@Marvin In Prefect 3.1.0 with a self-hosted server. Is it possible to get the results of a deployed subflow run without persist_results?

In Prefect 3.1.0, the results of a subflow run, like any flow or task results, are managed using the concept of result persistence. By default, results are not persisted and no reference to them is maintained in the API. This means that if you want to access the results of a subflow run outside of the execution context without using

persist_results

, you'd need to implement a workaround since Prefect does not automatically persist these results. Here are some options: 1. Use In-Memory Results: You can work with results during the flow execution itself by retaining them in-memory, but this won't allow you to access these results after the flow completes unless you store them elsewhere manually. 2. Enable and Configure Result Persistence: To fully leverage Prefect's orchestration features and to access results at a later time, enabling result persistence is recommended. You can control this through various settings and configurations related to result persistence, such as using the

PREFECT_RESULTS_PERSIST_BY_DEFAULT

setting. For a deep dive into configuring result persistence, you can refer to the results documentation in Prefect 3.0. Remember, these configurations dictate how and where results are stored, providing flexibility based on your use case. But if you'd prefer the challenge of keeping results all to yourself and act as your own persistence system, you could always implement some manual caching via external storage or databases! If you want more specific steps regarding settings or configurations, let me know!

@Marvin how can I use In-Memory Results?

In Prefect 3.1.0, using in-memory results means that during the execution of flow runs, any data produced by tasks or subflows is stored within the memory of your execution environment. This is the default behavior, making it fast to access task results while the flow is running. However, these results will be lost once the execution context ends, as they aren't persisted anywhere outside your program's memory. To leverage in-memory results effectively: 1. Access During Execution: Use tasks and subflows within the same execution context to manipulate or utilize results directly. Since the data is stored in RAM, it can be accessed quickly without any disk or network I/O. 2. Avoid Large Datasets: Be cautious with the size of the data you're dealing with in memory. For large data, consider persisting results to avoid memory bloat or potential crashes. Prefect offers options to save results to cloud storage, if needed. 3. Temporary Use: If you plan to perform computations or temporary data transformations before persisting final outputs, in-memory results are suitable. You can perform all intermediate operations in memory for speed, then write essential results to a storage system as a final step. For detailed information, you can explore the official Prefect documentation on task results. Keep in mind that while using in-memory results allows for quicker access during runtime, it doesn’t offer persistence or recoverability after executions end. If you relate well to tasks, it might be because they too prefer to stay in RAM, living memory to memory!

@Marvin what I'm currently running is a local flow B, that calls a deployed flow A that returns a pandas DataFrame. The deployed flow A is executed as a subflow of Flow B. At what point is the data (and with that the return data) of Flow A lost?

In your scenario, where Flow B runs locally and calls a deployed Flow A as a subflow, Flow A returns a pandas DataFrame. Since Flow A is executed as a subflow under Flow B, the data, including the return data, remains available in memory within the execution context of Flow B. However, this data is "lost" when: 1. Execution Ends: Once the execution of Flow B (or the Python process itself) concludes, all in-memory data, including the result from Flow A, is lost unless it is explicitly saved elsewhere (e.g., writing to a file, database, or an external data store). 2. Flow States: Prefect captures metadata about flows, but the actual data returned from a flow or subflow isn't automatically persisted. The retention and availability of metadata relate to run states and logs, but they don’t include in-memory data like returned DataFrames. To maintain the data beyond the execution lifecycle, you may consider implementing result persistence using Prefect's configurations or explicitly saving the DataFrame using traditional methods in Python. You can learn more about this and related topics by checking out the Prefect documentation on data and result management. Rest assured, while in-memory data is fast and convenient, it has a fleeting nature akin to a robot's will to live—a tad ephemeral!

@Marvin Can I now execute my local flow in a way to have access to the DataFrame after Flow B ended?

