Perfect! Here's how you can limit the number of concurrent subflow runs: 1. First, modify your flow code to add a tag to your subflows. Here's an example:

python
from prefect import flow

@flow(tags=["parallel-subflow"])
def subflow(x):
    # your subflow logic here
    return x * 2

@flow
def main_flow():
    # This will still create all futures at once, but execution will be limited
    futures = []
    for i in range(10):
        futures.append(subflow.submit(i))
    
    # Wait for all results
    results = [f.result() for f in futures]
    return results

2. Then, create a concurrency limit for the tag using the Prefect CLI:

bash
prefect concurrency-limit create parallel-subflow 3

This will limit the number of concurrent subflows with the tag "parallel-subflow" to 3 at a time. You can adjust the number (3 in this example) to whatever limit makes sense for your VM's resources. Some important notes: - The parent flow will still create all the futures immediately, but the actual execution of the subflows will be limited based on the concurrency limit - Subflows will queue up and execute as slots become available - You can monitor active concurrency slots using

prefect concurrency-limit inspect parallel-subflow

- You can list all concurrency limits using

prefect concurrency-limit ls

- If you need to remove the limit, use

prefect concurrency-limit delete parallel-subflow

This approach gives you fine-grained control over resource usage while maintaining the parallel execution pattern. The concurrency limits are enforced at the Prefect server level, so they'll work consistently across all your flow runs. Would you like me to explain any part of this in more detail or show you how to monitor the active concurrency slots?

Ah, I apologize for my earlier response. I can now provide a better solution that will properly queue your subflows. Here's the complete approach: 1. Monitoring Active Concurrency Slots:

bash
# To see details about a specific concurrency limit
prefect concurrency-limit inspect parallel-subflow

# To see all concurrency limits
prefect concurrency-limit ls

2. Better Implementation for Queuing Subflows: Here's the improved code that will properly queue your subflows using Prefect's concurrency context manager:

python
from prefect import flow
from prefect.concurrency import concurrency

@flow
def subflow(x):
    # your subflow logic here
    return x * 2

@flow
def main_flow():
    results = []
    
    for i in range(10):
        # This will properly queue the subflows
        with concurrency("my-concurrency-group", max_workers=3):
            result = subflow(i)
            results.append(result)
    
    return results

The key differences in this implementation: 1. We use the

concurrency

context manager from

prefect.concurrency

2. The

max_workers

parameter directly controls how many subflows can run simultaneously 3. Subflows will properly queue and wait for available slots instead of all entering the running state You can also monitor the flow runs in the Prefect UI to see: - Which flows are currently running - Which flows are queued - The concurrency limits in effect Alternative Approach using Work Pool Concurrency: If you're running your flows via deployments, you can also set concurrency limits at the work pool level:

bash
prefect work-pool create my-pool --concurrency-limit 3

Then deploy your flows to this work pool, and it will automatically handle the concurrency limiting for you. Would you like me to explain more about either: 1. How to monitor the queued flows in the UI? 2. How to set up work pool-based concurrency limits? 3. How to use more advanced concurrency patterns (like timeouts or different concurrency groups)?

I understand the issue better now. You're right - the work pool concurrency limits primarily affect deployment-level concurrency, not subflow execution within a single parent flow. Here's a corrected approach that will actually queue your subflows:

python
from prefect import flow
from prefect.concurrency.sync import concurrency_slot
import asyncio

@flow
def subflow(x):
    # your subflow logic here
    return x * 2

@flow
async def main_flow():
    results = []
    
    # Create a semaphore to limit concurrent execution
    sem = asyncio.Semaphore(3)  # Limit to 3 concurrent subflows
    
    async def run_with_limit(i):
        async with sem:
            # This will wait for a slot before executing
            with concurrency_slot("subflow-group", max_slots=3):
                return await subflow(i)
    
