@Marvin I have a flow that runs tasks in parallel using dask. Different tasks run for different lengths of time. I notice that tasks that have completed without error rerun while the longer tasks are still running. These tasks do not depend on each other and I have not made any changes from the default number of task retries. What’s causing this?

To assist you better, I need to know which version of Prefect you are using, as the version can impact behavior significantly. Could you please specify if you're using Prefect 2.x or 3.x? If you're unsure, checking your environment's package version might help.

I’m using Prefect 2.x

It seems like there may be a misunderstanding in the configuration of task retries or some global settings that could lead to tasks being retried unexpectedly. In Prefect 2.x, tasks have retry capabilities, and you can globally configure the number of retries and retry delays. If tasks are rerunning even without errors, check if there are any global settings applied using

prefect config

commands, such as:

plaintext
prefect config set PREFECT_TASK_DEFAULT_RETRIES=2

This command sets a default retry of 2 for all tasks, which could explain why tasks are rerunning. Ensuring that these configurations are as intended for your purposes is crucial. Additionally, verify if there are any retry settings defined directly in your tasks using the

@task

decorator, as these can override global settings. For more detailed guidance on troubleshooting this issue, reviewing the Prefect 2.x task concept documentation might be helpful. If misconfiguration is not causing the issue, sharing more code specifics might help to further pinpoint the problem.

@Marvin there is no assignment of PREFECT_TASK_DEFAULT_RETRIES in prefect config and no retry settings are given in the task decorator. The logs show the task

Finished in state Completed()

but without other logging info, it restarts. Is this related to a restart of dask?

It seems that your issue might be related to how Prefect handles infrastructure failures or restarts when using Dask. When a Dask worker restarts or fails (which could include scenarios like running on spot/preemptible instances), Prefect may not be able to detect and handle these states gracefully, leading to tasks being rerun unexpectedly. Here are a few key points from related GitHub issues: 1. Infrastructure-Level Failures: If a task fails at the infrastructure level, such as when a Dask worker gets preempted, Prefect may not detect the task's failure and might keep it in a

RUNNING

state on the backend. This can result in the task being retried by Dask, especially if Dask's

config

is set to retry tasks a certain number of times. - Referencing issue: Support task reruns when infrastructure fails 2. Mapped Tasks and Autoscaling: Tasks can also rerun in environments with autoscaling, such as GKE Autopilot, due to race conditions or other factors involving cluster resource management. - Referencing issue: Mapped tasks trigger multiple times on GKE Autopilot If your current setup uses Dask in an environment with potential restarts or preemptions, and you see tasks unexpectedly rerunning, ensure your cluster and task retry configurations are set to minimize unintended restarts. Consider checking Dask's task retry policies and Prefect's infrastructure handling settings. You may also need to explore mechanisms for better handling state transitions under infrastructure events in your specific deployment environment.

My task doesn’t have a return statement. Might that be what’s keeping the task in a running state in the backend?

If your task does not have a return statement, it should still complete normally in most cases, provided it reaches the end without any errors. However, if a task is "hanging" or not communicating completion back to Prefect due to a lack of return or an unexpected code path, it can appear as if it remains in a running state. Here's what you can do to address this: 1. Ensure Task Completes Correctly: Verify that the task completes without any unhandled exceptions or bottle-necks. Even without a return statement, the task should transition to a completed state if it finishes executing. 2. Explicit Return: For clarity and to aid debugging, you might add a simple return statement, even if it's just

return None

. This ensures that there is an explicit completion point for Prefect's backend to recognize. 3. Logging and Monitoring: Incorporate logging within your task to assert that it reaches the end of execution. This can help verify whether the task logic is completing as expected. If tasks continue to restart or stay in a running state after these changes, further diagnostics of your Dask worker and Prefect setup might be needed. This could involve inspecting network stability, resource availability, or potential error handling that inadvertently causes task reruns under certain conditions.