Based on the research, I'll create a practical example that demonstrates how to recursively process a tree structure using parallel subflows in Prefect. Here's a complete example that shows how to: 1. Define a tree structure 2. Create a subflow for processing nodes 3. Process nodes in parallel using async patterns

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

# Example tree structure (you can modify this to match your actual data structure)
class TreeNode:
    def __init__(self, value: str, children: Optional[List['TreeNode']] = None):
        self.value = value
        self.children = children or []

@flow(name="process-single-node")
async def process_node(node_value: str) -> str:
    """This is the subflow that processes a single node"""
    # Replace this with your actual node processing logic
    await asyncio.sleep(1)  # Simulate some work
    return f"Processed node: {node_value}"

@flow(name="process-tree")
async def process_tree(node: TreeNode) -> List[str]:
    """Main flow that recursively processes the tree structure"""
    
    # Process current node
    current_node_result = await process_node(node.value)
    
    # If there are no children, return just this node's result
    if not node.children:
        return [current_node_result]
    
    # Process all children in parallel using asyncio.gather
    child_tasks = [
        process_tree(child)
        for child in node.children
    ]
    
    # Wait for all child results
    child_results = await asyncio.gather(*child_tasks)
    
    # Flatten results and combine with current node result
    all_results = [current_node_result]
    for result_list in child_results:
        all_results.extend(result_list)
    
    return all_results

# Example usage
@flow(name="main-tree-processor")
async def main():
    # Create a sample tree
    tree = TreeNode("root", [
        TreeNode("A", [
            TreeNode("A1"),
            TreeNode("A2")
        ]),
        TreeNode("B", [
            TreeNode("B1"),
            TreeNode("B2")
        ])
    ])
    
    # Process the tree
    results = await process_tree(tree)
    
    # Print results
    for result in results:
        print(result)

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

This example demonstrates several important concepts: 1. Tree Structure: We define a simple

TreeNode

class to represent our tree structure. You can modify this to match your specific needs. 2. Subflow Definition: The

process_node

flow is our subflow that processes individual nodes. You should replace its contents with your actual node processing logic. 3. Parallel Processing: The

process_tree

flow uses

asyncio.gather

to process child nodes in parallel. This ensures that all children at the same level are processed concurrently. 4. Recursive Pattern: The flow recursively traverses the tree structure, ensuring that all nodes are processed. When you run this script, it will: - Process the root node - Concurrently process all its children - For each child, recursively process its children in parallel - Collect and return all results The output will look something like:

Processed node: root
Processed node: A
Processed node: B
Processed node: A1
Processed node: A2
Processed node: B1
Processed node: B2

Some important notes: 1. This example uses async/await patterns, which is recommended for concurrent execution in Prefect 3.x. 2. The subflows run in the same process by default. If you need them to run in separate processes or machines, you would need to create deployments for them. 3. You can add error handling and retries by using Prefect's built-in retry mechanisms:

python
@flow(name="process-single-node", retries=3, retry_delay_seconds=10)
async def process_node(node_value: str) -> str:
    # ... your code ...

4. The Prefect UI will show you the full hierarchy of flow runs, making it easy to monitor and debug the execution.

Would you like me to explain any part of this in more detail or show how to modify it for specific use cases?

@Marvin ok, please rewrite it such that it works without asyncio (unless that is really needed) can i call submit on a flow method? also, I'd like to process the children before the node

Based on the research, I'll rewrite the example using Prefect's

DaskTaskRunner

for parallel execution. This approach will process children before their parents and won't require asyncio:

python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner
from typing import List, Optional
from concurrent.futures import Future

class TreeNode:
    def __init__(self, value: str, children: Optional[List['TreeNode']] = None):
        self.value = value
        self.children = children or []

@task
def process_node(node_value: str) -> str:
    """Task that processes a single node"""
    # Replace this with your actual node processing logic
    return f"Processed node: {node_value}"

@flow(name="process-tree", task_runner=DaskTaskRunner())
def process_tree(node: TreeNode) -> List[str]:
    """Main flow that recursively processes the tree structure"""
    results = []
    
    # First, process all children recursively
    child_results = []
    for child in node.children:
        # Recursively process each child and collect results
        child_result = process_tree(child)
        child_results.extend(child_result)
    
    # Add all child results first (maintaining bottom-up order)
    results.extend(child_results)
    
    # Process current node after children
    current_result = process_node.submit(node.value)
    results.append(current_result.result())
    
    return results

@flow(name="main-tree-processor")
def main():
    # Create a sample tree
    tree = TreeNode("root", [
        TreeNode("A", [
            TreeNode("A1"),
            TreeNode("A2")
        ]),
        TreeNode("B", [
            TreeNode("B1"),
            TreeNode("B2")
        ])
    ])
    
