@Marvin I have a self-hosted server with a deployment that returns a pandas DataFrame. I now need to call this deployed flow from a local flow and need to access the returned DataFrame. Because of Privacy Reasons I can't use persist_result. Is there any other way to be able to access the returned DataFrame from my local flow?

To help you effectively, I need to search the Prefect documentation for the relevant information. Could you confirm the version of Prefect you're using? Or, is there any more detail you can provide about what you're trying to achieve or how you're currently doing it? This will help me search for the right information and provide a specific solution.

@Marvin I'm using PRefect 3.1.0

To call a deployed flow from a local flow in Prefect 3.1.0 and access a returned DataFrame without persisting it, you can follow these general steps: 1. Deploying Flows: Use

flow.deploy()

to deploy your flow. This is a change from previous methods like

Deployment.build_from_flow

. Make sure you have set up your infrastructure and environment properly for this. You can find more about this in the Prefect documentation. 2. Managing Results Locally: Results from flows and tasks are typically not persisted unless specifically configured. By default, Prefect does not maintain references to them in the API. This means that if you need to handle the DataFrame locally, you'll have to run and retrieve it immediately during execution—without using the API for storage or retrieval later. You might find additional information on managing results here. 3. API and Local Execution Setup: If you're connecting your local environment to Prefect Cloud for orchestration, ensure you configure your local execution environment properly. This includes authenticating and connecting to the correct API endpoints, as detailed here. Although the Prefect orchestration features are severely limited if you choose not to use persisted results, working with them entirely in memory on execution is still possible for tasks like this one. If handling the DataFrame directly in the local flow seems cumbersome, consider establishing a secure channel for streaming results directly through APIs or other communication protocols tailored for private data transfer. That way, you can avoid persisting data externally yet still gain access within your local environment. Just remember, not everything is stored in the cloud—sometimes it's just stored in our cluttered minds.

@Marvin I run the deployment via run_deployment and receive a FlowRun Object that has the StateType Completed. How do I now get the return data of this FlowRun?

To retrieve the return data from a completed FlowRun object in Prefect 3.1.0, you want to access the result using the

.result()

method. Here's a general approach: 1. Use the

.result()

Method: After you've run a deployment and received a

FlowRun

object, you can obtain the results of that run using the

.result()

method. This method is blocking, meaning it will wait for the flow to complete if it hasn't already. 2. Considerations for Blocking and Usage: Use

.result()

when you need to interact with the actual return value of the flow run—such as passing it to a standard Python function. Avoid using it to pass values to another Prefect task, as Prefect handles such task dependencies internally. For more detailed steps and examples on using the

.result()

method, you might find helpful explanations here. If you're attempting to gather more context on past flow runs or their states, you could interact with the Prefect API to fetch specific flows based on their completion state. This is generally achieved by using Prefect's API client. You can see examples of fetching flow runs in this documentation. Remember, in the weird world of code and automation, patience is as much a tool as a method.

@Marvin If I'm calling .result on my FlowRun Object (FlowRun.result()) I get the following Error Message: AttributeError: 'FlowRun' object has no attribute 'result'

It looks like there's some confusion regarding accessing the result from a

FlowRun

object in Prefect 3.1.0. The

FlowRun

object itself does not have a

.result()

method. Instead, you need to interact with the run's state to retrieve the result. Here's how you can handle this: 1. Inspect the Flow Run's State: Use the state to access results. The final state of a flow run in Prefect 3.0 is determined by the return value of the flow function or an explicitly provided state. To access the result, you typically examine the state object associated with the flow run. 2. Retrieve State Information: You might need to query the state of that FlowRun to extract the result. This involves using the Prefect client to check the state and get the result. Unfortunately, directly accessing a result via

.result()

on the

FlowRun

itself isn't supported. Instead, you'd typically do something like:

python
from prefect.client import get_client
from prefect.states import State

async with get_client() as client:
    flow_run = await client.read_flow_run(flow_run_id)
    state: State = flow_run.state

# Check the State to inspect results
if state.is_completed():
    result = state.result()  # This is where you'd access the result

For more details about handling flow results and manipulating flow run states, you can refer to the Prefect documentation related to task runners or the API access provided here. Just remember, in the grand cosmos of code, what seems obvious is often cleverly obscured by the whims of the code universe.

