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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 logging
import os
from datetime import datetime
from airflow import models
from airflow.decorators import task
from airflow.models import Connection
from airflow.models.baseoperator import chain
from airflow.operators.bash import BashOperator
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
from airflow.settings import Session
from airflow.utils.trigger_rule import TriggerRule
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "example_dataprep"
[docs]CONNECTION_ID = f"connection_{DAG_ID}_{ENV_ID}".replace("-", "_")
[docs]DATAPREP_TOKEN = os.environ.get("SYSTEM_TESTS_DATAPREP_TOKEN", "")
[docs]GCP_PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT")
[docs]GCS_BUCKET_NAME = f"dataprep-bucket-{DAG_ID}-{ENV_ID}"
[docs]GCS_BUCKET_PATH = f"gs://{GCS_BUCKET_NAME}/task_results/"
[docs]DATASET_URI = "gs://airflow-system-tests-resources/dataprep/dataset-00000.parquet"
[docs]DATASET_NAME = f"dataset_{DAG_ID}_{ENV_ID}".replace("-", "_")
[docs]DATASET_WRANGLED_NAME = f"wrangled_{DATASET_NAME}"
[docs]DATASET_WRANGLED_ID = "{{ task_instance.xcom_pull('create_wrangled_dataset')['id'] }}"
[docs]FLOW_ID = "{{ task_instance.xcom_pull('create_flow')['id'] }}"
[docs]FLOW_COPY_ID = "{{ task_instance.xcom_pull('copy_flow')['id'] }}"
[docs]RECIPE_NAME = DATASET_WRANGLED_NAME
[docs]WRITE_SETTINGS = {
"writesettings": [
{
"path": GCS_BUCKET_PATH + f"adhoc_{RECIPE_NAME}.csv",
"action": "create",
"format": "csv",
},
],
}
[docs]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:
[docs] create_bucket_task = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=GCS_BUCKET_NAME,
project_id=GCP_PROJECT_ID,
)
@task
def create_connection(**kwargs) -> None:
connection = Connection(
conn_id=CONNECTION_ID,
description="Example Dataprep connection",
conn_type="dataprep",
extra={"token": DATAPREP_TOKEN},
)
session: Session = Session()
if session.query(Connection).filter(Connection.conn_id == CONNECTION_ID).first():
log.warning("Connection %s already exists", CONNECTION_ID)
return None
session.add(connection)
session.commit()
create_connection_task = create_connection()
@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,
)
delete_connection = BashOperator(
task_id="delete_connection",
bash_command=f"airflow connections delete {CONNECTION_ID}",
trigger_rule=TriggerRule.ALL_DONE,
)
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],
)
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)
