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"""
This is an example DAG which uses the DatabricksSubmitRunOperator.
In this example, we create two tasks which execute sequentially.
The first task is to run a notebook at the workspace path "/test"
and the second task is to run a JAR uploaded to DBFS. Both,
tasks use new clusters.
Because we have set a downstream dependency on the notebook task,
the spark jar task will NOT run until the notebook task completes
successfully.
The definition of a successful run is if the run has a result_state of "SUCCESS".
For more information about the state of a run refer to
https://docs.databricks.com/api/latest/jobs.html#runstate
"""
import os
from datetime import datetime
from airflow import DAG
from airflow.providers.databricks.operators.databricks_sql import (
DatabricksCopyIntoOperator,
DatabricksSqlOperator,
)
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "example_databricks_sql_operator"
with DAG(
dag_id=DAG_ID,
schedule_interval='@daily',
start_date=datetime(2021, 1, 1),
tags=['example'],
catchup=False,
) as dag:
[docs] connection_id = 'my_connection'
sql_endpoint_name = "My Endpoint"
# [START howto_operator_databricks_sql_multiple]
# Example of using the Databricks SQL Operator to perform multiple operations.
create = DatabricksSqlOperator(
databricks_conn_id=connection_id,
sql_endpoint_name=sql_endpoint_name,
task_id='create_and_populate_table',
sql=[
"drop table if exists default.my_airflow_table",
"create table default.my_airflow_table(id int, v string)",
"insert into default.my_airflow_table values (1, 'test 1'), (2, 'test 2')",
],
)
# [END howto_operator_databricks_sql_multiple]
# [START howto_operator_databricks_sql_select]
# Example of using the Databricks SQL Operator to select data.
select = DatabricksSqlOperator(
databricks_conn_id=connection_id,
sql_endpoint_name=sql_endpoint_name,
task_id='select_data',
sql="select * from default.my_airflow_table",
)
# [END howto_operator_databricks_sql_select]
# [START howto_operator_databricks_sql_select_file]
# Example of using the Databricks SQL Operator to select data into a file with JSONL format.
select_into_file = DatabricksSqlOperator(
databricks_conn_id=connection_id,
sql_endpoint_name=sql_endpoint_name,
task_id='select_data_into_file',
sql="select * from default.my_airflow_table",
output_path="/tmp/1.jsonl",
output_format="jsonl",
)
# [END howto_operator_databricks_sql_select_file]
# [START howto_operator_databricks_sql_multiple_file]
# Example of using the Databricks SQL Operator to select data.
# SQL statements should be in the file with name test.sql
create_file = DatabricksSqlOperator(
databricks_conn_id=connection_id,
sql_endpoint_name=sql_endpoint_name,
task_id='create_and_populate_from_file',
sql="test.sql",
)
# [END howto_operator_databricks_sql_multiple_file]
# [START howto_operator_databricks_copy_into]
# Example of importing data using COPY_INTO SQL command
import_csv = DatabricksCopyIntoOperator(
task_id='import_csv',
databricks_conn_id=connection_id,
sql_endpoint_name=sql_endpoint_name,
table_name="my_table",
file_format="CSV",
file_location="abfss://container@account.dfs.core.windows.net/my-data/csv",
format_options={'header': 'true'},
force_copy=True,
)
# [END howto_operator_databricks_copy_into]
(create >> create_file >> import_csv >> select >> select_into_file)
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)
