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"""Example DAG for using the DatabricksWorkflowTaskGroup and DatabricksNotebookOperator."""
from __future__ import annotations
import os
from datetime import timedelta
from airflow.models.dag import DAG
from airflow.providers.databricks.operators.databricks import (
DatabricksNotebookOperator,
DatabricksTaskOperator,
)
from airflow.providers.databricks.operators.databricks_workflow import DatabricksWorkflowTaskGroup
from airflow.utils.timezone import datetime
[docs]EXECUTION_TIMEOUT = int(os.getenv("EXECUTION_TIMEOUT", 6))
[docs]DATABRICKS_CONN_ID = os.getenv("DATABRICKS_CONN_ID", "databricks_default")
[docs]DATABRICKS_NOTIFICATION_EMAIL = os.getenv("DATABRICKS_NOTIFICATION_EMAIL", "your_email@serviceprovider.com")
[docs]GROUP_ID = os.getenv("DATABRICKS_GROUP_ID", "1234").replace(".", "_")
[docs]USER = os.environ.get("USER")
[docs]QUERY_ID = os.environ.get("QUERY_ID", "d3773b5a-56f9-422c-ae60-048eaa90aa33")
[docs]WAREHOUSE_ID = os.environ.get("WAREHOUSE_ID", "368fe30b92228713")
# job_cluster_spec example for Databricks on Azure
[docs]job_cluster_spec = [
{
"job_cluster_key": "Shared_job_cluster",
"new_cluster": {
"cluster_name": "",
"spark_version": "11.3.x-scala2.12",
"azure_attributes": {
"availability": "ON_DEMAND_AZURE",
"spot_bid_max_price": -1,
},
"num_workers": 1,
"spark_conf": {},
"node_type_id": "Standard_D3_v2",
"ssh_public_keys": [],
"custom_tags": {},
"spark_env_vars": {"PYSPARK_PYTHON": "/databricks/python3/bin/python3"},
"cluster_source": "JOB",
"init_scripts": [],
},
}
]
[docs]dag = DAG(
dag_id="example_databricks_workflow",
start_date=datetime(2022, 1, 1),
schedule=None,
catchup=False,
tags=["example", "databricks"],
)
with dag:
# [START howto_databricks_workflow_notebook]
[docs] task_group = DatabricksWorkflowTaskGroup(
group_id=f"test_workflow_{USER}_{GROUP_ID}",
databricks_conn_id=DATABRICKS_CONN_ID,
job_clusters=job_cluster_spec,
notebook_params={"ts": "{{ ts }}"},
notebook_packages=[
{
"pypi": {
"package": "simplejson==3.18.0", # Pin specification version of a package like this.
"repo": "https://pypi.org/simple", # You can specify your required Pypi index here.
}
},
],
extra_job_params={
"email_notifications": {
"on_start": [DATABRICKS_NOTIFICATION_EMAIL],
},
},
)
with task_group:
notebook_1 = DatabricksNotebookOperator(
task_id="workflow_notebook_1",
databricks_conn_id=DATABRICKS_CONN_ID,
notebook_path="/Shared/Notebook_1",
notebook_packages=[{"pypi": {"package": "Faker"}}],
source="WORKSPACE",
job_cluster_key="Shared_job_cluster",
execution_timeout=timedelta(seconds=600),
)
notebook_2 = DatabricksNotebookOperator(
task_id="workflow_notebook_2",
databricks_conn_id=DATABRICKS_CONN_ID,
notebook_path="/Shared/Notebook_2",
source="WORKSPACE",
job_cluster_key="Shared_job_cluster",
notebook_params={"foo": "bar", "ds": "{{ ds }}"},
)
task_operator_nb_1 = DatabricksTaskOperator(
task_id="nb_1",
databricks_conn_id=DATABRICKS_CONN_ID,
job_cluster_key="Shared_job_cluster",
task_config={
"notebook_task": {
"notebook_path": "/Shared/Notebook_1",
"source": "WORKSPACE",
},
"libraries": [
{"pypi": {"package": "Faker"}},
],
},
)
sql_query = DatabricksTaskOperator(
task_id="sql_query",
databricks_conn_id=DATABRICKS_CONN_ID,
task_config={
"sql_task": {
"query": {
"query_id": QUERY_ID,
},
"warehouse_id": WAREHOUSE_ID,
}
},
)
notebook_1 >> notebook_2 >> task_operator_nb_1 >> sql_query
# [END howto_databricks_workflow_notebook]
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: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)