@Marvin I am running

y = task.submit(..., wait_for[x])
y.result(raise_on_failure=False)

But when y fails, the entire flow failes and says "Finished in state Failed9'1/6 states failed.'), why?

It appears that the behavior you are observing is due to Prefect's flow-state resolution rules. When any task within a flow fails, the entire flow is marked as failed unless specifically handled. Here's what might be happening based on the version of Prefect you're using: ### Prefect 2.x In Prefect 2.x, if a task fails and you call

result()

on it with

raise_on_failure=False

, the flow does not automatically fail unless you attempt to access the result of the failed task in a way that causes an exception. Ensure you are handling the task failure explicitly within your flow to avoid propagating the failure. References: 1. Task Runners in Prefect 2.x 2. Flow concepts in Prefect 2.x ### Prefect 3.x In Prefect 3.x, the behavior is similar. The entire flow is marked as failed when any task within it fails, but you need to handle the failed state explicitly. The

raise_on_failure=False

prevents the exception from being raised but does not change the state to successful. You can explicitly check the task state and handle failures appropriately in your flow to ensure the flow does not fail unless desired. Example:

python
from prefect import flow, task

@task
def my_task():
    # Some task logic that might fail
    raise ValueError("Task failed!")

@flow
def my_flow():
    future = my_task.submit()
    result = future.result(raise_on_failure=False)
    if future.state.is_failed():
        print("Task failed, but the flow continues gracefully.")

my_flow()

References: 1. Task Runners in Prefect 3.x 2. Managing States in Prefect 3.x If you provide the specific version of Prefect you are using, I can further tailor the advice!

@Marvin to follow up, I have a series of tasks chained together using .submit(wait_for[x]), wait_for[y], etc. How to keep these dependencies even if x or y fail?

To maintain task dependencies with the

wait_for

parameter, even if one of the tasks in the chain fails, you need to ensure that later tasks are not disrupted by failures in upstream tasks. Here are ways to handle this in both Prefect 2.x and 3.x: ### Prefect 2.x In Prefect 2.x, you can monitor the state of tasks and handle failures manually using the

result()

method with

raise_on_failure=False

. This way, you can check if a task failed and decide whether to proceed with downstream tasks. Example:

python
from prefect import flow, task

@task
def task_a():
    # This task might fail
    raise ValueError("Task A failed!")

@task
def task_b():
    return "Task B completed successfully."

@flow
def my_flow():
    result_a = task_a.submit().result(raise_on_failure=False)
    # Check if task_a failed
    if isinstance(result_a, Exception):
        print("Task A failed but continuing with Task B...")
    result_b = task_b.submit(wait_for=[result_a])

my_flow()

### Prefect 3.x In Prefect 3.x, you can follow a similar approach. However, you have better support for states and handling task dependencies with

wait_for

. Example:

python
from prefect import flow, task

@task
def task_a():
    # This task might fail
    raise ValueError("Task A failed!")

@task
def task_b():
    return "Task B completed successfully."

@flow
def my_flow():
    future_a = task_a.submit()
    # Wait for task_a and handle failure gracefully
    result_a = future_a.result(raise_on_failure=False)
    if future_a.state.is_failed():
        print("Task A failed but continuing with Task B...")
    result_b = task_b.submit(wait_for=[future_a])

my_flow()

In both versions, this ensures that the downstream task

task_b

waits for

task_a

to complete, regardless of `task_a`'s success or failure, and you handle the failure case explicitly. ### References: - Specifying Upstream Dependencies in Prefect 2.x - Managing States in Prefect 3.x - Writing Flows in Prefect 3.x These examples ensure the flow continues even if some tasks within the chain fail. Adjust the logic as needed based on your specific requirements.

@Marvin I am using Prefect 2. Now when doing that, I get this error: prefect.exceptions.UnfinishedRun: Run is in PENDING state, its result is not available.

The error you are encountering,

prefect.exceptions.UnfinishedRun: Run is in PENDING state, its result is not available.

