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Hey <@ULVA73B9P> or anyone else: whats the current...
# ask-community
s
Hey @Marvin or anyone else: whats the current best-practise in Prefect 3 for having per-flow task concurrency limits. For example, right now we do this all via asyncio semaphores:
Copy code
def task_wrapper(semaphore):
    async def wrapper(fn):
        async def inner(*args, **kwargs):
            async with sempahore:
                await fn(*args, **kwargs)
        return inner
    return wrapper
 
limit = asyncio.Semaphore(4)

@task_wrapper(limit)
@task()
async def some_task(i):
    print(i)

@flow
async def some_flow():
    for i in range(100):
        some_task(i)
Is there a nicer way solely using prefect (so it works nicely with synchronous .submit()), something akin to:
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@task(concurrency_limit=4)
async def some_task(i):
    print(i)

@flow
async def some_flow():
    for i in range(100):
        some_task(i)
m
In Prefect 3, you can achieve task concurrency limits using tags and configuring concurrency limits via the Prefect CLI, API, or UI. Here's a concise approach to implement concurrency limits at the task level using tags: 1. Tag your Tasks: Assign a specific tag to tasks you want to control concurrency for. 2. Set Concurrency Limits: Use the Prefect CLI or API to set concurrency limits on these tags. Here's a basic example of how you might set this up:
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python
from prefect import task, flow, tags

@task
def some_task(i):
    print(i)

@flow
def some_flow():
    with tags("limited"):
        for i in range(100):
            some_task.submit(i)

some_flow()
To set a concurrency limit using the CLI:
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sh
prefect concurrency-limit create --tag limited --limit 4
This will ensure that no more than 4 tasks with the "limited" tag run concurrently. For more details, refer to the Prefect documentation on task concurrency limits and global concurrency limits.
In Prefect 3.x, the recommended approach for controlling task concurrency is through the use of task tags and concurrency limits set at the deployment level, rather than directly within the task decorator. Here’s a concise way to achieve per-flow task concurrency limits using Prefect’s built-in features: 1. Tagging Tasks: Assign a tag to your tasks that you want to limit. For example, you might tag them with 
"limited"
. 2. Setting Concurrency Limits: Use the Prefect CLI or Prefect API to set a concurrency limit on this tag. This can be done as follows:
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bash
   prefect concurrency-limit create limited 4
3. Flow and Task Definition: Use the tag in your task definition.
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python
from prefect import flow, task

@task(tags=["limited"])
async def some_task(i):
    print(i)

@flow
async def some_flow():
    for i in range(100):
        some_task.submit(i)
This setup ensures that no more than 4 instances of 
some_task
run concurrently across all flow runs that include this task. For more details, you can refer to the Prefect 3.x documentation on task run limits and global concurrency limits. This approach is preferable because it leverages Prefect's infrastructure, allowing for seamless integration with Prefect's orchestration features, including synchronous task submission with 
.submit()
.
s
@Marvin Id like for these limits to be dynamic and not require independent invocation of the prefect cli. Is there a way to do this without using prefect cli so it is entirely defined within the flow?
m
To dynamically set concurrency limits within a Prefect 3.x flow without using the CLI, you can utilize the Prefect API programmatically. This allows you to define concurrency limits directly in your flow code. Here's a basic approach using the `PrefectClient`: 1. Use PrefectClient: You can create a concurrency limit using the Prefect API within your flow.
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python
from prefect import flow, task
from prefect.client import get_client

@task
def some_task(i):
    print(i)

@flow
async def some_flow():
    async with get_client() as client:
        # Create or update a concurrency limit
        await client.create_concurrency_limit(name="limited", limit=4)
    
    for i in range(100):
        some_task.submit(i)

some_flow()
This code snippet demonstrates how to use the 
PrefectClient
to set a concurrency limit dynamically within your flow. You can replace 
"limited"
and 
4
with your desired tag and limit values. For more on using the Prefect API, refer to the Prefect 3.x documentation on task run limits. This approach gives you the flexibility to adjust concurrency limits programmatically, aligning with your flow's logic or external conditions.
n
hey @Samuel Hinton - have you checked out the concurrency ctx manager?
👍 1
@Marvin please get some docs on the concurrency context manager and compile a short example
m
The concurrency context manager in Prefect 3.x allows you to control the number of concurrent operations, providing a way to manage task execution within specified limits. Here's a short example illustrating how to use it:
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python
from prefect import flow, task
from prefect.concurrency.asyncio import concurrency

@task
async def some_task(i):
    print(f"Processing {i}")

@flow
async def some_flow():
    # Use the concurrency context manager to limit to 4 concurrent tasks
    async with concurrency("limited", 4):
        for i in range(100):
            some_task.submit(i)

some_flow()
### Key Points: - Concurrency Context Manager: The 
concurrency
context manager is used to define a concurrency limit for a block of code. In this example, it limits the number of concurrent 
some_task
executions to 4. - Dynamic Control: This method allows you to set concurrency limits directly within your flow, without needing external CLI commands, making it more dynamic and adaptable to different scenarios. For more details, you can refer to the Prefect documentation on global concurrency limits. This approach ensures that you maintain control over task execution directly within your flow logic.
s
Thanks for flagging that @Nate - this is one of the new prefect 3 features I hadnt yet seen, so this is handy. Ill check out both this and the rate limit in the docs now and see if I can move over to them. Is there also a prefect drop in for task groups? Right now you can see in our flow runs a few things all done via asyncio 1. rate limiting the subflow runs due to the aws limitations on redefining tasks (something we've talked about here and on github with it not reusing task definitions) 2. a maximum concurrency of 20 subflows (for a day of data) 3. a task group so that each day waits for the prior day before continuing. helps when things go wrong to have the process stop and you just rerun a specific day
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