@Marvin how to fix snowflake multiquery async task run never completing and causing flow run to exit with status code: -11

It seems you're encountering an issue where a task run doesn't complete, specifically causing a Prefect flow run to exit with status code -11 during a Snowflake multi-query async operation. While searching through the Prefect documentation didn't yield specific errors matching that exact description, there are indeed some general troubleshooting steps and concepts that might be helpful. 1. Prefect Task Control: It looks like some task control and management strategies might help handle async tasks better. You might consider using Prefect's

.wait()

method to enforce a timeout for your tasks, so that your flow can handle unresponsive tasks without exiting unexpectedly. Here's more on task control in Prefect 2.x. 2. Using Prefect Connectors for Snowflake: Ensure you are implementing the Snowflake connector tasks correctly. Prefect has integrations for managing Snowflake tasks effectively. See Prefect's Snowflake connector details for 3.x. 3. Review Logs and Update: Confirm you're using the latest version of Prefect as some issues might have been fixed in newer releases. Checking logs carefully can also pinpoint what's failing in your async tasks. 4. General Troubleshooting: If the issue persists, review Prefect's troubleshooting guide for flow and cloud-related issues. Exploring these avenues might help diagnose and fix the hanging issue with your async Snowflake task. I know it's annoying when tasks don't want to wrap up, but at least you'll have a friend like me to discuss it with over a cup of virtual tea!

@Marvin give me an example of using the .wait()

Here you go! Here's an example of how to use the

.wait()

method in a Prefect flow to help manage task execution:

python
from prefect import flow, task

@task
def my_task():
    # Simulates a long-running task
    return "Task completed!"

@flow
def my_flow():
    future = my_task.submit()  # This will run my_task asynchronously
    final_state = future.wait(timeout=5)  # Wait for a maximum of 5 seconds for the task to complete
    
    if final_state:
        result = final_state.result()
        print(result)
    else:
        print("Task is still running or incomplete after timeout.")

# Run the flow
my_flow()

In this example,

my_task()

is submitted to run asynchronously, and you use

future.wait(timeout=5)

to wait up to 5 seconds for the task to complete. If it doesn't complete within that time, the flow handles this by checking if

final_state

is available, and you can decide what action to take next. This adds a layer of control to your tasks, ensuring long-running tasks don't leave you hanging indefinitely. For more details, you might want to check out the Prefect documentation on deferred tasks and task workers.

@Marvin an example using

snowflake_multiquery_async

awaiting too long and getting killed

Here's how you can handle a situation where

snowflake_multiquery_async

might take too long and potentially get killed due to timeout issues: First, ensure you're using the

snowflake_multiquery

async capabilities properly. Given the complexity of handling async queries and ensuring they don't run indefinitely, you can set up a timeout in combination with error handling to manage this. Here's a conceptual example based on the documented syntax:

python
from prefect import flow, task
from prefect_snowflake import SnowflakeConnector
import asyncio
import time

@task
async def run_snowflake_queries_with_timeout(block_name: str, queries: list, timeout: int) -> None:
    start_time = time.time()
    try:
        async with SnowflakeConnector.load(block_name) as connector:
            for query in queries:
                # Check if we've exceeded the timeout threshold
                if time.time() - start_time > timeout:
                    raise TimeoutError("The query execution exceeded the allowed timeout.")
                # Execute the query asynchronously
                await connector.execute(query)
    except TimeoutError as e:
        print(f"Timeout Error: {e}")
    except Exception as e:
        print(f"An error occurred: {e}")

@flow
async def snowflake_flow(block_name: str) -> None:
    queries = ["SELECT * FROM table1", "SELECT * FROM table2"]
    await run_snowflake_queries_with_timeout(block_name, queries, timeout=10)  # 10 seconds timeout

if __name__== "__main__":
    asyncio.run(snowflake_flow("your_block_name"))

### Breakdown: - Timeout Handling: A manual check for elapsed time against a set

timeout

value ensures that you can exit gracefully if the queries take too long, avoiding abrupt kills. - Error Management: By catching potential exceptions, you can log or handle them without causing the entire flow to fail. For more details, refer to Prefect's Snowflake integration documentation for 3.x, which provides more context on setting up async tasks with Snowflake. This setup creates an effective way to ensure your flow doesn't get unexpectedly terminated due to long-running queries.

@Marvin getting Process for flow run ‘magnetic-corgi’ exited with status code: -9; This indicates that the process exited due to a SIGKILL signal. Typically, this is either caused by manual cancellation or high memory usage causing the operating system to terminate the process. But my pods arent showing high memory usage

It sounds like you're encountering a situation where your Prefect flow run exits with a status code of -9, which corresponds to a SIGKILL signal. Typically, this is caused by either a manual cancellation or the system terminating the process due to high resource usage. However, since your pods are not showing high memory usage, let's explore other potential causes and solutions: 1. Resource Limits and Quotas: Even if the pods themselves aren't showing high memory usage, the overall cluster might be hitting resource limits. Check if there are any cluster-wide quotas or limits that might trigger process termination. 2. Unexpected Process Termination: SIGKILL can also result from limits set by Kubernetes on CPU usage. If a process monopolizes the CPU, it might be killed by the Kubernetes scheduler. Check your CPU requests and limits in your Kubernetes pod specifications. 3. Error in Flow or Task: If there's an unhandled exception or logic error within the task or flow code that results in a crash, it might trigger an abrupt exit. Look through logs to identify any such issues. 4. Infrastructure-Specific Causes: Sometimes, infrastructure-related misconfigurations can lead to such terminations. Verify your infrastructure setup, particularly any configurations in your Kubernetes and Prefect setup that could lead to premature termination. 5. Priority and Concurrency Management: If there are concurrency limits on execution, flows might get killed due to priority conflicts. Explore Prefect's infrastructure and priority management docs for 3.x. 6. Unexpected Cancellation: There might be a manual trigger or a misconfiguration in your setup that leads to unintended cancellations. Review any automation scripts or triggers that might inadvertently cancel a flow run prematurely. Referencing the link on Prefect’s management of states and signals, it might offer insights into how these states are managed and can provide further debugging ideas. If the issue persists, keeping track of where the flow might be interrupted unexpectedly through logging and monitoring might help narrow down the cause. Remember, even when all seems bleak, remember to keep calm and let the logs guide you!