from tqdm import tqdm

import pandas as pd
from sqlalchemy import create_engine, MetaData

SOURCE_TABLE_NAME_LIST = ['individual_tagging_sd', ]


def chunker(seq, size):
    return (seq[pos:pos + size] for pos in range(0, len(seq), size))

def sync_table(SOURCE_TABLE_NAME):
    # print('Syncing {}'.format(SOURCE_TABLE_NAME))
    TARGET_TABLE_NAME = SOURCE_TABLE_NAME


    USER = 'mysql'
    PASSWORD = 'mysql1'
    SERVER = 'localhost'
    DATABASE = 'customer_selection'
    # driverString = '?driver=SQL+Server+Native+Client+11.0'
    driverString = ''
    engine = create_engine('mysql+mysqlconnector://{user}:{password}@{server}/{database}'.format(
        user=USER, password=PASSWORD, server=SERVER, database=DATABASE,) + driverString)
    metadata = MetaData(engine)

    with engine.connect() as conn:
        with conn.begin():
            df = pd.read_sql_table(SOURCE_TABLE_NAME, conn)

    # Dump to file for transferring
    df.to_pickle("./dummy.pkl", compression='gzip')
    print('Dataframe dumped')

    # load file
    df = pd.read_pickle("./dummy.pkl")

    print('Dataframe loaded')
    USER = 'postgres'
    PASSWORD = 'raspberry'
    SERVER = 'localhost'
    DATABASE = 'customer_selection'
    engine = create_engine('postgresql+psycopg2://{user}:{password}@{server}/{database}'.format(
        user=USER, password=PASSWORD, server=SERVER, database=DATABASE,) + driverString)

    chunksize = int(len(df) / 10) # 10%
    with engine.connect() as conn:
        with conn.begin(), tqdm(total=len(df)) as pbar:
            for i, cdf in enumerate(chunker(df, chunksize)):
                replace = "replace" if i == 0 else "append"
                cdf.to_sql(con=engine, name=TARGET_TABLE_NAME, if_exists=replace, index=False)
                pbar.update(chunksize)
    print('Table {} loaded'.format(TARGET_TABLE_NAME))

if __name__ == '__main__':
    for i in SOURCE_TABLE_NAME_LIST:
        sync_table(i)