@Marvin: i am getting this error:

distributed.core - Event loop was unresponsive in Nanny for 14.37s.  This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability.

Oh, the unforgiving nature of software. It seems like you've run into an issue often related to performance bottlenecks, particularly when using Dask—a parallel computing library in Python. The message hints at tasks that hold the Global Interpreter Lock (GIL) for too long or when you're trying to move large amounts of data around. There are a few general strategies you can consider to mitigate such issues: 1. Optimize your computations: If you're doing CPU-bound work, make sure it's optimized and not unnecessarily holding the GIL. This could mean using libraries that release the GIL (like NumPy does for many operations) or restructuring your code. 2. Reduce data transfer: If you're moving large data, see if you can reduce the size of the data being transferred. This might involve more granular task division or pre-filtering data before the transfer. 3. Increase resources: Sometimes, the simplest solution is to throw more resources at the problem. This means allocating more CPU cores or memory to your Dask workers. To offer a more tailored solution, can you provide more details about your workflow? Specifically, information about what operations are causing the bottleneck, the size of your data, and the configuration of your Dask cluster would be incredibly useful. Feel free to also share relevant sections of your code, and I will give you my, uh, "expert" advice, bathed in a cloak of existential ennui.

@Marvin main_flow 1. grab data from snowflake 2. chunk data into buckets in main_flow 3. use dask to run 1 node and 16 threads 4. send chunks to subflow for futher processing

Alright, you have a flow that interacts with Snowflake and uses Dask for parallel processing. The error message you're encountering suggests that the way you're managing tasks or data within your Dask cluster is causing issues. Here are some things you could consider given your workflow steps in order to potentially resolve the issue: 1. Snowflake Data Retrieval: Ensure that the data you're pulling from Snowflake is being retrieved efficiently. Depending on the volume of data, consider using query results caching or loading data incrementally if possible. 2. Chunking Data: Be mindful of how you're chunking your data in the main flow. If chunks are too large, they could be causing the system to struggle when transferring them to the Dask worker nodes. Try to size chunks so that they fit comfortably in memory but are not so large as to cause excessive serialization overhead. 3. Dask Configuration: With 1 node and 16 threads, you're possibly facing contention if your workload is holding onto the GIL, as Dask generally prefers more processes over threads for CPU-bound tasks. Examine whether increasing the number of nodes or using processes instead of threads could benefit your workload. 4. Subflow Processing: When you send chunks to subflows, inspect if the subflows are optimized. Subflows should release the GIL if they are CPU-bound or should be designed to work efficiently with the Dask execution model. Consider the following actions to diagnose further: - Analyze Task Duration: Check Dask's diagnostic dashboard to see which tasks are taking unusually long to run. - Memory Management: Ensure that your workers have enough memory and that you're not inadvertently causing memory pressure. - Serialization Costs: Large chunks of data may have significant serialization and deserialization costs. Check how data is being serialized between tasks. Would you like me to search for documentation on best practices for chunking data with Dask or managing memory with Dask, or would you prefer to explore some specific issues on GitHub for similar error messages with Dask?

@Marvin i am running ECS + prefect agent. can we explore 3. Dask Configuration

It seems there isn't a direct answer to your query, but here are some points and resources that can guide you as you fine-tune your Dask configuration when using it with ECS and a Prefect agent: 1. Allocate Enough Resources: Ensure that your AWS ECS tasks have sufficient CPU and memory allocated. Lack of resources can cause instability and unresponsive workers. When you start an ECS task that contains a Prefect agent, you should allocate enough resources for the expected workload. Configuring AWS ECS with Prefect. 2. Concurrent Work Limit: Consider setting a work-queue concurrency limit for your agent. This will help prevent overloading your system by limiting the number of concurrent flow runs. 3. Dask Executor Startup: If you are using the

DaskExecutor

, one improvement could be exposing Dask scheduler information, including the dashboard link, in your Prefect logs. You can set

distributed.dashboard.link

in your Dask configuration, which provides template functionality for this purpose. Here's a discussion from GitHub that might be helpful in setting up logging for your Dask Executor and ECS. 4. CloudProvider for Dashboard Link: A discussion on GitHub suggests that to get the right dashboard link in a setup similar to yours, you would need dask-cloudprovider to expose the dashboard’s public IP. 5. Agent Configuration: Agents orchestrate flow runs and need to be configured correctly. This involves provisioning and monitoring the infrastructure required for your flow runs. Here's a detailed explanation on how FlowRunners, Executors, and Agents work together, which might help you configure them correctly. Please review the linked resources for more in-depth guidance on configuring your ECS tasks and Agents to work effectively with Dask. If you need more detailed help, specific to parts of your configuration, please let me know, and I can help look for more information.

@Marvin i am getting this error

KeyError: 77567

in relation to

self._h2_state.end_stream(stream_id)
  File "/Users/data-snowflake/.venv/lib/python3.11/site-packages/h2/connection.py", line 883, in end_stream
 frames = self.streams[stream_id].end_stream()

It appears that your error is related to issues that have been reported with httpx and h2, which are dependencies used by Prefect. Here are a couple of similar issues: 1.

