"""Snowflake connector class for the iTunes Tickets tasks."""
from datetime import datetime

from snowflake_connector.etl_connector import SQLLoader

from feed_ingestion.common.staging_raw_sf.base_executor import \
    SnowflakeSQLExecutorSR
from feed_ingestion.flows.itunes_tickets import config


sql_loader = SQLLoader(__file__)


class ITunesTicketsSF(SnowflakeSQLExecutorSR):
    """Helper class to abstract Snowflake operations.

    This class inherits from SnowflakeSQLExecutor class, which provides basic
    set of methods. This class extends SnowflakeSQLExecutor with some specific
    methods, which are useful to encapsulate some flow specific operations.
    """

    @property
    def feed_name(self):
        """Name of the feed.

        Should match dir name of this feed, feed_name in config.py of a feed.

        Returns:
            str: Feed name
        """
        return config.feed_name

    def create_temp_staging_raw_table(self, temp_staging_raw_table):
        """Create a temporary staging raw table.

        Args:
            temp_staging_raw_table (str): A table name in Snowflake.
        """
        for table_type in config.table_types:
            if table_type in temp_staging_raw_table:
                sql_template = 'create_temp_{table_type}'.format(
                    table_type=table_type)
                params = dict(
                    db=self.sf_config['db'],
                    schema=self.sf_config['schema'],
                    temp_staging_raw_table=temp_staging_raw_table)
                self.execute_query(sql_loader, sql_template, params)

    def load_temp_staging_raw_table(
            self, temp_staging_raw_table, aws, key_dir):
        """Load temp staging raw table.

        Args:
            temp_staging_raw_table (str): A table name in Snowflake.
            aws (dict): AWS credentials to fill a template of COPY SQL
                statement.
            key_dir (str): A S3 path to load files from.
        """
        sql_template = 'load_temp_table'
        aws_params = self.get_aws_params()
        params = dict(
            db=self.sf_config['db'],
            schema=self.sf_config['schema'],
            temp_staging_raw_table=temp_staging_raw_table,
            s3_path=key_dir,
            **aws_params,
        )
        self.execute_query(sql_loader, sql_template, params)

    def clean_staging_raw_table(self, staging_raw_table, date, **kwargs):
        """Delete rows from previous unsuccessful workflow run.

        Args:
            staging_raw_table (str): A table name in Snowflake.
            date (str): Date of the data being process (YYYY-MM-DD).
        """
        for table_type in config.table_types:
            staging_raw_table = config.snowflake_table_names[
                'staging_raw'].format(table_type=table_type)
            query_name = 'delete_from_staging_raw'

            params = dict(
                db=self.sf_config['db'],
                schema=self.sf_config['schema'],
                date=date,
                staging_raw_table=staging_raw_table)
            self.execute_query(sql_loader, query_name, params)

    def load_staging_raw_table(
            self, temp_staging_raw_table, staging_raw_table, date, **kwargs):
        """Load the temp_staging_raw data to the feed's staging_raw table.

        Args:
            temp_staging_raw_table (str): A table name in Snowflake.
            staging_raw_table (str): A table name in Snowflake.
            date (str): Date of the data being process (YYYY-MM-DD).
        """
        date_obj = datetime.strptime(date, '%Y-%m-%d')

        for table_type in config.table_types:
            temp_table = config.snowflake_table_names[
                'temp_staging_raw'].format(
                table_type=table_type, date=date_obj)
            staging_raw_table = config.snowflake_table_names[
                'staging_raw'].format(table_type=table_type)
            query_name = f'load_staging_raw_{table_type}'

            params = dict(
                db=self.sf_config['db'],
                schema=self.sf_config['schema'],
                date=date,
                temp_staging_raw_table=temp_table,
                staging_raw_table=staging_raw_table)
            self.execute_query(sql_loader, query_name, params)