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"""
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",
        notify=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
