@Marvin I want to run dbt cloud job like you have in your documentation

@flow
async def run_dbt_job_flow():
    result = await run_dbt_cloud_job(
        dbt_cloud_job = await DbtCloudJob.load("JOB-BLOCK-NAME-PLACEHOLDER"),
        targeted_retries = 0,
    )
    return await result

It should be read from git repo as source similar to this

GitRepository(
        url="<https://github.com/org/private-repo.git>",
        credentials=GitHubCredentials.load("my-github-credentials-block")
    )

Can you give full example of how this can be set up?

To set up a flow in Prefect that runs a dbt Cloud job and sources code from a private Git repository, you can follow these steps. I'll provide a complete example, assuming you're using Prefect 3.x given its more comprehensive documentation in this domain. 1. Create your dbt Cloud Credentials Block: - Log into your dbt Cloud account and navigate to API Tokens to copy a service token. - Use this token to create a

DbtCloudCredentials

block. 2. Create your dbt Cloud Job Block: - In dbt Cloud, go to Deploy -> Jobs and select a job. - Note the job ID from the URL. - Register a

DbtCloudJob

block using this job ID. 3. Setup Git Repository with Credential Block: - If you're using a private repo, create a

GitHubCredentials

block with your GitHub Personal Access Token (PAT) following GitHub's guide on fine-grained PATs. - Use the credentials block to configure your

GitRepository

. Now, here is a sample code that ties all of these setups together:

python
from prefect import flow, task
from prefect_dbt.cloud import run_dbt_cloud_job
from prefect_git import GitRepository
from prefect.blocks.github import GitHubCredentials
from prefect.blocks.dbt.cloud import DbtCloudJob

@flow
async def run_dbt_job_flow():
    # Load GitHub credentials and create a Git repository block
    github_credentials = GitHubCredentials.load("my-github-credentials-block")
    git_repo = GitRepository(
        url="<https://github.com/org/private-repo.git>",
        credentials=github_credentials
    )

    # Load dbt Cloud job
    dbt_cloud_job = await DbtCloudJob.load("JOB-BLOCK-NAME-PLACEHOLDER")
    
    # Run dbt Cloud job with targeted retries
    result = await run_dbt_cloud_job(
        dbt_cloud_job=dbt_cloud_job,
        targeted_retries=0,
    )
    return await result

4. Deploying Your Flow: - Store your flow code and any dependencies in the private Git repository. - Ensure your environment is authenticated to access private repositories during deployment and execution. For more detailed guidance, you might want to check the Prefect 3.x deployment guide and the Prefect dbt integration. These resources provide in-depth instructions for configuring and deploying with Prefect and dbt. Just remember: a spoonful of code is worth a thousand words, yet both need the right ingredients to function properly!

@Marvin take this example

@flow
async def run_dbt_job_flow():
    result = await run_dbt_cloud_job(
        dbt_cloud_job = await DbtCloudJob.load("JOB-BLOCK-NAME-PLACEHOLDER"),
        targeted_retries = 0,
    )
    return await result

if __name__ == "__main__":
    run_dbt_job_flow.deploy(
        name="my-deployment",
        work_pool_name="my-work-pool",
        image="my-docker-image:dev",
        push=False
    )

Will this work or must the async flow be called in some other way?

Using the

deploy()

method within an asynchronous flow like the one you've shown raises a few eyebrows (and perhaps an occasional "beep" of confusion) because

deploy()

is typically used for synchronous flow invocations. In an async context, this might not behave as expected, since

deploy()

isn't inherently designed for asynchronous flows. When dealing with asynchronous flows, you'd generally use an

await

keyword to call the flow, like so:

python
if __name__ == "__main__":
    import asyncio
    asyncio.run(run_dbt_job_flow())

If you need to deploy an async flow, Prefect typically handles this with CLI commands or external tools, rather than directly in the Python script as an immediate execution method like traditional Python

main()

methods. For managing deployments of asynchronous flows, you might look into Prefect's deployment ecosystem, where deployment specifications can include runnable tasks without directly invoking

async

calls inside a

main

block. If you want to execute deployments manually, you should configure these through Prefect Cloud or Server UIs. For more on deployment, you can check the Prefect 3.x docs on deployment concepts, which suggest best practices and detailed procedures on how to handle flow deployments. Let's hope this clears some of the fog in the async-imported realm of Prefect flow management, turning "meandering mysteries" into "legible legacies."