<@ULVA73B9P> Is it possible from within a task, to trigger the execution of another task, based on a...

Gunnar Oehmichen

07/29/2024, 9:39 AM

@Marvin Is it possible from within a task, to trigger the execution of another task, based on a specific error-message/exception? Within the task in question, a function is called to insert (or update) into a postgres table via sqlalchemy. Sometimes this results in an

sqlalchemy.exc.IntegrityError

. The imported function looks as following:

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def insert_bla(...): # some parameters
    # some function logic
        with SessionDB() as session:
           with session.begin():

              try:    
                session.execute(sql_insert)
                session.commit()
                print(f"{TableMainSupabase.__table__.name}: Inserted new {len(df_open_mastr_main_insert)} rows into supabase.")

              except Exception as e:
                session.rollback()
                raise e

In the task this of course would then look like:

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from utils import insert_bla

@task
... # some task-logic, preprocessing
  
def update_insert_table_bla(...):
    # some preprocessing
    insert_bla(df_bla, 
               ...).  # some connection parameters

So what I guess, I'd need to to, is again put the call of insert_bla() into a try except to catch the specific error and then, how to point to another task?

Marvin

07/29/2024, 9:40 AM

To achieve your goal, you can catch the specific exception within your task and then call another task based on that. Here is a code example demonstrating how to do this in Prefect: 1. Define your tasks:

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python
from sqlalchemy.exc import IntegrityError
from prefect import flow, task
from utils import insert_bla

@task
def update_insert_table_bla(df_bla, *args, **kwargs):
    try:
        insert_bla(df_bla, *args, **kwargs)
    except IntegrityError:
        handle_integrity_error_task(df_bla)  # Trigger another task on exception
    except Exception as e:
        raise e

@task
def handle_integrity_error_task(df_bla):
    # Define logic to handle IntegrityError
    print(f"Handling IntegrityError for data: {df_bla}")

@flow
def main_flow():
    df_bla = ...  # Your dataframe or data
    update_insert_table_bla(df_bla, ...)

if __name__ == "__main__":
    main_flow()

2. Explanation: - update_insert_table_bla
Task: This task attempts to execute the

insert_bla

function. If it catches an

IntegrityError

, it triggers the

handle_integrity_error_task

. - handle_integrity_error_task
Task: This task defines the logic to handle the

IntegrityError

exception. - main_flow
Flow: This is your main flow that orchestrates the execution of tasks. For more information on handling state and exceptions in Prefect, see these links: - State Handling and Exceptions: Prefect State Handling - Task Retry Conditions: Custom Retry Handler If you need further customization or examples, let me know.

Gunnar Oehmichen

07/29/2024, 9:58 AM

@Marvin what are the advantages of working with states instead of basic-python approach you've shown above?

Marvin

07/29/2024, 9:59 AM

Using states in Prefect for handling exceptions and control flow has several advantages over a basic Python approach. Here’s a breakdown: 1. Clearer Control Flow: - Prefect states provide a structured way to manage control flow in your workflows. You can easily decide whether to retry, skip, or trigger different tasks based on the state without nesting or complex try/except logic. 2. Improved Observability: - With states, you gain better observability over your tasks and flows. Prefect UI and logs will provide a clear view of task states (e.g., Pending, Running, Failed, Success), making it easier to debug and monitor workflows. 3. Automatic Task Retry and Policies: - Prefect allows you to specify retry policies and conditions directly via states. For instance, you can define retry conditions, delays, and limits without manually handling retries within your tasks. 4. Flexible Error Handling: - You can control the workflow based on specific state conditions, including error states, without convoluting your task logic. This separation of concerns leads to cleaner and more maintainable code. 5. Asynchronous Execution: - Prefect's state management integrates well with asynchronous task execution, ensuring that tasks run concurrently where applicable, without manual intervention. Here’s an example using states to handle exceptions and trigger another task:

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python
from sqlalchemy.exc import IntegrityError
from prefect import flow, task
from prefect.states import Failed, State

@task
def insert_bla_task(df_bla, *args, **kwargs):
    try:
        insert_bla(df_bla, *args, **kwargs)
    except IntegrityError as e:
        raise e from None  # To ensure the state captures the exception
    except Exception as e:
        raise e from None

@task
def handle_integrity_error_task(df_bla):
    print(f"Handling IntegrityError for data: {df_bla}")

@task
def conditional_task(state: State, df_bla):
    if isinstance(state.result(), IntegrityError):
        handle_integrity_error_task(df_bla)
    else:
        return state.result()

@flow
def main_flow():
    df_bla = ...  # Your dataframe or data
    state = insert_bla_task(df_bla, ..., return_state=True)
    conditional_task(state, df_bla)

if __name__ == "__main__":
    main_flow()

### Explanation: 1. insert_bla_task
Task: Attempts to execute and raises exceptions to influence state. 2. handle_integrity_error_task
Task: Handles the

IntegrityError

separately. 3. conditional_task
Task: Takes the state output and triggers the error-handling task if an

IntegrityError

is encountered. 4. main_flow
Flow: Manages the task execution and the conditional logic using states. Documentation References: - Working with States: Prefect States - Handling Task Failures: Handling Task Failures Using states effectively leverages Prefect’s full potential, streamlining error handling and making your workflows more robust and maintainable.

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