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#
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"""
Example Airflow DAG that shows how to use Google Dataprep.
This DAG relies on the following OS environment variables
* SYSTEM_TESTS_DATAPREP_TOKEN - Dataprep API access token.
For generating it please use instruction
https://docs.trifacta.com/display/DP/Manage+API+Access+Tokens#:~:text=Enable%20individual%20access-,Generate%20New%20Token,-Via%20UI.
"""
from __future__ import annotations
import json
import logging
import os
from datetime import datetime
from airflow import models
from tests_common.test_utils.version_compat import AIRFLOW_V_3_0_PLUS
if AIRFLOW_V_3_0_PLUS:
from airflow.sdk import task
else:
# Airflow 2 path
from airflow.decorators import task # type: ignore[attr-defined,no-redef]
from airflow.models.baseoperator import chain
from airflow.providers.google.cloud.hooks.dataprep import GoogleDataprepHook
from airflow.providers.google.cloud.operators.dataprep import (
DataprepCopyFlowOperator,
DataprepDeleteFlowOperator,
DataprepGetJobGroupOperator,
DataprepGetJobsForJobGroupOperator,
DataprepRunFlowOperator,
DataprepRunJobGroupOperator,
)
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.sensors.dataprep import DataprepJobGroupIsFinishedSensor
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
from system.google.gcp_api_client_helpers import create_airflow_connection, delete_airflow_connection
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
DAG_ID = "dataprep"
IS_COMPOSER = bool(os.environ.get("COMPOSER_ENVIRONMENT", ""))
CONNECTION_ID = f"connection_{DAG_ID}_{ENV_ID}".replace("-", "_")
DATAPREP_TOKEN = os.environ.get("SYSTEM_TESTS_DATAPREP_TOKEN", "")
GCP_PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
GCS_BUCKET_NAME = f"dataprep-bucket-{DAG_ID}-{ENV_ID}"
GCS_BUCKET_PATH = f"gs://{GCS_BUCKET_NAME}/task_results/"
DATASET_URI = "gs://airflow-system-tests-resources/dataprep/dataset-00000.parquet"
DATASET_NAME = f"dataset_{DAG_ID}_{ENV_ID}".replace("-", "_")
DATASET_WRANGLED_NAME = f"wrangled_{DATASET_NAME}"
DATASET_WRANGLED_ID = "{{ task_instance.xcom_pull('create_wrangled_dataset')['id'] }}"
FLOW_ID = "{{ task_instance.xcom_pull('create_flow')['id'] }}"
FLOW_COPY_ID = "{{ task_instance.xcom_pull('copy_flow')['id'] }}"
RECIPE_NAME = DATASET_WRANGLED_NAME
WRITE_SETTINGS = {
"writesettings": [
{
"path": GCS_BUCKET_PATH + f"adhoc_{RECIPE_NAME}.csv",
"action": "create",
"format": "csv",
},
],
}
log = logging.getLogger(__name__)
with models.DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1), # Override to match your needs
catchup=False,
tags=["example", "dataprep"],
render_template_as_native_obj=True,
) as dag:
create_bucket_task = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=GCS_BUCKET_NAME,
project_id=GCP_PROJECT_ID,
)
@task
def create_connection(connection_id: str) -> None:
create_airflow_connection(
connection_id=connection_id,
connection_conf={
"description": "Example Dataprep connection",
"conn_type": "dataprep",
"extra": json.dumps({"token": DATAPREP_TOKEN}),
},
is_composer=IS_COMPOSER,
)
create_connection_task = create_connection(connection_id=CONNECTION_ID)
@task
def create_imported_dataset():
hook = GoogleDataprepHook(dataprep_conn_id=CONNECTION_ID)
response = hook.create_imported_dataset(
body_request={
"uri": DATASET_URI,
"name": DATASET_NAME,
}
)
return response
create_imported_dataset_task = create_imported_dataset()
@task
def create_flow():
hook = GoogleDataprepHook(dataprep_conn_id=CONNECTION_ID)
response = hook.create_flow(
body_request={
"name": f"test_flow_{DAG_ID}_{ENV_ID}",
"description": "Test flow",
}
)
return response
create_flow_task = create_flow()
@task
def create_wrangled_dataset(flow, imported_dataset):
hook = GoogleDataprepHook(dataprep_conn_id=CONNECTION_ID)
response = hook.create_wrangled_dataset(
body_request={
"importedDataset": {"id": imported_dataset["id"]},
"flow": {"id": flow["id"]},
"name": DATASET_WRANGLED_NAME,
}
)
return response
create_wrangled_dataset_task = create_wrangled_dataset(create_flow_task, create_imported_dataset_task)
@task
def create_output(wrangled_dataset):
hook = GoogleDataprepHook(dataprep_conn_id=CONNECTION_ID)
response = hook.create_output_object(
body_request={
"execution": "dataflow",
"profiler": False,
