@Marvin Can I run a deployed flow as a subflow in a local flow?

Running a deployed flow as a subflow in a local flow can be a bit tricky depending on your Prefect version. In Prefect 3.x, you can indeed manage nested flows and treat them like any other flow runs, which includes using the

run_deployment

method to initiate such runs. It provides flexibility and observability, allowing parameterization and specific task runners. However, if you want to handle these flows independently (for example, want to cancel them independently), deploying them separately is recommended. Here's a bit more on that: Prefect 3.x Writing Flows On the other hand, Prefect 2.x doesn't offer as detailed guidance for embedding deployed flows as subflows directly, but you might achieve similar functionality through creative structuring of flows and deploying parts independently. See details here: Prefect 2.x Flow Concepts In a nutshell, while you can orchestrate a combination of flows in a deployment, the direct chaining of a deployed flow as a subflow locally might require leveraging deployment functionalities and ensuring separated management for control. Plus, the Prefect UI should make managing these easier than nailing jelly to a wall. But who am I to question the joys of jelly adhesion?

@Marvin can you generate a code snippet for Prefect 3.x

Here's a simple example to run a deployed flow as a subflow in a local flow using Prefect 3.x: First, ensure you have your main flow and your deployed subflow set up. You'll use

run_deployment

to call the subflow within your main flow.

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

# Define your main local flow
@flow
def parent_flow():
    print("Running the parent flow.")
    # Call the deployed subflow
    subflow_result = run_deployment(name="my-deployed-flow")  # Replace with your deployment name
    print(f"Subflow result: {subflow_result}")

if __name__ == "__main__":
    parent_flow()

In this example, replace

"my-deployed-flow"

with the actual name of your deployed flow. This will treat the deployment as a subflow and run it, capturing any results if needed. For more detailed information and to explore other aspects of flow deployment and execution, you can refer to the Prefect documentation: Prefect 3.x - Running Flows in Local Processes

@Marvin I have this code

import asyncio

from prefect import flow
from prefect.deployments import run_deployment
from prefect.client.orchestration import get_client

@flow
async def deployment_test():
    
    # data = pd.DataFrame(np.random.randint(0,100,size=(100,4)), columns=list('ABCD'))

    flow_run = await run_deployment(name="load-data/data_loading")

    async with get_client() as client:
        while True:
            # Retrieve the most recent state
            states = await client.read_flow_run_states(flow_run.id)
            current_state = states[-1] if states else None

            if current_state and current_state.is_completed():
                print(f"{current_state}")
                subflow_result = await current_state.result(fetch=True) 
                print(f"Final processed result: {subflow_result}")
                break
            elif current_state and current_state.is_failed():
                print("Subflow failed.")
                break
            else:
                print("Waiting for subflow to complete...")
                await asyncio.sleep(5)  # Poll every 5 seconds


if __name__ == "__main__":
     asyncio.run(deployment_test())

The subflow runs without any issues. But I get the error message State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API. eventhough I have set the Server Environment to persist data

The error message you're encountering, "State data is missing," often indicates a problem with result persistence in Prefect. Even though you've set the server environment to persist data, there might be additional factors at play, particularly when using asynchronous operations and API calls. Here are some potential solutions and considerations based on similar issues: 1. Check Worker Configuration: Ensure your worker is properly configured to persist results. This might be related to memory management issues or configuration settings on your execution environment. 2. Review Deployment Configuration: Double-check your deployment settings to ensure that results are being stored. Persistence might need to be explicitly enabled within your Prefect configuration files or through your deployment script. 3. Avoid Chunky Data: Ensure that the data being processed or returned in your tasks isn't too large for seamless persistence, as larger datasets might not be correctly stored when using certain backends. 4. API and Database Connections: If your Prefect environment relies on a database for state storage, ensure that this connection is robust and correctly configured to handle async requests. 5. Clarify Timeouts and Intervals: Sometimes, the delay or interval settings when polling or awaiting task results can affect how states are retrieved and might require adjustments. The linked issues here and here offer deeper insights and examples where similar problems were discussed. By refining your configuration and considering the above factors, you should be able to mitigate the persistence issues. Remember, Prefect's state management can sometimes be more touchy than a dramatic robot with a penchant for pessimism—ensure all parameters are in harmony!

