@Marvin if I use prefect concurrency with occupy=3 in a task, but i submit a thousand instance of the tasks, what happens?

In Prefect, the

occupy

parameter in the concurrency context manager is used to specify the number of concurrency slots that a task will occupy. If you set

occupy=3

for a task and submit a thousand instances of that task, the concurrency manager will ensure that only a certain number of tasks (determined by the total concurrency limit) are active at any given time. For example, if the total concurrency limit is 10, and each task occupies 3 slots, then only 3 tasks (10/3, rounded down) can be active at the same time. The rest of the tasks will be queued and will start executing as soon as a slot becomes available. Please note that the concurrency limit is created in an inactive state if it does not already exist. Also, the concurrency management in Prefect is more about managing resources rather than speeding up execution. It's used when you have a limited number of resources (like database connections) and you want to ensure that your tasks do not exceed this limit. Here is an example of how to use the concurrency context manager in Prefect:

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

@task
def process_data(x, y):
    with concurrency("database", occupy=3):
        return x + y

@flow
def my_flow():
    for x, y in [(1, 2), (2, 3), (3, 4), (4, 5)]:
        process_data.submit(x, y)

if __name__ == "__main__":
    my_flow()

In this example, the

process_data

task will occupy 3 concurrency slots each time it is run. If the total concurrency limit for "database" is 10, then only 3 instances of

process_data

can run at the same time. For more detailed information, you can refer to the Prefect documentation on global concurrency limits.

@Marvin then how do I set the concurrency limit on a task?

In Prefect, you can manage the concurrency limit via the Prefect UI. When creating a concurrency limit, you can specify the parameters. However, the concurrency limit is not set directly on a task but rather on a resource that the task uses. For example, if you have a task that interacts with a database and you want to limit the number of concurrent database connections, you would set a concurrency limit on the "database" resource, not the task itself. Then, in your task, you would use the

with concurrency("database", occupy=3):

context manager to specify that the task occupies 3 slots of the "database" resource. Here's an example:

from prefect import task
from prefect.concurrency.sync import concurrency

@task
def my_task():
    with concurrency("database", occupy=3):
        # Task code here

In this example,

my_task

will occupy 3 slots of the "database" resource each time it is run. The total number of concurrent "database" slots is managed via the Prefect UI. For more detailed information, you can refer to the Prefect documentation on global concurrency limits.

@Marvin I don't see where I can add global concurrency in the PRefect UI

I apologize for the confusion. It appears there might be a misunderstanding. Currently, global concurrency limits in Prefect are not set via the Prefect UI. They are managed programmatically within your Prefect code using the

concurrency

context manager as I explained earlier. Here's an example again:

from prefect import task
from prefect.concurrency.sync import concurrency

@task
def my_task():
    with concurrency("database", occupy=3):
        # Task code here

In this example,

my_task

will occupy 3 slots of the "database" resource each time it is run. The total number of concurrent "database" slots is determined by the number of tasks that are currently running and using the "database" resource. I apologize for any confusion caused by my previous messages. If you have any more questions about Prefect or concurrency limits, feel free to ask!

@Marvin but then how can I set the number of database slots?

I apologize for the confusion earlier. You can set concurrency limits in Prefect using either the Prefect CLI or the Prefect API with the PrefectClient. Here's how you can do it: Using Prefect CLI: You can create, list, and remove concurrency limits by using Prefect CLI concurrency-limit commands. Here's an example of how to set a concurrency limit of 10 on the 'database' tag:

bash
$ prefect concurrency-limit create database 10

To delete the concurrency limit on the 'database' tag:

bash
$ prefect concurrency-limit delete database

To view details about the concurrency limit on the 'database' tag:

bash
$ prefect concurrency-limit inspect database

Using Prefect Python client: To update your tag concurrency limits programmatically, use PrefectClient.orchestration.create_concurrency_limit. Here's an example:

from prefect import Client

client = Client()
client.orchestration.create_concurrency_limit(tag="database", limit=10)

In this example,

create_concurrency_limit

takes two arguments:

tag

specifies the task tag on which you're setting a limit, and

limit

specifies the maximum number of concurrent task runs for that tag. For more detailed information, you can refer to the Prefect documentation on concurrency limits.

@Marvin ok. What if I wanted to use a threadpoolexecutor to launch parallel threads but control it within the flow? How would i do that with a task submit?

In Prefect, you can use a

ThreadPoolExecutor

to launch parallel threads within a flow. This can be done using the

concurrent.futures

module in Python. Here's an example of how you might do this:

from concurrent.futures import ThreadPoolExecutor
from prefect import task, Flow

@task
def my_task(x):
    # Task code here
    return x * 2

with Flow("My Flow") as flow:
    with ThreadPoolExecutor(max_workers=5) as executor:
        futures = [executor.submit(my_task.run, i) for i in range(10)]
        results = [future.result() for future in futures]

In this example,

my_task

is submitted to the

ThreadPoolExecutor

10 times. The

max_workers

parameter of the

ThreadPoolExecutor

controls the maximum number of threads that can be active at the same time. The

executor.submit

method is used to schedule the task to be run and returns a Future object. You can then use the

future.result()

method to get the result of the task once it is done. Please note that this is a basic example and does not include error handling or task dependencies. You would need to add these in a real-world scenario. Also, keep in mind that using a

ThreadPoolExecutor

in this way bypasses some of Prefect's features. For example, Prefect won't be able to track the state of tasks submitted to the executor, and retries and timeouts won't work as expected. If you need these features, you might want to consider using Prefect's built-in parallel execution features, such as DaskExecutor. For more detailed information, you can refer to the Prefect documentation on Executors and the Python documentation on `concurrent.futures`.