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BigData / Apache Airflow Interview Questions

What is branching in Airflow and how is BranchPythonOperator used?

Branching lets a DAG conditionally execute one or more downstream paths based on runtime logic. The BranchPythonOperator runs a Python callable that returns the task_id (or list of task_ids) of the branch(es) to follow. All other branches are skipped.

from airflow.operators.python import BranchPythonOperator

def choose_branch():
    import random
    return 'branch_a' if random.random() > 0.5 else 'branch_b'

branch = BranchPythonOperator(
    task_id='choose',
    python_callable=choose_branch
)
branch >> [task_a, task_b]

Tasks not selected by the branch get a skipped state, so downstream join tasks often need trigger_rule='none_failed_min_one_success'.

What state do unselected branches receive in a BranchPythonOperator flow?

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