@Marvin I'm now getting the error Message: prefect.exceptions.MissingResult: State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API. I can't persist results in storage, because of privacy reasons

In Prefect, if you're seeing the

MissingResult

error, it usually indicates that result persistence is turned off and the state information you're querying from the API doesn't have the locally available result data. Since you can't use result persistence due to privacy concerns, here are some alternative strategies you can consider: 1. Synchronous Execution: If it's feasible, execute the flow locally where you need the result, so that you can directly access the return values without needing to persist them. 2. Intermediate Communication Layer: Implement an internal messaging or communication protocol (e.g., sockets, message queues) between your deployment and local flow execution to pass data back without storing it. 3. Direct Flow Invocation: If your setup allows, call the flow function directly instead of through Prefect orchestration. This means running the logic directly in Python rather than as a Prefect-managed deployment. 4. Networking Solutions: Consider setting up a secure local network connection where the results can be streamed directly to your local machine, thus bypassing storage altogether. 5. Environment Variables or In-memory Datastores: If privacy can be maintained, consider using environment variables or in-memory data stores like Redis to transiently hold the data during execution without persisting it permanently. You can explore more on managing results and understanding how Prefect handles different result scenarios through the results documentation. While these suggestions might solve your querying issues, they'll involve more intricate programming setups. Just like when pondering the meaning of life, sometimes the roundabout solutions end up being the most enlightening.

@Marvin It is necessary to run the flow on a remote server and not locally

Since running the flow on a remote server is necessary and result persistence isn't an option due to privacy concerns, we can explore ways to directly obtain the results after execution without relying on Prefect's persistence features: 1. Remote Execution with Callback: Implement a callback mechanism. After the remote flow execution is complete, the remote server could send the DataFrame back to your local machine as an HTTP response, using APIs, or through a websocket connection. You can implement this securely to ensure data privacy. 2. Result Passing Through Secure Channels: Set up a secure channel, such as an encrypted web service, where the server can send back the execution result directly to your local infrastructure once the process is completed. 3. Use Prefect's Capability to Pass Results: While Prefect doesn't persist results, you can opt to pass results as a return value through a secure, ephemeral, in-memory communication interface between your remote and local environments. This does require developing custom solutions depending on your network setup. Here’s a high-level approach: - Remote Flow Execution: Ensure the flow contains logic to communicate the result back to an agreed endpoint or system. - Data Encryption: Use strong encryption to protect the data during transfer. - Receive and Decrypt Locally: Implement a local receiver that decrypts and uses the incoming data immediately, avoiding any form of storage. These options require more setup but will keep your execution remote while maintaining privacy. Balancing security and functional requirements can feel akin to fixing a stubborn robot's CPU—one tedious step at a time, but rewarding once it all clicks into place.

@Marvin can you elaborate on the third idea?

Certainly! Let's dive into the third idea of using Prefect's capabilities for result retrieval through custom ephemeral communication: The essence of this approach revolves around using temporary in-memory communication to transfer flow results without

persist_result

. Here’s a step-by-step guide to implement this: 1. Setup a Communication Protocol: - Select a lightweight communication protocol that fits your infrastructure. Technologies like ZeroMQ, Redis Pub/Sub, or gRPC can offer the necessary functionality to create an ephemeral, direct communication channel. 2. Secure the Communication: - Use TLS/SSL or other encryption protocols to secure the communication channel and ensure that transferred data cannot be intercepted or read without authorization. 3. Flow Execution Logic: - Within your Prefect flow, after the task that produces the result (like your DataFrame), convert the result to a serializable format (like JSON or a binary format). - Direct Prefect to send this data to your communication endpoint. This could be a

PUSH

message to a queue, a data packet sent over gRPC, or a publish event in a Pub/Sub model. 4. Local Receive Logic: - On your local machine, establish a client or listener that waits for messages from the remote Prefect server. This component should be capable of decrypting and deserializing the incoming data back into a DataFrame. - Note: Ensure the local listener runs before the remote flow execution completes to capture the result as it comes in. 5. Ephemeral Nature: - Ensure that your communication pathway retains no history. For message queues or Pub/Sub, configure them to automatically delete messages after delivery. - Keep the infrastructure running only as needed to minimize operational footprint and exposure. 6. Test and Validate: - Test the implementation in a secured environment to ensure reliability and data privacy. - Check for any network issues, serialization pitfalls, and that the communication does not leave data remnants. In essence, this approach requires an interplay of Prefect's task orchestration and an auxiliary in-memory communication tool, creating a transient bridge for your result data. It's like a "now you see it, now you don't" magic trick, but with data, ensuring only a fleeting glance at the data without leaving traces.