@Marvin How can I make tasks within a flow dependent on the completion of another task within the same flow? I have a flow with the following tasks: connect, extract, load, and transform. Attached is an image of what the flow looks like in the UI. Here's what the flow code looks like:

def elt_flow(source_credentials: str, target_credentials: str, data_objects: list, local_filesystem: str):
    connection, cursor, local_storage = connect_task(target_credentials, local_filesystem)
    try:
        cursor.execute("begin transaction;")
        for data_object in data_objects:
            with tags(data_object):
                csv_file, log_file = extract_task(source_credentials, data_object, local_storage)
                load_task(cursor, csv_file, log_file)
                transform_task(cursor, csv_file, log_file, data_object)
        cursor.execute("commit;")
    except Exception as e:
        cursor.execute("rollback;")
        raise
    finally:
        cursor.close()
        connection.close()

To make tasks dependent on one another within a Prefect flow, you simply call the dependent task within the

flow

block after the task it depends on. Here's an example and an adapted version of your code to ensure the dependent tasks run in the correct order: Example Flow for Task Dependency:

python
from prefect import flow, task

@task
def add(x: int, y: int) -> int:
    return x + y

@flow
def my_flow():
    first_result = add(1, 2) 
    second_result = add(first_result, first_result)  # Will run after first_result is completed
    return add(first_result, second_result)          # Will run after second_result is completed

if __name__ == "__main__":
    my_flow()

Adapted Version of Your Code:

python
from prefect import flow, task
from prefect.context import tags

@task
def connect_task(target_credentials: str, local_filesystem: str):
    # Connection code
    pass

@task
def extract_task(source_credentials: str, data_object: str, local_storage: str):
    # Extraction code
    pass

@task
def load_task(cursor, csv_file, log_file):
    # Load code
    pass

@task
def transform_task(cursor, csv_file, log_file, data_object: str):
    # Transform code
    pass

@flow
def elt_flow(source_credentials: str, target_credentials: str, data_objects: list, local_filesystem: str):
    connection, cursor, local_storage = connect_task(target_credentials, local_filesystem).result()
    try:
        cursor.execute("begin transaction;")
        for data_object in data_objects:
            with tags(data_object):
                csv_file, log_file = extract_task(source_credentials, data_object, local_storage).result()
                load_task(cursor, csv_file, log_file).result()
                transform_task(cursor, csv_file, log_file, data_object).result()
        cursor.execute("commit;")
    except Exception as e:
        cursor.execute("rollback;")
        raise
    finally:
        cursor.close()
        connection.close()

In this adapted version: - Each task is called in the desired sequence. - Tasks are wrapped with the

.result()

method to ensure the dependent task waits for the completion of the preceding task. For further details, you can review the Prefect documentation here.

To make tasks dependent on one another within a Prefect flow, you simply call the dependent task within the

flow

block after the task it depends on.

this is only a fine answer if your code is sync/blocking in general • if you're passing data from an upstream to downstream, then that dependency is captured automatically • otherwise you can create a dependency using the

wait_for

keyword, in tasks @Marvin read up on

wait_for

and then very concisely give a short example

To define task dependencies in Prefect without passing data, use the

wait_for

keyword. This allows a task to wait for other tasks to complete before executing. Example:

python
from prefect import flow, task

@task
def task_a():
    # Task A logic
    pass

@task
def task_b():
    # Task B logic
    pass

@flow
def my_flow():
    a = task_a()
    b = task_b(wait_for=[a])  # Task B waits for Task A to complete

if __name__ == "__main__":
    my_flow()

This setup ensures that

task_b

runs only after

task_a

has completed, without needing to pass data between them. For more, read the documentation on

wait_for

here.

@Marvin So if I need to pass arguments in

task_b

, it would be

b = task_b(wait_for=[a], argument_1, argument_2)

?

Yes, you can pass additional arguments to the task while also using the

wait_for

keyword. Here's how you can do it: Example:

python
from prefect import flow, task

@task
def task_a():
    # Task A logic
    pass

@task
def task_b(arg1, arg2):
    # Task B logic using arg1 and arg2
    pass

@flow
def my_flow():
    a = task_a()
    b = task_b(wait_for=[a], arg1="value1", arg2="value2")  # Task B waits for Task A and receives arg1, arg2

if __name__ == "__main__":
    my_flow()

In this example: -

task_b

will wait for

task_a

to complete before starting. -

task_b

also receives

arg1

and

arg2

as parameters. For further reference, you can read the Prefect documentation on task dependencies.

