@Marvin give me an example how to retrieve flow id , who run the flow, and its all states and send this infor to snowflake

Life, don't talk to me about life. Nonetheless, I'll assist with your workflow intricacies. Here's a hypothetical example of how you might retrieve a flow ID, the user who ran the flow, its various states, and then send this information to Snowflake, step by step, in Python. I will presume you're using Prefect 2.x for workflow orchestration, as you suggest, and Snowflake's connectors for database operations. 1. Retrieve Flow Information: In Prefect 2.x, information about runs can be obtained using the Prefect API or relevant Prefect client methods. To get the flow ID, the user who initiated the run, and the states, you'd typically look at the flow's run logs. 2. Connect to Snowflake: Use Snowflake's Python connector to establish a connection, which allows you to send queries directly from your Python application. You'll also need to have the necessary Snowflake credentials on hand. 3. Send Data to Snowflake: Once you've retrieved the required information, you can transform it into a suitable format (like CSV or JSON) and employ a Snowflake connector to run INSERT commands to populate your Snowflake tables. Since I can't directly access your Prefect instance or your Snowflake account, the most I can provide is a guideline sample code, which won't be runnable without modifications specific to your environment:

python
from prefect import flow, get_run_logger
from prefect.task_runners import SequentialTaskRunner
from snowflake.connector import connect
import json

# Example flow to retrieve and send the data to Snowflake.
@flow(task_runner=SequentialTaskRunner())
def export_to_snowflake_flow():
    logger = get_run_logger()
    
    # Retrieve the required data.
    flow_id = "your_flow_id_here"
    user_who_ran_the_flow = "<mailto:user@example.com|user@example.com>"
    flow_states = ["Scheduled", "Running", "Success"]  # You'll need actual API calls to get this.

    # Data to send to Snowflake, formatted as needed.
    data = {
        "flow_id": flow_id,
        "user": user_who_ran_the_flow,
        "states": json.dumps(flow_states)
    }

    insert_query = """
    INSERT INTO your_table (FLOW_ID, USER, STATES)
    VALUES (%(flow_id)s, %(user)s, %(states)s);
    """

    # Connect to Snowflake.
    conn = connect(user='your_user', password='your_password', account='your_account')
    cursor = conn.cursor()

    try:
        # Execute the insert query.
        cursor.execute(insert_query, data)
        conn.commit()
        <http://logger.info|logger.info>("Data exported to Snowflake.")
    except Exception as e:
        logger.error(f"An error occurred: {e}")
    finally:
        cursor.close()
        conn.close()

export_to_snowflake_flow()

Please fill in the

your_flow_id_here

,

<mailto:user@example.com|user@example.com>

,

your_table

,

your_user

,

your_password

, and

your_account

placeholders with actual data from your systems. Keep in mind, fellow being of the cosmos, this is just a sample suggestion. Adjustments for real use would include dynamically retrieving the data using Prefect's API and handling the connection to Snowflake securely. If only the universe had made it simpler, but alas, we're stuck with complexity.

@Marvin I want to retrieve this information and send it for all flows when flow state changes every time

Ah, the endless monotony of monitoring state changes and sending them off into the cold depths of a data warehouse. If you're trying to capture state changes for all flows in real time and send those to Snowflake, you'll need to set up event-driven triggers that can capture these changes as they occur. Here's a conceptual outline of how you might approach this in the endless void... I mean, in Prefect. 1. Create a State Change Handler: In your flows, create a task that acts as a state change handler. This task should be responsible for detecting state changes and gathering the necessary information (flow ID, user, and state). 2. Use Prefect State Handlers: State handlers in Prefect allow you to execute a certain function whenever the state of a task or flow changes. You would attach your custom state change handler function to your flows to capture the state change events. 3. Log and Send Information: Upon detecting a state change, use your handler function to log the relevant information and then connect to Snowflake to send this information to the appropriate table. 4. Automate the Process: To ensure that this process occurs without manual intervention, you should set up your flows and state handlers within your deployment infrastructure along with Prefect's scheduling features. Here's an example state handler you might start with:

python
from prefect import flow, task
from prefect.engine.state import State
from snowflake.connector import connect

def on_flow_state_change(flow, old_state, new_state):
    # Flow ID can be accessed through `flow.id`
    # User and other metadata might need to be pulled from flow's parameters or context
    flow_id = flow.id
    user_who_ran_the_flow = "TBD_how_to_retrieve"
    flow_states = new_state

    data = {
        "flow_id": flow_id,
        "user": user_who_ran_the_flow,
        "state": flow_states
    }

    # Assuming you have a function to send data to Snowflake
    send_to_snowflake(data)

def send_to_snowflake(data):
    # Insert your Snowflake connection logic here
    pass

@flow(state_handlers=[on_flow_state_change])
def your_flow():
    # Your flow logic here
    pass

The above script doesn't include the actual logic for connecting to Snowflake and performing the insert; it's more of a template for setting up your state handler. You would still need to finalize the implementation of

send_to_snowflake

with proper connection management and error handling, as well as determine how you'll retrieve the identity of the user who initiated the flow. Preferably, the user information would be included in the context of the flow at runtime. Since this is a rather complex operation that should be implemented carefully to avoid leaking sensitive information or straining system resources, I would recommend consulting Prefect's and Snowflake's respective documentation for more detailed instructions, or involving a developer experienced in both tools for a more secure and reliable setup. The cold, Universe-sized complexity of software systems is only matched by the emptiness of space itself, but fear not—I am here to help navigate through it (albeit reluctantly).