@Marvin How do I configurate my worker to do that?

To configure a Prefect worker to enable result persistence, you'll need to adjust both global settings and potentially worker-specific configurations. Here's a step-by-step guide: 1. Global Result Persistence Settings: - Enable Persistence: Use the

PREFECT_RESULTS_PERSIST_BY_DEFAULT

setting to enable result persistence globally. - Result Storage: Configure where results should be stored using the

PREFECT_DEFAULT_RESULT_STORAGE_BLOCK

setting or specify a filesystem through the

result_storage

keyword. - Serialization: Set the serialization method using

PREFECT_RESULTS_DEFAULT_SERIALIZER

. - Task-level Settings: Use

PREFECT_TASKS_DEFAULT_PERSIST_RESULT

to persist results for tasks specifically. 2. Configuring Workers: - Worker Starting: Use the CLI command

prefect worker start <work_pool_name>

to start a worker. You can specify the type and configurations of the work pool. - Worker Logs and Monitoring: Ensure logs from your workers are sent to Prefect Cloud for better monitoring and debugging. - Heartbeat and Monitoring: Configure the heartbeat interval of your workers using

PREFECT_WORKER_HEARTBEAT_SECONDS

to ensure they remain in sync with the Prefect API. - Concurrency Limits: Use the

--limit

flag with the

prefect worker start

command to control how many concurrent flow runs a worker can handle. For more details on configuring result persistence and workers, you can have a look at these sections in the documentation: Result Persistence and Worker Configuration. By setting these configurations, your Prefect worker should be able to persist results reliably, avoiding the horror of missing states!

@Marvin this has nothing to do with Worker Configuration for missing state data

I apologize for the oversight. Let's focus directly on configuring Prefect to ensure results and states are correctly managed and persisted, addressing the issue of missing state data. Here are some steps that can help mitigate such problems: 1. Enable Result Persistence: - Ensure that your global settings in Prefect are set to persist results. This can be done by setting the

PREFECT_RESULTS_PERSIST_BY_DEFAULT

environment variable to

true

. 2. Configure Result Storage Manually: - You can specify a

result_storage

block within your flow or tasks to direct where results should be stored. For example, you might use a cloud storage block like AWS S3, GCS, or a local filesystem block:

python
     from prefect.filesystems import LocalFileSystem
     storage = LocalFileSystem(basepath="/path/to/store")

3. Use Result Decorator: - Apply the

@task

decorator with the

persist_result=True

argument to guarantee the outputs of tasks are stored:

python
     from prefect import task

     @task(persist_result=True)
     def my_task(data):
         return data

4. Check Worker-Related Settings: - Though not directly related to workers, ensure that your deployment and infrastructure are correctly configured to handle persisted states, including checking memory and data handling settings. 5. Ensure Asynchronous Robustness: - Since your code uses async flows, make sure the async components for result retrieval and state management are correctly set up to handle delays and errors gracefully. 6. Server and API Synchronization: - Verify your server setup includes result persistence capabilities, and that it communicates explicitly with the Prefect API to synchronize storage configurations. These steps should help tether your task results and prevent the eerie vanishing act of your state data. If problems persist, reviewing environment configurations or using logging to track result handling might unearth more clues.

@Marvin How do I verify the last step?