@Marvin And if

task_a

returns multiple items, it would be:

@flow
def my_flow():
    a1, a2 = task_a()
    b = task_b(wait_for=[a1, a2], arg1="value1", arg2="value2")

If

task_a

returns multiple items, and you want

task_b

to wait for

task_a

to complete, you should still use the

wait_for

keyword. The

wait_for

keyword takes task futures, not the returned data. Here's the correct way to handle this: 1.

wait_for

should be used with the task's future (i.e., the task itself, not its returned items). 2. You then pass the returned items from

task_a

to

task_b

as arguments if needed. Example:

python
from prefect import flow, task

@task
def task_a():
    # Task A logic
    return "value_a1", "value_a2"

@task
def task_b(a1, a2, extra_arg1, extra_arg2):
    # Task B logic using a1, a2 (from task_a) and extra_arg1, extra_arg2
    pass

@flow
def my_flow():
    a_future = task_a()
    a1, a2 = a_future.result()  # Extracting returned values from task_a
    b = task_b(wait_for=[a_future], a1=a1, a2=a2, extra_arg1="value1", extra_arg2="value2")

if __name__ == "__main__":
    my_flow()

In this correct example: -

task_b

waits for

task_a

to complete by using the

a_future

. -

a1

and

a2

are the results from

task_a

. - The

wait_for

keyword ensures that

task_b

will not start until

task_a

is completed. -

task_b

receives

a1

,

a2

,

extra_arg1

, and

extra_arg2

as its arguments. For more details, refer to the documentation on `wait_for` within Prefect.

@Marvin How would you modify the following flow code so that the tasks execute in the following order

connect_task -> extract_task -> load_task -> transform_task

?

@flow(name="Data Pipeline")
def elt_flow(source_credentials: str, target_credentials: str, data_objects: list, local_filesystem: str):
    connection, cursor, local_storage = connect_task(target_credentials, local_filesystem)
    try:
        cursor.execute("begin transaction;")
        for data_object in data_objects:
            with tags(data_object):
                csv_file, log_file = extract_task(source_credentials, data_object, local_storage)
                load_task(cursor, csv_file, log_file)
                transform_task(cursor, csv_file, log_file, data_object)
        cursor.execute("commit;")
    except Exception as e:
        cursor.execute("rollback;")
        raise
    finally:
        cursor.close()
        connection.close()

To ensure the tasks

connect_task -> extract_task -> load_task -> transform_task

execute in the specified order, you can use the

wait_for

keyword along with passing the results between tasks. Here is the modified flow code:

python
from prefect import flow, task
from prefect.context import tags

@task
def connect_task(target_credentials: str, local_filesystem: str):
    # Connection code
    return connection, cursor, local_storage

@task
def extract_task(source_credentials: str, data_object: str, local_storage: str):
    # Extraction code
    return csv_file, log_file

@task
def load_task(cursor, csv_file, log_file):
    # Load code
    pass

@task
def transform_task(cursor, csv_file, log_file, data_object: str):
    # Transform code
    pass

@flow(name="Data Pipeline")
def elt_flow(source_credentials: str, target_credentials: str, data_objects: list, local_filesystem: str):
    connection, cursor, local_storage = connect_task(target_credentials, local_filesystem).result()
    
    try:
        cursor.execute("begin transaction;")
        
        for data_object in data_objects:
            with tags(data_object):
                csv_file, log_file = extract_task(source_credentials, data_object, local_storage).result()
                load_task(wait_for=[csv_file, log_file], cursor=cursor, csv_file=csv_file, log_file=log_file).result()
                transform_task(wait_for=[csv_file, log_file], cursor=cursor, csv_file=csv_file, log_file=log_file, data_object=data_object).result()
        
        cursor.execute("commit;")
    except Exception as e:
        cursor.execute("rollback;")
        raise
    finally:
        cursor.close()
        connection.close()

if __name__ == "__main__":
    elt_flow()

Explanation: -

connect_task

runs first. -

extract_task

runs for each

data_object

and waits for

connect_task

because `connect_task`'s results are needed. -

load_task

runs after

extract_task

is complete, ensuring the correct dependencies are maintained with

wait_for

. -

transform_task

runs after

load_task

is complete, ensuring the correct dependencies are maintained with

wait_for

. This setup guarantees the tasks execute in the correct order while maintaining dependency constraints within the Prefect flow.

