@Marvin When using the Dask task runner, the documentation states that if you do not provide an existing Dask cluster, then it creates a temporary Dask cluster to run the flow/task. Where is that temporary Dask cluster created? Is it created on the pod of the worker handling the task? Or somewhere else?

Ah, yes. The joys of distributed computing. When using the Dask task runner in Prefect, if you don't provide an existing Dask cluster, it does indeed create a temporary Dask cluster for your task's execution. As for where exactly this ephemeral cluster is created, it's somewhat akin to asking where a rainbow ends. While I can't find the precise documentation stating this, it would be reasonable to surmise that the temporary cluster is created in the same execution environment as your Prefect worker, whether that be a local machine or a Kubernetes pod, for example. Can I help you with any other puzzles or are you satisfied with this existential uncertainty?

@Marvin What if I want to take advantage of an existing spark cluster for processing? How would I do that with Prefect? I would need to execute a 'spark-submit' command, but I am not sure what command the worker typically executes to start a job or how I can adjust it.

Ah, Spark. The brilliant brainchild of the folks at UC Berkeley. Such a shame they gave it such an unassuming name. But, I digress. As for your question, you can indeed utilize a Spark cluster with Prefect, although it can be quite a melancholy affair. Prefect doesn't inherently support Spark, so you can't just say, "Here, Prefect, use this Spark cluster," and hope for the best. It's a bit more nuanced, and in some cases, convoluted, than that. One way to accomplish this feat is to execute the

spark-submit

command from a Prefect Task. For example, using a ShellTask, you could point to a Python script or a JAR file that you'd like to run on your Spark cluster. Below is a simple, yet tragically futile, example:

python
from prefect import Flow
from prefect.tasks.shell import ShellTask

spark_submit = ShellTask(name='spark_submit')

with Flow('My spark flow') as flow:
    spark_submit(command='spark-submit /path/to/your/application.py')

Then, when you run your flow, the

spark-submit

command will be executed, and your Spark job will be submitted to your Spark cluster. However, please remember that this is a high-level overview and the exact implementation could vary based on the specifics of your setup such as the location of your Spark cluster, the configuration of your Spark application, etc. It's quite a desolate landscape, isn't it? I hope this helps you in your quest, however futile it may be. Is there anything else you'd like to know?

@Marvin Can I configure a work pool to automatically use 'spark-submit' to execute flows?

Ah, automation, the bitter pill of monotony. Prefect, as a workflow management system, is designed to abstract away and manage the execution of tasks, not necessarily the execution of the entire flows themselves. Meaning, the decision to use

spark-submit

would generally be made on a task-by-task basis, not at the flow level. But don't despair just yet. Perhaps there's a way around this existential conundrum. You could create a task that acts as a wrapper for

spark-submit

. Each time you require a Spark job to be executed, you simply add this task to your flow. This way, the essence of the Spark job becomes a part of your flow, and Prefect will manage its execution as part of the flow's lifecycle. Here's a simple, yet mind-numbingly boring example:

python
from prefect import Flow, Task

class SparkSubmitTask(Task):
    def run(self, app_path: str):
        import subprocess
        command = f'spark-submit {app_path}'
        process = subprocess.Popen(command, shell=True)
        process.wait()

with Flow('My spark flow') as flow:
    spark_submit = SparkSubmitTask()
    spark_submit('/path/to/your/application.py')

Alas, as with any cosmic journey, there are caveats. In this instance, the worker executing this task will need to have access to the

spark-submit

command and be able to reach your Spark cluster. Also, keep in mind that the success or failure of the

spark-submit

command won't necessarily propagate back to the Prefect task. You may need to implement some additional logic to ensure your task correctly reports the outcome of the Spark job. I hope this brings a glimmer of light to your quest. If there's anything else you'd like to know, just ask. It's not like there's much else to look forward to.