    # Process the tree
    results = process_tree(tree)
    
    # Print results
    for result in results:
        print(result)

if __name__ == "__main__":
    main()

Key changes and explanations: 1. Task vs Flow: -

process_node

is now a

@task

instead of a

@flow

- We can use

.submit()

on tasks but not on flows - Tasks within a flow will run in parallel when using the DaskTaskRunner 2. Parallel Execution: - We're using

DaskTaskRunner()

which handles parallel execution of tasks - The

submit()

method on tasks returns a Future object that we can wait for using

.result()

3. Bottom-up Processing: - The code now processes children before their parent nodes - Results are collected in order: children first, then parent 4. Simplified Structure: - Removed async/await patterns - Using synchronous code that's easier to understand and debug The output will now show nodes being processed from bottom to top:

Processed node: A1
Processed node: A2
Processed node: A
Processed node: B1
Processed node: B2
Processed node: B
Processed node: root

For more complex scenarios, you could add additional features: 1. Add retries to the task:

python
@task(retries=3, retry_delay_seconds=10)
def process_node(node_value: str) -> str:
    # ... your code ...

2. Add parallel processing of siblings:

python
@flow(name="process-tree", task_runner=DaskTaskRunner())
def process_tree(node: TreeNode) -> List[str]:
    results = []
    
    # Process all children recursively
    child_futures = []
    for child in node.children:
        # Start processing each child branch
        child_future = process_tree(child)
        child_futures.append(child_future)
    
    # Collect all child results
    for future in child_futures:
        results.extend(future)
    
    # Process current node after children
    current_result = process_node.submit(node.value)
    results.append(current_result.result())
    
    return results

3. Add logging or monitoring: ```python @task def process_node(node_value: str) -> str: from prefect import get_run_logger logger = get_run_logger() logger.info(f"Starting to process node: {node_value}") result = f"Processed node: {node_value}"

logger.info(f"Finished processing node: {node_value}") return result ``` Would you like me to explain any part of this in more detail or show how to modify it for specific use cases?

Let me explain how parallel execution works in Prefect and clarify when you need different task runners. ### Parallel Execution in Prefect There are several ways to achieve parallel execution in Prefect: 1. Task-Level Parallelism (Within a Single Flow)

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

@task
def my_task(x):
    return x * 2

@flow(task_runner=ConcurrentTaskRunner())
def my_flow():
    # These tasks will run in parallel
    results = [my_task.submit(i) for i in range(10)]
    # Get all results
    return [r.result() for r in results]

2. Flow-Level Parallelism (Multiple Flow Runs)

python
from prefect import flow

@flow
def my_subflow(x):
    return x * 2

@flow
def parent_flow():
    # These subflows will run in parallel
    futures = []
    for i in range(10):
        future = my_subflow.submit(i)
        futures.append(future)
    
    # Wait for all results
    return [f.result() for f in futures]

### Task Runners Explained You don't necessarily need DaskTaskRunner for parallel execution. Prefect offers several task runners: 1. ConcurrentTaskRunner (Default)

python
from prefect.task_runners import ConcurrentTaskRunner

@flow(task_runner=ConcurrentTaskRunner(max_workers=4))
def my_flow():
    # Tasks will run in parallel using threads
    pass

2. DaskTaskRunner

python
from prefect.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner())
def my_flow():
    # Tasks will run in parallel using Dask (multiple processes/machines)
    pass

3. SequentialTaskRunner

python
from prefect.task_runners import SequentialTaskRunner

@flow(task_runner=SequentialTaskRunner())
def my_flow():
    # Tasks will run sequentially (no parallelism)
    pass

### When to Use Each Task Runner 1. ConcurrentTaskRunner (Default) - Best for: I/O-bound tasks (API calls, database operations) - Uses: Python threads - Limitation: Doesn't bypass Python's GIL - Example use case:

python
@flow(task_runner=ConcurrentTaskRunner())
def download_files():
    futures = []
    for url in urls:
        future = download_task.submit(url)
        futures.append(future)
    return [f.result() for f in futures]

2. DaskTaskRunner - Best for: CPU-bound tasks or distributed computing - Uses: Multiple processes/machines - Advantage: True parallelism, can use multiple cores/machines - Example use case:

python
@flow(task_runner=DaskTaskRunner())
def process_large_datasets():
    futures = []
    for dataset in large_datasets:
        future = heavy_computation_task.submit(dataset)
        futures.append(future)
    return [f.result() for f in futures]

