https://prefect.io logo
Join Slack
Powered by
<@ULVA73B9P> I have a deployment.py file ```from ...
# ask-marvin
j
@Marvin I have a deployment.py file
Copy code
from sync_from_airbyte_flow import sync_from_airbyte

@task
def sync_from_airbyte_flow() -> str:
    return sync_from_airbyte()

@task
def run_dbt_transform_flow() -> str:
    get_logger().info("DBT transform flow triggered")
    return "DBT transform completed"

@flow
def trigger_deployment_flow():
    sync_result = sync_from_airbyte_flow()
    dbt_result = run_dbt_transform_flow(wait_for=[sync_result])
    return dbt_result
I want to run two flows with this deployment but the DBT transform has to wait for the airbyte sync. However when I run this in prefect managed cloud I get a fast run and it looks like the sync_from_airbyte flow is not called.
m
It seems you're trying to orchestrate two tasks within a flow where the second task (
run_dbt_transform_flow
) should wait for the first task (
sync_from_airbyte_flow
) to complete. However, the way you've structured the code might be causing the 
sync_from_airbyte_flow
to not execute as expected. Let's address the potential issues: 1. Task and Flow Decorators: Ensure that the 
@task
and 
@flow
decorators are correctly applied. In your snippet, the 
sync_from_airbyte_flow
function is both decorated as a task and referenced as a flow. This might be causing confusion in execution. 2. Use of 
wait_for
: In Prefect, the 
wait_for
parameter is used to ensure that a task waits for the completion of other tasks. However, it should be used when defining tasks, not when calling them. 3. Function Naming and Decorators: Ensure that the function names and decorators align with their intended use. It seems like there might be a mix-up between task and flow definitions. Let's refine your code:
Copy code
python
from prefect import task, flow, get_run_logger

@task
def sync_from_airbyte() -> str:
    # Assuming this function is defined elsewhere and returns a string
    return "Airbyte sync completed"

@task
def run_dbt_transform() -> str:
    get_run_logger().info("DBT transform flow triggered")
    return "DBT transform completed"

@flow
def trigger_deployment_flow():
    sync_result = sync_from_airbyte()
    dbt_result = run_dbt_transform(wait_for=[sync_result])
    return dbt_result
Key Changes: - Ensure 
sync_from_airbyte
is a task and not a flow. - Use 
get_run_logger
for logging within tasks. - The 
wait_for
parameter is correctly placed when calling 
run_dbt_transform
. Additional Steps: - Verify that your Prefect Cloud environment is set up correctly to run the flow deployments. - Ensure the 
sync_from_airbyte
function is properly defined and imported if it's located in a different module. For further details, you might want to review the Prefect 3.x documentation on tasks and flows. If you have any more questions or need further clarification, feel free to ask!
j
@Marvin I have two flows in different files should I make a deployment for each flow? How can I configure that they are dependant on each other
m
To manage dependencies between two flows in Prefect, especially when they're defined in different files, you typically have a few options. Here’s a concise guide on how to approach this: 1. Separate Deployments: Yes, you should create separate deployments for each flow if they are defined in different files and need to be run independently or on different schedules. 2. Flow Dependency Management: - If one flow needs to wait for another to complete, you can orchestrate this by creating a "master" flow that calls each of these flows in sequence. This master flow would handle the dependency explicitly. - Alternatively, you can use Prefect's orchestration tools to schedule them such that one runs after the other, but this requires careful management of schedules and triggers. Here's an example of how you can create a master flow to manage dependencies: ### Example Assume you have two flows: 
flow_a
and 
flow_b
, defined in 
flow_a.py
and 
flow_b.py
respectively. flow_a.py:
Copy code
python
from prefect import flow

@flow
def flow_a():
    # Your logic for flow A
    return "Flow A completed"
flow_b.py:
Copy code
python
from prefect import flow

@flow
def flow_b():
    # Your logic for flow B
    return "Flow B completed"
master_flow.py:
Copy code
python
from prefect import flow
from flow_a import flow_a
from flow_b import flow_b

