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from __future__ import annotations

import contextlib
import json
from datetime import datetime

import boto3

from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.models.dag import DAG
from import QuickSightCreateIngestionOperator
from import (
from import QuickSightSensor
from airflow.utils.trigger_rule import TriggerRule
from import ENV_ID_KEY, SystemTestContextBuilder

1. The account which runs this test must manually be activated in Quicksight here:
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' """
[docs]def get_aws_account_id() -> int: return boto3.client("sts").get_caller_identity()["Account"]
[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"]
[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", )
[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)
[docs]def delete_dataset(aws_account_id: str, dataset_name: str): boto3.client("quicksight").delete_data_set(AwsAccountId=aws_account_id, DataSetId=dataset_name)
[docs]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:
[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/
[docs]test_run = get_test_run(dag)

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