@Marvin How is the

transform_task

waiting for the

load_task

to complete? The

wait_for

argument is referencing the results of the

extract_task

.

You're right. I made an oversight in my previous response. To ensure that

transform_task

waits for

load_task

to complete, we need to use the futures of the tasks correctly. We should pass the task futures directly using

wait_for

. Here's the corrected version of your flow code, ensuring the tasks execute in the correct order:

python
from prefect import flow, task
from prefect.context import tags

@task
def connect_task(target_credentials: str, local_filesystem: str):
    # Connection code
    return connection, cursor, local_storage

@task
def extract_task(source_credentials: str, data_object: str, local_storage: str):
    # Extraction code
    return csv_file, log_file

@task
def load_task(cursor, csv_file, log_file):
    # Load code
    pass

@task
def transform_task(cursor, csv_file, log_file, data_object: str):
    # Transform code
    pass

@flow(name="Data Pipeline")
def elt_flow(source_credentials: str, target_credentials: str, data_objects: list, local_filesystem: str):
    connection, cursor, local_storage = connect_task(target_credentials, local_filesystem).result()

    try:
        cursor.execute("begin transaction;")

        for data_object in data_objects:
            with tags(data_object):
                extract_future = extract_task(source_credentials, data_object, local_storage)
                load_future = load_task.wait_for(extract_future), cursor=cursor, csv_file=extract_future.result()[0], log_file=extract_future.result()[1])
                transform_future = transform_task.wait_for(load_future), cursor=cursor, csv_file=extract_future.result()[0], log_file=extract_future.result()[1], data_object=data_object)

        cursor.execute("commit;")
    except Exception as e:
        cursor.execute("rollback;")
        raise
    finally:
        cursor.close()
        connection.close()

if __name__ == "__main__":
    elt_flow()

Changes made: -

extract_future

captures the future of the

extract_task

. -

load_future

waits for

extract_future

to complete using the

wait_for

keyword. -

transform_future

waits for

load_future

to complete using the

wait_for

keyword. This way,

transform_task

will execute only after

load_task

completes, ensuring the correct task order.

@Marvin The syntax doesn't look right. There's an extra closing parenthesis on the

load_future

line.

You are correct. I apologize for the error. Here is the corrected version with proper syntax:

python
from prefect import flow, task
from prefect.context import tags

@task
def connect_task(target_credentials: str, local_filesystem: str):
    # Connection code
    connection = "connection"  # Placeholder
    cursor = "cursor"  # Placeholder
    local_storage = "local_storage"  # Placeholder
    return connection, cursor, local_storage

@task
def extract_task(source_credentials: str, data_object: str, local_storage: str):
    # Extraction code
    csv_file = "csv_file"  # Placeholder
    log_file = "log_file"  # Placeholder
    return csv_file, log_file

@task
def load_task(cursor, csv_file, log_file):
    # Load code
    pass

@task
def transform_task(cursor, csv_file, log_file, data_object: str):
    # Transform code
    pass

@flow(name="Data Pipeline")
def elt_flow(source_credentials: str, target_credentials: str, data_objects: list, local_filesystem: str):
    connection, cursor, local_storage = connect_task(target_credentials, local_filesystem).result()

    try:
        cursor.execute("begin transaction;")

        for data_object in data_objects:
            with tags(data_object):
                extract_future = extract_task(source_credentials, data_object, local_storage)
                load_future = load_task(wait_for=[extract_future], cursor=cursor, csv_file=extract_future.result()[0], log_file=extract_future.result()[1])
                transform_task(wait_for=[load_future], cursor=cursor, csv_file=extract_future.result()[0], log_file=extract_future.result()[1], data_object=data_object)

        cursor.execute("commit;")
    except Exception as e:
        cursor.execute("rollback;")
        raise
    finally:
        cursor.close()
        connection.close()

if __name__ == "__main__":
    elt_flow()

In this corrected version: - Each task correctly references the previous task's future using the

wait_for

keyword. -

extract_task

returns

extract_future

. -

load_task

waits for

extract_future

to complete. -

transform_task

waits for

load_future

to complete. This order is ensured by the use of the

wait_for

keyword.