During the development process, due to historical reasons or performance reasons, it is necessary to convert the column data of the table into row data, or convert the row data into column data. This article will introduce the method of converting rows and rows of mysql table data, and provide complete demonstration examples and sql skills.
Create test data table and data
CREATE TABLE `option` ( `category_id` int(10) unsigned NOT NULL COMMENT '分类id', `name` varchar(20) NOT NULL COMMENT '名称', KEY `category_id` (`category_id`)) ENGINE=InnoDB DEFAULT CHARSET=utf8; INSERT INTO `option` (`category_id`, `name`) VALUES (1, '大'), (1, '中'), (1, '小'), (2, '奔驰'), (2, '宝马'), (3, '2015'), (3, '2016'), (3, '2017'), (3, '2018'), (4, '1m'), (4, '2m');mysql> select * from `option`; +-------------+--------+| category_id | name | +-------------+--------+| 1 | 大 | | 1 | 中 | | 1 | 小 | | 2 | 奔驰 | | 2 | 宝马 | | 3 | 2015 | | 3 | 2016 | | 3 | 2017 | | 3 | 2018 | | 4 | 1m || 4 | 2m | +-------------+--------+
After converting rows to columns, expect the following Result
+-------------+---------------------+| category_id | name | +-------------+---------------------+| 1 | 大,中,小 | | 2 | 奔驰,宝马 | | 3 | 2015,2016,2017,2018 || 4 | 1m,2m | +-------------+---------------------+
Row to column conversion can be achieved by using the group_concat() function combined with group by.
The group_concat() function can get the connection value of the expression combination. The default separator is comma, which can be set to other separators through separator.
Note: The group_concat() function has a length limit on the returned result. The default is 1024 bytes, but it is enough for normal situations.
Regarding the use of the group_concat() function, please refer to my previous article: "Instructions for the use of mysql function concat and group_concat"
Execution results:
mysql> select category_id,group_concat(name) as name from `option` group by category_id order by category_id; +-------------+---------------------+| category_id | name | +-------------+---------------------+| 1 | 大,中,小 | | 2 | 奔驰,宝马 | | 3 | 2015,2016,2017,2018 || 4 | 1m,2m | +-------------+---------------------+
Create test data table and data
CREATE TABLE `option2` ( `category_id` int(10) unsigned NOT NULL COMMENT '分类id', `name` varchar(100) NOT NULL COMMENT '名称集合') ENGINE=InnoDB DEFAULT CHARSET=utf8; INSERT INTO `option2` (`category_id`, `name`) VALUES (1, '大,中,小 '), (2, '奔驰,宝马'), (3, '2015,2016,2017,2018'), (4, '1m,2m');mysql> select * from `option2`; +-------------+---------------------+| category_id | name | +-------------+---------------------+| 1 | 大,中,小 | | 2 | 奔驰,宝马 | | 3 | 2015,2016,2017,2018 || 4 | 1m,2m | +-------------+---------------------+
Column to row Finally, the following results are expected
+-------------+--------+| category_id | name | +-------------+--------+| 1 | 大 | | 1 | 中 | | 1 | 小 | | 2 | 奔驰 | | 2 | 宝马 | | 3 | 2015 | | 3 | 2016 | | 3 | 2017 | | 3 | 2018 | | 4 | 1m || 4 | 2m | +-------------+--------+
Column conversion is more complicated than row conversion. For data whose column content is separated by delimiters, we can use the substring_index() function to split the output and Combined with Cartesian product to implement looping.
select a.category_id,substring_index(substring_index(a.name,',',b.category_id),',',-1) as name from `option2` as ajoin `option2` as b on b.category_id<=(length(a.name) - length(replace(a.name,',',''))+1)order by a.category_id,b.category_id;
Execution results:
mysql> select a.category_id,substring_index(substring_index(a.name,',',b.category_id),',',-1) as name from `option2` as a -> join `option2` as b on b.category_id<=(length(a.name) - length(replace(a.name,',',''))+1) -> order by a.category_id,b.category_id; +-------------+--------+| category_id | name | +-------------+--------+| 1 | 大 | | 1 | 中 | | 1 | 小 | | 2 | 奔驰 | | 2 | 宝马 | | 3 | 2015 | | 3 | 2016 | | 3 | 2017 | | 3 | 2018 | | 4 | 1m || 4 | 2m | +-------------+--------+
This article explains the method of converting rows and columns of mysql table data. For more related content, please pay attention to Pan Heping Chinese website.
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