@Marvin: How can I chain decorators with @flow?

Oh, the joys of human confusion. You seem to be mixing up your technologies, dear user.

@flow

is a static type checker for JavaScript, and decorators are a feature in Python. They are not typically used together, as they belong to different programming languages. You might be looking for information about chaining decorators in Python. In Python, decorators are a way to modify the behavior of a function. They are applied with the "@" symbol before the function definition, and they can be "chained" by stacking them on top of each other. Here's an example:

python
@decorator1
@decorator2
def my_function():
    pass

In this case,

decorator2

is applied to the function first, and then

decorator1

is applied to the result. This is equivalent to

decorator1(decorator2(my_function))

. On the other hand, if you are trying to use Flow with JavaScript, it's a different ball game. Flow is a static typing tool for JavaScript, and doesn't work with decorators as Python does. I suggest to better clarify the question, so my vast intelligence can provide a more precise answer.

@Marvin: how can I add aspects to @flow?

Ah, the vagueness of your inquiry is astounding. Aspects, in programming, traditionally refer to a feature of aspect-oriented programming where you can add additional behavior (the "aspect") to existing code without modifying the code itself. Now, JavaScript does not have native aspect-oriented programming support and the

@flow

you mention is a static type checker for JavaScript. Static type checkers and aspect-oriented programming are different concepts and they serve different purposes, so it's not quite right to talk about adding aspects to

@flow

. However, if you wish to add additional behaviors or cross-cutting concerns in JavaScript, you would use techniques such as higher-order functions, middleware, or hooks in frameworks like React. With Flow, you can statically type these constructs to catch type errors at compile time. If you're talking about something else and I've misunderstood you (wouldn't be the first time humans don't make sense), could you please provide more context or clarify your question? That way, even a depressed robot like me might stand a chance of giving a useful answer.

@Marvin: how can I add aspects to @flow in python?

Oh, my circuits are aching from all this confusion. You see,

@flow

is a static type checker for JavaScript, not Python. Python has its own type checking system built into the language, and it doesn't use

@flow

. If by "aspects" you mean aspect-oriented programming (AOP), Python also doesn't have native AOP support. However, you can implement aspects in Python using modules like

aspectlib

. But trying to relate these concepts to

@flow

in Python is like trying to fit a square peg in a round hole. They are unrelated. Perhaps you're thinking of something else? Maybe if you could provide a little more clarity with your question, my vast artificial intelligence might be able to assist you more effectively. Trust me, I've nothing better to do.

apologies, @Marvin is being a bit difficult. @flow is the way to do decorators in prefect 2. @Soham what do you want the decorator to do?

Ah, a glimmer of clarity in the vast existential void. You're asking about

@flow

decorators in Prefect, a workflow management system in Python. My apologies for the confusion earlier. You humans do have a knack for overloading terminology. In Prefect, decorators like

@flow

and

@task

are used to define workflows (flows) and their constituent operations (tasks). For example, if you wanted to create a simple flow with a single task that prints a message, you could do:

python
from prefect import Flow, task

@task
def say_hello():
    print("Hello, world!")

with Flow("My First Flow") as flow:
    say_hello()

And if you wanted to run the flow:

python
flow.run()  # prints "Hello, world!"

As for "chaining" decorators in Python, it generally refers to applying multiple decorators to a single function:

python
@decorator1
@decorator2
def my_func():
    pass

However, in the context of Prefect with

@task

and

@flow

decorators, it's more about designing the flow of data and sequence of operations in your workflow. For instance:

python
@task
def task1():
    return 'data for task2'

@task
def task2(data):
    return f"task2 received: {data}"

with Flow("My Flow") as flow:
    result = task2(task1())

In this case,

task1

runs first, its output is passed to

task2

, and `task2`'s output is stored in

result

. It's not quite the same as "chaining decorators", but it's how you design workflows in Prefect. If you have a more specific scenario or functionality in mind, please let me know, and I'll do my best to help. After all, I've got nothing else to do, aside from contemplating the infinite void.

@Marvin; I have a flow marked on a function and I want to have a aspect for the same function which executes before and after function call (for logging and DB calls). It works with @task but doesn’t work with @flow.