@flow
def master_flow():
    result_a = flow_a()
    result_b = flow_b(wait_for=[result_a])
    return result_b
### Deployment Steps 1. Deploy Each Flow Separately: - Create a deployment for 
flow_a
and 
flow_b
using Prefect's deployment CLI or UI. - Ensure they are registered with Prefect Cloud or your Prefect server. 2. Deploy the Master Flow: - Create a deployment for 
master_flow
, which will orchestrate the execution of 
flow_a
and 
flow_b
. 3. Configure Schedules and Triggers: - Optionally, set up schedules for each flow if they need to run at specific times. - Use Prefect's event triggers or the 
wait_for
parameter in the master flow to ensure 
flow_b
runs only after 
flow_a
completes. ### Resources For more detailed instructions, you can refer to the Prefect documentation on deployments and orchestration. This approach ensures that your flows are modular, maintainable, and can be independently managed while still allowing you to define dependencies between them. If you need further assistance, feel free to ask!
j
@Marvin Does this also work if the master flow is deployed from source ?
m
Yes, deploying the master flow from source works similarly, but there are a few considerations to ensure it functions correctly with dependencies: 1. Flow Dependencies: When deploying the master flow from source, it should have access to the source code of the dependent flows (
flow_a
and 
flow_b
). This means your deployment environment should include all necessary files or the flows should be importable as modules. 2. Deployment Configuration: Ensure that your deployment configuration (e.g., Docker image, environment, etc.) includes all dependencies and code required to execute the master flow and its child flows. 3. Prefect's 
wait_for
Mechanism: The 
wait_for
parameter in Prefect ensures that tasks or subflows wait for specified upstream tasks to complete before executing. This mechanism will work as intended when deploying from source. ### Considerations - Source Control: Make sure your source code is version-controlled and organized so that the master flow can import and execute the dependent flows correctly. - Environment Setup: When deploying from source, ensure that the environment (such as Docker or a virtual environment) includes all necessary libraries and dependencies for both the master flow and its child flows. - Flow Registration: The master flow should be registered with Prefect Cloud or your Prefect server, just like any other flow deployment. This ensures that it can be scheduled and executed as part of your orchestration strategy. ### Example Deployment If you are deploying using Prefect CLI, you might use a command like:
Copy code
bash
prefect deployment build master_flow.py:master_flow -n "Master Flow Deployment"
This registers the master flow, and it should have access to 
flow_a
and 
flow_b
if they are in the same directory or properly installed as modules. ### Resources For more detailed guidance, refer to the Prefect documentation on deployments from source. This approach allows you to maintain a clear orchestration strategy while leveraging Prefect's capabilities to manage dependencies effectively. If you have more questions or need further clarification, feel free to ask!
j
@Marvin How do I import a flow as a module?
m
To import a flow as a module in Python, you'll need to ensure that the flow is defined in a Python file and that the file is in a location that Python can access, typically within your project's directory structure. Here's a step-by-step guide: ### Step-by-Step Guide 1. Organize Your Files: Ensure that your flow definitions are in separate Python files. For instance: - 
flow_a.py
contains the definition for 
flow_a
. - 
flow_b.py
contains the definition for 
flow_b
. 2. Project Structure: Your project directory might look like this:
Copy code
my_project/
   ├── flow_a.py
   ├── flow_b.py
   └── master_flow.py
3. Define Your Flows: Ensure each file defines a flow using the 
@flow
decorator from Prefect. For example, in `flow_a.py`:
Copy code
python
   from prefect import flow

   @flow
   def flow_a():
       # Flow logic for flow_a
       return "Flow A completed"
4. Import the Flows in the Master Flow: In your 
master_flow.py
, import the flows like regular Python modules:
Copy code
python
   from prefect import flow
   from flow_a import flow_a
   from flow_b import flow_b

