Source code for tests.system.providers.databricks.example_databricks_sql

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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

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

import os
from datetime import datetime

from airflow import DAG
from airflow.providers.databricks.operators.databricks_sql import (

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "example_databricks_sql_operator"
with DAG( dag_id=DAG_ID, schedule='@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://", 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/
[docs]test_run = get_test_run(dag)

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