<gh_stars>1-10
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

import os
import textwrap
from datetime import datetime

from airflow import DAG
from airflow.providers.qubole.sensors.qubole import QuboleFileSensor, QubolePartitionSensor

START_DATE = datetime(2021, 1, 1)

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
DAG_ID = "example_qubole_sensor"


with DAG(
    dag_id=DAG_ID,
    schedule_interval=None,
    start_date=START_DATE,
    tags=['example'],
) as dag:
    dag.doc_md = textwrap.dedent(
        """
        This is only an example DAG to highlight usage of QuboleSensor in various scenarios,
        some of these tasks may or may not work based on your QDS account setup.

        Run a shell command from Qubole Analyze against your Airflow cluster with following to
        trigger it manually `airflow dags trigger example_qubole_sensor`.

        *Note: Make sure that connection `qubole_default` is properly set before running
        this example.*
        """
    )

    # [START howto_sensor_qubole_run_file_sensor]
    check_s3_file = QuboleFileSensor(
        task_id='check_s3_file',
        poke_interval=60,
        timeout=600,
        data={
            "files": [
                "s3://paid-qubole/HadoopAPIExamples/jars/hadoop-0.20.1-dev-streaming.jar",
                "s3://paid-qubole/HadoopAPITests/data/{{ ds.split('-')[2] }}.tsv",
            ]  # will check for availability of all the files in array
        },
    )
    # [END howto_sensor_qubole_run_file_sensor]

    # [START howto_sensor_qubole_run_partition_sensor]
    check_hive_partition = QubolePartitionSensor(
        task_id='check_hive_partition',
        poke_interval=10,
        timeout=60,
        data={
            "schema": "default",
            "table": "my_partitioned_table",
            "columns": [
                {"column": "month", "values": ["{{ ds.split('-')[1] }}"]},
                {"column": "day", "values": ["{{ ds.split('-')[2] }}", "{{ yesterday_ds.split('-')[2] }}"]},
            ],  # will check for partitions like [month=12/day=12,month=12/day=13]
        },
    )
    # [END howto_sensor_qubole_run_partition_sensor]

    check_s3_file >> check_hive_partition

    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)
test_run = get_test_run(dag)

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

import os
import textwrap
from datetime import datetime

from airflow import DAG
from airflow.providers.qubole.sensors.qubole import QuboleFileSensor, QubolePartitionSensor

START_DATE = datetime(2021, 1, 1)

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
DAG_ID = "example_qubole_sensor"


with DAG(
    dag_id=DAG_ID,
    schedule_interval=None,
    start_date=START_DATE,
    tags=['example'],
) as dag:
    dag.doc_md = textwrap.dedent(
        """
        This is only an example DAG to highlight usage of QuboleSensor in various scenarios,
        some of these tasks may or may not work based on your QDS account setup.

        Run a shell command from Qubole Analyze against your Airflow cluster with following to
        trigger it manually `airflow dags trigger example_qubole_sensor`.

        *Note: Make sure that connection `qubole_default` is properly set before running
        this example.*
        """
    )

    # [START howto_sensor_qubole_run_file_sensor]
    check_s3_file = QuboleFileSensor(
        task_id='check_s3_file',
        poke_interval=60,
        timeout=600,
        data={
            "files": [
                "s3://paid-qubole/HadoopAPIExamples/jars/hadoop-0.20.1-dev-streaming.jar",
                "s3://paid-qubole/HadoopAPITests/data/{{ ds.split('-')[2] }}.tsv",
            ]  # will check for availability of all the files in array
        },
    )
    # [END howto_sensor_qubole_run_file_sensor]

    # [START howto_sensor_qubole_run_partition_sensor]
    check_hive_partition = QubolePartitionSensor(
        task_id='check_hive_partition',
        poke_interval=10,
        timeout=60,
        data={
            "schema": "default",
            "table": "my_partitioned_table",
            "columns": [
                {"column": "month", "values": ["{{ ds.split('-')[1] }}"]},
                {"column": "day", "values": ["{{ ds.split('-')[2] }}", "{{ yesterday_ds.split('-')[2] }}"]},
            ],  # will check for partitions like [month=12/day=12,month=12/day=13]
        },
    )
    # [END howto_sensor_qubole_run_partition_sensor]

    check_s3_file >> check_hive_partition

    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)
test_run = get_test_run(dag)
# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements.  See the NOTICE file # distributed with this work for additional information # regarding copyright ownership.  The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License.  You may obtain a copy of the License at # #   http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied.  See the License for the # specific language governing permissions and limitations # under the License. This is only an example DAG to highlight usage of QuboleSensor in various scenarios,         some of these tasks may or may not work based on your QDS account setup.          Run a shell command from Qubole Analyze against your Airflow cluster with following to         trigger it manually `airflow dags trigger example_qubole_sensor`.          *Note: Make sure that connection `qubole_default` is properly set before running         this example.* # [START howto_sensor_qubole_run_file_sensor] # will check for availability of all the files in array # [END howto_sensor_qubole_run_file_sensor] # [START howto_sensor_qubole_run_partition_sensor] # will check for partitions like [month=12/day=12,month=12/day=13] # [END howto_sensor_qubole_run_partition_sensor] # This test needs watcher in order to properly mark success/failure # when "tearDown" task with trigger rule is part of the DAG # noqa: E402 # Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)