Cloud Composer - run same task in parallel with different arguments

[ayushmaheshwari]

Hi @ms4446 and all

I just wanted to ask if this is a good practice. I have an Airflow DAG being run in Cloud Composer. This is my flow:

        create_my_table
        >> data_validation
        >> export_data_to_gcs
        >> [xy_gcs_to_s3_transfer, ab_gcs_to_s3_transfer]

Now, xy_gcs_to_s3_transfer and ab_gcs_to_s3_transfer use the same Python callable (same task id) but different arguments. IS this a good practice?

My task id is: buckets_transfer_task  and I am noticing the task appearing in the ui as: buckets_transfer_task and buckets_transfer_task__1

Is anything wrong in my approach (it is working). But I am confused if its right practice because of the way composer ui appears

[ms4446 · Accepted solution]

Hi @ayushmaheshwari ,

Your approach to running the same task in parallel with different arguments in Cloud Composer is a valid and common practice, leveraging the concepts of task dynamism and parallelism to achieve efficient data processing. This method is particularly advantageous for several reasons, which I will explain, along with an analysis of the observed UI behavior.

First, the reuse of the same Python callable for different transfer operations demonstrates a good practice in task reusability. By using the buckets_transfer_task for both xy_gcs_to_s3_transfer and ab_gcs_to_s3_transfer, you promote code maintainability and avoid unnecessary duplication. This approach ensures that your codebase remains clean and manageable, as you do not need to create separate functions for each transfer operation. Instead, you use the same callable with different arguments, making your code more efficient and easier to update or debug.

Second, your method capitalizes on Airflow’s ability to execute tasks concurrently, thereby enhancing the workflow's speed and efficiency. By running tasks in parallel, particularly when transferring data to multiple destinations, you can significantly reduce the overall execution time. This parallelism is a core feature of Airflow, designed to handle large-scale data processing pipelines effectively.

Third, the use of dynamic tasks in Airflow is a powerful feature that allows for greater flexibility and scalability. By generating tasks dynamically based on input parameters, you can easily manage varying workloads. This dynamic generation of tasks enables your DAG (Directed Acyclic Graph) to adapt to different data processing requirements without the need for extensive modifications.

Regarding the UI behavior you observed, it is important to note that it is expected and intended. When you create dynamic tasks with the same task ID, Airflow automatically appends a suffix (such as __1, __2, etc.) to differentiate them in the UI. This suffix is purely for display purposes and does not affect the actual task execution. For example, the task IDs buckets_transfer_task and buckets_transfer_task__1 indicate the original and dynamically generated tasks, respectively. This automatic differentiation helps you to visually distinguish between the parallel tasks in the Airflow UI, ensuring that you can monitor and manage them effectively.

While your current approach is sound, there are optional improvements you could consider for enhanced clarity and maintainability:

1. Task Group: If xy_gcs_to_s3_transfer and ab_gcs_to_s3_transfer represent logically related tasks, you can group them under a TaskGroup. This would visually organize them in the UI.

2. Custom Task IDs: Instead of relying on the auto-generated suffixes, you could provide more descriptive task IDs for each dynamic task. This would make the UI more informative.

from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.utils.task_group import TaskGroup
from datetime import datetime

def buckets_transfer_task(bucket_name, **kwargs):
    # Your transfer logic here
    pass

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 1, 1),
    'retries': 1,
}

with DAG(dag_id='my_dag', default_args=default_args, schedule_interval='@daily') as dag:
    create_my_table = PythonOperator(
        task_id='create_my_table',
        python_callable=create_my_table_function
    )

    data_validation = PythonOperator(
        task_id='data_validation',
        python_callable=data_validation_function
    )

    export_data_to_gcs = PythonOperator(
        task_id='export_data_to_gcs',
        python_callable=export_data_to_gcs_function
    )

    with TaskGroup("s3_transfers") as s3_transfers_group:
        xy_transfer_task = PythonOperator(
            task_id="xy_gcs_to_s3_transfer",
            python_callable=buckets_transfer_task,
            op_kwargs={"bucket_name": "xy_bucket"},
        )

        ab_transfer_task = PythonOperator(
            task_id="ab_gcs_to_s3_transfer",
            python_callable=buckets_transfer_task,
            op_kwargs={"bucket_name": "ab_bucket"},
        )

    create_my_table >> data_validation >> export_data_to_gcs >> s3_transfers_group