"flowNodeId": wrangled_dataset["id"],
}
)
return response
create_output_task = create_output(create_wrangled_dataset_task)
@task
def create_write_settings(output):
hook = GoogleDataprepHook(dataprep_conn_id=CONNECTION_ID)
response = hook.create_write_settings(
body_request={
"path": GCS_BUCKET_PATH + f"adhoc_{RECIPE_NAME}.csv",
"action": "create",
"format": "csv",
"outputObjectId": output["id"],
}
)
return response
create_write_settings_task = create_write_settings(create_output_task)
# [START how_to_dataprep_copy_flow_operator]
copy_task = DataprepCopyFlowOperator(
task_id="copy_flow",
dataprep_conn_id=CONNECTION_ID,
project_id=GCP_PROJECT_ID,
flow_id=FLOW_ID,
name=f"copy_{DATASET_NAME}",
)
# [END how_to_dataprep_copy_flow_operator]
# [START how_to_dataprep_run_job_group_operator]
run_job_group_task = DataprepRunJobGroupOperator(
task_id="run_job_group",
dataprep_conn_id=CONNECTION_ID,
project_id=GCP_PROJECT_ID,
body_request={
"wrangledDataset": {"id": DATASET_WRANGLED_ID},
"overrides": WRITE_SETTINGS,
},
)
# [END how_to_dataprep_run_job_group_operator]
# [START how_to_dataprep_dataprep_run_flow_operator]
run_flow_task = DataprepRunFlowOperator(
task_id="run_flow",
dataprep_conn_id=CONNECTION_ID,
project_id=GCP_PROJECT_ID,
flow_id=FLOW_COPY_ID,
body_request={},
)
# [END how_to_dataprep_dataprep_run_flow_operator]
# [START how_to_dataprep_get_job_group_operator]
get_job_group_task = DataprepGetJobGroupOperator(
task_id="get_job_group",
dataprep_conn_id=CONNECTION_ID,
project_id=GCP_PROJECT_ID,
job_group_id="{{ task_instance.xcom_pull('run_flow')['data'][0]['id'] }}",
embed="",
include_deleted=False,
)
# [END how_to_dataprep_get_job_group_operator]
# [START how_to_dataprep_get_jobs_for_job_group_operator]
get_jobs_for_job_group_task = DataprepGetJobsForJobGroupOperator(
task_id="get_jobs_for_job_group",
dataprep_conn_id=CONNECTION_ID,
job_group_id="{{ task_instance.xcom_pull('run_flow')['data'][0]['id'] }}",
)
# [END how_to_dataprep_get_jobs_for_job_group_operator]
# [START how_to_dataprep_job_group_finished_sensor]
check_flow_status_sensor = DataprepJobGroupIsFinishedSensor(
task_id="check_flow_status",
dataprep_conn_id=CONNECTION_ID,
job_group_id="{{ task_instance.xcom_pull('run_flow')['data'][0]['id'] }}",
)
# [END how_to_dataprep_job_group_finished_sensor]
# [START how_to_dataprep_job_group_finished_sensor]
check_job_group_status_sensor = DataprepJobGroupIsFinishedSensor(
task_id="check_job_group_status",
dataprep_conn_id=CONNECTION_ID,
job_group_id="{{ task_instance.xcom_pull('run_job_group')['id'] }}",
)
# [END how_to_dataprep_job_group_finished_sensor]
# [START how_to_dataprep_delete_flow_operator]
delete_flow_task = DataprepDeleteFlowOperator(
task_id="delete_flow",
dataprep_conn_id=CONNECTION_ID,
flow_id="{{ task_instance.xcom_pull('copy_flow')['id'] }}",
)
# [END how_to_dataprep_delete_flow_operator]
delete_flow_task.trigger_rule = TriggerRule.ALL_DONE
delete_flow_task_original = DataprepDeleteFlowOperator(
task_id="delete_flow_original",
dataprep_conn_id=CONNECTION_ID,
flow_id="{{ task_instance.xcom_pull('create_flow')['id'] }}",
trigger_rule=TriggerRule.ALL_DONE,
)
@task(trigger_rule=TriggerRule.ALL_DONE)
def delete_dataset(dataset):
hook = GoogleDataprepHook(dataprep_conn_id=CONNECTION_ID)
hook.delete_imported_dataset(dataset_id=dataset["id"])
delete_dataset_task = delete_dataset(create_imported_dataset_task)
delete_bucket_task = GCSDeleteBucketOperator(
task_id="delete_bucket",
bucket_name=GCS_BUCKET_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
@task(task_id="delete_connection")
def delete_connection(connection_id: str) -> None:
delete_airflow_connection(connection_id=connection_id, is_composer=IS_COMPOSER)
delete_connection_task = delete_connection(connection_id=CONNECTION_ID)
chain(
# TEST SETUP
create_bucket_task,
create_connection_task,
[create_imported_dataset_task, create_flow_task],
create_wrangled_dataset_task,
create_output_task,
create_write_settings_task,
# TEST BODY
copy_task,
[run_job_group_task, run_flow_task],
[get_job_group_task, get_jobs_for_job_group_task],
[check_flow_status_sensor, check_job_group_status_sensor],
# TEST TEARDOWN
delete_dataset_task,
[delete_flow_task, delete_flow_task_original],
[delete_bucket_task, delete_connection_task],
)
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)