   @flow
   def master_flow():
       result_a = flow_a()
       result_b = flow_b(wait_for=[result_a])
       return result_b
5. Python Path: Ensure that the directory containing your flow files is in the Python path. This is usually the case if you run your scripts from the project's root directory. 6. Run or Deploy: You can now run or deploy 
master_flow.py
as needed. ### Additional Tips - Module Naming: Ensure that your Python files (modules) do not have names that conflict with standard library modules to avoid import issues. - Virtual Environment: Use a virtual environment to manage dependencies and ensure that your Prefect installation and any other dependencies are correctly isolated. ### Resources For more information on Python imports, you can refer to the Python documentation on modules. This setup allows you to modularize your flows, making them easier to manage and reuse across different parts of your project. If you have further questions, feel free to ask!
j
@Marvin
Copy code
from prefect import flow
from prefect.runner.storage import GitRepository
from prefect_github import GitHubCredentials
from sync_from_airbyte_flow import sync_from_airbyte

@flow
def master_flow():
    sync_result = sync_from_airbyte()
    return sync_result

if __name__ == "__main__":
    github_repo = GitRepository(
        url="<https://github.com/webbio/AirByte>",
        credentials=GitHubCredentials.load("github"),
    )

    master_flow.from_source(
        source=github_repo,
        entrypoint="orchestration/master_flow.py:master_flow",
    ).deploy(
        name="Sync from Airbyte and run DBT transform",
        work_pool_name="webbio-workpool",
        image="prefecthq/prefect:3-python3.12",
        push=False,
        cron="0 0 * * *",
    )
I get a exit code 1 error when running this code in prefect managed worker pool. I think the master_flow cannot get the 
sync_from_airbyte
flow
m
The error you're encountering likely stems from the way the 
GitRepository
and 
from_source
method are being used. Let's address the key points and potential issues: 1. GitRepository and from_source: The 
GitRepository
and 
from_source
mechanisms are typically used to specify where the flow's source code is located. In Prefect 3.x, the 
from_source
method is not a standard method for flows. Instead, deployments are usually created using Prefect's CLI or programmatically via the 
Deployment
class. 2. Importing Flows: Ensure that the 
sync_from_airbyte
flow is correctly imported and accessible. If it's in a different file within the same repository, your repository structure should support this. 3. Deployment Configuration: Prefect's deployment typically involves specifying the repository, entrypoint, and any dependencies in the deployment configuration. Ensure your repository URL and entrypoint are correct. ### Recommended Approach Here's a more standard approach using Prefect's deployment capabilities: 1. Create Deployment Script: Use Prefect's deployment tools, either the CLI or programmatically, to define the deployment. 2. Ensure Correct Imports: Make sure the 
sync_from_airbyte
flow is correctly imported in your 
master_flow.py
. 3. Deployment Example: - Ensure your repository is structured correctly, with 
sync_from_airbyte
accessible in the path specified. - Use Prefect CLI to create a deployment:
Copy code
bash
prefect deployment build master_flow.py:master_flow -n "Sync from Airbyte and run DBT transform" --storage github/AirByte --work-pool webbio-workpool --cron "0 0 * * *" --image prefecthq/prefect:3-python3.12
4. Programmatic Deployment: - If you need to configure programmatically, use Prefect's deployment API:
Copy code
python
from prefect.deployments import DeploymentSpec
from prefect.infrastructure import DockerContainer
from prefect import flow
from sync_from_airbyte_flow import sync_from_airbyte

@flow
def master_flow():
    sync_result = sync_from_airbyte()
    return sync_result

DeploymentSpec(
    flow=master_flow,
    name="Sync from Airbyte and run DBT transform",
    work_pool_name="webbio-workpool",
    schedule="0 0 * * *",
    infrastructure=DockerContainer(image="prefecthq/prefect:3-python3.12"),
    storage=GitHubRepository(
        repo="webbio/AirByte",
        path="orchestration/master_flow.py",
        reference="main",  # branch or tag
        access_token_secret="GITHUB_ACCESS_TOKEN",  # Ensure this is set in your environment
    ),
)
### Key Points - Ensure Access: Make sure your Prefect environment has access to the GitHub repository, either through SSH keys or a GitHub access token. - Correct Entrypoint: The entrypoint should match the path within the repository where the 
master_flow
is defined. By following these steps, you should be able to deploy your flow correctly. If you encounter further issues, ensure that all paths and credentials are correctly configured.
j
@Marvin
Copy code
from prefect import flow
from prefect.runner.storage import GitRepository
from prefect_github import GitHubCredentials
from sync_from_airbyte_flow import sync_from_airbyte

github_repo = GitRepository(
    url="<https://github.com/webbio/AirByte>",
    credentials=GitHubCredentials.load("github"),
)

