我有下面的查询,它获取我需要的行的 ID。
SELECT `wp_posts`.`ID`, `wp_posts`.`post_modified_gmt`, `wp_term_relationships`.`term_taxonomy_id`
FROM `wp_posts`
LEFT JOIN `wp_term_relationships` ON
`wp_posts`.`ID` = `wp_term_relationships`.`object_id`
WHERE `wp_posts`.`post_type` = 'shop_order'
AND `wp_term_relationships`.`term_taxonomy_id` = 14
AND `wp_posts`.`post_modified` > '2013-04-25 16:02:00'
ORDER BY `wp_posts`.`post_modified` ASC;
+----+---------------------+------------------+
| ID | post_modified | term_taxonomy_id |
+----+---------------------+------------------+
| 41 | 2013-04-25 16:02:43 | 14 |
| 43 | 2013-04-25 18:40:37 | 14 |
+----+---------------------+------------------+
2 rows in set (0.00 sec)
然后我遍历所有 ID 并使用 ID 运行下面的查询以匹配查询中的 post_id。
SELECT `wp_postmeta`.`post_id`, `wp_postmeta`.`meta_key`, `wp_postmeta`.`meta_value`
FROM `wp_postmeta`
WHERE `post_id` = 43 # ID from last query
AND (`wp_postmeta`.`meta_key` = '_shipping_first_name'
OR `wp_postmeta`.`meta_key` = '_shipping_last_name'
OR `wp_postmeta`.`meta_key` = '_shipping_address_1'
OR `wp_postmeta`.`meta_key` = '_shipping_address_2'
OR `wp_postmeta`.`meta_key` = '_shipping_city'
OR `wp_postmeta`.`meta_key` = '_shipping_state'
OR `wp_postmeta`.`meta_key` = '_shipping_postcode'
OR `wp_postmeta`.`meta_key` = '_shipping_country'
OR `wp_postmeta`.`meta_key` = '_order_total'
OR `wp_postmeta`.`meta_key` = '_order_shipping'
OR `wp_postmeta`.`meta_key` = '_order_discount'
OR `wp_postmeta`.`meta_key` = '_cart_discount'
OR `wp_postmeta`.`meta_key` = '_order_tax'
OR `wp_postmeta`.`meta_key` = '_order_shipping_tax');
+---------+----------------------+------------------+
| post_id | meta_key | meta_value |
+---------+----------------------+------------------+
| 43 | _shipping_country | GB |
| 43 | _shipping_first_name | Joe |
| 43 | _shipping_last_name | Bloggs |
| 43 | _shipping_address_1 | 18 Street Name |
| 43 | _shipping_address_2 | |
| 43 | _shipping_city | Manchester |
| 43 | _shipping_state | Lancashire |
| 43 | _shipping_postcode | MM1 1MM |
| 43 | _order_shipping | 0.00 |
| 43 | _order_discount | 0.00 |
| 43 | _cart_discount | 0.00 |
| 43 | _order_tax | 0.00 |
| 43 | _order_shipping_tax | 0.00 |
| 43 | _order_total | 224.00 |
+---------+----------------------+------------------+
14 rows in set (0.00 sec)
我想知道是否有办法合并这些查询,所以我将对服务器的查询量减半。
我不确定你是否可以这样做,但我想将 meta_key 值更改为列名并使用 meta_value 作为值?
最终结果示例
+----+---------------------+------------------+-------------------+----------------------+---------------------+---------------------+---------------------+----------------+-----------------+--------------------+-----------------+-----------------+----------------+------------+---------------------+--------------+
| ID | post_modified | term_taxonomy_id | _shipping_country | _shipping_first_name | _shipping_last_name | _shipping_address_1 | _shipping_address_2 | _shipping_city | _shipping_state | _shipping_postcode | _order_shipping | _order_discount | _cart_discount | _order_tax | _order_shipping_tax | _order_total |
+----+---------------------+------------------+-------------------+----------------------+---------------------+---------------------+---------------------+----------------+-----------------+--------------------+-----------------+-----------------+----------------+------------+---------------------+--------------+
| 43 | 2013-04-25 18:40:37 | 14 | GB | Joe | Bloggs | 18 Street Name | | Manchester | Lancashire | MM1 1MM | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 224.00 |
+----+---------------------+------------------+-------------------+----------------------+---------------------+---------------------+---------------------+----------------+-----------------+--------------------+-----------------+-----------------+----------------+------------+---------------------+--------------+
Next row...
Next row...
