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Example Airflow DAG that creates, gets, lists, updates, purges, pauses, resumes
and deletes Queues in the Google Cloud Tasks service in the Google Cloud.

Required setup:
- GCP_APP_ENGINE_LOCATION: GCP Project's App Engine location `gcloud app describe | grep locationId`.

from __future__ import annotations

import os
from datetime import datetime

from google.api_core.retry import Retry
from import Queue
from google.protobuf.field_mask_pb2 import FieldMask

from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from import (
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "cloud_tasks_queue"
[docs]LOCATION = os.environ.get("GCP_APP_ENGINE_LOCATION", "europe-west2")
[docs]QUEUE_ID = f"queue-{ENV_ID}-{DAG_ID.replace('_', '-')}"
with DAG( dag_id=DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "tasks"], ) as dag: @task(task_id="random_string")
[docs] def generate_random_string(): """ Generate random string for queue and task names. Queue name cannot be repeated in preceding 7 days and task name in the last 1 hour. """ import random import string return "".join(random.choices(string.ascii_uppercase + string.digits, k=8))
random_string = generate_random_string() # [START create_queue] create_queue = CloudTasksQueueCreateOperator( location=LOCATION, task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=0.5)), queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", retry=Retry(maximum=10.0), timeout=5, task_id="create_queue", ) # [END create_queue] # [START delete_queue] delete_queue = CloudTasksQueueDeleteOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="delete_queue", ) # [END delete_queue] delete_queue.trigger_rule = TriggerRule.ALL_DONE # [START resume_queue] resume_queue = CloudTasksQueueResumeOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="resume_queue", ) # [END resume_queue] # [START pause_queue] pause_queue = CloudTasksQueuePauseOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="pause_queue", ) # [END pause_queue] # [START purge_queue] purge_queue = CloudTasksQueuePurgeOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="purge_queue", ) # [END purge_queue] # [START get_queue] get_queue = CloudTasksQueueGetOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="get_queue", ) get_queue_result = BashOperator( task_id="get_queue_result", bash_command=f"echo {get_queue.output}", ) # [END get_queue] # [START update_queue] update_queue = CloudTasksQueueUpdateOperator( task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=1)), location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", update_mask=FieldMask(paths=["stackdriver_logging_config.sampling_ratio"]), task_id="update_queue", ) # [END update_queue] # [START list_queue] list_queue = CloudTasksQueuesListOperator(location=LOCATION, task_id="list_queue") # [END list_queue] chain( random_string, create_queue, update_queue, pause_queue, resume_queue, purge_queue, get_queue, get_queue_result, list_queue, delete_queue, ) 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/
[docs]test_run = get_test_run(dag)

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