Source code for tests.system.providers.amazon.aws.example_quicksight

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from __future__ import annotations

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.
"""


[docs]DAG_ID = "example_quicksight"



[docs]sys_test_context_task = SystemTestContextBuilder().build()



[docs]SAMPLE_DATA_COLUMNS = ["Project", "Year"]


[docs]SAMPLE_DATA = """'Airflow','2015'
    'OpenOffice','2012'
    'Subversion','2000'
    'NiFi','2006'
"""



@task

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



@task

[docs]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

[docs]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)

[docs]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)

[docs]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)

[docs]def delete_ingestion(aws_account_id: str, dataset_name: str, ingestion_name: str) -> None:
    client = boto3.client("quicksight")
    try:
        client.cancel_ingestion(
            AwsAccountId=aws_account_id,
            DataSetId=dataset_name,
            IngestionId=ingestion_name,
        )
    except client.exceptions.ResourceNotFoundException:
        # Ingestion has already terminated on its own.
        pass



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

[docs]    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)

[docs]test_run = get_test_run(dag)

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