pymysql中 execute 和 executemany 性能对比

本文主要是介绍pymysql中 execute 和 executemany 性能对比，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

一.性能对比

今天突然想测试一下, pymysql中exectue ,和 executemany 插入数据库的效率,
看看区别大不大.
pymysql 中 execute 和 executemany 性能对比

users表结构

CREATE TABLE `users` (                                                            `id` int(11) NOT NULL AUTO_INCREMENT,`email` varchar(255) DEFAULT NULL,`password` varchar(255) DEFAULT NULL,PRIMARY KEY (`id`)
) ENGINE=InnoDB  DEFAULT CHARSET=utf8;


import pymysql# 数据库配置信息
SHOUFUYOU_USER_PROFILE_CONFIG = {'host': 'xxx.xxx.xxx.xxx','port': xxxx,'user': 'xxxx','password': 'xxxxxxx','db': 'xxxxx','charset': 'utf8',
}def fn_timer(fn):"""计算 fn 的运算时间:param fn::return:"""@wraps(fn)def function_timer(*args, **kwargs):start = now()result = fn(*args, **kwargs)logger.info(f'{fn.__name__} total running time {now() - start} seconds')return resultreturn function_timer@fn_timer
def test_execute(connection, sql):rows_count = 0with connection.cursor(pymysql.cursors.DictCursor) as cursor:for i in range(1000):rows_count += cursor.execute(sql, ('frank@python.org', 'test' + str(i)))connection.commit()return rows_count@fn_timer
def test_execute_many(connection, sql):with connection.cursor(pymysql.cursors.DictCursor) as cursor:datas = [('webmaster@python.org', 'test' + str(i)) for i in range(1,1000)]rows_count = cursor.executemany(sql, datas)connection.commit()return rows_countif __name__ == '__main__':insert_sql = "INSERT INTO `users` (`email`, `password`) VALUES (%s, %s)"connection = pymysql.connect(**SHOUFUYOU_USER_PROFILE_CONFIG)test_execute(connection, insert_sql)test_execute_many(connection, insert_sql)Connected to pydev debugger (build 173.4127.16)test_execute total running time 33.28491401672363 seconds
test_execute_many total running time 0.12204098701477051 seconds