Apache YARN

What is Apache YARN?

Apache YARN (Yet Another Resource Negotiator) is a part of Apache Hadoop system, acting as a resource management and job scheduling tool. Initially designed for managing resources and scheduling tasks in Hadoop clusters, YARN has expanded its scope and now supports various data processing workloads including but not limited to MapReduce, Spark, and even services like Apache HBase and Apache TEZ.


Apache YARN was introduced as a part of Hadoop 2.0, improving on its predecessor by separating resource management and scheduling tasks from data processing. It was created by Arun C. Murthy and Vinod Kumar Vavilapalli at Yahoo, and has undergone multiple updates and enhancements since its inception.

Functionality and Features

Key features of Apache YARN include:

  • Resource Management: YARN can manage and allocate resources among multiple data processing frameworks operating on the same cluster.
  • Scalability: It can scale up to thousands of nodes, processing petabytes of data.
  • Multi-tenancy: YARN enables multi-tenancy, allowing multiple organizations to share a single Hadoop cluster, each with their own separate resource quotas.
  • Flexibility: It can handle various workloads beyond just MapReduce.


Apache YARN consists of a Resource Manager, Node Manager, and Application Master. Resource Manager manages the resource allocation, Node Manager maintains node operation, and Application Master communicates resource needs for individual applications.

Benefits and Use Cases

YARN is widely used for big data analytics tasks, machine learning, and real-time data processing. It benefits businesses with improved cluster utilization, increased data processing capabilities and flexibility to support various data processing workloads.

Challenges and Limitations

Despite its advantages, there are challenges associated with YARN, such as its complexity, limited fine-tuning opportunities, and issues with long-running applications. Moreover, for non-Hadoop workloads, alternatives like Kubernetes may provide a more modern, container-based approach to resource management.

Integration with Data Lakehouse

In a Data Lakehouse setting, YARN can operate as the resource management layer, managing workloads across the data lake environment. However, with newer data lake technologies like Dremio, the need for YARN is reduced, as Dremio facilitates direct querying on data lake storage, bypassing the need for Hadoop and YARN.

Security Aspects

YARN supports Kerberos for authentication and integrates with common authorization tools like Apache Ranger and Apache Sentry.


YARN significantly enhances the performance of Hadoop clusters by effectively managing and utilizing resources, thus ensuring efficient data processing.


What is Apache YARN? Apache YARN is a resource management and job scheduling tool in Apache Hadoop.

What role does YARN play in a Hadoop ecosystem? YARN manages resources and schedules jobs for various data processing tasks in Hadoop.

How does YARN integrate with a Data Lakehouse? YARN can manage workloads in a data lakehouse environment, though this need is reduced with newer technologies like Dremio.

What are the limitations of YARN? YARN has limitations such as its complexity, limited tuning opportunities, and issues handling long-running applications.

What alternatives exist to YARN? Alternatives to YARN include resource management systems like Kubernetes and Mesos.


Hadoop: An open-source software framework for storing data and running applications on clusters of commodity hardware.

MapReduce: A programming model and software framework for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes.

Data Lakehouse: A new, open architecture that combines the best elements of data lakes and data warehouses.

Dremio: A data lake engine that enables fast, efficient, and direct querying on data lake storage.

Kubernetes: An open-source platform for automating deployment, scaling, and management of containerized applications.

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