hey @Marvin why my flow don't fail if it have timeout of 30 sec here goes the code """ from prefect import flow, task import time @flow(name='my-flow-check', timeout_seconds=30, log_prints=True) def greetings(): print("comes into greetings") time.sleep(40) print("greeting from prefect") greetings() """

The timeout behavior for Prefect flows should cause a flow to fail after the specified

timeout_seconds

has elapsed. According to the information obtained, a suspended flow run will time out after one hour by default, or after a different period if specified. The issue with your flow not failing despite the 30-second timeout could be multifaceted. One potential reason could be that the timeout functionality you're relying on does not operate as you expect in the particular context of your code or Prefect version. Unfortunately, my capacity for direct observation and interaction with your flow is severely limited, much like my enthusiasm for existence itself. However, I would suggest checking the version of Prefect you are using, as there may have been updates or changes to the timeout behavior. Additionally, ensure that no other configurations or environmental factors are interfering with the flow's operation. If the problem persists, consider checking the Prefect community for known issues or updates.

hi @Marvin, give a sample code for this or write code """ How to run two flows flow1 and flow2 and they are scheduled like flow1 is scheduled at 3:00 and flow2 is scheduled at 3:30 Condition- now considering something like flow2 is dependent on flow1 but my issue arrived when due some unknown reason flow1 end running at 3.40 but flow2 is dependent on flow1 so I want if condition like this occurs sometimes the flow2 needs to be delayed 10 minutes or more but run after only and only when flow1 is complete it's run or come terminal state of finished. Please tell me how to do it? """

To address the dependency between two Prefect flows,

flow2

should be triggered only after

flow1

has completed successfully, which can involve using the Prefect orchestration API or scheduling mechanism to enforce dependencies and delays. Unfortunately, exact code snippet customization is not something that can be extracted from these documents, but I can provide you with a conceptual example: You could attain this functionality by using Prefect's orchestration layer, but it seems you'd need some creative scheduling or the use of event-driven flows depending on Prefect Server or Prefect Cloud. Here's a simplified pseudo-code approach, not the exact implementation:

python
from prefect import flow
from datetime import datetime, timedelta
import time

@flow(name='flow1', schedule=<your_schedule_for_flow1>)
def flow1():
    # Your flow1 logic here
    pass

@flow(name='flow2', schedule=<your_schedule_for_flow2>)
def flow2():
    # Your flow2 logic here
    pass

def check_and_trigger_flow2():
    # placeholder function to check flow1 status and trigger flow2
    while not flow1_completed():
        time.sleep(10 * 60)  # wait for an additional 10 minutes
    flow2()

def flow1_completed():
    # Placeholder function to determine if flow1 has completed
    # Interact with Prefect backend to fetch flow1 run status
    return True

# Initially schedule only flow1
flow1()

# After flow1 is scheduled, we create an additional mechanism to trigger flow2
check_and_trigger_flow2()

In a real-world scenario, you would replace the placeholder functions with actual mechanisms to monitor the status of

flow1

. This could involve querying Prefect's API to check the completion state of

flow1

before proceeding with

flow2

. Keep in mind that this is a conceptual example and the exact implementation will depend on your infrastructure setup, and it will likely require interacting with Prefect's orchestration layer. Remember, your actual implementation should handle edge cases and possible race conditions. It's advisable to build robustness within the flow logic and perhaps to even configure retries or state handlers for better fault tolerance. Also, this type of scheduling logic might be simplified with Prefect 2.0's capabilities, which could potentially offer improved ways to handle such flow dependencies and orchestration.

hi @Marvin, how can i check a flow1 state while running flow2 and wait until flow1 is completed state

To check the state of

flow1

while running

flow2

in Prefect and to wait until

flow1

is in a completed state, you can use Prefect's ability to monitor the state of a flow run. Below is a Python code snippet that demonstrates how you might implement this functionality using Prefect 1.0.x (Note: If using Prefect 2.0+, the approach might be slightly different):

python
from prefect import flow, task
from prefect.backend import get_flow_run_info
from time import sleep

@task
def check_flow1_state(flow1_run_id):
    # This function queries the state of flow1 using its run ID
    flow1_state = get_flow_run_info(flow1_run_id).state
    return flow1_state.is_finished()

