@Marvin For a period of time my deployment was spewing the following message "WARNING asyncio - socket.send() raised exception" I have dozens of these, but my flow seems to have resumed after these events. What causes this? Should I look for a bug somewhere?

The warning "asyncio - socket.send() raised exception" typically indicates that there was an issue with sending data over a network socket, which can be caused by network connectivity problems or an issue with the underlying network stack. Here are some potential causes and considerations: 1. Network Instability: Temporary network issues might lead to such warnings. Since your flow resumed, it suggests that the issue was transient. 2. Resource Limits: Check if there are any resource constraints or limits on your system that might affect network operations, such as file descriptor limits. 3. Concurrency Issues: If your deployment is highly concurrent, it could potentially lead to socket exhaustion or other network-related issues. 4. Prefect Configuration: Ensure that your Prefect deployment is configured correctly, including any network-related settings. Since the flow resumed, it might not be a critical issue, but it's worth monitoring to see if it recurs. If it does, you might want to investigate your network setup or consider logging more detailed information around these events. For more information on Prefect's asyncio usage, you can explore the Prefect source code or Prefect's documentation.

@Marvin My flow is decorated with @flow(task_runner=ThreadPoolTaskRunner(max_workers=3)) is that sufficient to ensure that prefect internals will not open too many sockets, or do I also need to limit the number of subtasks I create at a time?

The

ThreadPoolTaskRunner

in Prefect uses a thread pool to execute tasks concurrently. The

max_workers

parameter you specified limits the number of threads used by the task runner, which can help in controlling the number of concurrent tasks and, consequently, the number of sockets opened by those tasks. However, the

max_workers

parameter only limits the number of threads for tasks directly managed by the task runner. If your flow creates additional subtasks or if tasks themselves create further parallel operations, you'll need to manage those separately to ensure you don't exceed your system's socket limits. Here are some considerations: 1. Limit Subtasks: If your tasks spawn further subtasks or parallel operations, you should implement additional controls to limit their concurrency, such as using a semaphore or similar concurrency control mechanism. 2. Resource Constraints: Consider system-level constraints like file descriptors and sockets, and ensure they are appropriately configured for your workload. 3. Prefect Configuration: Review your Prefect deployment's configuration to ensure it aligns with your concurrency requirements. For more details, you can explore the Prefect task runners documentation or the source code for task runners.

@Marvin So when my flow or a task inside that flow calls task.submit, it goes to some other task_runner besides the one I specified in my decorator?

When you use the

@flow

decorator with a specified

task_runner

, such as

ThreadPoolTaskRunner

, it sets the task runner for all tasks executed within that flow. The

task.submit

method, when called within this flow, should indeed use the task runner specified in the decorator. However, if tasks themselves spawn additional subtasks or operations that are not directly managed by the flow's task runner, those operations might not be constrained by the

max_workers

setting of the

ThreadPoolTaskRunner

. In such cases, you might need to manage concurrency explicitly within those tasks. For more details, you can refer to the Prefect task runners documentation to understand how task runners and concurrency are managed within Prefect flows.

@Marvin I'm confused by your answer. Please rewrite your answer being very specific about when you are referring to prefect tasks versus using the word "task" to refer some other kind of spawned work or a generic asyncio task.

Certainly, let's clarify: 1. Prefect Tasks: When you use the

@flow

decorator with a

task_runner

like

ThreadPoolTaskRunner

, this task runner is responsible for executing all Prefect tasks within that flow. So, when you call

task.submit

within a Prefect flow, it should use the

ThreadPoolTaskRunner

specified, adhering to the

max_workers

limit you set. 2. Other Work (e.g., asyncio tasks): If a Prefect task internally creates other types of work, such as asyncio tasks, threads, or processes, these are not managed by the Prefect

task_runner

. You will need to implement your own concurrency controls for these to ensure they do not exceed system limits. In summary, the

ThreadPoolTaskRunner

manages Prefect tasks within the flow, but any additional work spawned inside those tasks (like asyncio tasks) needs separate management.