What is Hadoop YARN?

Written by: Jeff Tse

October 24, 2024

Time to read 5 min

Table of content

What is Hadoop YARN? The Need for YARN Key Features of YARN YARN Architecture 2. Node Manager (NM) 3. Application Master (AM) 4. Containers How YARN Works Advantages of Using YARN Use Cases for YARN FAQs About Hadoop YARN More Yarn Guides

What is Hadoop YARN?

Hadoop YARN (Yet Another Resource Negotiator) is a critical component of the Hadoop ecosystem, introduced in version 2.0 to address the limitations of the original Hadoop MapReduce framework. YARN serves as a resource management layer that enables multiple data processing engines to run on a single Hadoop cluster, thereby enhancing the flexibility, scalability, and efficiency of big data processing. This article will provide an in-depth exploration of Hadoop YARN, its architecture, components, functionalities, and its impact on the Hadoop ecosystem.

The Need for YARN

Before the introduction of YARN, Hadoop's architecture relied heavily on a single Job Tracker for resource management and job scheduling. This design posed several challenges:

Scalability Issues : The single Job Tracker became a bottleneck as the cluster size increased. It struggled to manage thousands of nodes and concurrent tasks effectively.
Inefficient Resource Utilization : The original MapReduce framework was limited to batch processing, which meant that other processing paradigms like stream processing and interactive querying could not be efficiently executed.
Limited Flexibility : The rigid structure of MapReduce restricted the ability to run various types of applications simultaneously on the same cluster.

To overcome these challenges, YARN was developed to decouple resource management from data processing, allowing for a more versatile and efficient computing environment.

Key Features of YARN

YARN introduces several key features that enhance the functionality of Hadoop:

Multi-Tenancy : YARN allows different applications to share resources dynamically, enabling multiple processing frameworks (like Spark, Storm, etc.) to run concurrently on the same cluster.
Improved Resource Management : It provides better resource allocation through its Resource Manager and Node Manager architecture, ensuring optimal utilization of cluster resources.
Support for Various Processing Models : YARN supports batch processing, interactive processing, stream processing, and graph processing, making it suitable for a wide range of data applications.
Scalability : With YARN, Hadoop can scale to manage thousands of nodes efficiently without performance degradation.

YARN Architecture

The Resource Manager is the master daemon in YARN responsible for managing cluster resources. It performs two main functions:

Resource Allocation : The RM allocates resources to various applications based on their requirements and available resources in the cluster.
Job Scheduling : It schedules jobs by communicating with Node Managers and Application Masters.

The RM consists of two main components:

Scheduler : The Scheduler is responsible for allocating resources to different running applications based on predefined policies (e.g., Capacity Scheduler or Fair Scheduler). It does not monitor application status or restart failed tasks.
Application Manager : This component manages application submissions and negotiates resources for launching Application Masters.

2. Node Manager (NM)

The Node Manager is a per-machine framework agent responsible for managing containers on individual nodes within the cluster. Its primary responsibilities include:

Container Management : The NM launches containers as requested by Application Masters and monitors their resource usage (CPU, memory, disk).
Health Monitoring : It reports the health status of nodes back to the Resource Manager and handles any failures by reallocating resources as necessary.

3. Application Master (AM)

Each application running on YARN has its own Application Master that negotiates resources with the Resource Manager and works with Node Managers to execute tasks. The AM is responsible for:

Resource Negotiation : It requests containers from the RM based on its resource requirements.
Task Monitoring : The AM tracks the progress of tasks running in containers and handles any failures by requesting new containers as needed.

4. Containers

Containers are the fundamental unit of resource allocation in YARN. A container encapsulates a specific amount of CPU and memory resources allocated for executing a task. Each container runs an instance of an application’s code within its allocated resources.

How YARN Works

The workflow in YARN can be broken down into several key steps:

Job Submission : A client submits a job to the Resource Manager.
Application Master Creation : The RM allocates a container for the Application Master associated with that job.
Resource Negotiation : The Application Master requests additional containers from the Resource Manager based on its needs.
Task Execution : Once containers are allocated, they are launched by Node Managers where tasks are executed.
Monitoring and Reporting : The Application Master monitors task execution and reports progress back to the Resource Manager.
Completion : Upon completion of all tasks, the Application Master deregisters itself from the Resource Manager.

Advantages of Using YARN

YARN brings numerous advantages to Hadoop users:

Enhanced Performance : By separating resource management from data processing, YARN improves overall performance and efficiency in job execution.
Flexibility : Users can run different types of applications (batch, interactive, streaming) simultaneously without conflicts over resources.
Better Resource Utilization : Dynamic allocation ensures that resources are used efficiently across various applications running on the cluster.
Scalability : Organizations can scale their clusters easily without worrying about performance bottlenecks or limitations inherent in earlier versions of Hadoop.
Support for Multiple Frameworks : With YARN's architecture, frameworks like Apache Spark and Apache Flink can operate alongside traditional MapReduce jobs seamlessly.

Use Cases for YARN

YARN has been adopted widely across industries due to its versatility and efficiency in handling big data workloads:

Real-Time Data Processing : Organizations use YARN with frameworks like Apache Storm or Apache Spark Streaming to process real-time data feeds efficiently.
Interactive Analytics : Data scientists leverage tools like Apache Hive or Impala running on top of YARN for interactive querying against large datasets stored in HDFS.
Batch Processing Workloads : Traditional MapReduce jobs continue to run effectively under YARN while benefiting from improved resource management.
Machine Learning Applications : With frameworks like Apache Mahout operating on top of YARN, organizations can build sophisticated machine learning models using large datasets.

FAQs About Hadoop YARN

1. How does YARN improve scalability?

YARN allows multiple applications to run concurrently on a single Hadoop cluster by efficiently allocating resources. This decoupling of resource management from job execution helps avoid bottlenecks associated with a single Job Tracker.

2. What types of applications can run on YARN?

YARN supports various data processing models, including batch processing (MapReduce), real-time processing (Apache Storm), interactive querying (Apache Hive), and stream processing (Apache Spark).

3. How does resource allocation work in YARN?

When an application is submitted, the Resource Manager allocates containers based on the application's resource requirements. The Application Master negotiates with the Resource Manager to secure these resources and manage task execution.

4. What is the role of the Application Master?

The Application Master coordinates the execution of an application within the cluster, negotiating resources with the Resource Manager and managing task execution across Node Managers.

5. Can YARN handle failures?

Yes, YARN includes mechanisms for handling failures. If a task fails, the Application Master can request new containers to restart the task, ensuring that applications continue running smoothly.

6. What are some advantages of using YARN?

Key advantages include improved resource utilization, support for multiple processing frameworks, enhanced scalability, and better fault tolerance compared to earlier versions of Hadoop.

7. Is YARN compatible with existing MapReduce applications?

Yes, YARN maintains compatibility with existing MapReduce applications while allowing new processing frameworks to operate alongside them without conflicts.

8. How can I monitor YARN applications?

YARN provides a web interface that allows users to monitor application status, resource usage, and overall cluster health. Administrators can also use command-line tools for monitoring purposes.