Pass variables from one task into cosmos dbt dag

[DanMawdsleyBA]

I'm try to build a dag that consists of a task which retreive some metadata and then have a comsos task group which has the values of the metadata from the previous task as part of the env_vars as part of the project config (These values have to be generated from a task). I've tried many combinations but haven't been able to get it to work. Anybody have any idea of how this would work?

[dosubot]

Based on your request, it seems you want to pass variables from one task to another in an Apache Airflow DAG, specifically for the purpose of using the values of metadata retrieved from a previous task as part of the environment variables in a dbt project configuration. This can be achieved using Airflow's XCom feature, which allows tasks to exchange messages, enabling the sharing of data between tasks.

Here's a simplified example of how you might achieve this:

1. Pushing a Value to XCom: In the task that retrieves metadata, you would push this metadata to XCom. This is typically done by returning the value from the task function or using the xcom_push method.

def retrieve_metadata(**kwargs):
    # Your logic to retrieve metadata
    metadata = {"key": "value"}  # Example metadata
    kwargs['ti'].xcom_push(key='metadata', value=metadata)

2. Pulling the Value from XCom: In the subsequent dbt task where you need to use this metadata as part of the environment variables, you would pull this value from XCom.

def dbt_task(**kwargs):
    ti = kwargs['ti']
    metadata = ti.xcom_pull(task_ids='previous_task_id', key='metadata')
    # Use the metadata as needed, for example, setting it as an environment variable
    os.environ['METADATA_KEY'] = metadata['key']

3. Defining the Tasks in your DAG: You would define these tasks in your DAG file and set their dependencies appropriately.

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
import os

default_args = {
    'start_date': datetime(2021, 1, 1),
}

with DAG('my_dag', default_args=default_args, schedule_interval='@daily') as dag:
    
    retrieve_metadata_task = PythonOperator(
        task_id='retrieve_metadata',
        python_callable=retrieve_metadata,
    )
    
    dbt_task = PythonOperator(
        task_id='dbt_task',
        python_callable=dbt_task,
        provide_context=True,
    )

    retrieve_metadata_task >> dbt_task

In this example, retrieve_metadata_task retrieves metadata and pushes it to XCom. The dbt_task then pulls this metadata from XCom and can use it, for example, to set environment variables. Note that provide_context=True is used in the PythonOperator for dbt_task to ensure that the task instance and other context variables are passed to the function, allowing it to pull from XCom.

Remember, this is a simplified example to illustrate the concept. You'll need to adapt it to fit the specifics of your dbt project and Airflow setup.

As for how Cosmos handles environment variables in the dbt project configuration, it uses the env_vars parameter within ProjectConfig to store a dictionary of environment variables that are used for both rendering and execution. Rendering with environment variables is supported when using the RenderConfig.LoadMode.DBT_LS load mode. This approach allows users to define environment variables that can be utilized during the dbt project's lifecycle within Cosmos.

Here's the relevant excerpt from the ProjectConfig class definition:

class ProjectConfig:
    """
    Class for setting project config.
    ...
    :param env_vars: Dictionary of environment variables that are used for both rendering and execution. Rendering with
        env_vars is only supported when using ``RenderConfig.LoadMode.DBT_LS`` load mode.
    ...
    """

    ...
    env_vars: dict[str, str] | None = None
    ...

This configuration enables Cosmos to integrate environment variables into the dbt project's execution and rendering processes, providing flexibility for dynamic configurations based on the environment in which the dbt project is run.

I hope this helps! If you have any further questions or need more clarification, feel free to ask.

Sources

• Add example/docs on using Airflow's context in a dbt project
• cosmos/airflow/dag.py
• cosmos/config.py

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