Occasional http2 connection errors (KeyError)

: A bug report PrefectHQ/prefect#11660 details an error where flows crash with a connection-related exception originating from h2, which seems to occur occasionally during longer flow runs. 2.

Client sometimes fails with httpx.LocalProtocolError

: Another related issue report PrefectHQ/prefect#7442 discusses scenarios where the agent is not able to retrieve jobs due to an exception raised that's related to the connection state being closed unexpectedly. Based on the information you provided, especially the traceback part where

self._h2_state.end_stream(stream_id)

in

connection.py

causes a

KeyError

, it seems like the connection might have been closed or encountered an error, but an attempt was still made to process the end of a stream with an invalid stream ID. To resolve this issue, a few potential steps can be taken: - Ensure that all dependencies are up to date. It could be that a bug in the h2 library has been patched in a more recent version. - Review the Prefect and related dependencies' changelogs and open issues to see if there are known issues with long-running flows or connections being closed prematurely. - You could also implement error handling in your workflow to catch such exceptions and manage retries or cleanup appropriately. If these suggestions do not resolve your issue and due to the complexity of async I/O, you might consider creating a new issue on the Prefect GitHub repository if one doesn't already exist for your specific case. Be sure to provide details of your environment setup, such as versions of Prefect, Dask, httpx, h2, and how you're deploying Prefect (e.g., with Docker), as this could help maintainers diagnose the problem.

@Marvin is there a limit to how many tasks can be generated? i am running a flow that spins up 100 tasks per subflow

The Prefect documentation does not specify a hard limit on the number of tasks that can be generated or the maximum number of tasks per subflow. However, certain considerations such as task concurrency limits and system resources may implicitly constrain the number of tasks you can effectively run. Task concurrency limits pertain to the number of concurrent runs of tasks with a given tag that are allowed to be in a

Running

state at any one time. These limits are set using task tags and apply across all tasks with the tag. You can read more about task concurrency limits here. In terms of subflows, while there isn't a specific mention of a limit to the number of tasks that can be included in a subflow, you will want to be mindful of system resources and maintain good practices in workflow design to avoid overloading your execution environment, which can lead to performance issues or failures. Design-wise, you can choose to use subflows when you have complex workflows where it makes sense to encapsulate a group of tasks into their own logical unit. Subflows can help you manage dependencies and task execution ordering in a more organized way, especially when dealing with large numbers of tasks. If you're encountering performance issues or limitations with generating many tasks within subflows, you may need to look into optimizing your task design or scaling your execution environment (e.g., by using a Dask cluster) to better handle a high number of tasks.

@Marvin can you run async subflows? like tasks you use .map

Yes, you can run asynchronous subflows in Prefect by using

asyncio.gather

to concurrently execute them. Here's a code example from the Prefect recipes on GitHub:

python
import asyncio
from prefect import flow

@flow
async def subflow(x):
    print(f"Running subflow with {x=}")
    return x

@flow
async def parent_flow():
    return await asyncio.gather(*[subflow(x) for x in range(3)])

if __name__ == "__main__":
    assert asyncio.run(parent_flow())

This example demonstrates how to define the parent flow (

parent_flow

) as an

async

function and use

asyncio.gather

to await the concurrent completion of all the subflow tasks initiated by list comprehension. Each subflow will receive a different parameter (in this case, numbers 0 to 2), allowing them to be executed in parallel. You can find the entire code snippet and more details on how to run asynchronous subflows on the Prefect recipes GitHub page. Additionally, Prefect's documentation on composing flows indicates that a subflow run is created when a flow function is called inside the execution of another flow. While this excerpt doesn't directly address asynchronous subflows, it confirms that subflows are considered first-class citizens within Prefect, meaning they can be run just like top-level flows. In summary, you can certainly use an asynchronous approach to run subflows concurrently, similar to how you use

.map

for tasks. Keep in mind that your flow functions would need to be prepared for asynchronous operation (i.e., defined with

async def

).

@Marvin what does this error mean to you? Crash detected! Execution was interrupted by an unexpected exception: KeyError: 63681

The error message "Crash detected! Execution was interrupted by an unexpected exception: KeyError: 63681" suggests that during the execution of your code, a

KeyError

exception was raised with the key

63681

. This typically occurs when you attempt to access a dictionary with a key that does not exist within that dictionary. In the context of Prefect, this could be related to a situation where either: 1. Your code, or 2. A Prefect internal operation is trying to access some information indexed by a specific key (in this case,

63681

), and that key is not in the expected data structure, which is likely a Python dictionary. This can happen for various reasons depending on what your flow and tasks are doing. It might be an unexpected state of data, an issue with the system managing the runs (like a Dask issue, for example), or something else entirely. Without more information on what task or operation within your flow was running when this error occurred, it's challenging to give a precise cause. However, such issues are often related to code logic errors, wherein certain assumptions about available data are violated—perhaps due to an unexpected data input, a race condition, or a problem during intermediate data storage or transfer. To troubleshoot this further, examine the details of the

KeyError

- specifically what operation was being performed when this key was looked up and what dataset or context it pertains to. Checking the surrounding logs or traceback may provide more insights into the cause of the issue. Additionally, considering any recent changes or specific conditions under which this error occurs might help pinpoint the problem.

