MySQL,由两个不同的列组成

时间:2015-11-12 10:13:19

标签: php mysql datetime

我的表格如下:

╔═════╦═════════════════════╦═════════════════════╦═════════════╗
║ id  ║        begin        ║         end         ║ employeesId ║
╠═════╬═════════════════════╬═════════════════════╬═════════════╣
║   4 ║ 2015-11-11 00:00:00 ║ 2015-11-11 09:00:00 ║           8 ║
║  80 ║ 2015-11-11 09:00:00 ║ 2015-11-11 12:00:00 ║           8 ║
║  49 ║ 2015-11-11 00:00:00 ║ 2015-11-11 08:00:00 ║          61 ║
║  32 ║ 2015-11-11 08:00:00 ║ 2015-11-12 06:00:00 ║          61 ║
║  42 ║ 2015-11-12 07:00:00 ║ 2015-11-12 13:00:00 ║          61 ║
║  17 ║ 2015-11-11 00:00:00 ║ 2015-11-11 08:00:00 ║          22 ║
║  42 ║ 2015-11-11 09:00:00 ║ 2015-11-11 12:00:00 ║          22 ║
╚═════╩═════════════════════╩═════════════════════╩═════════════╝

我想合并列employeesId相等的行,并且一行的begin时间等于另一行的end时间。像这样:

╔═════════════════════╦═════════════════════╦═════════════╗
║        begin        ║         end         ║ employeesId ║
╠═════════════════════╬═════════════════════╬═════════════╣
║ 2015-11-11 00:00:00 ║ 2015-11-11 12:00:00 ║           8 ║
║ 2015-11-11 00:00:00 ║ 2015-11-12 06:00:00 ║          61 ║
║ 2015-11-12 07:00:00 ║ 2015-11-12 13:00:00 ║          61 ║
║ 2015-11-11 00:00:00 ║ 2015-11-11 08:00:00 ║          22 ║
║ 2015-11-11 09:00:00 ║ 2015-11-11 12:00:00 ║          22 ║
╚═════════════════════╩═════════════════════╩═════════════╝

编辑:

  

我需要合并连续的日期时间,在这个例子中你可以看到结束时间'2015-11-11 12:00:00'为employeesId = 8替换结束时间'2015-11-11 09:00:00'在第一行,因为第二行的开头等于第一行的结尾。

2 个答案:

答案 0 :(得分:2)

Try join

SELECT a.`begin_date`, IF(b.`end_date` IS NOT NULL, b.`end_date`, a.`end_date`), a.`employer_id`
FROM `mytable` a
LEFT JOIN `mytable` b ON a.`employer_id` = b.`employer_id` AND a.`end_date` = b.`begin_date`
LEFT JOIN `mytable` c ON a.`employer_id` = c.`employer_id` AND a.`begin_date` = c.`end_date`
WHERE c.`id` IS NULL

But you should rebuild your schema.

答案 1 :(得分:0)

You can use MIN(),MAX() if you want to get earlier and later times.

SELECT MIN(begin),MAX(end),employeesId
    FROM table_name
    GROUP BY employeesId

Hope this helps