheduling from job processing. In earlier versions of Hadoop, the MapReduce framework was responsible for both processing and resource management, which limited flexibility and scalability. With YARN, the Resource Manager allocates resources more efficiently across multiple applications running on the cluster, allowing for better utilization and enabling frameworks other than MapReduce to operate concurrently.
• Discuss the roles of the Resource Manager and Application Master in YARN's architecture.
  • In YARN's architecture, the Resource Manager serves as the central authority that manages resources across the entire cluster. It schedules jobs and allocates resources based on application requirements. Each application submitted to YARN has its own Application Master, which is responsible for negotiating resources with the Resource Manager and managing the execution of tasks across the cluster. This division of responsibilities allows for improved efficiency and fault tolerance.
• Evaluate how YARN's flexibility in supporting various processing frameworks impacts big data analytics.
  • YARN's flexibility significantly enhances big data analytics by allowing organizations to utilize different processing frameworks tailored to specific use cases on a single Hadoop cluster. This means that businesses can run batch processes with MapReduce while also executing real-time analytics with Spark or Flink simultaneously. The ability to optimize resource allocation across diverse workloads leads to better performance and efficiency, enabling more complex analyses and faster insights from large data sets.

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 used for processing large data sets with a distributed algorithm on a cluster.

Resource Manager: The master daemon in YARN that manages resources and schedules applications running in the cluster.