#!/usr/bin/python
# -*- coding: utf-8 -*-
import sys
import getopt
from datetime import datetime
import pandas as pd
from sqlalchemy import create_engine

if __name__ == "__main__":

    #Задаём входные параметры
    unixOptions = "s:e"
    gnuOptions = ["start_dt=", "end_dt="]

    fullCmdArguments = sys.argv
    argumentList = fullCmdArguments[1:] #excluding script name

    try:
        arguments, values = getopt.getopt(argumentList, unixOptions, gnuOptions)
    except getopt.error as err:
        print (str(err))
        sys.exit(2)

    start_dt = '2019-09-24 18:00:00+03'
    end_dt = '2019-09-24 19:00:00+03'
    for currentArgument, currentValue in arguments:
        if currentArgument in ("-s", "--start_dt"):
            start_dt = currentValue
        elif currentArgument in ("-e", "--end_dt"):
            end_dt = currentValue

    db_config = {'user': 'my_user',
                 'pwd': 'my_user_password',
                 'host': 'localhost',
                 'port': 5432,
                 'db': 'zen'}

    connection_string = 'postgresql://{}:{}@{}:{}/{}'.format(db_config['user'],
                                     db_config['pwd'],
                                 db_config['host'],
                                 db_config['port'],
                                 db_config['db'])
    engine = create_engine(connection_string)

    # Теперь выберем из таблицы только те строки,
    # которые были выпущены между start_dt и end_dt
    query = ''' SELECT
                event_id
                ,age_segment
                ,event
                ,item_id
                ,item_topic
                ,item_type
                ,source_id
                ,source_topic
                ,source_type
                ,TO_TIMESTAMP(ts/1000) AT TIME ZONE 'Etc/UTC' as dt
                ,user_id
            FROM log_raw
            WHERE TO_TIMESTAMP(ts/1000) AT TIME ZONE 'Etc/UTC'  BETWEEN '{}'::TIMESTAMP AND '{}'::TIMESTAMP;
        '''.format(start_dt, end_dt)

    data_raw = pd.io.sql.read_sql(query, con = engine, index_col = 'event_id')
    columns_str = ['age_segment', 'item_topic', 'source_topic', 'source_type','event']
    columns_numeric = ['item_id', 'source_id', 'user_id']
    columns_datetime = ['dt']
    for column in columns_str: data_raw[column] = data_raw[column].astype(str)
    for column in columns_numeric: data_raw[column] = pd.to_numeric(data_raw[column], errors='coerce')
    for column in columns_datetime: data_raw[column] = pd.to_datetime(data_raw[column])

    dash_visits = data_raw.groupby(['item_topic', 'source_topic', 'age_segment', 'dt']).agg({'user_id': 'count'})
    dash_visits = dash_visits.rename(columns = {'user_id':'visits'})
    dash_visits = dash_visits.fillna(0).reset_index()


    #dash_engagement
    dash_engagement = data_raw.groupby(['dt', 'item_topic', 'event', 'age_segment']).agg({'user_id':'nunique'})
    dash_engagement = dash_engagement.rename(columns = {'user_id':'unique_users'})
    dash_engagement = dash_engagement.fillna(0).reset_index()



    #Удаляем старые записи между start_dt и end_dt
    tables = {'dash_visits': dash_visits, 'dash_engagement': dash_engagement}

    for table_name, table_data in tables.items():
        query = '''
                  DELETE FROM {} WHERE dt BETWEEN '{}'::TIMESTAMP AND '{}'::TIMESTAMP
                '''.format(table_name, start_dt, end_dt)
        engine.execute(query)

        table_data.to_sql(name = table_name, con = engine, if_exists = 'append', index = False)

    print('All done.')