python实现批量化查询耗时SQL

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, timedelta

conn = 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_time
        conn.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)

写在最后

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