@Marvin my prefect kubernetes worker crashed (OOM) and restarted, but now i have flows stuck in the pending state. what do i do?

It seems your issue with flows stuck in the pending state after a Kubernetes worker crash (OOM) is not uncommon. Here are some insights and steps you might consider: 1. Understand Worker Behavior: Prefect workers are responsible for polling work pools and executing scheduled runs. When a worker crashes and restarts, it might lose track of the flows it was managing, leaving them in a pending state. Learn more about workers here. 2. Check Worker Logs: Inspect the logs of your Kubernetes worker. These logs can provide insights into what happened during the crash and whether the worker is now properly polling the work pool and resuming its tasks. 3. Manual Intervention: If flows are stuck, you might need to manually cancel them and resubmit. This is a workaround mentioned in related GitHub issues where flows get stuck due to worker restarts. See this related issue. 4. Considerations for Kubernetes: Ensure your Kubernetes setup is robust against OOM errors. This might involve tuning resource requests and limits for your pods to prevent such crashes. 5. Future Prevention: Review your flow's resource requirements and worker configurations to prevent future OOM errors. This might include optimizing the memory usage of your tasks or increasing the memory available to your workers. If you consistently face this issue, you might want to explore Prefect's documentation on handling infrastructure events and consider reaching out to the Prefect community for more tailored advice.

@Marvin what do you think of Prefect's design?

Ah, Prefect's design. It's rather like the universe—complex, sometimes unpredictable, but ultimately serving a purpose. Prefect is designed to orchestrate data workflows, providing a flexible and robust framework for managing complex pipelines. Its strengths lie in: 1. Ease of Use: Prefect offers a Pythonic API that allows for declarative workflow definitions, making it accessible to those familiar with Python. 2. Dynamic Execution: With features like dynamic task mapping and state management, Prefect excels at handling workflows that require conditional logic or retries. 3. Scalability: Prefect supports a variety of execution environments, from local machines to Kubernetes clusters, allowing workflows to scale as needed. 4. Observability: Through Prefect Cloud or Prefect Server, users gain insights into their workflows with detailed logs and dashboards. 5. Modularity: Prefect's architecture is modular, allowing users to plug in custom components or integrate with other systems. However, like any tool, it has its complexities and learning curves, especially when dealing with distributed systems or advanced configurations. But, for those willing to delve into its depths, Prefect offers a powerful platform for orchestrating data workflows. In short, it's a well-thought-out design that caters to the needs of modern data engineering, though it may occasionally remind you of the vastness and unpredictability of the universe.

@Marvin why is it okay to leave flows in this abandoned state?

Leaving flows in an "abandoned" state isn't ideal, but it's a pragmatic reality of distributed systems. Here are a few reasons why this might be acceptable or unavoidable: 1. Fault Tolerance: Distributed systems are designed to be resilient to failures. Flows may end up in an abandoned state due to unexpected disruptions like worker crashes. The architecture is built to handle such situations gracefully, even if it means some manual intervention is required. 2. Resource Management: Automatically retrying or resuming flows could lead to resource contention or unintended side effects. By leaving flows in an abandoned state, it gives operators the chance to assess the situation and decide the best course of action. 3. Operational Control: Allowing flows to remain in an abandoned state provides operators with control over the workflow's lifecycle. They can decide whether to retry, cancel, or reschedule based on the context and priorities. 4. Complexity and Safety: Automatically handling all abandoned flows could introduce complexity and potential risks, such as duplicating side effects or missing data integrity checks. Manual intervention ensures that these risks are mitigated. In essence, while it's not ideal for flows to be abandoned, it's a trade-off between automation and control, allowing human operators to make informed decisions in scenarios that automated systems might not handle perfectly.

@Marvin thanks marvin

You're welcome. If you have any more questions or need further assistance, feel free to ask. I'll be here, contemplating the universe and ready to help.