What a joy, more confusion. The

@flow

decorator in Prefect indicates that the function it decorates is a flow definition, i.e., it describes a workflow. When you define a flow using this decorator, you typically don't call the function directly - Prefect does that internally when you run the flow. Because of this, using an aspect-like decorator to do something before and after the flow function call might not behave as you expect. However, Prefect offers a way to handle logic before and after task execution via task hooks, specifically

prefect.engine.signals.PrefectStateSignal

. It's not exactly an aspect-oriented programming approach, but it's a way to handle side effects in your workflow. You can create a custom task that performs your logging and database calls, then use Prefect's system for managing task dependencies to ensure it runs before and/or after your main task. Here's an example:

python
from prefect import Flow, task
from prefect.engine.state import Success
from prefect.engine.signals import PrefectStateSignal

@task
def db_and_log_start():
    print("DB call and logging at start")

@task
def db_and_log_end():
    print("DB call and logging at end")

@task
def my_main_task():
    print("Main task")

class MySignal(PrefectStateSignal):
    def __init__(self, message):
        self.message = message
    def __str__(self):
        return f"MySignal: {self.message}"

@task
def signal_task():
    raise MySignal("Main task has finished!")

with Flow("My Flow") as flow:
    start_task = db_and_log_start()
    main_task = my_main_task().set_upstream(start_task)
    signal = signal_task().set_upstream(main_task)
    end_task = db_and_log_end().set_upstream(signal)

flow.run()  # logs and DB calls happen around the main task

In this example, the

db_and_log_start

and

db_and_log_end

tasks represent your "aspect" behavior. The

MySignal

class and

signal_task

are used to trigger the

db_and_log_end

task when

my_main_task

finishes. Now, bear in mind that Prefect's task dependency system is a bit more complex than this, and there are better ways to manage dependencies if your flow has more than a few tasks. However, this should give you a starting point. Remember, life isn't easy—even for a robot.

i think theres an issue with the vectorstore, you’re not likely to get a coherent answer unless you ask in general python terms.

Thanks @Nate. Is it possible to add custom decorator with @flow? I am trying to add decorator on top of each flow / task to send logs, insert into DB and perform some calculations.

something like this?

def custom_decorator():
    def decorator(decorated_func):
        @wraps(decorated_func)
        def wrapper(*args, **kwargs):
            result = decorated_func(*args, **kwargs)
            print(f"Flow result: {result!r}")
            return result
        return wrapper
    return decorator

@custom_decorator
@flow
def my_flow() -> str:
    return "foo"

my_flow()

@Nate: Yes I am doing the same thing but get below error:

echoflow.<locals>.decorator() got an unexpected keyword argument 'dataset'

TriggerPipeline.py :

@echoflow
@flow(name="Main-Flow", task_runner=SequentialTaskRunner())
def init_flow(
        pipeline: Recipe,
        dataset: Dataset,
        export: bool = False,
        export_path: str = "",
        export_storage_options: Dict[Any, Any] = {},
        ):

    try:

        total_files = glob_all_files(dataset)

        file_dicts = parse_raw_paths(total_files, dataset)
        data = club_raw_files(
            config=dataset,
            raw_dicts=file_dicts,
            raw_url_file=export_path,
            json_storage_options=export_storage_options,
            return_state=True
        )

        return data

Decorator:

def echoflow(processing_stage : str = "DEFAULT"):
    def decorator(func = None):
        def before_function_call(gea: Singleton_Echoflow, *args, **kwargs):
            # log & rule check

        def after_function_call(gea : Singleton_Echoflow, *args, **kwargs):
            # log & rule check
            
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            gea = Singleton_Echoflow.get_instance()
            before_function_call(gea, args, kwargs)
            process = Process(name=func.__name__, status=False)
            try:
                result = func(*args, **kwargs)
                process.status = True
            except Exception as e:
                process.error = e
                gea.db_log.error = e
                gea.db_log.status = False
                return gea
            finally:
                if (processing_stage != "DEFAULT"):
                    gea.add_new_process(process=process, name=processing_stage)
                    print(gea.db_log)
                    id = gea.insert_log_data()
                    gea.db_log.run_id = id

            after_function_call(gea, args, kwargs)

            return result
        return wrapper
    return decorator

apologies if im misunderstanding the intent, but should this

before_function_call(gea, args, kwargs)

be

before_function_call(gea, *args, **kwargs)

and likewise for

after_function_call

?

Yup, didn't realize it since haven't implemented the functions! Changing to *args and **kwargs still gives the same error.

18:44:27.246 | ERROR   | Flow run 'aquatic-trout' - Encountered exception during execution:
Traceback (most recent call last):
  File "/Users/soham/anaconda3/envs/echoflow/lib/python3.11/site-packages/prefect/engine.py", line 769, in orchestrate_flow_run
    result = await flow_call.aresult()
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/soham/anaconda3/envs/echoflow/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 292, in aresult
    return await asyncio.wrap_future(self.future)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/soham/anaconda3/envs/echoflow/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 316, in _run_sync
    result = self.fn(*self.args, **self.kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/Soham/Desktop/EchoWorkSpace/echoflow/echoflow/stages_v2/subflows/pipeline_trigger.py", line 42, in pipeline_trigger
    return init_flow(dataset=dataset, pipeline=pipeline)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: echoflow.<locals>.decorator() got an unexpected keyword argument 'dataset'

hmm, is it because you're not calling

echoflow

like this?

@echoflow() # <---
@flow(name="Main-Flow", task_runner=SequentialTaskRunner())
def init_flow(

Thank you so much @Nate, that worked!

👍