Mastering Task Dependencies in Apache Airflow
Oct 11, 2024
·
1 min read
Welcome to Day 5! Today we explore defining task dependencies for complex, real-world data workflows.
Basic Dependencies
Linear Dependencies
extract >> transform >> load
Fan-out/Fan-in Patterns
download_launches >> [get_pictures, download_metadata]
[get_pictures, download_metadata] >> notify
Branching
Use BranchPythonOperator for conditional logic:
from airflow.operators.python import BranchPythonOperator
def choose_path():
return "task_A" if condition else "task_B"
branch = BranchPythonOperator(
task_id='branch_task',
python_callable=choose_path
)
Trigger Rules
| Trigger Rule | Behavior |
|---|---|
all_success | (default) All parent tasks completed successfully |
all_failed | All parent tasks failed |
all_done | All parents done, regardless of state |
one_failed | At least one parent failed |
one_success | At least one parent succeeded |
none_failed | No parents failed (succeeded or skipped) |
Sharing Data with XComs
task_A.xcom_push(key='data', value=my_data)
task_B.xcom_pull(task_ids='task_A', key='data')
Note: XComs are stored in the metadata database - use for small data only, not large datasets.
Taskflow API
Simplify Python task chaining:
@task
def extract():
return data
@task
def transform(data):
return transformed_data
@task
def load(transformed_data):
print("Loading data")
The @task decorator converts each function into an Airflow task automatically.
Authors
Aditya Paliwal
(he/him)
Data Engineer
Data Engineer with 4+ years of experience in implementing and deploying
end-to-end data pipelines in production environments. Passionate about
combining data engineering with cutting-edge machine learning and AI
technologies to create intelligent, data-driven products.