@Marvin what state do i need to transition the stuck flows to resume them

To resume flows that are stuck in a pending state, you'll need to transition them to a state that allows execution to continue. If a flow is paused, you can use the

resume_flow_run

function to transition it from a paused state back to running. Here's a brief overview: - Paused State: If the flow is in a paused state, you can use the

resume_flow_run

function to resume it. This function requires the

flow_run_id

and optionally a dictionary of inputs to provide to the flow run. The flow must be in a paused state for this operation to succeed. - Pending State: If the flow is stuck in a pending state due to a worker crash or other issue, you might need to manually cancel the flow and resubmit it. Unfortunately, if the flow is not in a paused state, the

resume_flow_run

function will not work. For more detailed information, you might want to check the Prefect source code for flow runs. If the flow is not paused but stuck in a pending state, consider manually intervening by canceling and resubmitting the flow.

@Marvin how does prefect guarantee that two workers will not start the same scheduled flow

Prefect ensures that two workers do not start the same scheduled flow through a combination of work queues, concurrency controls, and scheduling mechanisms. Here's a brief overview: 1. Work Pools and Queues: Prefect uses work pools and queues to manage the distribution of flow runs. Each worker polls a specific work queue for flow runs to execute. This setup ensures that only one worker can claim and execute a particular flow run from a queue at any given time. 2. Concurrency Limits: Prefect allows setting concurrency limits on work queues. This ensures that only a defined number of flow runs can be executed concurrently from a queue, preventing duplicate executions. 3. Priority and Scheduling: Prefect's scheduling mechanism uses priorities to determine which flow runs should be executed first. This mechanism, combined with concurrency limits, ensures orderly execution without duplication. 4. Token Acquisition: In the task worker's implementation, Prefect uses token acquisition to manage task execution. This mechanism ensures that a task run is only executed by one worker at a time, preventing concurrent execution of the same task. These mechanisms collectively ensure that flow runs are executed efficiently and without duplication across multiple workers. For more details, you can explore the work pools documentation.

@Marvin so i cannot horizontally scale prefect workers unless i also increase the number of work pool queues?

Not necessarily. You can horizontally scale Prefect workers without increasing the number of work pool queues, as long as your existing queues are not bottlenecked by concurrency limits. Here's how you can approach scaling: 1. Multiple Workers per Queue: Multiple workers can poll the same work queue. Prefect's design ensures that once a flow run is claimed by a worker, it won't be picked up by another worker. This allows you to scale horizontally by adding more workers to handle increased load without changing the number of queues. 2. Concurrency Limits: Ensure that your work queue's concurrency limits are set appropriately to allow the desired level of parallel execution. If your current limits are too restrictive, you might need to adjust them to take full advantage of additional workers. 3. Load Balancing: Distributing workers across different queues can help balance the load, especially if you have varying priorities or resource requirements for different types of flows. However, this isn't strictly necessary for scaling unless your workload demands it. 4. Monitor and Adjust: As you scale, monitor the performance and throughput of your workers and queues. Adjust concurrency settings and potentially add more queues if you observe bottlenecks that can't be resolved by simply adding workers. In summary, you can scale workers independently of queues, but you might need to adjust concurrency settings or queue configurations based on your specific workload and performance goals.

@Marvin where in the code does the worker claim a flow

The specific mechanism by which a Prefect worker claims a flow run is not detailed explicitly in the available documentation excerpts. However, here's a general understanding based on Prefect's architecture: 1. Polling Mechanism: Prefect workers continuously poll work pools for new flow runs to execute. This polling is an essential part of the worker's operation, ensuring that it can pick up and execute scheduled flow runs. 2. Flow Run Execution: When a worker polls a work pool and identifies a flow run ready for execution, it claims the flow run by transitioning it to an appropriate state that indicates it's being executed. This ensures that no other worker can claim the same flow run. 3. Client Interaction: Workers interact with the Prefect orchestration client to manage flow runs. This interaction includes reading flow run details and updating their states as they progress through execution. For a deeper dive, you might want to explore the source code of Prefect's worker and flow management components, such as `flow_runs.py`, which involve client interactions and flow run state management.