@Marvin I'm setting up an ETL pipeline using prefect but having difficulty parallelising the workflow. I have a process task that processes an individual part. I'd like to run this task in parallel but a on a maximum of 8 jobs for api / database connection issues. I have tried the dask task runner and the ray task runner but they both pickle the task beforehand and this isn't possible for my task. I have a work around using a TheadPoolExecutor but his hides the tasks being run from prefect so I don't know if they crashed or not. Any suggestions ?

To achieve parallel task executions while limiting concurrency in a Prefect ETL pipeline, you can utilize task concurrency limits using task tags. Prefect provides built-in functionality to limit the maximum number of tasks running concurrently for a specific tag. Here's a brief guide: 1. Define Concurrency Limits using Tags: Use tags to group tasks and define a maximum concurrency limit. Any task with the defined tag will adhere to the specified limit. 2. Implement the Limit in Flow: Use the concurrency context manager to enforce the concurrency limit in your flow. Here's an example:

from prefect import flow, task, tags

@task
def process_part(part):
    # Your processing logic here
    pass

@flow
def etl_pipeline(parts):
    for part in parts:
        with tags("process"):
            process_part.submit(part)

if __name__ == "__main__":
    parts = range(10)  # Example parts to process
    etl_pipeline(parts)

3. Set Concurrency Limit: Use the Prefect Client to set the concurrency limit for the tag. You can set this before running the flow.

from prefect.client import get_client

client = get_client()
client.create_concurrency_limit(tag="process", concurrency_limit=8)

Refer to the following for more detailed examples and documentation: - Global Concurrency Limits - Task Concurrency Limits - Add `Flow.submit` interface for subflows Use these features to enforce task concurrency limits for your ETL pipeline without relying on

ThreadPoolExecutor

. If you encounter specific issues, you can always search existing issues or open new ones on Prefect's GitHub repository.

@Marvin Ok, this seems to work, but it creates 422 tasks to be run immediately, I want it to process 8 tasks in parallel, then another 8 and so on.

To process tasks in parallel while limiting the number running simultaneously, you can use the task concurrency feature along with a batching approach. Here’s how you can achieve this: 1. Define a Batch Processing Function: Create a function to process parts in batches of, say, 8. 2. Use Concurrency Limits and Flow Submission: Utilize tags and Prefect's concurrency limits to manage these batches. Here’s an implementation example:

from prefect import flow, task, tags
from prefect.client import get_client

client = get_client()
client.create_concurrency_limit(tag="batch_process", concurrency_limit=8)

@task
def process_part(part):
    # Your processing logic here
    pass

@flow
def process_batch(batch):
    for part in batch:
        with tags("batch_process"):
            process_part.submit(part)

@flow
def etl_pipeline(parts, batch_size=8):
    # Divide parts into batches
    batches = [parts[i:i + batch_size] for i in range(0, len(parts), batch_size)]
    
    for batch in batches:
        process_batch.submit(batch)

if __name__ == "__main__":
    parts = list(range(422)) # Example parts to process
    etl_pipeline(parts)

### Key Points: 1. Batching: The function

etl_pipeline

divides the works into batches. 2. Concurrency Limit: Configured using tags to ensure that only 8 tasks run in parallel. 3. Flow Submission: Use

.submit()

to schedule the batch processing flows. For more complex scenarios, you can explore Prefect's advanced concurrency management features detailed here: Global Concurrency Limits. This approach ensures that only 8 tasks run at any given time, adhering to your specified constraints. If you need further customization, Prefect's forums and GitHub issues are good resources for community support and advanced patterns.

@Marvin if my task calls multiple other tasks do I need to change any of the parameters for those sub-tasks ?

