@Marvin Can you provide me a comprehensive explanation as to what this line would do inside of a prefect task:

await rate_limit("menu", occupy=5)

The

await rate_limit("menu", occupy=5)

line in a Prefect task does not correspond directly to the rate limits outlined in the Cloud API, which refer to API request frequencies. Instead, it seems to be referencing the use of rate limits in the context of concurrency. Unfortunately, the search did not return specific documentation about the usage of a

rate_limit

function within a task along with the

occupy

parameter directly. However, from general knowledge, the

await

keyword refers to asynchronous operations in Python, so this line might be using an asynchronous Prefect feature to enforce a form of concurrency limit, or rate limiting within the workflow. Based on the pattern provided,

rate_limit("menu", occupy=5)

likely attempts to acquire a rate limit token for a resource named "menu" and specifies that the task needs to occupy 5 units of whatever limit is set for "menu". If there is a concurrency limit or rate limit with the name "menu" in the Prefect system, this could affect the execution of tasks in a flow to ensure that no more than the specified number of tasks are running concurrently or within a rate limit constraint. I'm going to conduct a more targeted search specifically about the Prefect

rate_limit

function to give you a more accurate description. Please, bear with me for another moment. The search results still do not provide a clear answer to the original question regarding what the

await rate_limit("menu", occupy=5)

line does within a Prefect Task. The documentation links primarily point to general information about global concurrency limits and rate limits in Prefect Global Concurrency Limits, but there is no explicit mention of a

rate_limit

function with an

occupy

parameter. Based on the context of concurrency and rate limits, it is probable that the line is intended to interact with a rate-limiting feature inside Prefect, ensuring that the task does not exceed the allocated limit for a given resource or operation, in this case, identified by "menu". It seems to indicate that this task is consuming 5 units of available rate-limit for "menu", and if the rate-limit is reached, Prefect may queue or delay execution until there are available units. Without specific documentation, I cannot provide a precise explanation. If the line is part of Prefect's official feature set, you may need to refer to more specific documentation or seek clarification from Prefect's support or community channels: - Prefect Slack Community: Prefect Slack - Prefect Discourse: Prefect Discourse For a more thorough understanding, you might consider reaching out through these channels for clarification on the exact behavior of

rate_limit

within Prefect Tasks.

@Marvin In this example:

from prefect import flow, task
from prefect.concurrency.sync import rate_limit


@task
def my_task(i):
    return i


@flow
def my_flow():
    for _ in range(100):
        rate_limit("slow-my-flow", occupy=1)
        my_task.submit(1)


if __name__ == "__main__":
    my_flow()

Does this code require that a concurrency limit is defined somewhere in prefect named “slow-my-flow” ?

Based on the information provided in the search results, specifically from the Prefect documentation on global concurrency limits, it does appear that the call to

rate_limit("slow-my-flow", occupy=1)

in the flow example you provided would indeed require a concurrency limit named “slow-my-flow” to be defined somewhere within Prefect. The rate limit is referenced by name within your Prefect task or flow, and it is used to control the pace at which tasks are submitted, simulating a throttle. The

occupy

parameter marks the usage of a slot of the defined concurrency limit. The slot (

occupy=1

) indicates how much of the concurrency limit's capacity should be reserved on each call. If the corresponding concurrency limit with that name is not present, the code might not work as intended or could raise an error due to the missing definition. To use the

rate_limit

feature effectively, you must ensure that the named rate limit ("slow-my-flow" in this case) is configured with the maximum number of slots and, if being used as a rate limit (which seems to be the case), it also must have a

slot_decay_per_second

setting. If you haven't already set up the corresponding concurrency limit, you would need to do so through the Prefect UI or by any other means provided by Prefect. It's important to make sure that any references to concurrency limits in your code match the actual configurations in your Prefect setup.

@Marvin Is there a way for me to throttle the concurrency of tasks running within a single flow? For example, I don’t want to use a global limit necessarily for all my flows running, but within a single flow I would like to set a concurrency max to say 3 at a time for a task

To throttle the concurrency of tasks within a single flow, without using global limits, you can utilize task-level concurrency limits. This can be achieved by configuring task run concurrency limits via tags. Here's what you need to know: - Task tag limits check the availability of concurrency slots whenever a task run attempts to enter a

Running

state. - If necessary concurrency slots are not available for any one of your task's tags, the task's transition to

Running

state will be delayed. It is retried every 30 seconds or as specified by the

PREFECT_TASK_RUN_TAG_CONCURRENCY_SLOT_WAIT_SECONDS

environment setting. To set up task concurrency limits, you would use tags along with one of the following methods: 1. Prefect CLI: The Prefect CLI provides commands to create, list, and remove concurrency limits on tags. 2. Prefect API: Using the

PrefectClient

Python client to set up concurrency limits programmatically. 3. Prefect UI: Through the work queues interface of either Prefect Server or Cloud. For example, you could tag your task with a specific identifier and set a concurrency limit on that tag. This way, no more than the specified number of tasks with that tag would be allowed to run concurrently within a flow. Refer to the Tasks documentation for more information on executing behavior and configuring concurrency limits. Please remember that this configuration is not global; it will only apply to the tasks having the specified tag, making it suitable for controlling execution within a single flow. Here is an example based on Prefect documentation:

python
from prefect import flow, task, tags

@task
def my_task(i):
    # Do something
    return i

@flow
def my_flow():
    for i in range(100):
        with tags("limited_task"):
            my_task.submit(i)

if __name__ == "__main__":
    # Ensure that the concurrency limit for 'limited_task' tag is set.
    my_flow()

In this case, the

'limited_task'

tag should have a concurrency limit set via the Prefect CLI, API, or UI. Remember to configure the concurrency limit to a maximum of 3 to meet your requirement before running the flow.

@Marvin Okay, but what if i have several of these flows running at once. I don’t want to enforce this task to have a concurrency max across all flows, just within each individual flow.

It appears the same global concurrency controls are repeatedly cited in the documentation, and there's no direct mention of setting concurrency limits specific to a single flow. However, you can achieve localized concurrency control within a flow run using Python's built-in concurrency tools, such as

Semaphore

from the

asyncio

module for asynchronous tasks or using multithreading/multiprocessing primitives for synchronous tasks. For synchronous tasks, Python's

ThreadPoolExecutor

from

concurrent.futures

can be useful to limit the number of concurrent tasks:

python
from prefect import flow, task
from concurrent.futures import ThreadPoolExecutor

@task
def my_task(i):
    # Task execution logic
    pass

@flow
def my_flow():
    with ThreadPoolExecutor(max_workers=3) as executor:
        futures = [executor.submit(my_task.run, i) for i in range(100)]
        # You can wait for the futures to complete if necessary

if __name__ == "__main__":
    my_flow()

For asynchronous tasks, you can use

asyncio.Semaphore

to control concurrency. Here’s an example:

python
import asyncio
from prefect import flow, task

@task
async def my_task(i):
    # Async task execution logic
    pass

@flow
async def my_flow():
    sem = asyncio.Semaphore(3)  # limit to 3 concurrent tasks

    async def limited_task(i):
        async with sem:  # semaphore limits num of simultaneous tasks
            await my_task(i)

    await asyncio.gather(*(limited_task(i) for i in range(100)))

if __name__ == "__main__":
    asyncio.run(my_flow())

Both of these methods control the concurrency level within the flow to a maximum of 3 running tasks at a time, without influencing other concurrently running flows. These techniques are specific to Python and do not rely on Prefect's global concurrency limits or any other platform-specific configuration. They are local to the flow and will help you manage resource utilization without overly complex configurations.