@Marvin One flow with several tasks. Within the task at a certain point, I want to test wether a variable is empty and if it is so, stop this task and start the next. How?

To handle conditional execution within tasks and proceed to the next task when a variable is empty, you can structure your Prefect flow by using control flow logic in Python within each task. If a variable is found to be empty, you can simply return from the task, effectively skipping any subsequent code and allowing the flow to continue to the next task. There isn't a built-in Prefect control for this specific use case, but implementing a check within your task function allows you precise control over the flow's behavior. Here's a basic example using Python and Prefect constructs:

python
from prefect import flow, task

@task
def task_with_check(variable):
    if not variable:  # Check if the variable is empty
        print("Variable is empty, skipping rest of the task.")
        return  # Exit the task
    # Task logic here if the variable is not empty
    print("Variable is not empty, proceeding with task logic.")
    # ... rest of your task logic

@flow
def my_flow():
    # Suppose we have a list of variables for each task
    variables = ["data1", "", "data3"]  # Second item is an empty string

    # Executing tasks with a check for emptiness of the variable
    for variable in variables:
        result = task_with_check(variable)
        # Based on your logic, you can use `result` or proceed to the next iteration

my_flow()

In this code snippet,

task_with_check

performs a check on the passed

variable

. If the

variable

is empty, it prints a message and returns early, which stops further execution of that task and allows the flow to move on to the next task. Remember, however, that each task in Prefect is executed independently and asynchronously by default. It's up to you to control the flow of execution. If you are looking for ways to skip a task based on more complex conditions, you might want to explore state handlers or result handlers in Prefect, which offer more advanced control, including skipping tasks and handling outcomes based on previous results. You can use the provided example as a starting point and customize it according to your needs. Adjust the condition and the rest of the task's logic to match your specific use case.

@Marvin Ok. Since I am updating/inserting into a database, it is of great importance, that the tasks are actually executed subsequently to allow for the relationships. How do I control this?

For sequential task execution, where each task must be run one after the other especially when dealing with database operations that require maintaining the integrity of relationships, you can simply call tasks in order within a Prefect flow. Prefect will run tasks in the sequence they are called by default unless you specify parallel execution explicitly. Here's an example that demonstrates sequential task execution for database operations:

python
from prefect import flow, task

@task
def insert_into_database(data):
    # Logic for inserting data into a database
    print(f"Inserting {data} into the database.")
    # Simulate database insert and return some identifier
    return f"{data}_id"

@task
def subsequent_database_operation(previous_result):
    # Logic for an operation that depends on the previous insert
    print(f"Performing an operation that depends on {previous_result}.")

@flow
def my_database_flow():
    datas = ["data1", "data2", "data3"]
    
    for data in datas:
        if data:  # Skip if data is empty
            result = insert_into_database(data)  # Sequential execution ensured
            subsequent_database_operation(result)  # Wait for the insert to complete

my_database_flow()

In this code, tasks are called one after the other within the loop. The

subsequent_database_operation

task is only called after

insert_into_database

returns, ensuring that each operation waits for the previous insert to complete. To maintain strict order, do not use asynchronous task execution features like

TaskRunner

,

submit()

, or

wait()

methods that could otherwise cause tasks to run out of order. Remember to replace the

print

statements and simulated operations with actual database logic in your implementation. The sequential execution in this flow is guaranteed by the plain, synchronous calling of task functions one after another; Prefect's default behavior is to wait for a task to complete before continuing to the next line of code in the flow function.

@Marvin and what if each subsequent task uses a different task-function with different parameters? This would not be feasible within a loop

If each subsequent task uses a different function with different parameters, you still maintain control of the execution order by how you call these tasks within the flow function. You do not need a loop for sequential execution; you can simply call each task function one after the other, and Prefect will handle them in the order they are invoked. By default, tasks within a flow are scheduled and run sequentially unless you explicitly introduce concurrency. Here is an example flow with different tasks being executed in sequence:

python
from prefect import flow, task

@task
def task_one(param1):
    # Logic for the first task
    result = f"result_of_task_one_with_{param1}"
    print(result)
    return result

@task
def task_two(param2):
    # Logic for the second task
    result = f"result_of_task_two_with_{param2}"
    print(result)
    return result

@task
def task_three(param3):
    # Logic for the third task
    result = f"result_of_task_three_with_{param3}"
    print(result)
    return result

@flow
def my_sequential_flow():
    # Call each task one after the other with their respective parameters
    result_one = task_one("data1")
    result_two = task_two(result_one)  # using the result of the first task as a parameter
    result_three = task_three(result_two)  # using the result of the second task as a parameter

    # You may return or process the final result as needed
    return result_three

# Execute the flow
my_sequential_flow()

In this flow,

task_one

is executed first, and then

task_two

is executed only after

task_one

has completed and returned its result. The same goes for

task_three

, which waits for

task_two

to complete. This ensures that all tasks are executed in sequence and that any dependencies between tasks are respected. This pattern can be extended to as many tasks as you need, each with its own parameters and logic, and Prefect will ensure they run sequentially within the flow.

@Marvin after changing the code within the .py where the flow is defined, what steps do i need to take, to ensure that this change is reflected in an already deployed flow?

To update an already deployed flow after making changes to the code within the

.py

file where the flow is defined, you would typically follow these steps: 1. Make the Changes: Edit your flow's

.py

file and save the changes. 2. Update Deployment: Depending on how you have deployed your Prefect flow, you might need to rebuild any containers or packaging that encapsulates your flow if you're using Docker or server-based deployments. 3. Deployment Manifest: If you are using Prefect 2.0 and have a

deployment.yaml

file, ensure it references the updated flow code. Sometimes, you may need to increment the version if you maintain versioning for your deployments. 4. Apply Deployment: Use Prefect CLI to apply the updated deployment manifest. You would typically run a command like

prefect deployment apply deployment.yaml

. 5. Restart Services (if necessary): Depending on your infrastructure settings, you might need to restart services to pull the new code changes. If you're using Prefect Cloud or Server UI, you can go to the details page for a deployment, then select "Edit" from the commands menu to update the details of your deployment if needed. Any changes in parameters or schedules will be used for all future flow runs based on that deployment. For more details on managing deployments in Prefect 2.0, please refer to the Prefect documentation: - Migration Guide - Deployments - Block Based Remember to carefully test your flow after making changes to ensure that the new logic works as intended and doesn't introduce any regressions. If your flow is critical, you might want to run the updated flow manually or in a testing environment before updating the deployment.

🙅

prefect deployment apply deployment.yaml

you want

prefect deploy path/to/file.py:my_flow

or

my_flow.serve("my-deployment")

Ok. So the same as If deploying a newly created deployment

oh sorry, I actually didn't read the whole question the minimum to just have code changes reflect in your deployment depends on your code storage for example, if you keep code in github and you have a

pull

step on your deployment then you'd just have to push your code, since the worker will pull whatever lives in that repo when its time to run your flow if you have your flow code in some image, you'd have to rebuild the image if you change your entrypoint though, you'd have to update your deployment with one the methods i mentioned before

I see, thanks. At this point in testing the flow itself locally, it is probably to early to deploy

👍