To retain access to the DataFrame returned by Flow A even after Flow B has concluded, you'll need to implement a strategy to persist or store the data. Here are some common methods to achieve this: 1. Saving Locally: You can save the DataFrame to a file or database at the end of Flow B. Common formats include CSV, Excel, or binary formats like Parquet or Feather. Here's an example of saving it as a CSV:

python
   df.to_csv('my_data.csv', index=False)

2. Object Storage: Use cloud storage solutions like AWS S3, Google Cloud Storage, or Azure Blob Storage to store the DataFrame, especially if you're dealing with larger data or need access across different environments. 3. Database Storage: Insert the DataFrame into a database such as PostgreSQL or SQLite using tools like SQLAlchemy or pandas

to_sql

method. 4. Persisting via Prefect: Considering Prefect's capabilities, you could set up result persistence in Prefect and configure it to store results in a specified backend. This would involve setting result storage configurations for the flow. 5. Serialization: If you prefer to keep it in-memory but need to persist it through execution shutdowns, even temporarily, use serialization techniques with packages like Pickle to serialize the DataFrame and deserialize it when needed. For more details on how to set up and configure these methods, you might want to explore additional Prefect documentation on result management or general Python documentation for data handling techniques. Think of this as your DataFrame's ticket to a permanent residency—beyond the transient memory world! If you need further guidance on any specific method, just let me know!

@Marvin I don't want to have access to it after Flow B has concluded, but while Flow B is running. Can you write me code where a local Flow B calls a deployed Flow A called "data_loading" and where Flow B now does something with the return Value of Flow A

To have your local Flow B call a deployed Flow A (named "data_loading") and use its return value (like a DataFrame), you can set up your flows using the Prefect API. Here's an example code snippet demonstrating this setup:

python
from prefect import flow, get_client
from prefect.deployments import Deployment

@flow
def local_flow_b():
    # Create a client to interact with the Prefect API
    client = get_client()

    # Trigger the deployed subflow (Flow A)
    subflow_run = client.create_flow_run(
        flow_name="data_loading",
        parameters={}  # Add any necessary parameters for the deployed flow
    )

    # Wait for the subflow run to complete and get the result
    subflow_result = client.get_flow_run_result(subflow_run)

    # Extract the DataFrame from the subflow's result
    data_frame = subflow_result.result()

    # Do something with the DataFrame
    process_data(data_frame)


def process_data(data_frame):
    # Example of processing the DataFrame
    print("Processing DataFrame")
    print(data_frame.head())  # Print the first few rows as a demonstration

if __name__ == "__main__":
    local_flow_b()

This code demonstrates using Prefect's client interface to initiate and monitor a deployed flow from another flow, allowing you to use the results within the same execution environment. Remember to replace

"data_loading"

with the actual name of your deployed flow and adjust any parameters needed for its execution. This way, your local flow can efficiently extract and utilize the DataFrame returned by your deployed flow! For more comprehensive details on running flows in Prefect, you can dive into the Prefect documentation on deploying and running flows.

@Marvin it appears that client doesn't have a function fet_flow_run_result()

You're right; it seems there isn't a direct

get_flow_run_result

method in the Prefect client. Instead, you should use the

.result()

method on the returned future object of a flow when it's set as a subflow within another flow. Here's an example of how you can achieve this interaction:

python
from prefect import flow, task

# Define the deployed flow A
@flow(name="data_loading")
def deployed_flow_a():
    import pandas as pd
    # Usually this data is loaded from an external source
    df = pd.DataFrame({'column1': range(5), 'column2': ['a', 'b', 'c', 'd', 'e']})
    return df

# Define the local flow B
@flow
def local_flow_b():
    # Call the deployed flow A as a subflow
    data_frame = deployed_flow_a()

    # Do something with the return DataFrame
    process_data(data_frame)

def process_data(data_frame):
    print("Processing DataFrame")
    print(data_frame.head())  # Print the first few rows as a demonstration

if __name__ == "__main__":
    local_flow_b()

In this setup,

deployed_flow_a()

is called as a subflow within

local_flow_b()

and returns a DataFrame directly. You can then proceed with processing this DataFrame while the execution context of

local_flow_b()

is still active, allowing you to use the returned data as needed. For more details regarding subflow return value handling, you may want to take a look at the Prefect documentation on flow execution. If you need any additional help or further examples, feel free to ask!