### Flow-Level Parallelism Without Task Runners You can achieve parallel execution of flows without specifying any task runner:

python
from prefect import flow

@flow
def process_item(item):
    return item * 2

@flow
def main_flow():
    # These subflows will run in parallel regardless of task runner
    futures = []
    for i in range(10):
        # .submit() on a flow creates a new flow run
        future = process_item.submit(i)
        futures.append(future)
    
    # Collect results
    results = [f.result() for f in futures]
    return results

### Key Points to Remember: 1. Flow.submit() - Creates a new flow run - Runs in parallel by default - Returns a future object - No task runner required for flow-level parallelism 2. Task.submit() - Submits a task for execution - Parallelism depends on the task runner - Returns a future object - Requires appropriate task runner for parallel execution 3. Choosing Parallelism Level - Use task-level parallelism for fine-grained control within a flow - Use flow-level parallelism for independent, larger units of work - Can combine both approaches when needed Would you like me to elaborate on any of these aspects or provide more specific examples for your use case?

Yes, a task can create or trigger a flow run, but there are a few important patterns and considerations to be aware of. Here are the main approaches: ### 1. Direct Flow Call from Task The simplest approach - calling a flow directly from a task:

python
from prefect import flow, task

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

@task
def my_task(x: int):
    # Directly call the flow
    result = subflow(x)
    return result

@flow
def main_flow():
    result = my_task(5)
    return result

### 2. Creating Dynamic Flow Runs A task can create dynamic flow runs using deployment:

python
from prefect import flow, task
from prefect.client.orchestration import get_client

@task
def create_flow_run(data: dict):
    # Get the Prefect client
    client = get_client()
    
    # Create a flow run from an existing deployment
    flow_run = client.create_flow_run(
        deployment_id="deployment-id",
        parameters={"data": data}
    )
    
    return flow_run.id

@flow
def main_flow():
    data = {"value": 42}
    flow_run_id = create_flow_run(data)
    return flow_run_id

### 3. Parallel Flow Runs from Task A task can trigger multiple flow runs in parallel:

python
from prefect import flow, task

@flow
def process_item(item: int):
    return item * 2

@task
def process_batch(items: list):
    # Submit multiple flow runs in parallel
    futures = []
    for item in items:
        future = process_item.submit(item)
        futures.append(future)
    
    # Wait for all results
    return [f.result() for f in futures]

@flow
def main_flow():
    items = [1, 2, 3, 4, 5]
    results = process_batch(items)
    return results

### 4. Creating Deployments from Task A task can create new deployments programmatically:

python
from prefect import flow, task
from prefect.deployments import Deployment

@flow
def target_flow(x: int):
    return x * 2

@task
def create_deployment():
    deployment = Deployment.build_from_flow(
        flow=target_flow,
        name="programmatic-deployment",
        version="1",
        parameters={"x": 42}
    )
    deployment_id = deployment.apply()
    return deployment_id

@flow
def main_flow():
    deployment_id = create_deployment()
    return deployment_id

### Important Considerations 1. State Handling

python
@task
def trigger_flow_with_state(data: dict):
    try:
        result = subflow(data)
        return result
    except Exception as e:
        # Handle any flow run failures
        raise RuntimeError(f"Flow run failed: {e}")

2. Async Support

python
from prefect import flow, task

@task
async def async_task_with_flow():
    # Async tasks can call async flows
    result = await async_subflow()
    return result

@flow
async def async_subflow():
    return "async result"

@flow
async def main_flow():
    result = await async_task_with_flow()
    return result

3. Best Practices - Keep it Simple: Only create flows from tasks when there's a clear separation of concerns

python
@task
def prepare_flow_run(data: dict):
    # Task handles preparation logic
    processed_data = preprocess(data)
    # Then triggers the flow that handles the main processing
    return processing_flow(processed_data)

- Error Handling: Always include proper error handling when creating flow runs

python
@task(retries=3)
def safe_flow_trigger(data: dict):
    try:
        return subflow(data)
    except Exception as e:
        logger = get_run_logger()
        logger.error(f"Failed to trigger flow: {e}")
        raise

- Resource Management: Be mindful of resource usage when creating multiple flow runs

python
@task
def batch_flow_creator(items: list, batch_size: int = 10):
    # Create flow runs in batches to manage resources
    for batch in chunks(items, batch_size):
        futures = [process_flow.submit(item) for item in batch]
        # Wait for batch to complete before starting next batch
        [f.result() for f in futures]

Remember that while it's possible to create flows from tasks, you should have a good reason for doing so. Common valid use cases include: - Dynamic workflow generation based on runtime data - Parallel processing of large datasets - Complex orchestration patterns requiring runtime decision-making Would you like me to elaborate on any of these patterns or provide more specific examples for your use case?