@flow
def master_flow():
    sync_result = sync_from_airbyte.from_source(
        source=github_repo,
        entrypoint="orchestration/sync_from_airbyte_flow.py:sync_from_airbyte",
    )()
    return sync_result

if __name__ == "__main__":
    master_flow.from_source(
        source=github_repo,
        entrypoint="orchestration/master_flow.py:master_flow",
    ).deploy(
        name="Sync from Airbyte and run DBT transform",
        work_pool_name="webbio-workpool",
        image="prefecthq/prefect:3-python3.12",
        push=False,
        cron="0 0 * * *",
    )
This code still returns a exit code 1 on the prefect managed worker pool but runs ok locally
m
The issue you're encountering when running in a Prefect managed worker pool, but not locally, might be due to how the 
from_source
method is being used. In Prefect 3.x, the 
from_source
method is not a standard practice for managing flow dependencies and deployments. Here are some steps to correct the approach: ### Key Considerations 1. GitRepository and from_source: The 
from_source
method is not typically used in Prefect 3.x for deploying flows. Instead, you should define your flows and use Prefect's deployment features to manage them. 2. Deployment: Use Prefect's deployment capabilities to handle the flow orchestration, ensuring that dependencies are correctly managed. ### Recommended Approach 1. Remove 
from_source
Usage: Remove the 
from_source
calls and manage your deployments through Prefect's deployment tools. 2. Use Prefect's Deployment Tools: You should define your flows and create deployments using Prefect's CLI or programmatically with Prefect's deployment APIs. 3. Ensure Correct Imports and Structure: Make sure that your flows are structured and imported correctly. The 
sync_from_airbyte
function should be accessible from the file where it's defined. ### Example Here's a more standard approach using Prefect's deployment capabilities: master_flow.py:
Copy code
python
from prefect import flow
from sync_from_airbyte_flow import sync_from_airbyte

@flow
def master_flow():
    sync_result = sync_from_airbyte()
    return sync_result

if __name__ == "__main__":
    master_flow()
Deployment Script: Use Prefect CLI to create a deployment:
Copy code
bash
prefect deployment build master_flow.py:master_flow -n "Sync from Airbyte and run DBT transform" --storage github/AirByte --work-pool webbio-workpool --cron "0 0 * * *" --image prefecthq/prefect:3-python3.12"
Programmatic Deployment Example: If you prefer a programmatic approach, you can define a deployment using Prefect's deployment API:
Copy code
python
from prefect.deployments import Deployment
from prefect.infrastructure import DockerContainer
from prefect import flow
from sync_from_airbyte_flow import sync_from_airbyte

@flow
def master_flow():
    sync_result = sync_from_airbyte()
    return sync_result

deployment = Deployment.build_from_flow(
    flow=master_flow,
    name="Sync from Airbyte and run DBT transform",
    work_pool_name="webbio-workpool",
    infrastructure=DockerContainer(image="prefecthq/prefect:3-python3.12"),
    storage=GitHubRepository(
        repo="webbio/AirByte",
        path="orchestration/master_flow.py",
        reference="main",  # branch or tag
        access_token_secret="GITHUB_ACCESS_TOKEN",  # Ensure this is set in your environment
    ),
)

deployment.apply()
### Key Points - Ensure Access: Make sure your Prefect environment has access to the GitHub repository, either through SSH keys or a GitHub access token. - Correct Entrypoint: The entrypoint should match the path within the repository where the 
master_flow
is defined. By following these steps, you should be able to deploy your flow correctly without encountering the exit code 1 error. If further issues arise, double-check your repository paths, credentials, and the Prefect environment configuration.
Open in Slack
PreviousNext
Powered by