Understanding and Troubleshooting Out of Memory Error Code 137

If you've encountered the dreaded "exit code 137" error message while working with Docker, Kubernetes, or other containerized environments, you're not alone. This error can be frustrating and difficult to troubleshoot, but understanding its causes and solutions can help you keep your applications running smoothly. This comprehensive guide will delve into the intricacies of error code 137, its common scenarios, and strategies to resolve it.

# What is Exit Code 137?

Exit code 137 typically indicates that a process inside a container was killed by the system due to an out-of-memory (OOM) condition. When the operating system detects that it's running out of memory, it invokes the OOM killer, which terminates processes to free up memory. In the context of containers, this often results in the following error messages:

command terminated with exit code 137

container exited with a non-zero exit code 137

docker exit code 137

exit status 137

kubernetes exit code 137

exited with code 137

docker exited 137

oomkilled with exit code 137

k8s exit code 137

container killed on request. exit code is 137

linux exit code 137

container exit code 137

kubernetes error code 137

pod exit code 137

command failed with exit code 137

oomkilled - exit code: 137

command terminated with exit code 137 kubernetes

docker error 137

script returned exit code 137

docker exited with code 137

return code 137

Understanding these messages is crucial for diagnosing and resolving the underlying issues causing your containers to run out of memory.

# Common Scenarios Leading to Exit Code 137

Several scenarios can lead to a container running out of memory and subsequently being terminated with exit code 137. Here are the most common ones:

1. Memory Leaks

Memory leaks occur when a program consumes memory but fails to release it back to the system after use. Over time, this can lead to excessive memory consumption, eventually triggering the OOM killer.

2. High Memory Usage by Applications

Certain applications or processes may inherently require a significant amount of memory. If your container is not allocated sufficient memory resources to handle the application's demands, it will be prone to OOM errors.

3. Improper Resource Limits

Misconfigured resource limits in your container orchestration setup (like Kubernetes) can lead to containers exceeding their memory quotas, triggering the OOM killer.

4. Resource Contention

In environments with multiple containers or applications running simultaneously, resource contention can lead to some containers being starved of memory. This is particularly common in high-density deployments.

5. Configuration Errors

Errors in configuration files, such as incorrect memory limits or requests, can inadvertently cause containers to request more memory than is available, leading to OOM errors.

# Diagnosing Exit Code 137

Diagnosing the exact cause of exit code 137 involves a systematic approach. Here are the steps you can follow:

Step 1: Check Container Logs

The first step in diagnosing any issue with a container is to inspect its logs. Use the appropriate command to fetch the logs:

For Docker:

docker logs <container_id>

For Kubernetes:

kubectl logs <pod_name> -c <container_name>

Step 2: Inspect Resource Usage

Analyze the resource usage of your containers to identify if they are consuming excessive memory. In Docker, you can use the docker stats command:

docker stats <container_id>

In Kubernetes, you can get resource usage statistics using kubectl top:

kubectl top pod <pod_name> --containers

Step 3: Examine System Logs

Check the system logs for OOM killer events. In Linux, you can inspect the kernel log using:

dmesg | grep -i 'killed process'

This will show you details about processes that were terminated by the OOM killer, including their memory usage at the time of termination.

Step 4: Review Resource Limits

Verify the resource limits set for your containers. In Kubernetes, you can describe the pod to see its resource requests and limits:

kubectl describe pod <pod_name>

Ensure that the memory limits are set appropriately for your application's requirements.

# Mitigating Exit Code 137

Once you've diagnosed the cause of the out-of-memory error, you can implement strategies to mitigate it. Here are some approaches:

Optimize Memory Usage

• Identify and Fix Memory Leaks: Use profiling tools to identify memory leaks in your application and fix them.

• Optimize Code: Review your code for inefficiencies that could be consuming excessive memory.

Adjust Resource Limits

• Increase Memory Limits: If your application legitimately requires more memory, increase the memory limits for your containers.

• Set Resource Requests: Ensure that resource requests are set appropriately to reserve the necessary amount of memory for your container.

Improve Resource Allocation

• Use Resource Quotas: In Kubernetes, you can set resource quotas to manage the overall resource consumption of your namespace, ensuring fair distribution of resources among containers.

• Horizontal Pod Autoscaling: Use autoscaling to add more replicas of your pod when the load increases, distributing the memory usage across multiple instances.

Monitor and Scale

• Monitoring: Implement monitoring tools like Prometheus and Grafana to keep an eye on memory usage trends over time.

• Scaling: Scale your infrastructure to ensure there are sufficient resources available for your containers, especially during peak loads.

# Example: Mitigating Exit Code 137 in Kubernetes

Let's walk through an example of diagnosing and mitigating exit code 137 in a Kubernetes environment.

Step 1: Identify the Problem

First, check the logs of the affected pod to confirm it was terminated due to an OOM condition:

kubectl logs <pod_name>

Step 2: Inspect Resource Usage

Check the resource usage of the pod:

kubectl top pod <pod_name> --containers

If the memory usage is close to or exceeds the pod's memory limit, it indicates the pod is running out of memory.

Step 3: Adjust Resource Limits

Edit the pod's resource limits to provide more memory. You can do this by updating the pod's manifest file or using kubectl edit:

kubectl edit deployment <deployment_name>

Update the memory limits and requests:

resources:
  requests:
    memory: "512Mi"
  limits:
    memory: "1Gi"

Step 4: Apply Changes

Apply the changes to your deployment:

kubectl apply -f <deployment_file.yaml>

Step 5: Monitor

Monitor the pod to ensure it no longer exceeds its memory limits and does not get terminated by the OOM killer.

# Conclusion

Exit code 137 is a common and often frustrating error that developers encounter in containerized environments. Understanding its causes, such as memory leaks, high memory usage, improper resource limits, resource contention, and configuration errors, is key to diagnosing and resolving the issue. By following the steps outlined in this guide, you can effectively troubleshoot and mitigate out-of-memory errors, ensuring the stability and performance of your applications.

Regular monitoring, optimizing memory usage, and properly configuring resource limits are essential practices to prevent exit code 137 and keep your containerized applications running smoothly. With these strategies in place, you'll be better equipped to handle OOM conditions and maintain the health of your containerized infrastructure.