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"""Example Airflow DAG for testing Google Dataflow Beam Pipeline Operator with Asynchronous Python in the deferrable mode."""
from __future__ import annotations
import os
from datetime import datetime
from typing import Callable
from airflow.exceptions import AirflowException
from airflow.models.dag import DAG
from airflow.providers.apache.beam.hooks.beam import BeamRunnerType
from airflow.providers.apache.beam.operators.beam import BeamRunPythonPipelineOperator
from airflow.providers.google.cloud.hooks.dataflow import DataflowJobStatus
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.sensors.dataflow import (
DataflowJobAutoScalingEventsSensor,
DataflowJobMessagesSensor,
DataflowJobMetricsSensor,
DataflowJobStatusSensor,
)
from airflow.utils.trigger_rule import TriggerRule
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "dataflow_sensors_deferrable"
[docs]RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]GCS_TMP = f"gs://{BUCKET_NAME}/temp/"
[docs]GCS_STAGING = f"gs://{BUCKET_NAME}/staging/"
[docs]GCS_OUTPUT = f"gs://{BUCKET_NAME}/output"
[docs]GCS_PYTHON_SCRIPT = f"gs://{RESOURCE_DATA_BUCKET}/dataflow/python/wordcount_debugging.py"
[docs]LOCATION = "europe-west3"
[docs]default_args = {
"dataflow_default_options": {
"tempLocation": GCS_TMP,
"stagingLocation": GCS_STAGING,
}
}
with DAG(
DAG_ID,
default_args=default_args,
schedule="@once",
start_date=datetime(2024, 1, 1),
catchup=False,
tags=["example", "dataflow"],
) as dag:
[docs] create_bucket = GCSCreateBucketOperator(task_id="create_bucket", bucket_name=BUCKET_NAME)
start_beam_python_pipeline = BeamRunPythonPipelineOperator(
task_id="start_beam_python_pipeline",
runner=BeamRunnerType.DataflowRunner,
py_file=GCS_PYTHON_SCRIPT,
py_options=[],
pipeline_options={
"output": GCS_OUTPUT,
},
py_requirements=["apache-beam[gcp]==2.47.0"],
py_interpreter="python3",
py_system_site_packages=False,
dataflow_config={
"job_name": "start_beam_python_pipeline",
"location": LOCATION,
"wait_until_finished": False,
},
)
# [START howto_sensor_wait_for_job_status_deferrable]
wait_for_beam_python_pipeline_job_status_def = DataflowJobStatusSensor(
task_id="wait_for_beam_python_pipeline_job_status_def",
job_id="{{task_instance.xcom_pull('start_beam_python_pipeline')['dataflow_job_id']}}",
expected_statuses=DataflowJobStatus.JOB_STATE_DONE,
location=LOCATION,
deferrable=True,
)
# [END howto_sensor_wait_for_job_status_deferrable]
# [START howto_sensor_wait_for_job_metric_deferrable]
def check_metric_scalar_gte(metric_name: str, value: int) -> Callable:
"""Check is metric greater than equals to given value."""
def callback(metrics: list[dict]) -> bool:
dag.log.info("Looking for '%s' >= %d", metric_name, value)
for metric in metrics:
context = metric.get("name", {}).get("context", {})
original_name = context.get("original_name", "")
tentative = context.get("tentative", "")
if original_name == "Service-cpu_num_seconds" and not tentative:
return metric["scalar"] >= value
raise AirflowException(f"Metric '{metric_name}' not found in metrics")
return callback
wait_for_beam_python_pipeline_job_metric_def = DataflowJobMetricsSensor(
task_id="wait_for_beam_python_pipeline_job_metric_def",
job_id="{{task_instance.xcom_pull('start_beam_python_pipeline')['dataflow_job_id']}}",
location=LOCATION,
callback=check_metric_scalar_gte(metric_name="Service-cpu_num_seconds", value=100),
fail_on_terminal_state=False,
deferrable=True,
)
# [END howto_sensor_wait_for_job_metric_deferrable]
# [START howto_sensor_wait_for_job_message_deferrable]
def check_job_message(messages: list[dict]) -> bool:
"""Check job message."""
for message in messages:
if "Adding workflow start and stop steps." in message.get("messageText", ""):
return True
return False
wait_for_beam_python_pipeline_job_message_def = DataflowJobMessagesSensor(
task_id="wait_for_beam_python_pipeline_job_message_def",
job_id="{{task_instance.xcom_pull('start_beam_python_pipeline')['dataflow_job_id']}}",
location=LOCATION,
callback=check_job_message,
fail_on_terminal_state=False,
deferrable=True,
)
# [END howto_sensor_wait_for_job_message_deferrable]
# [START howto_sensor_wait_for_job_autoscaling_event_deferrable]
def check_autoscaling_event(autoscaling_events: list[dict]) -> bool:
"""Check autoscaling event."""
for autoscaling_event in autoscaling_events:
if "Worker pool started." in autoscaling_event.get("description", {}).get("messageText", ""):
return True
return False
wait_for_beam_python_pipeline_job_autoscaling_event_def = DataflowJobAutoScalingEventsSensor(
task_id="wait_for_beam_python_pipeline_job_autoscaling_event_def",
job_id="{{task_instance.xcom_pull('start_beam_python_pipeline')['dataflow_job_id']}}",
location=LOCATION,
callback=check_autoscaling_event,
fail_on_terminal_state=False,
deferrable=True,
)
# [END howto_sensor_wait_for_job_autoscaling_event_deferrable]
delete_bucket = GCSDeleteBucketOperator(
task_id="delete_bucket", bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE
)
(
# TEST SETUP
create_bucket
>> start_beam_python_pipeline
# TEST BODY
>> [
wait_for_beam_python_pipeline_job_status_def,
wait_for_beam_python_pipeline_job_metric_def,
wait_for_beam_python_pipeline_job_message_def,
wait_for_beam_python_pipeline_job_autoscaling_event_def,
]
# TEST TEARDOWN
>> delete_bucket
)
from tests.system.utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "teardown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests.system.utils import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)