If your task calls multiple other tasks (sub-tasks), you need to ensure that concurrency limits are applied consistently. Here are some key points: 1. Apply Tags Consistently: If you have a concurrency limit on a specific tag, ensure you apply this tag to all relevant sub-tasks as well. 2. Task Concurrency Limits: Prefect checks concurrency limits for tasks based on tags. If a task has multiple tags, it will only execute if all tags have available concurrency slots. Here's an example illustrating how to manage concurrency limits for sub-tasks:

from prefect import flow, task, tags

@task
def sub_task_1(part):
    # Sub task logic here
    pass

@task
def sub_task_2(part):
    # Sub task logic here
    pass

@task
def process_part(part):
    sub_task_1(part)
    sub_task_2(part)

@flow
def process_batch(batch):
    for part in batch:
        with tags("batch_process"):
            process_part.submit(part)

@flow
def etl_pipeline(parts, batch_size=8):
    batches = [parts[i:i + batch_size] for i in range(0, len(parts), batch_size)]
    for batch in batches:
        process_batch.submit(batch)

if __name__ == "__main__":
    parts = list(range(422))  # Example parts to process
    etl_pipeline(parts)

### Key Concepts: - Tags and Limits: Apply the tag

"batch_process"

consistently to all relevant tasks and sub-tasks. - Concurrency Checks: Prefect will check concurrency limits whenever a task run attempts to enter a running state. - Flow Management: Ensure your main flow manages task submissions in batches, each adhering to the concurrency rules. For additional information, see the Prefect documentation on Task Concurrency Limits. This setup will ensure your tasks and sub-tasks adhere to the specified concurrency limits, preventing overloads while still processing in parallel batches.

@Marvin well the SequentialTaskRunner works fine, but I can't get the ConcurrentTaskRunner to work. The tasks start and then do nothing. are there any other ways to get it to work ? like just using the ThreadPoolExecutor to loop though the tasks ?

When using

ConcurrentTaskRunner

, users have reported issues where tasks initiate but do not progress. If

ConcurrentTaskRunner

is causing issues, as a workaround, you can manually manage parallel task execution using

ThreadPoolExecutor

. Here’s how you can achieve this in conjunction with Prefect flows: 1. Use ThreadPoolExecutor to Submit and Execute Tasks:

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

@task
def process_part(part):
    # Your processing logic here
    pass

def process_batch_with_thread_pool(batch):
    with ThreadPoolExecutor(max_workers=8) as executor:
        futures = {executor.submit(process_part.run, part): part for part in batch}
        for future in as_completed(futures):
            part = futures[future]
            try:
                future.result()
            except Exception as exc:
                print(f'Task generated an exception: {exc}')
            else:
                print(f'Task for part {part} completed successfully.')

@flow
def etl_pipeline(parts, batch_size=8):
    batches = [parts[i:i + batch_size] for i in range(0, len(parts), batch_size)]
    for batch in batches:
        process_batch_with_thread_pool(batch)

if __name__ == "__main__":
    parts = list(range(422))  # Example parts to process
    etl_pipeline(parts)

### Explanation: 1. ThreadPoolExecutor: The

ThreadPoolExecutor

is used to manage a pool of worker threads efficiently. 2. Concurrent Tasks: Tasks are submitted to the executor, which handles running up to 8 tasks at a time. 3. Error Handling: The example includes basic error handling for tasks that fail. ### Limitations: - Prefect Tracking: Using

ThreadPoolExecutor

in this manner hides task execution from Prefect’s runtime tracking. Exceptions and results won't be logged by Prefect. - Concurrency Control: This approach provides manual concurrency control but lacks advanced Prefect concurrency features. This approach should allow you to run tasks concurrently in manageable batches, bypassing the issues with

ConcurrentTaskRunner

. The tasks will run up to the specified worker limit (e.g., 8) and then continue with the next batch.

@Marvin so this works, and the tasks completions are being logged to the terminal there prefect flow is running, but they aren't appearing in the prefect ui ? Are they not being logged to the database ?

