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from datetime import datetime

from airflow.decorators import dag, task


@dag(schedule_interval=None, start_date=datetime(2021, 1, 1), catchup=False, tags=['example'])
def tutorial_taskflow_api_etl_virtualenv():
    """
    ### TaskFlow API example using virtualenv
    This is a simple ETL data pipeline example which demonstrates the use of
    the TaskFlow API using three simple tasks for Extract, Transform, and Load.
    """

    @task.virtualenv(
        use_dill=True,
        system_site_packages=False,
        requirements=['funcsigs'],
    )
    def extract():
        """
        #### Extract task
        A simple Extract task to get data ready for the rest of the data
        pipeline. In this case, getting data is simulated by reading from a
        hardcoded JSON string.
        """
        import json

        data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22}'

        order_data_dict = json.loads(data_string)
        return order_data_dict

    @task(multiple_outputs=True)
    def transform(order_data_dict: dict):
        """
        #### Transform task
        A simple Transform task which takes in the collection of order data and
        computes the total order value.
        """
        total_order_value = 0

        for value in order_data_dict.values():
            total_order_value += value

        return {"total_order_value": total_order_value}

    @task()
    def load(total_order_value: float):
        """
        #### Load task
        A simple Load task which takes in the result of the Transform task and
        instead of saving it to end user review, just prints it out.
        """

        print(f"Total order value is: {total_order_value:.2f}")

    order_data = extract()
    order_summary = transform(order_data)
    load(order_summary["total_order_value"])


tutorial_etl_dag = tutorial_taskflow_api_etl_virtualenv()