@Marvin When using prefect server to start flow runs via a deployment, and prefect worker to execute flow runs, is the result returned by the flow function stored in the server?

In both Prefect 2.x and 3.x versions, the results from flow runs are not automatically stored on the Prefect server. Here's a quick overview: 1. Prefect 2.x and 3.x: By default, task results are stored in memory in the execution environment. This method keeps flow runs fast but isn't suited for large data handling, where you may want to write results to disk or cloud storage (like AWS S3) for persistence. This storage helps with re-running tasks using cached data, avoiding unnecessary computation the next time. 2. Result Storage Configuration: You can configure your flows and tasks to use result storage, which captures the result and persists it in a specified storage. This could include local disk storage, cloud storage, and other storage blocks provided by Prefect integrations. For more detailed insights and examples, you can check out the documentation links: - Prefect 2.x Details: Prefect 2.x Results Concept - Prefect 3.x Details: Prefect 3.x Big Data Handling Remember, storage is like a jazz solo—improvisation can sometimes lead to unexpected places!

@Marvin do functions annotated with

@flow

also support result_storage or just

@task

functions?

Both in Prefect 2.x and 3.x versions, functions annotated with

@flow

can indeed support result storage. ### Prefect 2.x: - You can specify result storage for flows using the

result_storage

option in the

@flow

decorator. This enables persistence for all tasks within the flow by default unless a task specifies its own storage. - Here's an example:

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)
  def my_task():
      ...

See more on Prefect 2.x Result Storage ### Prefect 3.x: - Similar to Prefect 2.x, you can enable result persistence using the

persist_result

and

result_storage

parameters on a

@flow

decorator. - This configuration affects all tasks within the flow unless overridden by the task-specific settings. Explore Prefect 3.x Flow Persistence In short, flows can take a load off tasks by storing results directly through the flow's configuration itself. Who knew flows had such range, right?

@Marvin does Prefect support a way to retrieve results from a flow (or task)? Or do applications need to keep track by some other way of what is the storage location of the results a flow or task produced?

Yes, Prefect provides a way to retrieve results from flow or task runs without needing to manually track the storage location. ### Prefect 2.x - Retrieving Results: You can retrieve the results of flow and task runs using the

State.result()

method. This method fetches the result state, and you can use

fetch=True

to automatically retrieve persisted results. - Example:

python
  import asyncio
  from prefect import flow, task

  @task
  async def my_task():
      return 1

  @flow
  async def my_flow():
      state = await my_task(return_state=True)
      result = await state.result(fetch=True)
      return result + 1

  asyncio.run(my_flow())

### Prefect 3.x - Result Management: Results, by default, are not persisted and no reference is kept in the API. However, enabling result persistence allows full access to orchestration features like caching and transactions. - Using `.result()`: For interactions with the return value of tasks, the

.result()

method can be used. It blocks until the task run completes and is particularly useful for direct interactions with Python functions, not just Prefect tasks. For more information, you can refer to: - Prefect 2.x Result Methods - Prefect 3.x Result Management With Prefect, you can opt-out of becoming your application's memory bank of data locations—Prefect's result retrieval handles it for you!

@Marvin consider an application that launches a flow run via a deployment using Prefect server, and a Prefect worker executes that flow run. How can the application later retrieve the return value of the flow function after the flow run has finished executing?

To retrieve the return value of a flow function after a flow run has finished executing via a deployment with Prefect, you can use the following approach: ### Prefect 2.x 1. Use the Prefect API: You need to interact with the Prefect API to get the state of the flow run, which contains the return value. 2. State.result() Method: Once you have the flow run state, you can use the

State.result()

method to get the flow's result. If the results are persisted, use

fetch=True

to ensure the results are fetched from storage. Here's a brief example:

python
from prefect import Client

client = Client()
flow_run_id = "your-flow-run-id"
state = client.get_flow_run_state(flow_run_id)
result = state.result(fetch=True)
print(result)

### Prefect 3.x 1. State Management: Utilize the state management features in Prefect to handle and query the flow's final state. 2. Direct State Access: While Prefect 3.x emphasizes direct API interaction for tracking and results retrieval, typically, you interact with state objects through the Prefect UI or API, and extract results similarly to Prefect 2.x by accessing the appropriate flow run state and reading the result. For more details and nuances about retrieving results: - Prefect 2.x Results Documentation - Prefect 3.x Manage States Documentation Remember, in this complex dance of flows and tasks, even data needs its moment in the spotlight—a Prefect API call or two makes sure it gets there!

@Marvin Keep the answers focused on Prefect 3.x only. No need to mention version 2.x. Produce Python code showing how an application can retrieve the return value of a flow function, not the state of the flow run, and not the return value of a task of the flow, but the final return value of the flow.

To retrieve the final return value of a flow function in Prefect 3.x, you can interact with the Prefect API to get the flow run's state and result. Here's a snippet of Python code demonstrating how you can achieve this:

python
from prefect import OrionClient
import asyncio

async def get_flow_run_result(flow_run_id):
    async with OrionClient() as client:
        # Retrieve the state of the flow run
        flow_run_state = await client.read_flow_run_state(flow_run_id)
        
        # Access the result of the flow run
        result = await flow_run_state.result()
        return result

flow_run_id = "your-flow-run-id"

# Run the async function
result = asyncio.run(get_flow_run_result(flow_run_id))
print("Flow run result:", result)

Ensure that your application is configured to connect with your Prefect server and replace

"your-flow-run-id"

with the actual ID of your flow run. This code will fetch the final result of your completed flow run, provided the result is serializable, directly from Prefect's API. For further details, you can also look into Prefect 3.x Write Flows Documentation. Now you have your flow's result without needing to perform detective work on where it ended up!