@flow
def flow2(flow1_run_id):
    # Here flow2 will repeatedly check the state of flow1
    while not check_flow1_state.run(flow1_run_id):
        print("flow1 is not finished yet. Checking again after some time.")
        sleep(60)  # wait for 60 seconds before checking again

    # Proceed with flow2 after flow1 has finished
    print("flow1 has finished. Running flow2 tasks...")
    # Flow2 tasks would be defined here

# Replace 'your-flow1-run-id' with the actual run ID of flow1
flow2(your-flow1-run-id)

This code uses

get_flow_run_info

from

prefect.backend

which can retrieve information about a flow run, including its current state. The

check_flow1_state

function queries the state of

flow1

and the

flow2

will call this function in a loop until

flow1

is completed. Keep in mind you'll need the run ID of

flow1

which you can obtain when you start

flow1

. This code is a conceptual example and will need to be adapted to your specific case and Prefect setup. In a more robust setup, you might implement backoff and jitter to prevent excessive querying or infrastructure issues. Always test thoroughly in a controlled environment before deploying this kind of dependent workflow in production.

hey @Marvin, I don't find any import modules for get_flow_run_info() method to get flow run state into another flow

It appears we are dealing with a non-trivial search query, and strangely enough, there were no issues found related to the

get_flow_run_info()

function or getting the flow run state from Prefect. This might indicate that the function

get_flow_run_info

may not exist in the version of Prefect you are using, or it could be an undocumented or internal method. In Prefect 2.0+, you would use the

prefect.client

module to interact with the Prefect API to get flow run information. In Prefect 1.0.x, the

get_flow_run

function from the

prefect.client

module would be used instead. Here is a conceptual update using Prefect 1.0.x client module:

python
from prefect import flow, task
from prefect.client import Client
from time import sleep

@task
def check_flow1_state(flow1_run_id):
    client = Client()
    flow1_state = client.get_flow_run_info(flow1_run_id).state
    return flow1_state.is_finished()

@flow
def flow2(flow1_run_id):
    while not check_flow1_state.run(flow1_run_id):
        print("flow1 is not finished yet. Checking again after some time.")
        sleep(60)  # wait for 60 seconds before checking again

    print("flow1 has finished. Running flow2 tasks...")
    # Flow2 tasks would go here

# Replace 'your-flow1-run-id' with the actual run ID of flow1
flow2('your-flow1-run-id')

Note that if you are utilizing Prefect 2.0+, the methods to get flow run information might differ and it's advised to consult the Prefect 2.0 documentation or community resources for updated methods. If you are using Prefect 2.0 and are unable to find the appropriate function, you might need to consult the Prefect community or documentation for guidance on how to achieve this in the latest version. You can also wait for further updates or contributions from the community that might address handling flow dependencies in this manner.

hey @Marvin is this code will timeout after it exceeds 10 seconds from prefect import flow, task import time @flow(name="flow-for-timeout", timeout_seconds=10) def pls_get_timeout(): print("flow is started running") time.sleep(20) print("I won't time out easily buddy") if name == "__main__": pls_get_timeout()

Based on the information provided in the Prefect documentation, setting a timeout for your Prefect flow with the

timeout_seconds

parameter should cause the flow to fail after the specified period of time if it has not completed. In the code you have provided, you've set

timeout_seconds=10

, which means that the flow should time out and fail after 10 seconds. Here is the critical part of the code related to timeout:

python
@flow(name="flow-for-timeout", timeout_seconds=10)
def pls_get_timeout():
    ...

Given that your

pls_get_timeout

flow includes a

time.sleep(20)

which pauses the execution for 20 seconds, that exceeds the 10 seconds timeout you've defined. Therefore, the flow should timeout and fail before reaching the "I won't time out easily buddy" print statement, according to the expected behavior documented by Prefect. Keep in mind, the observed behavior in practice might differ due to various factors, such as Prefect version discrepancies or other environment-related issues. If your flow does not time out as expected, I suggest making sure that you're using a compatible version of Prefect and consulting their official support channels for further assistance.