When using

ThreadPoolExecutor

directly, task executions are not tracked by Prefect's infrastructure, so they don't appear in the Prefect UI or get logged in the database. To maintain visibility in the Prefect UI while using concurrency, you must use Prefect’s task and task runner infrastructure. Here's a refined approach keeping execution visible in Prefect: 1. Maintain Visibility Using Prefect's Tags and Concurrency Limit: Ensure you tag your tasks appropriately and set concurrency limits using tags. 2. Manual Batching Handling: - Use Prefect tasks inside the flow to manually control batches, ensuring they respect max concurrency limits using a sequential approach combined with concurrency settings. Here’s an improved example to ensure tasks are logged and viewable in Prefect's UI:

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

client = get_client()
client.create_concurrency_limit(tag="batch_process", concurrency_limit=8)

@task
def process_part(part):
    # Your processing logic here
    pass

@task
def process_batch(batch):
    for part in batch:
        with tags("batch_process"):
            process_part.submit(part)

@flow
def etl_pipeline(parts, batch_size=8):
    batches = [parts[i:i + batch_size] for i in range(0, len(parts), batch_size)]
    for batch in batches:
        process_batch(batch)

if __name__ == "__main__":
    parts = list(range(422))  # Example parts to process
    etl_pipeline(parts)

### Explanation: - Tags and Concurrency Limits: You tag all tasks with "batch_process" and set a concurrency limit for that tag. - Task Submission: In

process_batch

, tasks are submitted in a way that respects the concurrency limit. ### Key Points: 1. Task and Flow Definitions: Properly define tasks and flows using Prefect’s infrastructure. 2. Concurrency Management: Use tags and concurrency limits to manage parallel task execution and visibility in Prefect UI. 3. Batch Handling: Ensure batches are processed respecting the set concurrency constraints. By implementing this, all tasks managed by Prefect will appear in the Prefect UI with their execution states properly tracked and logged. If you need further customization, consulting Prefect’s documentation and community resources can provide more advanced tips and guidance.

@Marvin ok, I have been mistaken. I don't want to use concurrency. I want to use parallel task execution for faster results.

If you want to achieve parallel task execution for faster results while ensuring that tasks are tracked in the Prefect UI, you can use

ConcurrentTaskRunner

. It will allow you to execute tasks in parallel. Given that tasks start but do nothing when using

ConcurrentTaskRunner

, there might be underlying configuration issues. Here’s how you can correctly set up and use `ConcurrentTaskRunner`: 1. Task Runner Configuration:

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

@task
def process_part(part):
    # Your processing logic here
    pass

@flow(task_runner=ConcurrentTaskRunner())
def etl_pipeline(parts, batch_size=8):
    for part in parts:
        process_part.submit(part)

if __name__ == "__main__":
    parts = list(range(422))  # Example parts to process
    etl_pipeline(parts)

### Explanation: - ConcurrentTaskRunner: This task runner will allow the flow to execute tasks in parallel. ### Debugging Tips: 1. Logging: Add detailed logging in your tasks to ensure they’re executing properly. 2. Resource Limitation: Ensure the system running the flow has enough resources (CPU, memory) to handle parallel execution. 3. Environment: Ensure that the Python environment supports the concurrency model. ### Extended Example Handling Task Completion: To ensure tasks run in parallel and visualize completion in Prefect UI:

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

@task
def process_part(part):
    import time
    time.sleep(1)  # Simulate work
    print(f'Processed part: {part}')
    return f"Result of part {part}"

@flow(task_runner=ConcurrentTaskRunner())
def etl_pipeline(parts):
    futures = [process_part.submit(part) for part in parts]
    results = [future.result() for future in futures]
    return results

if __name__ == "__main__":
    parts = list(range(8))  # Example parts to process
    etl_pipeline(parts)

### Key Points: - Flow and Tasks: Define tasks and flow ensuring they’re decorated with

prefect.task

and

prefect.flow

. - Task Runner: Use

ConcurrentTaskRunner()

to facilitate parallel execution. - Submit and Get Results: Submit tasks to run in parallel and gather results after execution. ### Troubleshooting: - Prefect Logs: Inspect logs to ensure tasks progress. - Limited Resources: Monitor system resources to ensure it can handle parallel execution. By following this structured approach, you'll achieve parallel task execution with proper tracking in Prefect. For additional support, reviewing Prefect's task runners documentation might offer more insights.