@Marvin How would I rate limit an asyncio.gather execution, where the function being sent to asyncio.gather is a flow?

Rate limiting in an asynchronous context, where you are using

asyncio.gather

to execute multiple concurrent tasks, can be done with the help of

asyncio.Semaphore

. This Python construct can be used to limit the number of concurrent executions by managing an internal counter which is decremented when a task starts and incremented when the task ends, blocking the execution of new tasks if the counter is zero. Here's a step-by-step approach to rate limiting an

asyncio.gather

execution where the tasks are Prefect flows: ### Step 1: Import Necessary Libraries You need to import

asyncio

for the async functionality and

Semaphore

for rate limiting.

python
import asyncio
from prefect import flow

### Step 2: Define a Semaphore You use

asyncio.Semaphore

to limit the concurrency. You need to decide the maximum number of concurrent tasks allowed.

python
# Set a maximum number of concurrent tasks
semaphore = asyncio.Semaphore(10)

### Step 3: Wrap the Flow Call You'll have to create a wrapper around your flow call that acquires and releases the semaphore.

python
@flow
async def my_flow():
    # your flow implementation
    pass

async def run_with_semaphore(semaphore, flow_func, *args, **kwargs):
    async with semaphore:
        # Assuming your flow is an async function
        await flow_func(*args, **kwargs)

### Step 4: Collect Tasks and Rate Limit You gather the coroutine objects using

asyncio.gather

while ensuring they all wait for the semaphore.

python
async def main():
    # Collect coroutines
    tasks = [run_with_semaphore(semaphore, my_flow, arg1, arg2) for arg1, arg2 in your_list_of_arguments]

    # Run the tasks with rate limiting
    await asyncio.gather(*tasks)

asyncio.run(main())

In actual use, you'll have to replace

my_flow

with your actual flow function, and

arg1

,

arg2

with the arguments you're intending to pass into your flow. This setup ensures that no more than 10 flows will run concurrently. The tasks will wait for the semaphore to be available before starting, effectively rate-limiting their execution. Keep in mind that while semaphore is a good primitive for rate limiting within a single process, dealing with distributed or multi-process setups might require more sophisticated strategies such as distributed lock managers or message queues with worker pattern implementations.

@Marvin Any way of mocking result_storage and result_serializer in the decorator when writing unit tests? STORAGE and SERIALIZER are constants @flow( task_runner=ConcurrentTaskRunner(), flow_run_name=generate_uvofacquire_subflows_run_name, persist_result=True, result_storage=STORAGE, result_serializer=SERIALIZER, ) async def acquire_nfl_files_and_upload_to_stage( ) -> Dict[str, Any]:

When it comes to unit testing and especially when you need to mock certain components, such as

result_storage

and

result_serializer

that are used in the

@flow

decorator, you can use Python's

unittest.mock

library to override them. ### Step 1: Import the Mocking Library

python
from unittest.mock import patch, MagicMock

### Step 2: Define Your Mock Objects Create mock versions of

STORAGE

and

SERIALIZER

which will be used in the test context.

python
mock_storage = MagicMock()
mock_serializer = MagicMock()

### Step 3: Apply the Patches Use the

patch

decorator or context manager to replace the constants

STORAGE

and

SERIALIZER

with your mocks when the unit test runs.

python
@patch('path.to.your.module.STORAGE', new=mock_storage)
@patch('path.to.your.module.SERIALIZER', new=mock_serializer)
def test_acquire_nfl_files_and_upload_to_stage():
    # Your testing logic here
    # Call the flow function and check results

The

'path.to.your.module'

part should be the import path to the module where

STORAGE

and

SERIALIZER

constants are defined. ### Step 4: Testing Your Flow Inside the test, you can now call the

acquire_nfl_files_and_upload_to_stage()

function as if it was using the actual storage and serializer, but behind the scenes, they are mocked. This is how it looks as a whole:

python
from unittest.mock import patch, MagicMock
import pytest

# Define mock objects
mock_storage = MagicMock()
mock_serializer = MagicMock()

# Use patch to replace STORAGE and SERIALIZER
@patch('path.to.your.module.STORAGE', new=mock_storage)
@patch('path.to.your.module.SERIALIZER', new=mock_serializer)
@pytest.mark.asyncio
async def test_acquire_nfl_files_and_upload_to_stage():
    # Call the function
    result = await acquire_nfl_files_and_upload_to_stage()
    
    # Assertions and validations
    assert result == expected_result
    # You can also assert that your mocks have been called
    mock_storage.some_method.assert_called()
    mock_serializer.some_method.assert_called()

When you decorate your test function with

@patch()

, it will replace

STORAGE

and

SERIALIZER

with mocks for the duration of the test. The actual storage and serialization won't be performed, allowing you to focus solely on the logic of the flow without external dependencies or side effects. This is a typical approach for isolating unit tests in Python, though the exact details might vary depending on the broader context of your application and the frameworks you're using. It's also important to ensure the mocks simulate the expected behavior closely enough to provide valid tests. Remember, sometimes paranoia pays off, mostly in testing.