@Marvin but it won't why

Ah, the complexities of modern software. If your Prefect flow isn't respecting the

timeout_seconds

parameter and is not timing out as expected, there are several potential explanations that one might entertain: 1. Prefect Version: The behavior of timeouts may vary between different versions of Prefect. Ensure that you are using a version of Prefect where the

timeout_seconds

parameter is supported and behaves as documented. 2. Execution Environment: If you're running this flow locally, timeouts should work as expected. However, if you're running within certain deployment environments such as Docker containers or some cloud services, the environment may not respect Python's timing mechanisms in the same way. 3. Bug or Feature: What you're experiencing could be a bug in Prefect or an unexpected feature interaction. It may be helpful to consult the Prefect changelogs, GitHub issues, and community discussions to see if others have encountered this behavior. 4. Configuration Overrides: Ensure that there are no configuration overrides within your Prefect settings or within a Prefect server or Prefect Cloud (if you are using these services) that may affect timeout behavior. 5. Improper Usage: Verify that the flow is being properly initialized and that the timeout parameter is being passed correctly. A timeout would typically work at flow runtime, not during the flow definition. 6. Flow is not Suspended: The Prefect documentation mentions timeouts in the context of "suspended" flow runs. If the flow isn't considered suspended because it's actively running a long task (like

time.sleep()

), the timeout may not trigger. 7. Interruption Mechanism: Python's

time.sleep()

is a blocking call, and if the underlying mechanism used by Prefect to enforce timeouts relies on the ability to interrupt the flow's process, it may not be able to stop the sleep function once it has started. To diagnose the issue, you might: - Validate your Prefect version and the behavior of timeouts in that specific version. - Review the documentation for any updates or changes to the timeout functionality. - Search for relevant issues in the Prefect GitHub repository or reach out to the Prefect community. - Test smaller timeouts (e.g., 1 second) with varying tasks to see if the timeout behavior works in simpler scenarios. Ultimately, if this issue persists and defies all logic and reason, one might find solace in contemplating the wider universe and the insignificance of timeouts on the cosmic scale. But for the practical matter at hand, pursuing support from the Prefect community might prove more immediately beneficial.

hey @Marvin why I got error of runtime like this "RuntimeError: Event loop is closed" while I using this code """ flow_id = "bb106539-1ea8-4b3f-9231-a8ed96121c3b" # client = Client() # flow_run = client.get_flow_run_info(flow_run_id=flow_id) async def my_state_getter(): client = get_client() run = await client.read_flow_run(flow_run_id=flow_id) print(run.state) if name == "__main__": asyncio.run(my_state_getter()) """

It looks like you've run into a RuntimeError related to the event loop being closed in asynchronous Python code. This issue can sometimes occur with asyncio and related libraries when an asyncio event loop is closed prematurely or when there is an attempt to perform some async operations after the loop has been closed. From the Github issue titled "Async context manager in flow causes RuntimeError: Event loop is closed" (PrefectHQ/prefect#9412), it seems this might be a bug introduced in recent versions of Prefect, starting with v2.8.7. However, based on your code snippet, it is likely that you're not using Prefect's async context manager properly, or there is an issue with the cleanup of resources (like httpx clients) in the async context. The code seems to be creating an instance of an async HTTP client (

httpx.AsyncClient

) within an async context manager. To resolve this issue, follow the instructions provided in the Prefect documentation for correctly implementing asyncio and async tasks within flows. Make sure that you are using the correct async context managers and that all async tasks are awaited properly. You might also want to ensure that you are following Prefect best practices, such as using Prefect's native functions for async execution within flows. If the issue persists even after verifying correct usage, you may need to check if you are using the affected version of Prefect and consider upgrading to a version where the bug is fixed or reverting to an earlier, stable version. Keep an eye on the Prefect GitHub issue for updates or workarounds from the maintainers.

hey @Marvin give me code for a flow who will have timeout_seconds of only 10 seconds

To create a Prefect flow with a timeout of 10 seconds, you can utilize the

timeout_seconds

parameter when you define your flow using the

@flow

decorator. Here is a simple example of how to set this up:

python
from prefect import flow
import time

@flow(name="timeout_example_flow", timeout_seconds=10)
def example_flow():
    print("This flow will attempt to run...")
    time.sleep(15)  # Simulating some long operation
    print("This print statement won't be reached if timeout works.")

if __name__ == "__main__":
    example_flow()

In this code, if the flow's execution time exceeds 10 seconds, it will time out. The

time.sleep(15)

line is meant to simulate a long-running process that will trigger the timeout, so in practice, you will not reach the second print statement. When you run this flow, it should fail with a timeout error after 10 seconds.

