BranchPythonOperator in Apache Airflow - Mini Project: Build & Apply

Using BranchPythonOperator in Airflow

📖 Scenario: You are setting up an Airflow DAG to decide which task to run based on a condition. This is like choosing a path in a choose-your-own-adventure story depending on a choice.

🎯 Goal: Build an Airflow DAG that uses BranchPythonOperator to select between two tasks based on a simple condition.

📋 What You'll Learn

Create a Python function that returns the task id to run next

Use BranchPythonOperator with the function

Create two dummy tasks to branch between

Print the chosen task id at the end

💡 Why This Matters

🌍 Real World

BranchPythonOperator is used in Airflow to control workflow paths based on conditions, like choosing different steps in a process depending on data or time.

💼 Career

Understanding branching in Airflow is important for building flexible and efficient data pipelines and automation workflows in many companies.

1

Create the DAG and tasks

Create an Airflow DAG called branch_dag with default args. Inside it, create two DummyOperator tasks named task_a and task_b.

Apache Airflow

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime

with DAG('branch_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    # Your code here

Need a hint?

Use DummyOperator to create simple placeholder tasks inside the DAG context.

2

Create the branching function

Define a Python function called choose_task that returns the string 'task_a'.

Apache Airflow

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime

with DAG('branch_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    task_a = DummyOperator(task_id='task_a')
    task_b = DummyOperator(task_id='task_b')

def choose_task():
    # Your code here

Need a hint?

The function should simply return the task id string to run next.

3

Add the BranchPythonOperator

Inside the DAG, create a BranchPythonOperator named branch_task that uses the choose_task function as its python_callable. Set branch_task to run before task_a and task_b.

Apache Airflow

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.operators.python import BranchPythonOperator
from datetime import datetime

with DAG('branch_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    task_a = DummyOperator(task_id='task_a')
    task_b = DummyOperator(task_id='task_b')
    branch_task = BranchPythonOperator(
        task_id='branch_task',
        python_callable=choose_task
    )
    branch_task >> [task_a, task_b]

Need a hint?

Use BranchPythonOperator to decide which task runs next. Use the >> operator to set task order.

4

Print the chosen task id

Add a print statement outside the DAG that calls choose_task() and prints the returned task id.

Apache Airflow

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.operators.python import BranchPythonOperator
from datetime import datetime

with DAG('branch_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    task_a = DummyOperator(task_id='task_a')
    task_b = DummyOperator(task_id='task_b')
    branch_task = BranchPythonOperator(
        task_id='branch_task',
        python_callable=choose_task
    )
    branch_task >> [task_a, task_b]

def choose_task():
    return 'task_a'

print(choose_task())  # Your code here

Need a hint?

Call the function and print its result to see which task will run.