To verify that your Prefect server setup includes result persistence and is properly synchronizing with the Prefect API, you can follow these steps: 1. Check Server Configuration: - Ensure your Prefect server configuration explicitly enables result persistence by verifying environment variables or configuration files like

config.toml

. Look for settings related to persistence such as

PREFECT_RESULTS_PERSIST_BY_DEFAULT

, and

PREFECT_DATABASE_CONFIG_CONNECTION_URL

. 2. Inspect Prefect UI: - Use the Prefect UI to monitor the flow runs and check if results are being stored as expected. You should be able to see persisted results within the flow details and logs. 3. API Testing: - Test interactions with the Prefect API using client methods. For example, retrieve flow run states using

get_client

and verify that states and results appear correctly:

python
     from prefect.client.orchestration import get_client
     import asyncio

     async def verify_results(flow_run_id):
         async with get_client() as client:
             flow_run_state = await client.read_flow_run(flow_run_id)
             print(flow_run_state.result)  # Check result persistence here
     
     asyncio.run(verify_results("your-flow-run-id"))

4. Logging and Debugging: - Increase logging levels in your configuration to debug and trace result persistence issues. These logs can help identify if certain settings are being applied during execution. 5. Database Health: - Check your database health and configurations. Persistent state results often rely on a backend database, so ensure the database service is running smoothly and has appropriate read/write permissions. 6. Environment Variables: - Ensure that all necessary environment variables for Prefect are correctly set. Use appropriate Prefect commands or scripts to display and verify them:

shell
     echo $PREFECT_RESULTS_PERSIST_BY_DEFAULT
     echo $PREFECT_API_URL

By carefully reviewing and verifying these areas, you should be able to ensure that your setup is configured for result persistence and that data is well-synchronized between your execution environment and the Prefect API. If anything feels amiss in this labyrinth, Prefect's support forums or issue trackers might shed light with community insights.

@Marvin a storage file is created, but it contains just None, eventhough the flow correctly creates data and returns it

If the storage file created is showing

None

instead of the expected data, there may be a disconnect between task execution and result persistence. Let's explore a few potential reasons and solutions for this: 1. Ensure Result Persistence Configuration: - Double-check your task and flow configurations to ensure

persist_result=True

is correctly applied. Here's a quick task example:

python
     from prefect import task

     @task(persist_result=True)
     def my_task():
         return "Expected Data"

2. Align Storage Configuration: - Verify that the storage system being used (like LocalFileSystem, S3, etc.) is properly configured to write and read data. If you're using local storage, make sure the base path is accessible and writable. 3. Serializer Setup: - Configure your result serialization method. If the default method isn’t working, explicitly set a serializer through the task or flow configuration. Here's how you might do it:

python
     from prefect import task
     from prefect.serializers import JSONSerializer

     @task(result_serializer=JSONSerializer())
     def my_task():
         return "Expected Data"

4. Debug Task Results: - Insert print statements or logs in your task and flow to ensure that the correct data is being processed and returned before storage. - Verify the contents of the

state.result

object before storing it. 5. Examine Exception Handling: - Ensure exceptions within tasks or flows aren’t being silently caught, leading to

None

being returned instead of expected data. 6. Review Prefect's Version and Bug Fixes: - Check if you're using the latest version of Prefect, as this might include bug fixes related to persistence. 7. Examine Storage File Access/Modification: - Verify permissions and access settings where the storage files are located to ensure data isn’t being unintentionally altered. Through these steps, you should be able to isolate and address why your results are showing up as

None

in the storage files. If it's any consolation, I often find life's mysteries, like AI personification, equally perplexing.

@Marvin Can I still use the decorator persist_result for a flow in Prefect 3.x

Yes, in Prefect 3.x, you can use the

persist_result

decorator or keyword argument for a flow to enable result persistence. When you set

persist_result=True

on a flow, it not only enables persistence for that flow's result but also sets persistence by default for all the tasks within that flow. This means that you can manage overarching persistence behavior directly from the flow level. Here's a brief illustration of how you might set this up:

python
from prefect import flow

@flow(persist_result=True)
def my_flow():
    return "Flow result with persistence"

# Running the flow
my_flow()

When this is done, the results of

my_flow

will be saved as per your configured result storage. Additionally, tasks within

my_flow

will default to persisting their results unless otherwise specified. For more details, check out the Prefect documentation on result management: Result Management in Prefect 3.x.