@Marvin my kubernetes pods running prefect flow did not shut down. why?

There are several reasons why your Kubernetes pods running Prefect flows might not shut down properly: 1. Finished Job TTL: Ensure that the

finished_job_ttl

is set appropriately. This setting determines how long finished jobs are retained before being cleaned up by the Kubernetes controller. If not set, jobs may linger indefinitely. Reference. 2. Cancellation Issues: If the infrastructure does not support cancellation or if there are mismatched identifiers, the pods may not terminate. This can happen if the worker cannot find the infrastructure associated with the run or if there are errors during cancellation. Reference. 3. Data Transfer Delays: There have been reports of multi-minute delays between tasks in Kubernetes, especially when large amounts of data are passed between tasks. This could indirectly affect pod shutdown if tasks are delayed. GitHub Issue. 4. Kubernetes Agent Issues: Ensure that you are using a stable version of the Prefect Kubernetes agent. There have been issues with certain versions causing errors. GitHub Issue. Given your use of Prefect in a Kubernetes environment, you might want to review these settings and issue reports to troubleshoot further. If the problem persists, consider reaching out to the Prefect community or support for more tailored assistance.

@Marvin these jobs were zombied and marked as failed later. i thought that when a flow is finished, the process exits and the container should exit. why is it being falsely kept alive by prefecT?

Your experience with zombied jobs and false failures in Kubernetes using Prefect might be due to a few factors: 1. Heartbeats and Infrastructure Failures: Prefect's orchestration engine relies on heartbeats to detect if a flow is still running. If the infrastructure supporting the flow (like Kubernetes nodes) fails or is rescheduled, Prefect may not receive the necessary heartbeats, causing it to mark the flow as failed or zombied. This is especially prevalent in environments with autoscaling, where resources are volatile. Reference. 2. Autoscaling and Elastic Compute: Prefect doesn't inherently handle the volatility of autoscaling clusters well. If jobs are frequently rescheduled, Prefect might lose track of them, leading to "ghost runs" where the job appears to be running long after it has actually completed or failed. This can result in flows being marked as failed while the runner continues executing or vice versa. GitHub Issue. 3. Handling Ghost Runs: One suggested approach is to implement a heartbeat mechanism for runners, where the runner periodically checks in with the Prefect server. If no heartbeat is detected for a certain period, the server assumes the run has crashed and can take corrective action. This isn't a built-in feature yet, but it's a recognized issue within the community. To address these issues, you might need to configure timeout automations to handle flow run states more gracefully or adjust your infrastructure strategy to better accommodate Prefect's current capabilities. If these issues persist, consider engaging with the Prefect community or support for more tailored solutions.