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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,
)

try:
    from airflow.sdk import TriggerRule
except ImportError:
    # Compatibility for Airflow < 3.1
    from airflow.utils.trigger_rule import TriggerRule  # type: ignore[no-redef,attr-defined]

from system.google import DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
DAG_ID = "dataproc_update"
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID

CLUSTER_NAME_BASE = f"cluster-{DAG_ID}".replace("_", "-")
CLUSTER_NAME_FULL = CLUSTER_NAME_BASE + f"-{ENV_ID}".replace("_", "-")
CLUSTER_NAME = CLUSTER_NAME_BASE if len(CLUSTER_NAME_FULL) >= 33 else CLUSTER_NAME_FULL
REGION = "europe-north1"


# Cluster definition
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]
CLUSTER_UPDATE = {
    "config": {"worker_config": {"num_instances": 3}, "secondary_worker_config": {"num_instances": 3}}
}
UPDATE_MASK = {
    "paths": ["config.worker_config.num_instances", "config.secondary_worker_config.num_instances"]
}
# [END how_to_cloud_dataproc_updatemask_cluster_operator]

TIMEOUT = {"seconds": 1 * 24 * 60 * 60}


with DAG(
    DAG_ID,
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "managed-spark"],
) as dag:
    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 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: contributing-docs/testing/system_tests.rst)
test_run = get_test_run(dag)