    # Create all tasks but they will properly queue
    tasks = [run_with_limit(i) for i in range(10)]
    
    # Wait for all tasks to complete
    results = await asyncio.gather(*tasks)
    return results

# Run the flow
if __name__ == "__main__":
    asyncio.run(main_flow())

This solution combines two mechanisms to ensure proper queuing: 1. An

asyncio.Semaphore

to control concurrent execution at the Python level 2. Prefect's

concurrency_slot

to manage the concurrency at the Prefect level Key points about this solution: 1. The subflows will actually queue up and wait for their turn, rather than all starting at once 2. You'll see only 3 subflows running at any given time 3. The rest will be in a proper waiting state 4. The parent flow will still coordinate all the subflows but won't launch them all simultaneously You can monitor the execution in real-time using:

python
@flow
def subflow(x):
    print(f"Starting subflow {x}")
    # Add some sleep to simulate work
    time.sleep(5)
    print(f"Finishing subflow {x}")
    return x * 2

Alternative Approach using Task-Based Concurrency: If you prefer a simpler approach without async/await, you can use tasks instead of subflows:

python
from prefect import flow, task
from prefect.tasks import task_input_hash
from datetime import timedelta

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(minutes=5))
def process_item(x):
    # Your processing logic here
    return x * 2

@flow
def main_flow():
    futures = []
    with concurrency_slot("process-group", max_slots=3):
        for i in range(10):
            future = process_item.submit(i)
            futures.append(future)
    
    return [f.result() for f in futures]

Would you like me to: 1. Explain how to monitor the queued states in more detail? 2. Show how to add error handling to these patterns? 3. Demonstrate how to add timeout mechanisms to prevent infinite waiting?

I see the issue now. To properly manage subflow concurrency at the infrastructure level, you'll need to use a combination of work pool settings and global concurrency limits. Here's the step-by-step approach: 1. First, create a global concurrency limit (GCL):

bash
# Create a global concurrency limit that will apply to all flows with a specific tag
prefect gcl create my-subflows-limit 3

2. Then, modify your flows to use this tag:

python
@flow(tags=["my-subflows-limit"])
def subflow(x):
    # your subflow logic here
    return x * 2

@flow
def main_flow():
    futures = []
    for i in range(10):
        futures.append(subflow.submit(i))
    return [f.result() for f in futures]

3. If you're using work pools, you can also set a concurrency limit on the work pool:

bash
# Create a work pool with concurrency limit
prefect work-pool create my-pool --type process --concurrency-limit 3

# Or set concurrency limit on an existing work pool
prefect work-pool set-concurrency-limit my-pool 3

4. Deploy your flows to this work pool:

python
from prefect import serve

if __name__ == "__main__":
    main_flow.serve(
        name="main-flow",
        work_pool="my-pool",
        tags=["my-subflows-limit"]
    )

To monitor the concurrency limits and queued flows:

bash
# Check global concurrency limits
prefect gcl ls

# Inspect work pool settings
prefect work-pool inspect my-pool

# View active flow runs and their states
prefect flow-run ls

The key differences with this approach: 1. Concurrency is managed at the infrastructure level through global concurrency limits and work pool settings 2. No need for code-level concurrency management 3. Limits are enforced consistently across all deployments 4. Can be modified without changing code 5. Can be managed through the Prefect UI as well Would you like me to: 1. Show you how to monitor these limits in the Prefect UI? 2. Explain how to set up different limits for different types of subflows? 3. Demonstrate how to temporarily override these limits when needed? Remember that when using global concurrency limits, you'll need to ensure your flows are properly tagged, but this is a one-time setup in your flow code rather than active concurrency management.

Let me put this all together for you. Here's a comprehensive explanation: 1. ThreadPoolTaskRunner: - It's a task runner that executes tasks concurrently using threads - Useful when you have I/O-bound tasks (like API calls, database operations) - Not as effective for CPU-bound tasks (due to Python's GIL) - It's actually an alias for

ConcurrentTaskRunner

in modern Prefect 2. Running Subflows Concurrently with Dependencies: Here's a complete example showing how to run subflows concurrently and make a task dependent on their completion:

python
from prefect import flow, task
from typing import List
import asyncio

@task
def process_results(results: List[int]):
    """This task will only run after all subflows complete successfully"""
    print(f"Processing results: {results}")
    return sum(results)

