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from __future__ import annotations

import contextlib
import json
from datetime import datetime

import boto3

from airflow import DAG
from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.providers.amazon.aws.operators.quicksight import QuickSightCreateIngestionOperator
from airflow.providers.amazon.aws.operators.s3 import (
    S3CreateBucketOperator,
    S3CreateObjectOperator,
    S3DeleteBucketOperator,
)
from airflow.providers.amazon.aws.sensors.quicksight import QuickSightSensor
from airflow.utils.trigger_rule import TriggerRule
from tests.system.providers.amazon.aws.utils import ENV_ID_KEY, SystemTestContextBuilder

"""
Prerequisites:
1. The account which runs this test must manually be activated in Quicksight here:
https://quicksight.aws.amazon.com/sn/console/signup?#
2. The activation process creates an IAM Role called `aws-quicksight-service-role-v0`.
 You have to add a policy named 'AWSQuickSightS3Policy' with the S3 access permissions.
 The policy name is enforced, and the permissions json can be copied from `AmazonS3FullAccess`.

NOTES:  If Create Ingestion fails for any reason, that ingestion name will remain in use and
 future runs will stall with the sensor returning a status of QUEUED "forever".  If you run
 into this behavior, changing the template for the ingestion name or the ENV_ID and re-running
 the test should resolve the issue.
"""

DAG_ID = "example_quicksight"

sys_test_context_task = SystemTestContextBuilder().build()

SAMPLE_DATA_COLUMNS = ["Project", "Year"]
SAMPLE_DATA = """'Airflow','2015'
    'OpenOffice','2012'
    'Subversion','2000'
    'NiFi','2006'
"""


@task
def get_aws_account_id() -> int:
    return boto3.client("sts").get_caller_identity()["Account"]


@task
def create_quicksight_data_source(
    aws_account_id: str, datasource_name: str, bucket: str, manifest_key: str
) -> str:
    response = boto3.client("quicksight").create_data_source(
        AwsAccountId=aws_account_id,
        DataSourceId=datasource_name,
        Name=datasource_name,
        Type="S3",
        DataSourceParameters={
            "S3Parameters": {"ManifestFileLocation": {"Bucket": bucket, "Key": manifest_key}}
        },
    )
    return response["Arn"]


@task
def create_quicksight_dataset(aws_account_id: int, dataset_name: str, data_source_arn: str) -> None:
    table_map = {
        "default": {
            "S3Source": {
                "DataSourceArn": data_source_arn,
                "InputColumns": [{"Name": name, "Type": "STRING"} for name in SAMPLE_DATA_COLUMNS],
            }
        }
    }

    boto3.client("quicksight").create_data_set(
        AwsAccountId=aws_account_id,
        DataSetId=dataset_name,
        Name=dataset_name,
        PhysicalTableMap=table_map,
        ImportMode="SPICE",
    )


@task(trigger_rule=TriggerRule.ALL_DONE)
def delete_quicksight_data_source(aws_account_id: str, datasource_name: str):
    boto3.client("quicksight").delete_data_source(AwsAccountId=aws_account_id, DataSourceId=datasource_name)


@task(trigger_rule=TriggerRule.ALL_DONE)
def delete_dataset(aws_account_id: str, dataset_name: str):
    boto3.client("quicksight").delete_data_set(AwsAccountId=aws_account_id, DataSetId=dataset_name)


@task(trigger_rule=TriggerRule.ALL_DONE)
def delete_ingestion(aws_account_id: str, dataset_name: str, ingestion_name: str) -> None:
    client = boto3.client("quicksight")
    with contextlib.suppress(client.exceptions.ResourceNotFoundException):
        # suppress ResourceNotFoundException: Ingestion has already terminated on its own.
        client.cancel_ingestion(
            AwsAccountId=aws_account_id,
            DataSetId=dataset_name,
            IngestionId=ingestion_name,
        )


with DAG(
    dag_id=DAG_ID,
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    tags=["example"],
    catchup=False,
) as dag:
    test_context = sys_test_context_task()
    account_id = get_aws_account_id()

    env_id = test_context[ENV_ID_KEY]
    bucket_name = f"{env_id}-quicksight-bucket"
    data_filename = "sample_data.csv"
    dataset_id = f"{env_id}-data-set"
    datasource_id = f"{env_id}-data-source"
    ingestion_id = f"{env_id}-ingestion"
    manifest_filename = f"{env_id}-manifest.json"
    manifest_contents = {"fileLocations": [{"URIs": [f"s3://{bucket_name}/{data_filename}"]}]}

    create_s3_bucket = S3CreateBucketOperator(task_id="create_s3_bucket", bucket_name=bucket_name)

    upload_manifest_file = S3CreateObjectOperator(
        task_id="upload_manifest_file",
        s3_bucket=bucket_name,
        s3_key=manifest_filename,
        data=json.dumps(manifest_contents),
        replace=True,
    )

    upload_sample_data = S3CreateObjectOperator(
        task_id="upload_sample_data",
        s3_bucket=bucket_name,
        s3_key=data_filename,
        data=SAMPLE_DATA,
        replace=True,
    )

    data_source = create_quicksight_data_source(
        aws_account_id=account_id,
        datasource_name=datasource_id,
        bucket=bucket_name,
        manifest_key=manifest_filename,
    )

    create_dataset = create_quicksight_dataset(account_id, dataset_id, data_source)

    # [START howto_operator_quicksight_create_ingestion]
    create_ingestion = QuickSightCreateIngestionOperator(
        task_id="create_ingestion",
        data_set_id=dataset_id,
        ingestion_id=ingestion_id,
    )
    # [END howto_operator_quicksight_create_ingestion]

    # QuickSightCreateIngestionOperator waits by default, setting as False to test the Sensor below.
    create_ingestion.wait_for_completion = False

    # If this sensor appears to freeze with a "QUEUED" status, see note above.
    # [START howto_sensor_quicksight]
    await_job = QuickSightSensor(
        task_id="await_job",
        data_set_id=dataset_id,
        ingestion_id=ingestion_id,
    )
    # [END howto_sensor_quicksight]
    await_job.poke_interval = 10

    delete_bucket = S3DeleteBucketOperator(
        task_id="delete_s3_bucket",
        trigger_rule=TriggerRule.ALL_DONE,
        bucket_name=bucket_name,
        force_delete=True,
    )

    chain(
        # TEST SETUP
        test_context,
        account_id,
        create_s3_bucket,
        upload_manifest_file,
        upload_sample_data,
        data_source,
        create_dataset,
        # TEST BODY
        create_ingestion,
        await_job,
        # TEST TEARDOWN
        delete_dataset(account_id, dataset_id),
        delete_quicksight_data_source(account_id, datasource_id),
        delete_ingestion(account_id, dataset_id, ingestion_id),
        delete_bucket,
    )

    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)
