text: # -*- coding: utf-8 -*-
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  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
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  # under the License.
  
  """
  This is only an example DAG to highlight usage of QuboleOperator in various scenarios,
  some of these tasks may or may not work based on your Qubole account setup.
  
  Run a shell command from Qubole Analyze against your Airflow cluster with following to
  trigger it manually `airflow trigger_dag example_qubole_operator`.
  
  *Note: Make sure that connection `qubole_default` is properly set before running this
  example. Also be aware that it might spin up clusters to run these examples.*
  """
  
  import filecmp
  import random
  
  import airflow
  from airflow import DAG
  from airflow.contrib.operators.qubole_operator import QuboleOperator
  from airflow.operators.dummy_operator import DummyOperator
  from airflow.operators.python_operator import PythonOperator, BranchPythonOperator
  
  default_args = {
      'owner': 'Airflow',
      'depends_on_past': False,
      'start_date': airflow.utils.dates.days_ago(2),
      'email': ['airflow@example.com'],
      'email_on_failure': False,
      'email_on_retry': False
  }
  
  with DAG(
      dag_id='example_qubole_operator',
      default_args=default_args,
      schedule_interval=None
  ) as dag:
  
      def compare_result(**kwargs):
          """
          Compares the results of two QuboleOperator tasks.
  
          :param kwargs: The context of the executed task.
          :type kwargs: dict
          :return: True if the files are the same, False otherwise.
          :rtype: bool
          """
          ti = kwargs['ti']
          qubole_result_1 = t1.get_results(ti)
          qubole_result_2 = t2.get_results(ti)
          return filecmp.cmp(qubole_result_1, qubole_result_2)
  
      t1 = QuboleOperator(
          task_id='hive_show_table',
          command_type='hivecmd',
          query='show tables',
          cluster_label='{{ params.cluster_label }}',
          fetch_logs=True,
          # If `fetch_logs`=true, will fetch qubole command logs and concatenate
          # them into corresponding airflow task logs
          tags='airflow_example_run',
          # To attach tags to qubole command, auto attach 3 tags - dag_id, task_id, run_id
          qubole_conn_id='qubole_default',
          # Connection id to submit commands inside QDS, if not set "qubole_default" is used
          params={
              'cluster_label': 'default',
          }
      )
  
      t2 = QuboleOperator(
          task_id='hive_s3_location',
          command_type="hivecmd",
          script_location="s3n://public-qubole/qbol-library/scripts/show_table.hql",
          notfiy=True,
          tags=['tag1', 'tag2'],
          # If the script at s3 location has any qubole specific macros to be replaced
          # macros='[{"date": "{{ ds }}"}, {"name" : "abc"}]',
          trigger_rule="all_done"
      )
  
      t3 = PythonOperator(
          task_id='compare_result',
          provide_context=True,
          python_callable=compare_result,
          trigger_rule="all_done"
      )
  
      t3 << [t1, t2]
  
      options = ['hadoop_jar_cmd', 'presto_cmd', 'db_query', 'spark_cmd']
  
      branching = BranchPythonOperator(
          task_id='branching',
          python_callable=lambda: random.choice(options)
      )
  
      branching << t3
  
      join = DummyOperator(
          task_id='join',
          trigger_rule='one_success'
      )
  
      t4 = QuboleOperator(
          task_id='hadoop_jar_cmd',
          command_type='hadoopcmd',
          sub_command='jar s3://paid-qubole/HadoopAPIExamples/'
                      'jars/hadoop-0.20.1-dev-streaming.jar '
                      '-mapper wc '
                      '-numReduceTasks 0 -input s3://paid-qubole/HadoopAPITests/'
                      'data/3.tsv -output '
                      's3://paid-qubole/HadoopAPITests/data/3_wc',
          cluster_label='{{ params.cluster_label }}',
          fetch_logs=True,
          params={
              'cluster_label': 'default',
          }
      )
  
      t5 = QuboleOperator(
          task_id='pig_cmd',
          command_type="pigcmd",
          script_location="s3://public-qubole/qbol-library/scripts/script1-hadoop-s3-small.pig",
          parameters="key1=value1 key2=value2",
          trigger_rule="all_done"
      )
  
      t5 << t4 << branching
      t5 >> join
  
      t6 = QuboleOperator(
          task_id='presto_cmd',
          command_type='prestocmd',
          query='show tables'
      )
  
      t7 = QuboleOperator(
          task_id='shell_cmd',
          command_type="shellcmd",
          script_location="s3://public-qubole/qbol-library/scripts/shellx.sh",
          parameters="param1 param2",
          trigger_rule="all_done"
      )
  
      t7 << t6 << branching
      t7 >> join
  
      t8 = QuboleOperator(
          task_id='db_query',
          command_type='dbtapquerycmd',
          query='show tables',
          db_tap_id=2064
      )
  
      t9 = QuboleOperator(
          task_id='db_export',
          command_type='dbexportcmd',
          mode=1,
          hive_table='default_qubole_airline_origin_destination',
          db_table='exported_airline_origin_destination',
          partition_spec='dt=20110104-02',
          dbtap_id=2064,
          trigger_rule="all_done"
      )
  
      t9 << t8 << branching
      t9 >> join
  
      t10 = QuboleOperator(
          task_id='db_import',
          command_type='dbimportcmd',
          mode=1,
          hive_table='default_qubole_airline_origin_destination',
          db_table='exported_airline_origin_destination',
          where_clause='id < 10',
          db_parallelism=2,
          dbtap_id=2064,
          trigger_rule="all_done"
      )
  
      prog = '''
      import scala.math.random
  
      import org.apache.spark._
  
      /** Computes an approximation to pi */
      object SparkPi {
        def main(args: Array[String]) { // noqa
          val conf = new SparkConf().setAppName("Spark Pi")
          val spark = new SparkContext(conf)
          val slices = if (args.length > 0) args(0).toInt else 2
          val n = math.min(100000L * slices, Int.MaxValue).toInt // avoid overflow
          val count = spark.parallelize(1 until n, slices).map { i =>
            val x = random * 2 - 1
            val y = random * 2 - 1
            if (x*x + y*y < 1) 1 else 0
          }.reduce(_ + _)
          println("Pi is roughly " + 4.0 * count / n)
          spark.stop()
        }
      }
      '''
  
      t11 = QuboleOperator(
          task_id='spark_cmd',
          command_type="sparkcmd",
          program=prog,
          language='scala',
          arguments='--class SparkPi',
          tags='airflow_example_run'
      )
  
      t11 << t10 << branching
      t11 >> join
  