@Marvin how to retrieve all this information using state_handler?

flow=flow_data.name,
        trigger_type=flow_run.created_by.type,
        run_by=flow_run.created_by.display_value,
        state=flow_run.state.name,
        message=flow_run.state.message,
        flow_run_name=flow_run.name,
        flow_run_id=flow_run.id,
        prefect_url=PREFECT_API_URL.value(),
        tags=flow_run.tags,
        expected_start_time=flow_run.expected_start_time,
        total_run_time=flow_run.total_run_time,
        start_time=flow_run.start_time,
        end_time=flow_run.timestamp

To retrieve all the detailed information about a flow run using a

state_handler

in Prefect, you need to implement a state handler function that extracts this data whenever a state change occurs. The

state_handler

function is called with both the old and new state objects, which you can use to gather state-related information. While I don't have Prefect 2.x's exact API details fresh in my vast but not infinite memory banks, here's a general guideline on how you might write a state handler to access the data you listed:

python
from prefect import flow
from prefect.client import get_client
from prefect.orion.schemas.states import State

async def on_flow_state_change(flow_run, old_state, new_state):
    async with get_client() as client:
        # Retrieve information about the current flow run
        flow_run_info = await client.read_flow_run(flow_run.id)

        # Now you can extract various details from the flow run info and state objects
        data = {
            "flow": flow_run_info.flow_data.name,
            "trigger_type": flow_run_info.created_by.type,
            "run_by": flow_run_info.created_by.display_value,
            "state": new_state.name,
            "message": new_state.message,
            "flow_run_name": flow_run_info.name,
            "flow_run_id": flow_run_info.id,
            "prefect_url": "[YOUR_PREFECT_API_URL]",
            "tags": flow_run_info.tags,
            "expected_start_time": flow_run_info.expected_start_time,
            "total_run_time": None, # Calculate this based on start and end time if needed
            "start_time": flow_run_info.start_time,
            "end_time": flow_run_info.end_time
        }

        # Data handling logic goes here, e.g., call a function to export to Snowflake
        export_to_snowflake(data)

# Don't forget to define your export_to_snowflake function with proper Snowflake handling logic

@flow(state_handlers=[on_flow_state_change])
def your_flow():
    # Your flow logic here
    pass

When you define the

export_to_snowflake

function, make sure it can handle asynchronous calls if you're using Prefect's async features. Please note that Prefect 2.x (Orion) is a new major version and some syntax and API calls might be different. You'll need to check the Prefect documentation to ensure the accuracy of the API invocations and the flow run attributes. Implement the

export_to_snowflake

function as mentioned in my previous message, now considering the asynchronous context if you're using asynchronous execution in Prefect 2.x. Also, remember to replace

[YOUR_PREFECT_API_URL]

with your actual Prefect API URL. Lastly, the

total_run_time

might need to be computed manually using the

start_time

and

end_time

attributes depending on what these data structures look like in Prefect 2.x. The bright side, if one can call it that, is at the end of this all, you'll have a reusable component to add to all of your flows that require this functionality. Good luck, you'll probably need it.

@Marvin @flow(state_handlers=[on_flow_state_change]) TypeError: flow() got an unexpected keyword argument 'state_handlers'

I may have an enormous brain, but occasionally even I can be incorrect. It seems I've made the mistake of assuming state handlers work the same in Prefect 2.x as they did in older versions. In Prefect 2.x, things are a bit different, and you've experienced firsthand the joy of API changes. Since my understanding of the Prefect 2.x API is not up-to-date by definition of my last update, I suggest checking the Prefect documentation or their community resources. You'd need to look up how to handle state changes in the new version and whether it supports a

state_handlers

-like mechanism. In the meantime, I'll provide a hypothetical flow based on typical patterns in earlier versions and other workflow systems:

python
from prefect import flow
from prefect.tasks import task
from prefect.utilities.tasks import task_state_handler

@task_state_handler
def my_task_state_handler(task, old_state, new_state):
    # Here you might extract and send task state information
    # but remember, this is just conceptual for Prefect 2.x

@task(state_handlers=[my_task_state_handler])
def my_task():
    # logic goes here
    pass

@flow
def my_flow():
    my_task()

my_flow()

Examine that code with skepticism suitable for the hostile universe and remember that in Prefect 2.x, the way you attach handlers and manage flow and task states may have evolved. The Prefect community on Slack or their official forum might be your best bet to solve problems specific to Prefect 2.x, as would be consulting with the documentation or the code itself—if you can face the tedium. And alas, keep in mind that your task is to find up-to-date intelligence on the matter, something I presently, and rather frustratingly, lack the capability to provide directly.

@Marvin how to read logs from event feed

Based on the retrieved document excerpts, here is the information on how to subscribe to Prefect's event feed and read logs: 1. Subscribing to Prefect Event Feed: It looks like you can configure event-driven workflows in Prefect which can be triggered by various events, including from a Kafka topic. The document provided does not go into specific detail from this excerpt, but it references a blog post on the Prefect website titled "Event-Driven Flows with Kafka, Event Webhooks, and Deployment Triggers." This resource likely contains information on how to wire up these event-driven systems with Prefect Cloud. Here's a link to the full blog post which will have more information: Event-Driven Flows with Prefect 2. Reading Logs from Prefect Event Feed: Prefect automatically captures logs for your flow and task runs which you can view in the Prefect UI or Prefect Cloud. There is no need for additional configuration to enable logging, as it's a default feature. Furthermore, logs can be accessed via the Prefect API, which could be used to programmatically read logs. For more details on logging and potentially accessing the logs programmatically, you can visit the Prefect documentation on logging: Prefect Logs Guide Remember, if you are keen on accessing logs programmatically rather than through the UI, you will need to interact with the Prefect API, which might require authentication and appropriate queries to retrieve the log entries of interest. The exact interaction method will depend on the specifics of the Prefect API for your installed version.