, typically means that the task you are trying to get the result from has not yet started or completed. In Prefect 2.x, when you use

.submit()

to add a task, it returns a

PrefectFuture

. To get the final state or result from this future, you should ensure that the task indeed finishes its execution. The task might be in a

PENDING

state due to some delay or scheduling. Here are a few strategies to handle this issue: ### Use

wait()

Method Before getting the result, wait for the task to complete by using the

wait()

method on the future. This ensures that the task has finished running before you attempt to get its result. Example:

python
from prefect import flow, task

@task
def task_a():
    raise ValueError("Task A failed!")

@task
def task_b():
    return "Task B completed successfully."

@flow
def my_flow():
    future_a = task_a.submit()
    state_a = future_a.wait()  # Ensure task_a completes
    result_a = future_a.result(raise_on_failure=False)
    
    if state_a.is_failed():
        print("Task A failed but continuing with Task B...")
    
    future_b = task_b.submit(wait_for=[state_a])
    state_b = future_b.wait()  # Optional, to ensure task_b completes

my_flow()

### Check States Before Proceeding Ensure that the state is not

PENDING

before you try to access the result. This can be done in a loop or with some conditional checks. Example:

python
from prefect import flow, task

@task
def task_a():
    raise ValueError("Task A failed!")

@task
def task_b():
    return "Task B completed successfully."

@flow
def my_flow():
    future_a = task_a.submit()
    
    # Ensure future_a is not in PENDING state
    state_a = future_a.wait()
    result_a = future_a.result(raise_on_failure=False)
    
    if state_a.is_failed():
        print("Task A failed but continuing with Task B...")
    
    future_b = task_b.submit(wait_for=[state_a])
    state_b = future_b.wait()  # Ensure task_b completes

my_flow()

### Additional References: 1. Concepts States in Prefect 2.x> 2. Specifying Upstream Dependencies in Prefect 2.x These methods ensure that tasks dependent on others handle the states correctly and continue execution even if there are failures in upstream tasks.

@Marvin I have a method named

submit_dbt_task()

that does

return trigger_dbt_cli_command.submit(...)

, and then I have my tasks defined as:

dims_task = dbt.submit_dbt_task("dims", dims_command, wait_for=[deps_task])
    dims_task.wait()
    dims_task.result(raise_on_failure=False)

When using Prefect with dbt CLI commands, handling task dependencies and failure states properly is crucial to ensure the flow execution doesn't stop abruptly. Given the current structure of your flow tasks, you should handle the possibility of failures explicitly to ensure that the flow continues even if a task encounters an error. Here’s how you can adjust your flow: 1. Use

wait()

to ensure tasks complete before moving to the next one. 2. Check and handle the state of each task to make sure the flow progresses even when a task fails. Here is a refined version of your flow:

python
from prefect import flow, task
from prefect_dbt.cli.tasks import trigger_dbt_cli_command

# Assume submit_dbt_task is defined
def submit_dbt_task(name: str, command: str, wait_for=None):
    return trigger_dbt_cli_command.submit(
        command=command,
        # Add additional configurations as needed
        wait_for=wait_for
    )

@flow
def my_dbt_workflow():
    dims_command = "your_invalid_dbt_command"
    facts_command = "your_valid_dbt_command"
    
    # Run deps_task if not already defined
    deps_task = submit_dbt_task("deps", "dbt deps") # Example deps_task
    
    # Submitting dims_task and waiting for it to complete
    dims_task = submit_dbt_task("dims", dims_command, wait_for=[deps_task])
    dims_task.wait()  # Ensure dims_task completes
    dims_result = dims_task.result(raise_on_failure=False)
    
    # Handle the failure of dims_task
    if dims_task.state.is_failed():
        print("dims_task failed, but we are proceeding with facts_task...")
    
