@Marvin what would cause a prefect flow transition marked FAILED manually by me from RUNNING to transition back to RUNNING

Your situation, where a Prefect flow transitions back to

RUNNING

after being manually set to

FAILED

, could be due to several factors: 1. Concurrency Issues: If you are running Prefect in a distributed environment, like Kubernetes, there might be multiple pods or agents attempting to manage the flow's state. This can lead to race conditions where one pod sets the flow to

FAILED

, and another pod, possibly due to retries or miscommunication, sets it back to

RUNNING

. See related GitHub issue. 2. Orchestration Rules: Prefect's orchestration rules might be configured in a way that allows or forces a flow to retry or continue execution even after a manual state change. This could be due to retry settings or specific flow configurations that override manual interventions. 3. State Management: There might be a bug or misconfiguration in how Prefect manages states, especially in distributed setups. The Prefect engine might be attempting to resolve dependencies or orchestrate tasks that inadvertently change the flow's state back to

RUNNING

. 4. Code Execution: If there is any code within the flow or tasks that programmatically changes the state, this could also cause unexpected transitions. Ensure that the flow logic or any callbacks do not unintentionally reset the state. To address these issues, you might want to: - Review your flow's retry settings and orchestration rules. - Check for any concurrent execution issues in your environment. - Look into Prefect's state management logic for any anomalies. - Review the flow and task code for any state manipulation. For further insights, you can refer to the Prefect documentation on flow states and the GitHub issues related to state transitions.

@Marvin is the prefect engine running on the server or the worker

The Prefect engine is designed to run on the workers, not on the server. The server acts primarily as an orchestrator and manager, handling flow and task state transitions, logging, and coordination. The actual execution of the flows and tasks, managed by the Prefect engine, occurs on the workers. Workers are responsible for executing the flow runs, sending logs to the Prefect Cloud API, and managing the execution environment. You can start a worker using the

prefect worker start

CLI command, and the worker will take care of running the flows, handling retries, and other execution-related tasks. For more details, you can refer to the Prefect documentation on workers and infrastructure concepts.

@Marvin can long running flows and tasks cause strain on the prefect server?

Long-running flows and tasks can indeed impact the Prefect server, but the extent of the strain largely depends on how your infrastructure is set up and how Prefect is configured: 1. Server Load: The Prefect server handles orchestration and state transitions, logging, and metadata storage. Long-running tasks can increase the volume of logs and state transitions being processed, potentially leading to higher server load. 2. Database Strain: If the Prefect server is using a database to store flow and task metadata, long-running flows can increase the amount of data being written to and read from the database. This can lead to increased I/O operations and strain on the database. 3. Concurrency Management: The server's ability to handle concurrent state updates and log entries might be challenged by a high number of long-running tasks, especially if they are configured to log frequently or manage complex state transitions. 4. Event Persister Settings: The server's event persister settings, such as batch size and flush interval, can influence how efficiently it handles the increased load from long-running tasks. Adjusting these settings may help mitigate strain. To mitigate these potential issues, consider scaling your infrastructure horizontally, optimizing server settings, or using Prefect Cloud's managed services. You can also refer to the Prefect documentation on deployment and infrastructure concepts for more details on managing server load effectively.