Source code for airflow.example_dags.example_branch_operator_decorator
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"""Example DAG demonstrating the usage of the branching TaskFlow API decorators.
It shows how to use standard Python ``@task.branch`` as well as the external Python
version ``@task.branch_external_python`` which calls an external Python interpreter and
the ``@task.branch_virtualenv`` which builds a temporary Python virtual environment.
"""
from __future__ import annotations
import random
import sys
import tempfile
import pendulum
from airflow.operators.python import is_venv_installed
if is_venv_installed():
from airflow.decorators import task
from airflow.models.dag import DAG
from airflow.operators.empty import EmptyOperator
from airflow.utils.edgemodifier import Label
from airflow.utils.trigger_rule import TriggerRule
[docs] PATH_TO_PYTHON_BINARY = sys.executable
with DAG(
dag_id="example_branch_python_operator_decorator",
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
schedule="@daily",
tags=["example", "example2"],
orientation="TB",
) as dag:
run_this_first = EmptyOperator(task_id="run_this_first")
options = ["a", "b", "c", "d"]
# Example branching on standard Python tasks
# [START howto_operator_branch_python]
@task.branch()
def branching(choices: list[str]) -> str:
return f"branch_{random.choice(choices)}"
# [END howto_operator_branch_python]
random_choice_instance = branching(choices=options)
run_this_first >> random_choice_instance
join = EmptyOperator(task_id="join", trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS)
for option in options:
@task(task_id=f"branch_{option}")
def some_task():
print("doing something in Python")
t = some_task()
empty = EmptyOperator(task_id=f"follow_{option}")
# Label is optional here, but it can help identify more complex branches
random_choice_instance >> Label(option) >> t >> empty >> join
# Example the same with external Python calls
# [START howto_operator_branch_ext_py]
@task.branch_external_python(python=PATH_TO_PYTHON_BINARY)
def branching_ext_python(choices) -> str:
import random
return f"ext_py_{random.choice(choices)}"
# [END howto_operator_branch_ext_py]
random_choice_ext_py = branching_ext_python(choices=options)
join >> random_choice_ext_py
join_ext_py = EmptyOperator(
task_id="join_ext_py", trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS
)
for option in options:
@task.external_python(task_id=f"ext_py_{option}", python=PATH_TO_PYTHON_BINARY)
def some_ext_py_task():
print("doing something in external Python")
t = some_ext_py_task()
# Label is optional here, but it can help identify more complex branches
random_choice_ext_py >> Label(option) >> t >> join_ext_py
# Example the same with Python virtual environments
# [START howto_operator_branch_virtualenv]
# Note: Passing a caching dir allows to keep the virtual environment over multiple runs
# Run the example a second time and see that it re-uses it and is faster.
VENV_CACHE_PATH = tempfile.gettempdir()
@task.branch_virtualenv(requirements=["numpy~=1.24.4"], venv_cache_path=VENV_CACHE_PATH)
def branching_virtualenv(choices) -> str:
import random
import numpy as np
print(f"Some numpy stuff: {np.arange(6)}")
return f"venv_{random.choice(choices)}"
# [END howto_operator_branch_virtualenv]
random_choice_venv = branching_virtualenv(choices=options)
join_ext_py >> random_choice_venv
join_venv = EmptyOperator(task_id="join_venv", trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS)
for option in options:
@task.virtualenv(
task_id=f"venv_{option}", requirements=["numpy~=1.24.4"], venv_cache_path=VENV_CACHE_PATH
)
def some_venv_task():
import numpy as np
print(f"Some numpy stuff: {np.arange(6)}")
t = some_venv_task()
# Label is optional here, but it can help identify more complex branches
random_choice_venv >> Label(option) >> t >> join_venv