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 result

    return function_timer




@fn_timer
def test_execute(connection, sql):
    rows_count = 0
    with 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_count


if __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

二. 总结

可以看出明显的区别, 如果需要批量插入数据库,还是用 executemany方法 这个和execute 不是一个数量级. 
如果只是插入比较少的数量量 用 exectue 比较好点.