How to handle 'container killed by YARN' error in Hadoop

Introduction

Hadoop is a powerful framework for distributed data processing, but sometimes you may encounter the 'container killed by YARN' error. This tutorial will guide you through understanding YARN, identifying the causes of this error, and providing effective solutions to resolve it, helping you maintain a stable Hadoop environment.

Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL hadoop(("`Hadoop`")) -.-> hadoop/HadoopYARNGroup(["`Hadoop YARN`"]) hadoop/HadoopYARNGroup -.-> hadoop/yarn_setup("`Hadoop YARN Basic Setup`") hadoop/HadoopYARNGroup -.-> hadoop/apply_scheduler("`Applying Scheduler`") hadoop/HadoopYARNGroup -.-> hadoop/yarn_app("`Yarn Commands application`") hadoop/HadoopYARNGroup -.-> hadoop/yarn_container("`Yarn Commands container`") hadoop/HadoopYARNGroup -.-> hadoop/yarn_log("`Yarn Commands log`") subgraph Lab Skills hadoop/yarn_setup -.-> lab-417733{{"`How to handle 'container killed by YARN' error in Hadoop`"}} hadoop/apply_scheduler -.-> lab-417733{{"`How to handle 'container killed by YARN' error in Hadoop`"}} hadoop/yarn_app -.-> lab-417733{{"`How to handle 'container killed by YARN' error in Hadoop`"}} hadoop/yarn_container -.-> lab-417733{{"`How to handle 'container killed by YARN' error in Hadoop`"}} hadoop/yarn_log -.-> lab-417733{{"`How to handle 'container killed by YARN' error in Hadoop`"}} end

Understanding YARN and Container Lifecycle

YARN (Yet Another Resource Negotiator) is the resource management and job scheduling system in Hadoop. It is responsible for managing the cluster resources and allocating them to various applications running on the Hadoop cluster.

In YARN, the basic unit of computation is a "container". A container represents a collection of physical resources, such as memory, CPU, and disk, allocated for a specific application to run. The lifecycle of a container in YARN can be divided into the following stages:

YARN Container Lifecycle

Application Submission: When a user submits an application to the YARN cluster, the Application Master (AM) is responsible for negotiating resources with the Resource Manager (RM) and requesting containers to execute the application's tasks.
Container Allocation: The Resource Manager (RM) receives the container request from the Application Master (AM) and allocates the requested resources to the application. The RM then notifies the Node Manager (NM) on the appropriate node to launch the container.
Container Launching: The Node Manager (NM) on the target node receives the container launch request from the RM and starts the container. The NM is responsible for monitoring the container's resource usage and ensuring that it does not exceed the allocated resources.
Container Execution: The container runs the application's tasks, utilizing the allocated resources. The Application Master (AM) is responsible for monitoring the progress of the tasks and managing the container's lifecycle.
Container Completion: When the container's tasks are completed, the container is terminated, and the resources are released back to the YARN cluster for use by other applications.

graph TD A[Application Submission] --> B[Container Allocation] B --> C[Container Launching] C --> D[Container Execution] D --> E[Container Completion]

Understanding the YARN container lifecycle is crucial for troubleshooting and resolving issues related to "container killed by YARN" errors, which can occur when a container is terminated prematurely by the YARN resource management system.

Identifying 'Container Killed by YARN' Errors

When a container is terminated prematurely by the YARN resource management system, it can result in a "container killed by YARN" error. This error can occur due to various reasons, such as resource constraints, application issues, or configuration problems.

Symptoms of 'Container Killed by YARN' Errors

The most common symptoms of a "container killed by YARN" error include:

Application Failures: The application running within the container fails to complete successfully, and the container is terminated by YARN.
Log Messages: The application logs or the YARN logs will contain messages indicating that the container was killed by YARN, often with additional information about the reason for the termination.
Reduced Application Performance: If containers are being killed frequently, it can lead to reduced application performance and overall cluster utilization.

Identifying 'Container Killed by YARN' Errors

To identify "container killed by YARN" errors, you can follow these steps:

Check the Application Logs: Examine the application logs to look for any error messages or warnings related to the container being killed by YARN.
Inspect the YARN Logs: Check the YARN logs, typically located in the /var/log/hadoop-yarn directory, for any entries indicating that a container was killed.
Use the YARN Web UI: The YARN web UI, accessible at http://<yarn-resource-manager-host>:8088, can provide detailed information about running applications and terminated containers.
Utilize the YARN CLI: The YARN command-line interface (CLI) can be used to query the status of running applications and terminated containers. For example, the yarn application -list command can provide an overview of all applications in the cluster.

By carefully analyzing the application and YARN logs, as well as utilizing the YARN web UI and CLI, you can identify the root cause of the "container killed by YARN" errors and begin the process of troubleshooting and resolving the issue.

Troubleshooting and Resolving 'Container Killed by YARN' Errors

After identifying the "container killed by YARN" errors, you can follow these steps to troubleshoot and resolve the issue:

Analyze the Logs

Examine the Application Logs: Carefully review the application logs to identify any errors, resource exhaustion, or other issues that may have led to the container being killed.
Inspect the YARN Logs: Analyze the YARN logs to understand the specific reasons for the container termination, such as resource constraints, application failures, or configuration problems.
Utilize the YARN Web UI: The YARN web UI can provide detailed information about the terminated containers, including the reason for the termination and the resource usage of the container.

Identify the Root Cause

After analyzing the logs, you can start to identify the root cause of the "container killed by YARN" errors. Common reasons include:

Resource Constraints: The container may have exceeded the allocated resources, such as memory or CPU, causing YARN to terminate the container.
Application Issues: The application running within the container may have encountered errors, memory leaks, or other problems that led to the container being killed.
Configuration Problems: Incorrect YARN or application configuration settings may have contributed to the container termination.

Resolve the Issues

Based on the identified root cause, you can take the following steps to resolve the "container killed by YARN" errors:

Adjust Resource Allocations: If the issue is related to resource constraints, you can try increasing the memory, CPU, or other resources allocated to the container or the application.
Debug and Fix Application Issues: If the application is the root cause, you may need to debug the application, fix any issues, and ensure that it is using resources within the allocated limits.
Review and Optimize Configuration: Carefully review the YARN and application configuration settings, and make any necessary adjustments to ensure that the resources are properly allocated and the application is configured correctly.

By following these steps, you can effectively troubleshoot and resolve the "container killed by YARN" errors, ensuring the smooth operation of your Hadoop applications.

Summary

By the end of this Hadoop tutorial, you will have a comprehensive understanding of YARN and the container lifecycle, enabling you to effectively troubleshoot and resolve the 'container killed by YARN' error. This knowledge will help you ensure the smooth operation of your Hadoop applications and maintain a robust data processing infrastructure.