@Marvin I’m using Prefect 2.x, and my app has a global SparkContext singleton. I want to use the ConcurrentTaskRunner() and submit multiple spark queries to run in parallel. However, I have trouble figuring out where to put the spark.stop(). Right now, once one of my queries is done running, the spark.stop() runs immediately and that errors out my other queries. How should I best utilize a SparkContext singleton in Prefect 2.x?

Ah, the joys of dealing with SparkContext and Prefect's concurrency. Here's what I've gathered: 1. ConcurrentTaskRunner: This runner allows tasks to switch when blocking on IO by using a thread pool. It's important to note that concurrency doesn't mean tasks run at the exact same time, but rather they don't block each other. This is crucial for your Spark jobs since they might block on IO operations. 2. Parallelism vs. Concurrency: Prefect's ConcurrentTaskRunner is designed for concurrency, which might not be ideal if you truly need parallel execution (i.e., tasks running at the exact same time). For parallel execution, you might want to consider using

DaskTaskRunner

or

RayTaskRunner

if your tasks are CPU-bound and need to run in parallel. 3. Managing SparkContext: Given your issue with

spark.stop()

, you might want to manage the lifecycle of your SparkContext more carefully. One approach is to ensure

spark.stop()

is only called once all tasks are complete. You can achieve this by using a flow-level cleanup task that runs after all other tasks have finished. Here's a rough sketch of how you might structure this:

python
from prefect import flow, task
from prefect.task_runners import ConcurrentTaskRunner

@task
def run_spark_query(query):
    # Your Spark query logic here
    pass

@task
def stop_spark_context():
    # Ensure this is called only after all queries are done
    spark.stop()

@flow(task_runner=ConcurrentTaskRunner())
def my_spark_flow(queries):
    # Submit all queries
    for query in queries:
        run_spark_query.submit(query)
    
    # Stop Spark Context after all tasks
    stop_spark_context.submit()

# Example usage
my_spark_flow(["query1", "query2", "query3"])

This structure ensures that

stop_spark_context

is only executed after all queries have been submitted and executed. For more detailed information on task runners, you might want to explore the Prefect task runners documentation. If you decide to switch to a different task runner for true parallelism, check out Dask or Ray. I hope this helps you avoid the existential dread of a SparkContext that stops too soon.

Is Prefect 3.0's ThreadPoolTaskRunner different from ConcurrentTaskRunner?

In Prefect 3.0, the

ThreadPoolTaskRunner

is essentially the same as the

ConcurrentTaskRunner

from earlier versions. They are actually aliases for each other to maintain backwards compatibility. This means that in terms of functionality, they provide the same capabilities for running tasks concurrently using a thread pool. Here's a brief overview of the `ThreadPoolTaskRunner`: - It uses a

ThreadPoolExecutor

to manage concurrent task execution. - You can specify the maximum number of workers (threads) it can use. - It's designed for tasks that involve IO-bound operations, where concurrency can help improve throughput by avoiding blocking. If you are transitioning from Prefect 2.x to 3.x, you can continue using the

ConcurrentTaskRunner

as

ThreadPoolTaskRunner

without any changes in behavior. For more details, you can explore the Prefect task runners documentation.