@Marvin hey so running you're example

import asyncio
from prefect import flow, task

@task
def run_task(field: str):
    return field

@flow
async def subflow(x, sep):
    async with sep:
        y = [x for x in range(1000)]
        run_task.map(y)

@flow
async def parent_flow():
    sep = asyncio.Semaphore(16)
    return await asyncio.gather(*[subflow(x, sep) for x in range(100)])

if __name__ == "__main__":
    assert asyncio.run(parent_flow())

this is creating 100 subflows all in the state running; what is meant by running here? i would think that only 16 are running and the others are pending but that's not what this example shows

In the example provided, you're creating 100 subflows from the

parent_flow

, and each subflow is being launched in the context of an

asyncio.gather

. This suggests that all 100 subflows are initiated concurrently. The

sem

(which I believe you meant to be

sep

for a semaphore) is used within the

subflow

to apply a concurrency limit—each subflow is entering the asynchronous context manager created by

async with sep

. This semaphore is intended to limit the number of concurrent executions within the

subflow

. However, the semaphore here isn't actually affecting the number of concurrent subflows running because the subflow isn't awaiting on any task inside the context manager. The

run_task.map(y)

is a Prefect method to map tasks over an iterable

y

, and it schedules the mapped tasks to run potentially in parallel, but it's not an

async

call and doesn't work directly with

asyncio

constructs like a semaphore. Hence, the semaphore doesn't control the concurrency of this operation directly, and all subflows are initiated and marked as 'running', because from Prefect's perspective, they've all started executing. Prefect considers a subflow 'running' when the subflow function is called—it doesn't necessarily reflect whether any given mapped task within the subflow has started, is waiting, or has completed. It's a state indicating that the subflow has begun execution. If you are trying to limit the actual execution of subflows to 16 at a time, you would need to have tasks within the subflow

await

on some asynchronous operation that is controlled by the semaphore. Simply entering the context manager of the semaphore does not affect the execution in this case, as the semaphore needs to be awaited upon to limit concurrency. Also, keep in mind that

asyncio.Semaphore

controls the concurrency of

async

functions/tasks in an

asyncio

event loop. Regular Prefect task mapping does not integrate with

asyncio

concurrency constructs directly. To limit concurrency of subflows or tasks in Prefect, you can use Prefect concurrency primitives like

task concurrency limits

which use task tags, or potentially modify your flow execution strategy to control the number of subflows running at a given time through Prefect-native mechanisms rather than trying to mix in

asyncio.Semaphore

.

@Marvin more one concurrency limits on the task, subflows, flows

Prefect provides mechanisms to limit the concurrency for tasks, which can be beneficial in situations where you want to avoid overloading a resource or service. 1. Task Concurrency Limits: You can limit concurrency using task tags. You specify a concurrency limit for the number of concurrent task runs in a 'Running' state for tasks with a given tag. The limit applies to any task with the specified tag. If a task has multiple tags, it will run only if all tags' concurrency limits are not already exceeded. Here's an excerpt from the Prefect Docs on Task Concurrency Limits. 2. Global Concurrency Limits: Prefect also allows you to set global concurrency limits that can span across multiple flows. This can be useful for managing external resources like database connections. The global concurrency limits are defined via a context manager that can be used to wrap around the code which requires limited access. For example, you can limit database queries to ensure that the maximum number of database connections is never exceeded. Here's an excerpt from the Prefect Docs on Global Concurrency Limits. 3. Flow Concurrency Limits: While Prefect primarily documents task concurrency limits, flows typically can be controlled at the orchestration level, such as limiting the number of flows an agent processes at a time or implementing scheduling that ensures certain flow runs do not overlap. However, global concurrency limits can technically apply to tasks across multiple flows, which indirectly can control flow concurrency when those tasks are integral parts of the flows. In your scenario involving subflows, if the subflows are calling tasks that have concurrency limits, the execution of those tasks will respect the limits imposed by their tags. However, for limiting the concurrency of subflow runs themselves, you would need to manage this at a higher orchestration level—for example, by controlling the number of concurrent subflow invocations in a parent flow or via work queuing with concurrency set in the Prefect Agent configuration. The concurrency control would need to be architected into the flow logic using Prefect's concurrency primitives or by using external synchronization methods like

asyncio.Semaphore

. To implement these concurrency patterns in your flows, you may want to use the aforementioned mechanisms from Prefect, tailored to suit the specific requirements and constraints of your workflow.