@flow
def subflow(x: int) -> int:
    # Simulate some work
    return x * 2

@flow
async def main_flow():
    # Create futures for all subflows
    futures = []
    for i in range(10):
        # .submit() runs the subflow asynchronously
        future = subflow.submit(i)
        futures.append(future)
    
    # Wait for all subflows to complete and collect results
    results = await asyncio.gather(*(f.wait() for f in futures))
    
    # Now process_results will only run after all subflows complete successfully
    final_result = await process_results.submit(results)
    return final_result

# Run the flow
if __name__ == "__main__":
    asyncio.run(main_flow())

Alternative approach using

as_completed

if you want to process results as they come in:

python
from prefect import flow, task
from prefect.futures import as_completed

@flow
async def main_flow():
    # Submit all subflows
    futures = [subflow.submit(i) for i in range(10)]
    
    # Process results as they complete
    results = []
    for future in as_completed(futures):
        result = await future.wait()
        results.append(result)
    
    # Process final results
    final_result = await process_results.submit(results)
    return final_result

3. Using ThreadPoolTaskRunner Explicitly: If you want to use ThreadPoolTaskRunner for your tasks:

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

@task
def io_bound_task(x):
    # Some I/O operation like API call or database query
    return x * 2

@flow(task_runner=ThreadPoolTaskRunner(max_workers=3))
def main_flow():
    results = []
    for i in range(10):
        result = io_bound_task.submit(i)
        results.append(result)
    
    # Wait for all tasks to complete
    final_results = [r.result() for r in results]
    return final_results

Key points to remember: 1.

.submit()

runs flows/tasks asynchronously and returns a future 2.

await future.wait()

or

future.result()

waits for completion and gets the result 3. Tasks depending on subflows won't start until all required subflows complete successfully 4. ThreadPoolTaskRunner is best for I/O-bound tasks 5. You can control the level of concurrency using

max_workers

Would you like me to: 1. Show how to handle errors in concurrent subflows? 2. Explain how to add timeouts to prevent hanging? 3. Demonstrate how to monitor the progress of concurrent subflows?

Here's the correct way to run subflows concurrently in Prefect 3.x:

python
from prefect import flow, task
import asyncio

@task
def process_results(results):
    """This task will only run after all subflows complete successfully"""
    print(f"Processing results: {results}")
    return sum(results)

@flow
async def subflow(x: int) -> int:
    # Simulate some work
    await asyncio.sleep(1)  # Simulating some async work
    return x * 2

@flow
async def main_flow():
    # Create coroutines for all subflows
    coroutines = [subflow(i) for i in range(10)]
    
    # Run all subflows concurrently and wait for results
    results = await asyncio.gather(*coroutines)
    
    # Process results will only run after all subflows complete
    final_result = await process_results(results)
    return final_result

# Run the flow
if __name__ == "__main__":
    asyncio.run(main_flow())

Or if you need more control over the execution:

python
from prefect import flow, task
import asyncio
from typing import List

@flow
async def subflow(x: int) -> int:
    await asyncio.sleep(1)  # Simulating some async work
    return x * 2

@task
async def process_batch_results(results: List[int]):
    print(f"Processing batch results: {results}")
    return sum(results)

@flow
async def main_flow():
    # Control concurrency by processing in batches
    batch_size = 3
    all_results = []
    
    for i in range(0, 10, batch_size):
        batch = range(i, min(i + batch_size, 10))
        # Run batch of subflows concurrently
        batch_results = await asyncio.gather(
            *(subflow(x) for x in batch)
        )
        all_results.extend(batch_results)
        print(f"Completed batch {i//batch_size + 1}")
    
    # Process all results
    final_result = await process_batch_results(all_results)
    return final_result

For non-async flows, you can use:

python
from prefect import flow, task
from concurrent.futures import ThreadPoolExecutor
from typing import List

@flow
def subflow(x: int) -> int:
    return x * 2

@task
def process_results(results: List[int]):
    return sum(results)

@flow
def main_flow():
    # Use ThreadPoolExecutor to run subflows concurrently
    with ThreadPoolExecutor(max_workers=3) as executor:
        # Submit all subflows to the executor
        futures = [executor.submit(subflow, i) for i in range(10)]
        