任何帮助都会很棒。
谢谢
编辑
在 meewoK 的帮助下,这是对我有用的查询。
SELECT (CASE WHEN `wp_postmeta`.`meta_key` = '_shipping_first_name' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_shipping_first_name',
(CASE WHEN `wp_postmeta`.`meta_key` = '_shipping_last_name' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_shipping_last_name',
(CASE WHEN `wp_postmeta`.`meta_key` = '_shipping_address_1' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_shipping_address_1',
(CASE WHEN `wp_postmeta`.`meta_key` = '_shipping_address_2' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_shipping_address_2',
(CASE WHEN `wp_postmeta`.`meta_key` = '_shipping_city' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_shipping_city',
(CASE WHEN `wp_postmeta`.`meta_key` = '_shipping_state' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_shipping_state',
(CASE WHEN `wp_postmeta`.`meta_key` = '_shipping_postcode' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_shipping_postcode',
(CASE WHEN `wp_postmeta`.`meta_key` = '_shipping_country' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_shipping_country',
(CASE WHEN `wp_postmeta`.`meta_key` = '_order_total' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_order_total',
(CASE WHEN `wp_postmeta`.`meta_key` = '_order_shipping' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_order_shipping',
(CASE WHEN `wp_postmeta`.`meta_key` = '_order_discount' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_order_discount',
(CASE WHEN `wp_postmeta`.`meta_key` = '_cart_discount' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_cart_discount',
(CASE WHEN `wp_postmeta`.`meta_key` = '_order_tax' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_order_tax',
(CASE WHEN `wp_postmeta`.`meta_key` = '_order_shipping_tax' THEN `wp_postmeta`.`meta_value` ELSE NULL END) AS '_order_shipping_tax',
`wp_posts`.`ID`,
`wp_posts`.`post_modified_gmt`,
`wp_term_relationships`.`term_taxonomy_id`
FROM `wp_postmeta`, `wp_posts`
LEFT JOIN `wp_term_relationships` ON `wp_posts`.`ID` = `wp_term_relationships`.`object_id`
WHERE `wp_posts`.`post_type` = 'shop_order'
AND `wp_term_relationships`.`term_taxonomy_id` = 14
AND `wp_posts`.`post_modified` > '2013-04-25 16:02:00'
AND `post_id` = `wp_posts`.`ID`
AND (`wp_postmeta`.`meta_key` = '_shipping_first_name'
OR `wp_postmeta`.`meta_key` = '_shipping_last_name'
OR `wp_postmeta`.`meta_key` = '_shipping_address_1'
OR `wp_postmeta`.`meta_key` = '_shipping_address_2'
OR `wp_postmeta`.`meta_key` = '_shipping_city'
OR `wp_postmeta`.`meta_key` = '_shipping_state'
OR `wp_postmeta`.`meta_key` = '_shipping_postcode'
OR `wp_postmeta`.`meta_key` = '_shipping_country'
OR `wp_postmeta`.`meta_key` = '_order_total'
OR `wp_postmeta`.`meta_key` = '_order_shipping'
OR `wp_postmeta`.`meta_key` = '_order_discount'
OR `wp_postmeta`.`meta_key` = '_cart_discount'
OR `wp_postmeta`.`meta_key` = '_order_tax'
OR `wp_postmeta`.`meta_key` = '_order_shipping_tax')
GROUP BY `wp_posts`.`ID`
ORDER BY `wp_posts`.`post_modified` ASC;
最终结果示例
+----+---------------------+------------------+-------------------+----------------------+---------------------+---------------------+---------------------+----------------+-----------------+--------------------+-----------------+-----------------+----------------+------------+---------------------+--------------+
| ID | post_modified | term_taxonomy_id | _shipping_country | _shipping_first_name | _shipping_last_name | _shipping_address_1 | _shipping_address_2 | _shipping_city | _shipping_state | _shipping_postcode | _order_shipping | _order_discount | _cart_discount | _order_tax | _order_shipping_tax | _order_total |
+----+---------------------+------------------+-------------------+----------------------+---------------------+---------------------+---------------------+----------------+-----------------+--------------------+-----------------+-----------------+----------------+------------+---------------------+--------------+
| 43 | 2013-04-25 18:40:37 | 14 | GB | Joe | Bloggs | 18 Street Name | | Manchester | Lancashire | MM1 1MM | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 224.00 |
+----+---------------------+------------------+-------------------+----------------------+---------------------+---------------------+---------------------+----------------+-----------------+--------------------+-----------------+-----------------+----------------+------------+---------------------+--------------+
Next row...
Next row...