Source code for tests.system.databricks.example_databricks_sensors

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# to you under the Apache License, Version 2.0 (the
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from __future__ import annotations

import os
import textwrap
from datetime import datetime

from airflow import DAG
from airflow.providers.databricks.sensors.databricks_partition import DatabricksPartitionSensor
from airflow.providers.databricks.sensors.databricks_sql import DatabricksSqlSensor

# [Env variable to be used from the OS]

[docs]
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")

# [DAG name to be shown on Airflow UI]

[docs]
DAG_ID = "example_databricks_sensor"


with DAG(
    dag_id=DAG_ID,
    schedule="@daily",
    start_date=datetime(2021, 1, 1),
    tags=["example"],
    catchup=False,
) as dag:
    dag.doc_md = textwrap.dedent(
        """

        This is an example DAG which uses the DatabricksSqlSensor
        sensor. The example task in the DAG executes the provided
        SQL query against the Databricks SQL warehouse and if a
        result is returned, the sensor returns True/succeeds.
        If no results are returned, the sensor returns False/
        fails.

        """
    )
    # [START howto_sensor_databricks_connection_setup]
    # Connection string setup for Databricks workspace.

[docs]
    connection_id = "databricks_default"

    sql_warehouse_name = "Starter Warehouse"
    # [END howto_sensor_databricks_connection_setup]

    # [START howto_sensor_databricks_sql]
    # Example of using the Databricks SQL Sensor to check existence of data in a table.
    sql_sensor = DatabricksSqlSensor(
        databricks_conn_id=connection_id,
        sql_warehouse_name=sql_warehouse_name,
        catalog="hive_metastore",
        task_id="sql_sensor_task",
        sql="select * from hive_metastore.temp.sample_table_3 limit 1",
        timeout=60 * 2,
    )
    # [END howto_sensor_databricks_sql]

    # [START howto_sensor_databricks_partition]
    # Example of using the Databricks Partition Sensor to check the presence
    # of the specified partition(s) in a table.
    partition_sensor = DatabricksPartitionSensor(
        databricks_conn_id=connection_id,
        sql_warehouse_name=sql_warehouse_name,
        catalog="hive_metastore",
        task_id="partition_sensor_task",
        table_name="sample_table_2",
        schema="temp",
        partitions={"date": "2023-01-03", "name": ["abc", "def"]},
        partition_operator="=",
        timeout=60 * 2,
    )
    # [END howto_sensor_databricks_partition]

    # Task dependency between the SQL sensor and the partition sensor.
    # If the first task(sql_sensor) succeeds, the second task(partition_sensor)
    # runs, else all the subsequent DAG tasks and the DAG are marked as failed.
    (sql_sensor >> partition_sensor)

    from tests_common.test_utils.watcher import watcher

    # This example does not need a watcher in order to properly mark success/failure
    # since it is a single task, but it is given here as an example for users to
    # extend it to their use cases.
    # 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)