[Python] How to orchestrate dependent async tasks with mixed I/O and CPU-bound workloads in...

I'm building a task orchestration system in Python where tasks may be either I/O-bound (like HTTP calls) or CPU-bound (like data transformation). Some tasks depend on the result of others, and I want to execute them concurrently while preserving the correct execution order.

I've tried combining asyncio for async I/O and concurrent.futures.ThreadPoolExecutor for CPU tasks. Here's a simplified example:

`python

import asyncio
from concurrent.futures import ThreadPoolExecutor
import time

executor = ThreadPoolExecutor()

async def fetch_data():
await asyncio.sleep(1) # Simulate I/O
return {"raw": "123,456,789"}

def transform_data(data):
time.sleep(2) # Simulate CPU-bound task
return list(map(int, data["raw"].split(",")))

async def store_data(data):
await asyncio.sleep(1) # Simulate I/O
print("Stored:", data)

async def pipeline():
raw = await fetch_data()
loop = asyncio.get_running_loop()
transformed = await loop.run_in_executor(executor, transform_data, raw)
await store_data(transformed)

asyncio.run(pipeline())


This works, but in a larger DAG (directed acyclic graph) of task dependencies, managing the scheduling becomes messy.

Question:

1. 
   What’s the best architectural pattern for managing complex, mixed async and CPU-bound workflows in Python?
   
   
2. 
   Are there task orchestration libraries (like Prefect, Dagster, or custom async DAGs) that handle both types of workloads well?
   
   
3. 
   Can asyncio.TaskGroup in Python 3.11+ help with dependency management between tasks?
   

I’m also interested in best practices for error handling and ensuring cleanup across different task layers. Any insight or references would be appreciated.

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