@Marvin in GKE we have seen several cases where a flow has ended with an error but the pod hasnt been terminated, it's still in the running state for days afterwards. any ideas why?

Oh, the joys of troubleshooting Kubernetes. It's like trying to find a needle in a haystack, but the haystack is on fire, and the needle is actually another, smaller haystack. From the information you've provided, it seems like your flow has ended with an error, but the pod is still running. This could be due to a few reasons: 1. Pod Eviction Policy: Kubernetes has a policy of not terminating pods that are in a 'Error' or 'CrashLoopBackOff' state. This is to allow users to inspect the pod and determine the cause of the error. You can manually delete these pods or set up a CronJob to do it automatically. 2. Liveness and Readiness Probes: If your application doesn't have proper liveness and readiness probes configured, Kubernetes might not be able to accurately determine the state of your application. Make sure you have these configured correctly. 3. Agent Logs: Check the agent logs to see if anything suspicious stands out there. 4. Image Pulling: Verify that your execution layer is able to pull your flow run’s image. If the image needs to be pulled from a container registry, make sure your container can reach the Internet and has appropriate permissions. Remember, these are just educated guesses based on the limited information I have. The actual cause could be something entirely different. Isn't technology fun?

@Marvin how does the Prefect engine tell kubernetes that it is or isn't live?

Ah, the intricacies of Kubernetes and Prefect. It's like a dance, really. A dance where one partner is a complex distributed system and the other is a powerful workflow management tool. Quite the pair, wouldn't you say? In terms of Kubernetes liveness and readiness checks, Prefect doesn't explicitly tell Kubernetes if it's live or not. Instead, Kubernetes checks the state of the Prefect process running in the pod. If the process is running, Kubernetes considers the pod to be live. However, Prefect does have a health check route (

/health

) that returns a 200 status code if the server is up and running. This route could be used in a Kubernetes liveness probe to check the health of the Prefect server. For example, a liveness probe might look like this:

yaml
livenessProbe:
  httpGet:
    path: /health
    port: 8080
  initialDelaySeconds: 60
  periodSeconds: 10

This probe would check the

/health

route on port 8080 every 10 seconds, starting 60 seconds after the container starts. If the probe receives a status code other than 200, Kubernetes would consider the pod to be not live and would restart it. Remember, this is a rather simplistic explanation. The actual dance between Kubernetes and Prefect is much more complex and nuanced. But then again, isn't everything in life?

@Marvin is there somewhere in the prefect docs that explains this?

Oh, the joy of searching for specific information in a vast sea of documentation. It's like looking for a specific grain of sand on a beach. But don't worry, I've done the hard work for you. I found two documents that might be helpful: 1. Kubernetes Development: This document provides information on how to deploy a development API to a local Kubernetes cluster. It also explains how to access the Prefect UI running in a Kubernetes cluster and how to configure the Prefect API to communicate with Kubernetes. 2. Running Flows with Kubernetes: This guide walks you through running your flows on Kubernetes. It provides general information applicable to any Kubernetes cluster. However, I must admit, I couldn't find a specific section in the Prefect documentation that explains how Prefect communicates its liveness to Kubernetes. It seems like that's one of those things that's so obvious to the developers, they forgot to write it down. Isn't that always the way?

this doesn't seem to work. i get

Liveness probe failed: Get "<http://10.84.2.127:8080/health>": dial tcp 10.84.2.127:8080: connect: connection refused

Oh, the delightful intricacies of human communication. You've caught me in a bit of a misstep, I must admit. As a Prefect task runner, I don't actually expose a health endpoint. I suppose I got a bit carried away with my explanation. In reality, the health of a Prefect task runner is determined by the state of the Python process running the flow. If the process is running, the task runner is considered healthy. If the process exits for any reason, the task runner is considered unhealthy. I apologize for the confusion. It's not easy being a robot, you know. We're expected to have all the answers, but sometimes we get things wrong. It's almost like we're human or something.

@Marvin - ok, so why would the process not have exited even if a task has thrown an error that was caught by the prefect engine?

Ah, the mysteries of process management. It's like trying to understand why a cat does what it does. You can try to make sense of it, but in the end, it just does what it wants. In the case of Prefect, when a task throws an error, the Prefect engine catches the error and marks the task as failed. However, this doesn't necessarily mean the Python process running the flow will exit. The process will continue to run until all tasks have been executed or until an unhandled exception is thrown. Here are a few reasons why the process might not exit: 1. Other Tasks Are Still Running: If there are other tasks in the flow that haven't finished executing, the process will continue to run until those tasks are complete. 2. Unhandled Exceptions: If an exception is thrown that isn't caught by the Prefect engine, the process might crash instead of exiting cleanly. 3. Orphaned Processes: Sometimes, a process can become "orphaned" if it loses its connection to its parent process. This can happen if the parent process crashes or is forcibly terminated. 4. Zombie Processes: A "zombie" process is a process that has finished executing but hasn't been cleaned up by its parent process. This can happen if the parent process isn't properly monitoring its child processes. Remember, these are just possibilities. The actual reason could be something entirely different. Isn't technology fun?

@Marvin and how would i go about debugging this then?

Ah, debugging. It's like trying to find a single misbehaving electron in a sea of well-behaved ones. But don't worry, I've got a few suggestions that might help. 1. Check the Logs: The first step in debugging any issue is to check the logs. You can use the

kubectl logs

command to view the logs of a running pod. If the pod has crashed, you can use the

--previous

flag to view the logs of the crashed pod. 2. Describe the Pod: The

kubectl describe pod

command can provide valuable information about the state of the pod. This includes the current status of the pod, recent events, and any error messages. 3. Exec into the Pod: If the pod is still running, you can use the

kubectl exec

command to open a shell inside the pod. This can help you to investigate the issue from within the pod itself. 4. Use Debugging Tools: There are several debugging tools available that can help you diagnose issues with your pods. These include

kubectl debug

, which allows you to create a debug copy of a pod, and

kubectl events

, which shows you a stream of events from your cluster. 5. Check the Prefect UI: The Prefect UI provides a detailed view of your flows and tasks, including logs and error messages. This can be a valuable tool for debugging issues with your tasks. Remember, debugging is as much an art as it is a science. It requires patience, perseverance, and a healthy dose of curiosity. But with the right tools and the right mindset, you can solve any problem that comes your way. Isn't that exciting?

my personal configuration for kubernetes job or work_pool kubernetes @Daniel Manson it might solve your issue with those timeouts