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<@ULVA73B9P> Is it possible to trigger deployed fl...
# ask-marvin
s
@Marvin Is it possible to trigger deployed flow from other flow? Is it possible to do it with python?
m
Yes, it is possible to trigger a deployed flow from another flow in Prefect, and you can do it using Python. Here's a concise explanation: 1. Using Deployment Triggers: You can use 
DeploymentTrigger
to set up conditions under which a flow will trigger another flow. This involves specifying the expected state of a related flow or resource. For example, you can configure a downstream flow to trigger when an upstream flow completes successfully. 2. Python Implementation: In Python, you can define flows and use Prefect's deployment and triggering mechanisms. You can create a deployment using the 
flow.deploy
method and set up triggers using Prefect's event schemas. Here's a snippet from the Prefect documentation for reference:
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python
from prefect import flow, tags
from prefect.events.schemas import DeploymentTrigger

TAG_NAME = "foobar"

@flow
def upstream():
    pass

@flow
def downstream():
    pass

if __name__ == "__main__":
    with tags(TAG_NAME):
        upstream()
        upstream()
        upstream()
    
    downstream.serve(
        name="downstream-of-tagged-upstream",
        triggers=[
            DeploymentTrigger(
                expect={"prefect.flow-run.Completed"},
                match_related=[
                    {"prefect.resource.role": "tag", "prefect.resource.id": f"prefect.tag.{TAG_NAME}"},
                    {"prefect.resource.role": "flow", "prefect.resource.name": "upstream"},
                ],
                threshold=3,
            )
        ]
    )
For more details, you can explore the Prefect documentation and related resources: - Prefect Deployment Example - Tagged Upstream Flow Example If you're using Prefect 3.x, remember that the API may differ slightly, so always check the latest documentation for your version.
s
@Marvin Is it possible to trigger downstream flow from upstream flow (not the other way around, as in examples)?
m
Yes, it is possible to trigger a downstream flow from an upstream flow in Prefect using Python. This can be achieved by utilizing the 
run_deployment
function in a callback that is executed upon the completion of the upstream flow. Here's an example of how you can set this up:
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python
from prefect import flow
from prefect.deployments import run_deployment

def trigger_downstream(flow, flow_run, state):
    run_deployment("downstream/deployment-b", parameters={"foo": "bar"}, timeout=0)

@flow(log_prints=True, on_completion=[trigger_downstream])
def upstream():
    print("Doing some work")

@flow(log_prints=True)
def downstream(foo: str):
    assert foo == "bar"
In this example: - The 
upstream
flow is defined with an 
on_completion
callback that calls 
trigger_downstream
. - The 
trigger_downstream
function uses 
run_deployment
to trigger the downstream flow named 
"downstream/deployment-b"
with specific parameters. For more detailed information, you can refer to the example in the Prefect documentation: Chain Example. This setup allows the upstream flow to trigger the downstream flow directly upon its completion, making it a straightforward way to manage dependent flows.
s
@Marvin is it possible to wait till the triggered downstream tasks are finished before marking upstream task as completed? I don't want other runs of upstream task to happen before all downstream tasks are done
m
To ensure that the upstream flow waits for the downstream flow to complete before marking itself as completed, you can use Prefect's 
run_deployment
function with a synchronous wait. This approach allows the upstream flow to block until the downstream flow finishes. Here's a conceptual approach to achieve this: 1. Trigger Downstream Flow: Use 
run_deployment
to start the downstream flow. 2. Wait for Completion: You can wait for the downstream flow to complete by checking its state in a loop or using Prefect's state management tools. Below is a simplified example:
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python
from prefect import flow
from prefect.deployments import run_deployment
from prefect.states import wait_for_flow_run

@flow
def upstream():
    # Trigger the downstream flow
    downstream_flow_run = run_deployment("downstream/deployment-b", parameters={"foo": "bar"}, timeout=0)
    
    # Wait for the downstream flow to complete
    wait_for_flow_run(downstream_flow_run)

    print("Downstream flow completed, proceeding with upstream tasks.")

