问如何在Google上提高网络效率

EN

while True:

    # GENERATES AN MYSQL QUERY BASED ON THE COLUMNS AND THEIR
    # RESPECTIVE TYPES, USING A DICTIONARY TO CONVERT
    # MYSQL D_TYPES TO PYTHON D_TYPES

    sql_query = gen_query(cols_dict=col_types, table=table,
                          pr_key=p_key, offset=offset)

    cursor = cnx.cursor(buffered=True)
    cursor.execute(sql_query)

    if cursor.rowcount == 0:
        break

    num_fields = len(cursor.description)
    field_names = [i[0] for i in cursor.description]

    records = cursor.fetchall()

    df = pd.DataFrame(records, columns=columns)
    offset += len(df.index)

    print('Ok, df structured')

    # CHECK FOR DATETIME COLUMNS

    col_parse_date = []

    for column in columns:
        if col_types[column] == 'datetime64':
            try:
                df[column] = df[column].astype(col_types[column])
                col_parse_date.append(column)

            except:
                df[column] = df[column].astype(str)
                for i in to_bgq:
                    if i['name'] == column:
                        i['type'] = 'STRING'

    # UPLOAD DATAFRAME TO GOOGLE BIGQUERY

    df.to_csv('carga_etl.csv', float_format='%.2f',
              index=False, sep='|')
    print('Ok, csv recorded')
    df = ''
    df = pd.read_csv('carga_etl.csv', sep='|')
    print('Ok, csv read')

    df.to_gbq(destination_table='tr.{}'.format(table),
              project_id='iugu-bi', if_exists='append', table_schema=to_bgq)