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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_update"
[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},
},
}
# 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"],
) as dag:
[docs] create_cluster = DataprocCreateClusterOperator(
task_id="create_cluster",
project_id=PROJECT_ID,
cluster_config=CLUSTER_CONFIG,
region=REGION,
cluster_name=CLUSTER_NAME,
retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
num_retries_if_resource_is_not_ready=3,
)
# [START how_to_cloud_dataproc_update_cluster_operator]
scale_cluster = DataprocUpdateClusterOperator(
task_id="scale_cluster",
cluster_name=CLUSTER_NAME,
cluster=CLUSTER_UPDATE,
update_mask=UPDATE_MASK,
graceful_decommission_timeout=TIMEOUT,
project_id=PROJECT_ID,
region=REGION,
retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
)
# [END how_to_cloud_dataproc_update_cluster_operator]
delete_cluster = DataprocDeleteClusterOperator(
task_id="delete_cluster",
project_id=PROJECT_ID,
cluster_name=CLUSTER_NAME,
region=REGION,
trigger_rule=TriggerRule.ALL_DONE,
)
(
# TEST SETUP
create_cluster
# TEST BODY
>> scale_cluster
# TEST TEARDOWN
>> delete_cluster
)
from dev.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 dev.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)