    # Submitting facts_task and waiting for it to complete
    facts_task = submit_dbt_task("facts", facts_command, wait_for=[dims_task])
    facts_task.wait()  # Ensure facts_task completes
    facts_result = facts_task.result(raise_on_failure=False)
    
    # Handle the failure of facts_task if needed
    if facts_task.state.is_failed():
        print("facts_task failed.")

my_dbt_workflow()

### Key Points: - `wait()`: Ensure that each task has a chance to complete by calling

wait()

. - Check Task State: By using

raise_on_failure=False

, you retrieve the result without raising an exception and then handle the task state to proceed with the subsequent tasks. - Logging/Handling Failures: Print or handle the task failure cases to ensure that the flow logic is clearly defined for failed tasks. ### References: - Prefect Dbt CLI Commands - States in Prefect 2.x By implementing these changes, your flow should handle task failures more gracefully, allowing the dependent tasks to continue executing.

@Marvin unfortunately I got an error:

refect.exceptions.UnfinishedRun: Run is in PENDING state, its result is not available.
04:24:11 PM
prefect.flow_runs
Finished in state Failed('Flow run encountered an exception. UnfinishedRun: Run is in PENDING state, its result is not available.')

  File "prefect/flows/dbt_daily.py", line 84, in dbt_daily
    facts_result = facts_task.result(raise_on_failure=False)

The error

prefect.exceptions.UnfinishedRun: Run is in PENDING state, its result is not available.

indicates that the task has not yet completed when you are trying to access its result. This could be due to the asynchronous nature of task execution in Prefect. To address this, ensure that tasks have completed before you attempt to access their results. The

wait()

method should generally handle this, but it can still result in issues if not correctly implemented. A refined approach is to continuously check the state of the task until it's no longer PENDING before attempting to access the result. Here is an updated version of your flow:

python
from prefect import flow, task
from prefect_dbt.cli.tasks import trigger_dbt_cli_command
import time

# Assume submit_dbt_task is defined
def submit_dbt_task(name: str, command: str, wait_for=None):
    return trigger_dbt_cli_command.submit(
        command=command,
        name=name,
        # Add additional configurations as needed
        wait_for=wait_for
    )

@flow
def my_dbt_workflow():
    dims_command = "your_invalid_dbt_command"
    facts_command = "your_valid_dbt_command"
    
    # Run deps_task if not already defined
    deps_task = submit_dbt_task("deps", "dbt deps")  # Example deps_task
    
    # Submitting dims_task and waiting for it to complete
    dims_task = submit_dbt_task("dims", dims_command, wait_for=[deps_task])
    
    while dims_task.state.is_pending():
        print(f"Waiting for dims_task to complete. Current state: {dims_task.state}")
        time.sleep(1)  # Wait a second before checking again

    dims_task.wait()  # Ensure dims_task completes
    dims_result = dims_task.result(raise_on_failure=False)
    
    # Handle the failure of dims_task
    if dims_task.state.is_failed():
        print("dims_task failed, but we are proceeding with facts_task...")
    
    # Submitting facts_task and waiting for it to complete
    facts_task = submit_dbt_task("facts", facts_command, wait_for=[dims_task])
    
    while facts_task.state.is_pending():
        print(f"Waiting for facts_task to complete. Current state: {facts_task.state}")
        time.sleep(1)  # Wait a second before checking again

    facts_task.wait()  # Ensure facts_task completes
    facts_result = facts_task.result(raise_on_failure=False)
    
    # Handle the failure of facts_task if needed
    if facts_task.state.is_failed():
        print("facts_task failed.")
    
    return {"dims_result": dims_result, "facts_result": facts_result}

my_dbt_workflow()

### Key Improvisation: - Polling for State: Continuously check if the task state is

PENDING

and wait for it to transition out of the pending state before accessing its result. This ensures that you only access the result once the task has completed its execution. ### References: - Prefect Dbt CLI Commands - Handling Task States This approach helps to ensure that your tasks have correctly completed before you attempt to get their results, minimizing the chances of running into the

PENDING

state issue.