MySQL 簡單造數

造數背景

主要造出一張時間字段的相關的數據，時間字段不能是一個固定值，數據範圍需要隨機分散在90天內的。

構建測試表

-- 測試表結構
CREATE TABLE `demo`.`order_info` (
  `ID` bigint NOT NULL AUTO_INCREMENT COMMENT '自增ID主鍵',
  `ORDER_NO` varchar(64) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '訂單編號',
  `GOODS_ID` int DEFAULT NULL COMMENT '商品編號',
  `USER_ID` bigint DEFAULT NULL COMMENT '用戶ID',
  `ORDER_AMOUNT` decimal(14,2) DEFAULT NULL COMMENT '訂單金額',
  `CREATE_DATE` datetime DEFAULT CURRENT_TIMESTAMP COMMENT '訂單創建時間',
  `MODIFY_DATE` datetime DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '訂單修改時間',
  PRIMARY KEY (`ID`),
  UNIQUE KEY `UK_ORDER_NO` (`ORDER_NO`),
  KEY `NK_MODIFY_DATE` (`MODIFY_DATE`)
);

構建插數存儲過程

• 其中ID自增無需指定字段插入
• 訂單編號生成唯一值
• 商品編號和用戶ID均隨機生成，允許生成重複值
• 商品金額隨機，保留2位小數
• 創建時間取當前時間減去90天，這個值可根據實際情況調整，一般與修改時間保持聯動
• 修改時間取90天內任意值，這個值可根據實際情況調整，一般與創建時間保持聯動

-- 插入數據的存儲過程
delimiter ;;
  create procedure proc_insert_order_data(in n int)
  begin
    declare i int;
    set i=1;
    while(i<=n)do
      insert into demo.order_info(ORDER_NO,GOODS_ID,USER_ID,ORDER_AMOUNT,CREATE_DATE,MODIFY_DATE) select concat('NET_',uuid_short()) as order_no,
       rpad(floor(rand()*1000),6,0) as GOODS_ID,
       rpad(floor(rand()*100000),8,0) as USER_ID,
       round(rand()*1000,2) as ORDER_AMOUNT,
       date_SUB(now(),interval 90 day) as CREATE_DATE,
       DATE_SUB(now(),interval floor(rand()*90) day) as MODIFY_DATE;
      set i=i+1;
    end while;
  end;;
delimiter ;

根據需要的數據量指定插數

-- 調用存儲過程插入需要的數據量
call proc_insert_order_data(100000);

-- 生成的數據示例
select * from order_info limit 10;
+----+--------------------------+----------+----------+--------------+---------------------+---------------------+
| ID | ORDER_NO                 | GOODS_ID | USER_ID  | ORDER_AMOUNT | CREATE_DATE         | MODIFY_DATE         |
+----+--------------------------+----------+----------+--------------+---------------------+---------------------+
|  1 | NET_10764588660109082634 |   658000 | 10694000 |       558.76 | 2022-08-28 11:43:16 | 2022-10-15 11:43:16 |
|  2 | NET_10764588660109082635 |   688000 | 24510000 |        56.45 | 2022-08-28 11:43:38 | 2022-11-08 11:43:38 |
|  3 | NET_10764588660109082636 |   874000 | 74479000 |       101.42 | 2022-08-28 11:43:38 | 2022-11-02 11:43:38 |
|  4 | NET_10764588660109082637 |   590000 | 47827000 |       213.53 | 2022-08-28 11:43:38 | 2022-10-01 11:43:38 |
|  5 | NET_10764588660109082638 |   523000 | 71922000 |        25.52 | 2022-08-28 11:43:38 | 2022-08-31 11:43:38 |
|  6 | NET_10764588660109082639 |   772000 | 95517000 |       456.94 | 2022-08-28 11:43:38 | 2022-10-20 11:43:38 |
|  7 | NET_10764588660109082640 |   724000 | 36747000 |       662.40 | 2022-08-28 11:43:38 | 2022-11-08 11:43:38 |
|  8 | NET_10764588660109082641 |   600000 | 67484000 |       192.07 | 2022-08-28 11:43:38 | 2022-09-03 11:43:38 |
|  9 | NET_10764588660109082642 |   102000 | 70681000 |       225.51 | 2022-08-28 11:43:38 | 2022-11-26 11:43:38 |
| 10 | NET_10764588660109082643 |   359000 | 77415000 |       793.43 | 2022-08-28 11:43:38 | 2022-09-29 11:43:38 |
+----+--------------------------+----------+----------+--------------+---------------------+---------------------+