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"""
Example Airflow DAG that show how to create a Dataproc cluster in Google Kubernetes Engine.

Required environment variables:
GKE_NAMESPACE = os.environ.get("GKE_NAMESPACE", f"{CLUSTER_NAME}")
A GKE cluster can support multiple DP clusters running in different namespaces.
Define a namespace or assign a default one.
Notice: optional kubernetes_namespace parameter in VIRTUAL_CLUSTER_CONFIG should be the same as GKE_NAMESPACE

"""
from __future__ import annotations

import os
from datetime import datetime

from airflow import models
from airflow.operators.bash import BashOperator
from airflow.providers.google.cloud.operators.dataproc import (
    DataprocCreateClusterOperator,
    DataprocDeleteClusterOperator,
)
from airflow.providers.google.cloud.operators.kubernetes_engine import (
    GKECreateClusterOperator,
    GKEDeleteClusterOperator,
)
from airflow.utils.trigger_rule import TriggerRule

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
DAG_ID = "dataproc-gke"
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")

REGION = "us-central1"
CLUSTER_NAME = f"cluster-{ENV_ID}-{DAG_ID}".replace("_", "-")
GKE_CLUSTER_NAME = f"cluster-{ENV_ID}-{DAG_ID}-gke".replace("_", "-")
WORKLOAD_POOL = f"{PROJECT_ID}.svc.id.goog"
GKE_CLUSTER_CONFIG = {
    "name": GKE_CLUSTER_NAME,
    "workload_identity_config": {
        "workload_pool": WORKLOAD_POOL,
    },
    "initial_node_count": 1,
}
GKE_NAMESPACE = os.environ.get("GKE_NAMESPACE", f"{CLUSTER_NAME}")
# [START how_to_cloud_dataproc_create_cluster_in_gke_config]

VIRTUAL_CLUSTER_CONFIG = {
    "kubernetes_cluster_config": {
        "gke_cluster_config": {
            "gke_cluster_target": f"projects/{PROJECT_ID}/locations/{REGION}/clusters/{GKE_CLUSTER_NAME}",
            "node_pool_target": [
                {
                    "node_pool": f"projects/{PROJECT_ID}/locations/{REGION}/clusters/{GKE_CLUSTER_NAME}/nodePools/dp",  # noqa
                    "roles": ["DEFAULT"],
                    "node_pool_config": {
                        "config": {
                            "preemptible": True,
                        }
                    },
                }
            ],
        },
        "kubernetes_software_config": {"component_version": {"SPARK": b"3"}},
    },
    "staging_bucket": "test-staging-bucket",
}

# [END how_to_cloud_dataproc_create_cluster_in_gke_config]


with models.DAG(
    DAG_ID,
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "dataproc", "gke"],
) as dag:
    create_gke_cluster = GKECreateClusterOperator(
        task_id="create_gke_cluster",
        project_id=PROJECT_ID,
        location=REGION,
        body=GKE_CLUSTER_CONFIG,
    )

    add_iam_policy_binding = BashOperator(
        task_id="add_iam_policy_binding",
        bash_command=f"gcloud projects add-iam-policy-binding {PROJECT_ID} "
        f"--member=serviceAccount:{WORKLOAD_POOL}[{GKE_NAMESPACE}/agent] "
        "--role=roles/iam.workloadIdentityUser",
    )

    # [START how_to_cloud_dataproc_create_cluster_operator_in_gke]
    create_cluster_in_gke = DataprocCreateClusterOperator(
        task_id="create_cluster_in_gke",
        project_id=PROJECT_ID,
        region=REGION,
        cluster_name=CLUSTER_NAME,
        virtual_cluster_config=VIRTUAL_CLUSTER_CONFIG,
    )
    # [END how_to_cloud_dataproc_create_cluster_operator_in_gke]

    delete_dataproc_cluster = DataprocDeleteClusterOperator(
        task_id="delete_dataproc_cluster",
        project_id=PROJECT_ID,
        cluster_name=CLUSTER_NAME,
        region=REGION,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    delete_gke_cluster = GKEDeleteClusterOperator(
        task_id="delete_gke_cluster",
        name=GKE_CLUSTER_NAME,
        project_id=PROJECT_ID,
        location=REGION,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    (
        # TEST SETUP
        create_gke_cluster
        >> add_iam_policy_binding
        # TEST BODY
        >> create_cluster_in_gke
        # TEST TEARDOWN
        >> [delete_gke_cluster, delete_dataproc_cluster]
    )

    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)
test_run = get_test_run(dag)
