@Marvin In prefect 2.19.3 how would I go about retrieving the results for a previous ran flow run through the python sdk. For example, using the FlowRun object when running run_deployment as well as using the flow_run id to retrieve the result later

To retrieve the results of a previously run flow in Prefect 2.19.3 using the Python SDK, you can utilize the

FlowRun

object and the Prefect client. Here's a general approach: 1. Using the FlowRun Object: If you have a

FlowRun

object from executing

run_deployment

, you can access its state to retrieve results. The state contains a

result

attribute, which can be accessed if the state is completed successfully. 2. Using the Flow Run ID: If you only have the flow run ID, you can use the Prefect client to query the flow run details and access the result. Here's a simplified example:

python
from prefect import get_client
from prefect.states import State

async def get_flow_run_result(flow_run_id):
    async with get_client() as client:
        flow_run = await client.read_flow_run(flow_run_id)
        if flow_run.state.is_completed():
            return flow_run.state.result
        else:
            print("Flow run is not completed yet or failed.")

# Usage
# result = asyncio.run(get_flow_run_result("your-flow-run-id"))

- Ensure that your flow and tasks are configured to persist results. You can specify result storage in your flow definition using Prefect's result storage options like S3 or local file system. For more detailed information, you can refer to the Prefect documentation on using the client and results handling. If you're interested in exploring more about Prefect's top-level API, you can check out the API reference.

@Marvin if the result is saved to s3, I recall having to set something like fetch=True

To retrieve results stored in S3 with Prefect, you may need to specify the

fetch=True

parameter when accessing the results. This ensures that the results are fetched from the remote storage (like S3) rather than relying on any cached or local copies. Here's a conceptual overview of how you can do this: 1. Persisting Results: When defining your flow and tasks, ensure that results are set to be persisted to S3 by specifying

persist_result=True

and defining a

result_storage_key

. 2. Fetching Results: When you need to retrieve these results, use the

fetch=True

parameter to ensure the results are fetched from S3. Here's an example setup:

python
from prefect import flow, task
from prefect.filesystems import S3

@flow(result_storage=S3(bucket_path="my-bucket"))
def my_flow():
    my_task()

@task(persist_result=True, result_storage_key="my_task.json")
def my_task():
    return "result_data"

# To run the flow
# my_flow()

# To fetch the result later, ensure to use fetch=True when accessing the task result

For more detailed information, you can refer to the results handling documentation. This will provide more context on how Prefect handles result persistence and retrieval.

@Marvin Where do I specify that?

It seems I couldn't find specific documentation on specifying

fetch=True

directly in the context of retrieving results from S3 using Prefect. However, typically, fetching a result from a remote storage like S3 would involve ensuring that the result is retrieved rather than using any cached version. In Prefect, when dealing with task results stored in remote locations, the retrieval process is usually handled by Prefect's result handling mechanism. If you have a flow or task configured to use S3 for result storage, the results should be automatically fetched when you access them, assuming the task or flow run has completed successfully. If you have any specific method or function call in mind where

fetch=True

is to be used, it might be a part of a custom implementation or an integration that uses Prefect's result handling. If you find any specific method or context where this parameter is used, please let me know, and I can help further!

@Mitch sorry didn't read super closely but fetch should not be required should just be something like

flow_run = client.read_flow_run(...) # or however you get the flow run object
print(flow_run.state.result()) # requires the flow run in question persisted the result

Thanks!