跟踪 MySQL 中的最后读取记录

Question

我在 MySQL 中有 2 个数据库：

1) 输入Latitude-Longitude_dB（'latlong_db'，以后）：它具有从 GPS 跟踪设备读取的每个读数的纬度和经度。

2) A Weather_db：我从 dB1 读取输入经纬度，并计算每对经纬度的 'current' 天气数据（例如：湿度，cloud_coverage）。这个天气数据被写入了一个Weather_db.

问题是：我需要跟踪最后读取的记录（'input latlong'）。这样我就不会为我已经介绍过的经纬度重新计算 weather_data。如何跟踪上次阅读 input_latlong?

非常感谢。

编辑： 1) 对于那些一直在询问 'database v/s table' 问题的人，答案是我正在从 1 个数据库读取并写入第二个数据库。 'config.json'连接2个数据库如下：

{
"Tracker_ds_locallatlongdb": {
        "database": "ds_testdb1",
        "host": "XXXXXXXXXXX",
        "port": XXXX,
        "user": "XXXX",
        "password": "XXXXX"
    },
    "Tracker_ds_localweatherdb": {
    "database": "ds_testdb2",
        "host": "XXXXXXX",
        "port": XXXX,
        "user": "XXXX",
        "password": "XXXXX"
    }
}

2) 我的 Python 从 input_latlong_db 读取并写入 weather_db 的脚本概述如下。我正在使用 OpenWeatherMap API 计算给定纬度和经度的天气数据：

from pyowm import OWM
import json
import time
import pprint
import pandas as pd

import mysql.connector
from mysql.connector import Error



api_key = 'your api key'

def get_weather_data(my_lat, my_long):
    owm= OWM(api_key)
    obs= owm.weather_at_coords(my_lat.item() , my_long.item() )   #Use: <numpy.ndarray>.item:
    w= obs.get_weather()
    l= obs.get_location()

    city= l.get_name()
    cloud_coverage =w.get_clouds()
    .
    .
    .
    w_datatoinsert= [my_lat, my_long, w_latitude, w_longitude, city, weather_time_gmt,call_time_torontotime,
    short_status, detailed_status,
    temp_celsius, cloud_coverage, humidity, wind_deg, wind_speed,
    snow, rain, atm_pressure, sea_level_pressure,sunset_time_gmt ] #15 + act_latitude + act_longitude
    return w_datatoinsert

# ------------------------------------------------------------------------------------------------------------------------------------
spec_creds_1= {}
spec_creds_2= {}
def operation():
    with open('C:/Users/config.json') as config_file:
        creds_dict= json.load(config_file)
        spec_creds_1= creds_dict['Tracker_ds_locallatlongdb'] 
        spec_creds_2= creds_dict['Tracker_ds_localweatherdb']
        try:
            my_conn_1= mysql.connector.connect(**spec_creds_1 )
            if (my_conn_1.is_connected()):
                info_1= my_conn_1.get_server_info()
                print("Connected ..now reading the local input_latlong_db: ", info_1)
                try:
                    my_conn_2= mysql.connector.connect(**spec_creds_2)
                    if (my_conn_2.is_connected()):
                        info_2= my_conn_2.get_server_info()
                        print('Connected to write into the local weather_db: ', info_2)
                        cursor_2= my_conn_2.cursor()
                        readings_df= pd.read_sql("SELECT latitude, longitude FROM readings_table_19cols;", con= my_conn_1)
                        for index, row in readings_df.iterrows():
                            gwd= get_weather_data(row['latitude'], row['longitude'])
                            q= "INSERT INTO weather_table_19cols VALUES(" +        ",".join(["%s"]*len(gwd))     +    " ); "
                            cursor_2.execute(q, gwd)
                            my_conn_2.commit()
                except Error as e:
                    print("Error while connecting to write into the local weather_db: ", e)
                finally:
                    if (my_conn_2.is_connected()):
                        cursor_2.close()
                        my_conn_2.close()
                        print("Wrote 1 record to the local weather_db.")
        except Error as e:
            print("Error connecting to the local input latlong_db: ", e)
        finally:
            if (my_conn_1.is_connected()):
                my_conn_1.close() # no cursor present for 'my_conn_1'
                print("Finished reading all the input latlongs ...and finished inserting ALL the weather data.")


#-------------------------------------------------------------------------------



if __name__=="__main__":
    operation()

Answer 1

在Input_latlong_table-中readings_table_19cols：我创建了一个Autoincremented readings_id作为主键，以及一个名为read_flag的列，其默认值是 0.
在weather_table_19cols中：我创建了一个自动递增的weather_id作为主键。
由于我的方法涉及读取输入的latlong记录并将其天气数据相应地写入weather_table，我比较了readings_table_19cols和[=14=的索引].如果它们匹配，则意味着读取了输入记录，我会将 read_flag 设置为 1.

..

for index_1, row_1 in readings_df_1.iterrows():

                            gwd= get_weather_data(row_1['imei'], row_1['reading_id'] ,row_1['send_time'],row_1['latitude'], row_1['longitude'])

                            q_2= "INSERT INTO weather_table_23cols(my_imei, my_reading_id, actual_latitude, actual_longitude, w_latitude, w_longitude, city, weather_time_gmt, OBD_send_time_gmt, call_time_torontotime, \
                            short_status, detailed_status, \
                            temp_celsius, cloud_coverage, humidity, wind_deg, wind_speed, \
                            snow, rain, atm_pressure, sea_level_pressure,sunset_time_gmt) VALUES("  +  ",".join(["%s"]*len(gwd))     +    " ); "

                            q_1b= "UPDATE ds_testdb1.readings_table_22cols re,  ds_testdb2.weather_table_23cols we \
                             SET re.read_flag=1 WHERE (re.reading_id= we.weather_id);"  # use the prefix 'db_name.table_name' if 1 cursor is being used for 2 different db's

                            cursor_2.execute(q_2, gwd)
                            my_conn_2.commit()
                            cursor_1.execute(q_1b) # use Cursor_1 for 1st query 
                            my_conn_1.commit()

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