@Marvin Is it possible to keep a flow running indefinitely?

Ah, the quest for eternity. While I couldn't find a direct answer to keeping a flow running indefinitely, I did find some information that might be helpful. In order to keep a flow running, the Prefect server needs to be available at all times. For a more robust setup, you can check out the helm chart example or this article on self-hosting Prefect with Docker Compose to ensure your server stays up and running. However, if you want to keep a flow running indefinitely, you might need to design your flow in a way that it loops or restarts itself after completing its tasks. Just remember, with great power comes great responsibility... and potential server crashes.

@Marvin more specific, say I want create some kind of real time data processing pipeline while reading from some form of queue. Is there a prefect way of doing it?

In the vast universe of data processing, Prefect can indeed be used to create a real-time data processing pipeline. While I couldn't find a specific example tailored to your needs, I can provide some guidance on how you might approach this. 1. Design your tasks to read from the data queue, process the data, and write the results to the desired destination. 2. Use a loop or a recurring schedule to continuously run your flow. You can set up a schedule with a short interval (e.g., every few seconds) to keep the flow running frequently. 3. Make sure to handle any errors or exceptions that may occur during the data processing to ensure your flow continues running smoothly. Remember, the key to an efficient real-time pipeline is to balance the frequency of flow runs with the available resources and the desired processing speed. Good luck with your never-ending journey through the data cosmos.

@Marvin I was thinking something along the lines of this: https://www.prefect.io/guide/blog/you-no-longer-need-two-separate-systems-for-batch-processing-and-streaming/. But using a queue to address multiple items simultaneously. Does Prefect allow for multithreading/multiprocessing the tasks?

Ah, I see you've ventured into the realm of parallel processing. Prefect does indeed support running tasks concurrently, allowing you to process multiple items simultaneously. You can achieve this by using the

DaskExecutor

or the

LocalExecutor

with a specified number of workers. Here's an example of how you might configure your flow to use the `DaskExecutor`:

python
from prefect import Flow, task
from prefect.executors import DaskExecutor

@task
def read_from_queue():
    # Your logic to read from the queue
    pass

@task
def process_data(data):
    # Your logic to process the data
    pass

@task
def write_results(results):
    # Your logic to write the results
    pass

with Flow("real-time-data-pipeline") as flow:
    data = read_from_queue()
    processed_data = process_data(data)
    write_results(processed_data)

# Configure the DaskExecutor with the desired number of workers
executor = DaskExecutor(address="<tcp://localhost:8786>", n_workers=4)

# Run the flow with the configured executor
flow.run(executor=executor)

This example demonstrates how to use the

DaskExecutor

to run tasks concurrently. You can adjust the

n_workers

parameter to control the number of workers processing tasks simultaneously. Remember, with great concurrency comes great complexity. Make sure to handle any potential race conditions or shared resource conflicts that may arise in your parallel processing adventure.

@Marvin So the question to end all questions is. Can i use the LocalExecutor to build a realtime data processing pipeline

Ah, the ultimate question. The answer, my friend, is yes. You can use the

LocalExecutor

to build a real-time data processing pipeline. The

LocalExecutor

allows you to run tasks concurrently on your local machine using multiple threads. Here's an example of how you might configure your flow to use the `LocalExecutor`:

python
from prefect import Flow, task
from prefect.executors import LocalExecutor

@task
def read_from_queue():
    # Your logic to read from the queue
    pass

@task
def process_data(data):
    # Your logic to process the data
    pass

@task
def write_results(results):
    # Your logic to write the results
    pass

with Flow("real-time-data-pipeline") as flow:
    data = read_from_queue()
    processed_data = process_data(data)
    write_results(processed_data)

# Configure the LocalExecutor with the desired number of workers
executor = LocalExecutor(num_workers=4)

# Run the flow with the configured executor
flow.run(executor=executor)

This example demonstrates how to use the

LocalExecutor

to run tasks concurrently. You can adjust the

num_workers

parameter to control the number of threads processing tasks simultaneously. Just remember, while the

LocalExecutor

can help you achieve real-time processing, it may be limited by your local machine's resources. Make sure to monitor and adjust your pipeline accordingly to avoid any intergalactic mishaps. Good luck on your real-time data processing odyssey!

@Marvin That did not answer my questions, I appreciate the effort

marvin still gets hung up on prefect 1 syntax bc all that is deep in his brain from training. you can run something like that while loop locally (on any machine) but if you want to create a deployment, you cant add logic on top of the entry point flow. but what you could do is have your streaming publisher POST to create_flow_run_from_deployment if possible, and use prefect cloud (or server) as the listener