The Ultimate Guide to Understanding Exit Code 143 in Docker Containers

If you’re familiar with exit codes in the context of a bar, you might reserve code 143 for those bargoers who gracefully finish their drinks and leave quietly at closing time. In the world of Docker container exit codes, 143 means that the container has shut down peacefully without causing any disruption.

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While exiting with code 143 is generally favorable compared to other scenarios, it is still important to troubleshoot why it happened and prevent it from occurring again. In this guide, we’ll explore the causes of exit code 143 and provide guidance on how to check for it, troubleshoot it, and prevent it from happening.

How to Check for Container Exit Code 143

To determine if your container exited with code 143, you can run the command:

docker container ls -a

The output will display information about your containers, including the exit code in the STATUS column.

Alternatively, if you only want to retrieve the exit code, you can use the following one-liner:

docker container inspect -f '{{.State.ExitCode}}' [container ID]

What Causes Exit Code 143?

Exit code 143 occurs when a Docker container receives and obeys the SIGTERM signal. SIGTERM is a type of signal that any process can send to ask another process to shut down gracefully. In the case of containers, it is a way of asking them to terminate and giving them the opportunity to do so peacefully. When a container responds to SIGTERM by shutting down, it exits with code 143.

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While code 143 is generally considered the “best” exit code, there are other exit codes that indicate scenarios where containers don’t shut down willingly. For instance, code 139 signifies a container being forcibly shut down because it tried to request memory that wasn’t assigned to it, and code 137 represents a container receiving a SIGKILL signal, which forces it to shut down immediately.

Possible Causes of Exit Code 143

Exit code 143 can have various causes, including:

• The container’s Pod being moved to a different server
• The cluster running out of resources and Kubernetes killing one Pod to free up resources for another
• The container using excessive memory resources, leading Kubernetes to request a shutdown for resource conservation
• Kubernetes wanting to remove the container’s host node from the cluster, resulting in a SIGTERM signal to drain the node
• A manual SIGTERM request from an administrator using commands like kill or docker stop

While some of these scenarios do not pose a significant problem as the containers should restart on their own, others indicate underlying issues. For example, a container being SIGTERMed due to high memory usage could imply memory leak problems or inefficient management of memory requests and limits.

How to Troubleshoot Exit Code 143

If a container stops unexpectedly with code 143 and does not restart automatically, you can look for information in the following places:

1. Operating System Logs

Check the operating system logs of the host node, typically found at /var/log/syslog on most Linux distributions. Use grep to search for keywords such as “terminated” or “SIGTERM” to identify events that might have triggered the SIGTERM signal.

2. Kubelet Log

Inspect the Kubelet log for the node, usually located at /var/log/kubelet.log. If the SIGTERM request originated from Kubernetes, you should find relevant information in the Kubelet log. The log records the interactions between nodes, containers, and the cluster.

3. Container Logs

If available, review the logs from the container itself. A well-designed container should log SIGTERM events, providing insight into when the container received the signal and its subsequent actions.

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By analyzing these logs, you can determine the cause of the SIGTERM request, whether it was triggered by misbehavior or routine events like node draining.

Advanced Troubleshooting Techniques

If the cause of exit code 143 cannot be identified through the syslog or kubelet.log, you may need to dive deeper and experiment with the container. Here are a few advanced troubleshooting techniques:

• Restart the container normally and monitor its resource consumption patterns with tools like docker stats or external container monitoring tools. An unexpected increase in memory or CPU usage without an apparent cause could indicate a memory management issue leading to a SIGTERM signal.
• Check the Pod’s priority levels. It is possible that Kubernetes is shutting down the container due to low-priority workload settings. Adjusting the priority level or allocating more resources to the cluster should prevent code 143 from recurring.
• Inspect other containers within the same Pod. If they are also exiting with code 143, the issue could be related to how Kubernetes manages the Pod rather than being specific to an individual container.

Best Practices to Prevent Exit Code 143

To avoid encountering exit code 143 in Docker containers, adhere to these best practices:

• Test the code included in the container to ensure its stability and absence of memory leaks.
• Enable logging of signal events within the container to facilitate troubleshooting based on container-specific log data.
• Avoid running multiple applications within the same container, as it can complicate the identification of the application responsible for the SIGTERM request.
• Properly configure Pod priority levels, resource requests, and resource limits in Kubernetes to prevent containers from consuming more resources than necessary.
• Utilize autoscaling features where available to ensure automatic resource acquisition, preventing container shutdowns due to resource scarcity.

By following these best practices, you can minimize the occurrence of exit code 143 and ensure the stable operation of your Docker containers.

Frequently Asked Questions

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Conclusion

Exit code 143 in Docker containers indicates a graceful shutdown triggered by the SIGTERM signal. While generally desirable, it is essential to troubleshoot the cause and prevent its recurrence. By understanding the causes, utilizing effective troubleshooting techniques, and implementing best practices, you can maintain the stability and reliability of your containerized applications.

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