@flow
def downstream(foo: str):
    print("Running downstream flow with parameter:", foo)
In this example: - 
run_deployment
starts the downstream flow. - 
wait_for_flow_run
is used to block the upstream flow until the downstream flow completes. This ensures that no other runs of the upstream flow occur until the downstream tasks are finished. This approach effectively synchronizes the upstream and downstream flows, preventing any premature completion of the upstream tasks. Always ensure that you handle potential exceptions and state transitions to avoid deadlocks or unexpected behavior.
s
@Marvin will the waiting upstream task occupy the worker slot while it waits? Is it possible to run other flows on that worker in the meantime?
m
When an upstream flow is waiting for a downstream flow to complete, it does occupy a worker slot. This means that the worker is effectively tied up with the waiting task and is not available to run other tasks or flows in the meantime. To allow other flows to run on the same worker while the upstream flow is waiting, you might consider the following strategies: 1. Concurrency Management: Use Prefect's work queue and concurrency settings to manage the execution of flows. You can set up multiple workers or adjust concurrency limits to ensure that other flows can still be executed while some flows are in a waiting state. 2. Separate Workers: Assign different workers for different flows or tasks. This way, even if one worker is occupied, others can continue processing available tasks. 3. Async/Await Patterns: Use asynchronous programming patterns to handle waiting states more efficiently, though this may require more advanced configuration and handling. For more details on managing concurrency and worker slots, you can refer to the Prefect documentation on infrastructure concepts and work pools. Unfortunately, Prefect doesn't inherently provide a straightforward way to release the worker slot while waiting, so these strategies can help mitigate the impact on your system's throughput.
s
@Marvin "adjust concurrency limits to ensure that other flows can still be executed while some flows are in a waiting state" . Can you give me an example?
m
To adjust concurrency limits in Prefect and ensure that other flows can still execute while some are in a waiting state, you can manage concurrency in work pools. Here's how you can do it: 1. Define Concurrency Limits: Set a concurrency limit for your work pool. This limit determines how many flow runs can be executed simultaneously. 2. Manage Slot Decay: Use slot decay to control how quickly slots become available again. This helps in managing the frequency of task execution. Here's an example of how you might configure these settings: ### Setting Concurrency Limits You can set concurrency limits using the Prefect CLI or directly through the Prefect API. For example, using the CLI:
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bash
prefect work-pool create my-work-pool --concurrency-limit 5
This command creates a work pool named 
my-work-pool
with a concurrency limit of 5, meaning only 5 flow runs can be active at the same time. ### Slot Decay Slot decay allows you to control how quickly occupied slots become available again. This can be set by specifying the 
slot_decay_per_second
parameter:
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python
from prefect import concurrency

# Set a global concurrency limit with slot decay
concurrency.set_global_concurrency_limit(
    "my-concurrency-limit",
    concurrency_limit=5,
    slot_decay_per_second=0.5
)
- 
concurrency_limit
: The maximum number of concurrent runs. - 
slot_decay_per_second
: Controls how quickly slots refresh. A higher value allows slots to refresh faster, enabling more frequent task executions. ### Example Usage By configuring both the concurrency limit and slot decay, you can ensure that your system efficiently manages the execution of flows, allowing other tasks to proceed even if some are waiting: - If an upstream flow is waiting, it occupies a slot. - Other flows can still run if there are available slots, thanks to the concurrency limit. - The slot decay ensures that slots are freed up over time, allowing new tasks to start. For more information, you can refer to Prefect's documentation on global concurrency limits and work pool management.
s
@Marvin Do concurrency limit refer to the number of tasks that are run on a single worker simultaneously?
m
No, concurrency limits in Prefect generally refer to the number of flow runs that can be executed simultaneously across all workers within a specific work pool, rather than the number of tasks on a single worker. Here's a breakdown: - Flow Runs: Concurrency limits are typically set at the level of flow runs. This means you can control how many instances of a flow can run at the same time across your infrastructure. - Work Pools: These limits are applied to work pools, which manage the distribution of flow runs to available workers. A work pool with a concurrency limit of 5, for instance, will only allow 5 flow runs to be active (running or pending) at any given time, regardless of the number of workers. - Tasks Within Flows: The execution of individual tasks within a flow is managed by the Prefect orchestrator and does not directly relate to the concurrency limit set on flow runs. Tasks within a single flow run will utilize the resources of the assigned worker. For more detailed information, you can refer to Prefect's documentation on work pools and concurrency. This documentation will give you a comprehensive understanding of how to manage concurrency across your Prefect deployments.
s
@Marvin "*Async/Await Patterns*: Use asynchronous programming patterns to handle waiting states more efficiently, though this may require more advanced configuration and handling." Can async allow me to run several flows on one worker simultaneously? If yes, can you give me an example?
m
Yes, using asynchronous programming patterns in Prefect allows you to run multiple flows concurrently on a single worker. Prefect supports asynchronous execution, enabling flows to be defined as coroutines that can be awaited or run concurrently. Here's a basic example to illustrate how you might set up asynchronous flows:
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python
from prefect import flow, task
import asyncio

@task
async def async_task(name):
    await asyncio.sleep(1)
    print(f"Task {name} completed")

@flow
async def async_flow():
    await asyncio.gather(
        async_task("A"),
        async_task("B"),
        async_task("C")
    )

@flow
def main_flow():
    asyncio.run(async_flow())

if __name__ == "__main__":
    main_flow()
### Explanation - Asynchronous Tasks: The 
async_task
function is defined as an asynchronous task using the 
async def
keyword. It simulates an asynchronous operation with 
await asyncio.sleep(1)
. - Asynchronous Flow: The 
async_flow
function is an asynchronous flow that uses 
asyncio.gather
to run multiple tasks concurrently. This allows tasks "A", "B", and "C" to execute simultaneously. - Main Flow: The 
main_flow
function runs the 
async_flow
using 
asyncio.run
, which is a standard way to execute asynchronous code in Python. ### Considerations - Concurrency Management: While asynchronous execution allows multiple tasks to run concurrently, the actual concurrency is subject to Python's Global Interpreter Lock (GIL) and the capabilities of the underlying infrastructure. - Resource Utilization: Running multiple flows or tasks concurrently on a single worker can lead to resource contention, so it's important to monitor and manage your system's resources appropriately. This approach can be particularly useful for I/O-bound operations where tasks can yield control while waiting for external resources, thereby improving the overall throughput of your workflows.
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