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I want to call a TaskGroup with a Dynamic sub-task id from BranchPythonOperator.

This is the DAG flow that I have:

branch_dag

My case is I want to check whether a table exists in BigQuery or not.

  • If exists: do nothing and end the DAG

  • If not exists: Ingest the data from Postgres to Google Cloud Storage

I know that to call a TaskGroup from BranchPythonOperator is by calling the task id with following format:
group_task_id.task_id

The problem is, my task group's sub task id is dynamic, depends on how many time I loop the TaskGroup. So the sub_task will be:

parent_task_id.sub_task_1
parent_task_id.sub_task_2
parent_task_id.sub_task_3
...
parent_task_id.sub_task_x

This is the following code for the DAG that I have:

import airflow
from airflow.providers.google.cloud.transfers.postgres_to_gcs import PostgresToGCSOperator
from airflow.utils.task_group import TaskGroup
from google.cloud.exceptions import NotFound
from airflow import DAG
from airflow.operators.python import BranchPythonOperator
from airflow.operators.dummy import DummyOperator
from google.cloud import bigquery

default_args = {
    'owner': 'Airflow',
    'start_date': airflow.utils.dates.days_ago(2),
}

with DAG(dag_id='branch_dag', default_args=default_args, schedule_interval=None) as dag:

    def create_task_group(worker=1):
        var  = dict()
        with TaskGroup(group_id='parent_task_id') as tg1:
            for i in range(worker):
                var[f'sub_task_{i}'] = PostgresToGCSOperator(
                    task_id = f'sub_task_{i}',
                    postgres_conn_id = 'some_postgres_conn_id',
                    sql = 'test.sql',
                    bucket = 'test_bucket',
                    filename = 'test_file.json',
                    export_format = 'json',
                    gzip = True,
                    params = {
                        'worker': worker
                    }
                )
        return tg1
    
    def is_exists_table():
        client = bigquery.Client()
        try:
            table_name = client.get_table('dataset_id.some_table')
            if table_name:
                return 'task_end'
        except NotFound as error:       
            return 'parent_task_id'

    task_start = DummyOperator(
        task_id = 'start'
        )

    task_branch_table = BranchPythonOperator(
        task_id ='check_table_exists_in_bigquery',
        python_callable = is_exists_table
        )

    task_pg_to_gcs_init = create_task_group(worker=3)

    task_end = DummyOperator(
        task_id = 'end',
        trigger_rule = 'all_done'
    )    

    task_start >> task_branch_table >> task_end
    task_start >> task_branch_table >> task_pg_to_gcs_init >> task_end

When I run the dag, it returns

**airflow.exceptions.TaskNotFound: Task parent_task_id not found **

But this is expected, what I don't know is how to iterate the parent_task_id.sub_task_x on is_exists_table function. Or are there any workaround?

This is the test.sql file if it's needed


SELECT 
id,
name,
country
FROM some_table
WHERE 1=1
AND ABS(MOD(hashtext(id::TEXT), 3)) = {{params.worker}};

-- returns 1M+ rows

I already seen this question as reference Question but I think my case is more specific.

Iqbal
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  • 6

2 Answers2

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When designing your data pipelines, you may encounter use cases that require more complex task flows than "Task A > Task B > Task C." For example, you may have a use case where you need to decide between multiple tasks to execute based on the results of an upstream task. Or you may have a case where part of your pipeline should only run under certain external conditions. Fortunately, Airflow has multiple options for building conditional logic and/or branching into your DAGs.

RIYA ROUNIYAR
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  • 1
  • I think this has nothing to do with the question, and I read this somewhere before. I suggest that you cite your source if it's taken from somewhere else. – Iqbal Nov 29 '22 at 11:08
0

I found a dirty way around it.

What I did is creating 1 additional task using DummyOperator called task_pass.

    task_pass = DummyOperator(
        task_id = 'pass_to_task_group'
    )

So the DAG flow now looks like this:

task_start >> task_branch_table >> task_end
task_start >> task_branch_table >> task_pass >> task_pg_to_gcs_init >> task_end

Also there is 1 mistake that I made from the code above, notice that the params I set was worker. This is wrong because worker is the constant while the thing that I need to iterate is the i variable. So I change it from:

params: worker

to:

params: i
Iqbal
  • 13
  • 6