Source code for tests.system.google.cloud.dataproc.example_dataproc_cluster_deferrable

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"""
Example Airflow DAG for DataprocUpdateClusterOperator.
"""

from __future__ import annotations

import os
from datetime import datetime

from google.api_core.retry import Retry

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.dataproc import (
    DataprocCreateClusterOperator,
    DataprocDeleteClusterOperator,
    DataprocUpdateClusterOperator,
)
from airflow.utils.trigger_rule import TriggerRule

from providers.tests.system.google import DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]DAG_ID = "dataproc_cluster_def"
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
[docs]CLUSTER_NAME_BASE = f"cluster-{DAG_ID}".replace("_", "-")
[docs]CLUSTER_NAME_FULL = CLUSTER_NAME_BASE + f"-{ENV_ID}".replace("_", "-")
[docs]CLUSTER_NAME = CLUSTER_NAME_BASE if len(CLUSTER_NAME_FULL) >= 33 else CLUSTER_NAME_FULL
[docs]REGION = "europe-north1"
# Cluster definition
[docs]CLUSTER_CONFIG = { "master_config": { "num_instances": 1, "machine_type_uri": "n1-standard-4", "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 32}, }, "worker_config": { "num_instances": 2, "machine_type_uri": "n1-standard-4", "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 32}, }, "secondary_worker_config": { "num_instances": 1, "machine_type_uri": "n1-standard-4", "disk_config": { "boot_disk_type": "pd-standard", "boot_disk_size_gb": 32, }, "is_preemptible": True, "preemptibility": "PREEMPTIBLE", }, }
# Update options # [START how_to_cloud_dataproc_updatemask_cluster_operator]
[docs]CLUSTER_UPDATE = { "config": {"worker_config": {"num_instances": 3}, "secondary_worker_config": {"num_instances": 3}} }
[docs]UPDATE_MASK = { "paths": ["config.worker_config.num_instances", "config.secondary_worker_config.num_instances"] }
# [END how_to_cloud_dataproc_updatemask_cluster_operator]
[docs]TIMEOUT = {"seconds": 1 * 24 * 60 * 60}
with DAG( DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "dataproc", "deferrable"], ) as dag: # [START how_to_cloud_dataproc_create_cluster_operator_async]
[docs] create_cluster = DataprocCreateClusterOperator( task_id="create_cluster", project_id=PROJECT_ID, cluster_config=CLUSTER_CONFIG, region=REGION, cluster_name=CLUSTER_NAME, deferrable=True, retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0), num_retries_if_resource_is_not_ready=3, )
# [END how_to_cloud_dataproc_create_cluster_operator_async] # [START how_to_cloud_dataproc_update_cluster_operator_async] update_cluster = DataprocUpdateClusterOperator( task_id="update_cluster", cluster_name=CLUSTER_NAME, cluster=CLUSTER_UPDATE, update_mask=UPDATE_MASK, graceful_decommission_timeout=TIMEOUT, project_id=PROJECT_ID, region=REGION, deferrable=True, retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0), ) # [END how_to_cloud_dataproc_update_cluster_operator_async] # [START how_to_cloud_dataproc_delete_cluster_operator_async] delete_cluster = DataprocDeleteClusterOperator( task_id="delete_cluster", project_id=PROJECT_ID, cluster_name=CLUSTER_NAME, region=REGION, trigger_rule=TriggerRule.ALL_DONE, deferrable=True, ) # [END how_to_cloud_dataproc_delete_cluster_operator_async] ( # TEST SETUP create_cluster # TEST BODY >> update_cluster # TEST TEARDOWN >> delete_cluster ) from tests_common.test_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_common.test_utils.system_tests 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)

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