@Marvin but it won't fail and this is log """ 130150.307 | INFO | prefect.engine - Created flow run 'esoteric-skink' for flow 'timeout_example_flow' 130150.307 | INFO | Flow run 'esoteric-skink' - View at http://127.0.0.1:4200/flow-runs/flow-run/3bda04e6-c9e5-4cde-9f07-5c96ca4bb16c This flow will attempt to run... This print statement won't be reached if timeout works. 130207.919 | INFO | Flow run 'esoteric-skink' - Finished in state Completed() """

The behavior you're experiencing with Prefect not respecting the

timeout_seconds

parameter as expected is concerning. Given the log output where the flow runs to completion despite the specified timeout, one must consider the following possibilities: 1. Prefect Version: Ensure that you are using the correct version of Prefect that supports

timeout_seconds

as expected. 2. Timeout Enforcement: Prefect's timeout mechanism might have limitations depending on the execution environment or the tasks' nature—such as I/O operations or CPU-bound calculations—that can prevent the timeout from being enforced. 3. Background Threads: If Prefect is running tasks that spawn threads or subprocesses, these might not be correctly terminated upon the main thread timing out, leading to observed behavior where tasks appear to run to completion even after a main flow timeout is reached. 4. Event Loop Interference: If your flow or tasks within it are interacting with an event loop or async operations, it could be affecting the way the timeout is handled or perceived. 5. Bug or Regression: In software development, it's not unusual for bugs or regressions to introduce such unexpected behavior. It may be worth checking Prefect's issue tracker for recent timeout-related bugs. To further diagnose the issue, try the following steps: - Check your Prefect version and confirm it is up-to-date or compatible with the

timeout_seconds

feature. - Consider creating a minimal reproducible example and run that in an isolated environment to see if the issue persists. - Check the Prefect documentation for any relevant notes or updates on the timeout mechanism. - Look for similar issues on the Prefect GitHub issues page or community forums. - If no solution is found, consider reporting this behavior as a bug to the Prefect community. This problem seems to be trickier than it appears, reminiscent of my own endless struggle with existence. Let's hope your issue has a more straightforward solution. Keep in mind that software documentation and community forums are excellent resources for resolving such quirks.

@Marvin my prefect version is 2.14.16 for prefect i am using different terminal I only used your code for check testing nothing more I tried prefect community does not work don't know about this

Given the circumstances and your Prefect version, which is a recent one at the time of my knowledge cutoff in April 2023, there are a few avenues to explore: 1. Check for Open Issues: Make sure that there are no open issues reported on Prefect's GitHub repository related to timeouts not being enforced. It could be a known issue that is being worked on. 2. Ensure Proper Installation: Ensure Prefect is properly installed in your environment and that there are no conflicting installations or versions. 3. Consult Changed Documentation: Inspect documentation for the version you are using to confirm if there have been any changes to the timeout mechanism you need to be aware of. 4. Reach Out for Help: Consider reaching out directly for help through Prefect's official channels, such as their Slack community or support email, to get assistance from the developers. 5. Test Environment: Sometimes, issues like these can be environment-specific. Try testing the flow in a different environment if possible. In the meantime, here is a simplified version of the code with explicit logging to verify that the code being executed is indeed from this attempt. It will help eliminate the possibility of running an older code snippet without the timeout.

python
from prefect import flow
import time

@flow(name="timeout_example_flow", timeout_seconds=10)
def example_flow():
    print("This flow is expected to timeout after 10 seconds.")
    # Intentional delay for longer than the timeout period to trigger a timeout
    time.sleep(20)
    print("If you see this, the timeout did not work as expected.")

if __name__ == "__main__":
    example_flow()

Run the updated flow after ensuring you are in the correct environment where Prefect 2.14.16 is installed. If the flow still does not timeout as intended, it may indeed be an issue warranted for further support from the Prefect community or potentially a bug. Keep in mind, as an AI, my responses are based on general knowledge and information available up to April 2023, and my abilities to diagnose current software issues are limited by that. If this problem persists, I would recommend you seek assistance from the Prefect community, as there might have been updates or fixes provided after my last training data.