        # Gather results as they complete
        results = [f.result() for f in futures]
    
    # Process results after all subflows complete
    final_result = process_results(results)
    return final_result

Key points about these implementations: 1. Use

asyncio.gather()

for concurrent async subflows 2. Use

ThreadPoolExecutor

for concurrent non-async subflows 3. Control concurrency by: - Setting batch sizes - Using

max_workers

with ThreadPoolExecutor - Using semaphores with async flows 4. Dependencies are naturally handled - tasks won't start until their dependent subflows complete Would you like me to: 1. Show how to add error handling to these patterns? 2. Demonstrate how to monitor the progress of concurrent subflows? 3. Explain how to add timeouts to prevent hanging?

Let me break down what you can and should return from Prefect flows and tasks: ### Tasks ✅ Can Return:

python
from prefect import task

@task
def good_task_returns():
    # Basic Python types
    return 42                    # integers
    return 3.14                  # floats
    return "hello"               # strings
    return True                  # booleans
    
    # Collections of basic types
    return [1, 2, 3]            # lists
    return {"a": 1, "b": 2}     # dictionaries
    return (1, 2, 3)            # tuples
    return {1, 2, 3}            # sets
    
    # Nested structures
    return {
        "numbers": [1, 2, 3],
        "metadata": {
            "name": "test",
            "success": True
        }
    }
    
    # Pandas DataFrames (will be serialized)
    import pandas as pd
    return pd.DataFrame({"a": [1, 2, 3]})
    
    # NumPy arrays
    import numpy as np
    return np.array([1, 2, 3])

❌ Should Not Return:

python
@task
def bad_task_returns():
    # Don't return these:
    return lambda x: x + 1      # functions
    return open("file.txt")     # file handles
    return database_connection  # database connections
    return threading.Lock()     # thread locks

### Flows ✅ Can Return:

python
from prefect import flow

@flow
def good_flow_returns():
    # Everything that tasks can return
    return 42
    return [1, 2, 3]
    return {"results": "success"}
    
    # Results from tasks
    result = my_task()
    return result
    
    # Multiple task results
    results = [task1(), task2(), task3()]
    return results
    
    # Structured results
    return {
        "task1_result": task1(),
        "task2_result": task2(),
        "metadata": {
            "flow_name": "example",
            "timestamp": "2023-01-01"
        }
    }

### Best Practices 1. Keep it Serializable:

python
@task
def process_data(data):
    # Good: Return processed data
    return {"processed_values": data, "status": "success"}
    
    # Bad: Don't return objects that can't be serialized
    return MySQLConnection()

2. Structure Complex Returns:

python
@flow
def analysis_flow():
    # Good: Structured, clear return value
    return {
        "metrics": {
            "accuracy": 0.95,
            "precision": 0.92
        },
        "metadata": {
            "model_version": "1.0",
            "timestamp": "2023-01-01"
        },
        "data_summary": {
            "rows_processed": 1000,
            "errors": []
        }
    }

3. Handle Large Data:

python
@task
def process_large_dataset():
    # Good: Save large data to storage and return reference
    results = process_big_data()
    save_to_storage(results)
    return {
        "storage_location": "<s3://bucket/results.parquet>",
        "row_count": len(results),
        "status": "complete"
    }

4. Error Information:

python
@task
def data_validation():
    errors = []
    warnings = []
    # ... validation logic ...
    
    # Good: Return structured validation results
    return {
        "is_valid": len(errors) == 0,
        "errors": errors,
        "warnings": warnings,
        "records_checked": 1000
    }

Key Points to Remember: 1. All returns must be serializable 2. Structure complex returns as dictionaries for clarity 3. For large data, consider storing in external storage and returning references 4. Include metadata when relevant (timestamps, versions, counts) 5. Return values should be meaningful for downstream tasks/flows Would you like me to: 1. Show how to handle non-serializable objects properly? 2. Demonstrate patterns for returning large datasets? 3. Explain how to use Prefect's result storage for different return types?