python实现批量化查询耗时SQL

今天发现最近多了一些耗时SQL的查询，例如我去数据库一张千万级表查询一天的数据需要耗时20分钟，我总共需要查询一年的数据，我总不能一条一条的手动执行吧，这样也太伤身体，属实难崩啊。就算将这些SQL都弄好放到navicat里面执行，一个窗口最多只能展示20个结果，根本不够用，所以只能另想他法。于是我就计划用python程序解决这个问题，将每次查询的结果保存到一个CSV文件里面，这样我就能等它查询之后，一键复制就行，真实老婆婆吃豆腐——放120个心，哈哈哈哈哈，下面开始上程序！！！

import csv
from pymysql import *
import time
from datetime import datetime, timedeltaconn = connect(host='xxxxxx',port=3306,user='xxxxxx',password='xxxxxx',database='xxxxxx',charset='utf8mb4')sites = ["bw_web", "bw_app"]data_list = []
start_date = datetime(2024, 1, 1)
end_date = datetime(2024, 7, 22)
current_date = start_date
while current_date <= end_date:date = current_date.strftime("%Y-%m-%d")print(date)rows_list = [date]for site in sites:cs = conn.cursor()  # 获取光标sql = f"SELECT count(1) as pv, count( DISTINCT ( token_id ) ) as uv FROM msg2024{current_date.month} WHERE server_day = '{date}' AND track_event = '0' AND site_id = '{site}'; \n"start_time = time.time()cs.execute(sql)rows = cs.fetchall()# 记录结束时间end_time = time.time()# 计算执行时间execution_time = end_time - start_timeconn.commit()pv_value = rows[0][0]uv_value = rows[0][1]print(f"======>track库共花费{execution_time:.6f}秒执行完毕,{sql},pv为{pv_value},uv为{uv_value}")rows_list.append(pv_value)rows_list.append(uv_value)data_list.append(rows_list)current_date += timedelta(days=1)print(data_list)with open('result.csv', 'w', newline='', encoding='utf-8-sig') as file:writer = csv.writer(file)csv_title = ['date', 'track-web-pv', 'track-web-uv', 'track-app-pv', 'track-app-uv']writer.writerow(csv_title)writer.writerows(data_list)