如何从 Airflow 中的另一个任务动态初始化任务？

Question

I am currently working on a DAG which does the same tasks for different datasets in one DAG definition. The list of the datasets and their schema needs to be accessed from some configuration. I have ideas about where to store the configuration, but I can't figure it out how to read the configuration, than start iterating on tasks based on the result of this configration.

My code currently looks like this:

# Configuration read logic will be implemented here, working with Variable during test phase.
def _read_config(ti):
    Variable.set("table_list", ["first", "second", "third"], serialize_json=True)

# The actual processing logic will be implemented here.
def _processing(table):
    print("Current table:")
    print(table)

def processing(table):
    return PythonOperator(
        task_id=f"processing_{table}",
        python_callable=_processing,
        op_kwargs={
            "table": table
        }
    )

def scheduling():
    for table in Variable.get("table_list", deserialize_json=True)():
        processing(table)

read_config = PythonOperator(
    task_id='read_config',
    python_callable=_read_config
    )

scheduling = PythonOperator(
        task_id='scheduling',
        python_callable=_scheduling
    )


read_config >> scheduling

But that results in the following graph:

What I want to achieve is this:

1. Read the configuration in a task.
2. Iterate on the result of this task in a main scheduler task (or any other alternative possibility)
3. From this scheduler task(?), initialize the instances of the processing task.

Is there a proper way to to this in Airflow? I am open to new suggestions, the only important thing is to perform these 3 steps properly.

Answer 1

Generating tasks dynamically in this way is not recommended in Airflow, instead Airflow provides since 2.3.0 a clean way to do it using Dynamic Task Mapping .

with DAG(dag_id="dag id", start_date=...) as dag:

    @task
    def read_config():
        # here load the config from variables, db, S3/GCS file, ...
        return ["first", "second", "third"]


    @task
    def processing(table: str):
        # implement your processing function
        print(f"Current table: {table}")

    config = read_config()
    processing.expand(table=config)

Airflow will store the config returned in the first task as a xcom, then the second task will pull it, and will run an instance for each element in the list. In the UI you will find one instance with a number of mapped instances, but don't worry, if you have multiple workers, all the instances will be executed in parallel on different workers (it depends on your parallelism config).