關於關係表的設計歸根結底有兩個方面。
第一，就是完全按照範式理論去設計，一般來說達到第三範式就可以了，或者你可以劃分的更細到達更上一層次。比如第四，第五，第六等等。這種設計有自己的可讀性很強，但是有一點，在檢索數據的時候增加了多張關係表來做關聯的開銷。
第二，就是在範式理論上適當的做些反範式，有的東西還是不要太剝離的好。（窄表以及寬表） 這點和軟件設計中的緊耦合鬆耦合理論一致。
下面我就以常用的LOG表來做下演示，其中有兩種表的實際，一種是窄表，一種是稍微寬一點的表。
窄表：log_ytt
mysql> show create table log_ytt; +-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Table | Create Table | +-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | log_ytt | CREATE TABLE `log_ytt` ( `ids` bigint(20) DEFAULT NULL, `log_time` datetime DEFAULT NULL, KEY `idx_u1` (`ids`,`log_time`) ) ENGINE=InnoDB DEFAULT CHARSET=utf8 | +-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 1 row in set (0.00 sec)
表記錄數
mysql> select * from log_ytt where ids > '4875000001'; +------------+---------------------+ | ids | log_time | +------------+---------------------+ | 7110000001 | 2014-05-20 21:56:42 | | 6300000001 | 2014-05-20 21:56:42 | | 6750000001 | 2014-05-20 21:56:42 | | 5310000001 | 2014-05-20 21:56:42 | | 7200000001 | 2014-05-20 21:56:42 | | 7380000001 | 2014-05-20 21:56:42 | | 5760000001 | 2014-05-20 21:56:42 | | 6930000001 | 2014-05-20 21:56:42 | | 6660000001 | 2014-05-20 21:56:42 | | 5670000001 | 2014-05-20 21:56:42 | | 6210000001 | 2014-05-20 21:56:42 | | 5850000001 | 2014-05-20 21:56:42 | | 6570000001 | 2014-05-20 21:56:42 | | 5580000001 | 2014-05-20 21:56:42 | | 5130000001 | 2014-05-20 21:56:42 | | 7290000001 | 2014-05-20 21:56:42 | | 6390000001 | 2014-05-20 21:56:42 | | 5490000001 | 2014-05-20 21:56:42 | | 5220000001 | 2014-05-20 21:56:42 | | 7560000001 | 2014-05-20 21:56:42 | | 7470000001 | 2014-05-20 21:56:42 | | 7020000001 | 2014-05-20 21:56:42 | | 6840000001 | 2014-05-20 21:56:42 | | 6030000001 | 2014-05-20 21:56:42 | | 6480000001 | 2014-05-20 21:56:42 | | 7650000001 | 2014-05-20 21:56:42 | | 5940000001 | 2014-05-20 21:56:42 | | 6120000001 | 2014-05-20 21:56:42 | | 7740000001 | 2014-05-20 21:56:42 | | 5400000001 | 2014-05-20 21:56:42 | | 5760000001 | 2014-05-21 03:19:07 | | 6840000001 | 2014-05-21 03:19:17 | | 7020000001 | 2014-05-21 03:19:32 | | 7200000001 | 2014-05-21 03:19:45 | | 7110000001 | 2014-05-21 03:19:46 | | 7380000001 | 2014-05-21 03:19:48 | | 5670000001 | 2014-05-21 03:19:58 | | 6930000001 | 2014-05-21 03:19:59 | | 6030000001 | 2014-05-21 03:20:00 | | 5940000001 | 2014-05-21 03:20:00 | | 7290000001 | 2014-05-21 03:20:02 | | 6120000001 | 2014-05-21 03:20:09 | | 5850000001 | 2014-05-21 03:20:18 | | 5580000001 | 2014-05-21 03:20:24 | | 6480000001 | 2014-05-21 03:25:05 | | 6390000001 | 2014-05-21 03:25:37 | | 6210000001 | 2014-05-21 03:25:45 | | 7470000001 | 2014-05-21 03:26:14 | | 6750000001 | 2014-05-21 03:27:17 | | 5310000001 | 2014-05-21 03:27:33 | | 5130000001 | 2014-05-21 03:27:34 | | 6570000001 | 2014-05-21 03:27:34 | | 7560000001 | 2014-05-21 03:27:45 | | 5220000001 | 2014-05-21 03:27:45 | | 5400000001 | 2014-05-21 03:27:53 | | 5490000001 | 2014-05-21 03:27:55 | | 6660000001 | 2014-05-21 03:28:07 | | 6300000001 | 2014-05-21 03:28:13 | | 7740000001 | 2014-05-21 03:28:26 | | 7650000001 | 2014-05-21 03:28:37 | +------------+---------------------+ 60 rows in set (0.00 sec)
接下來，我們要檢索所有IDS的平均時間。 有以下兩種方式：
第一， 對錶進行了兩次訪問，並且有GROUP BY 操作，不可取。
mysql> select sec_to_time(avg(timestampdiff(second,a.times,b.times))) as 'running' -> from -> (select ids,min(log_time) as times from log_ytt where 1 group by ids ) as a, -> (select ids,max(log_time) as times from log_ytt where 1 group by ids) as b where a.ids = b.ids; +---------------+ | running | +---------------+ | 05:27:08.8333 | +---------------+ 1 row in set (0.00 sec)
第二，雖然對錶進行了最少的訪問，但是也有一次GROUP BY 操作。也沒辦法，表設計如此。
mysql> SELECT SEC_TO_TIME(AVG(times)) AS 'Running' FROM -> ( -> SELECT TIMESTAMPDIFF(SECOND,MIN(log_time),MAX(log_time)) AS times FROM log_ytt GROUP BY ids -> ) AS T; +---------------+ | Running | +---------------+ | 05:27:08.8333 | +---------------+ 1 row in set (0.00 sec)
寬表：log_ytt_horizontal.
mysql> show create table log_ytt_horizontal; +------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Table | Create Table | +------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | log_ytt_horizontal | CREATE TABLE `log_ytt_horizontal` ( `ids` bigint(20) NOT NULL, `start_time` datetime DEFAULT NULL, `end_time` datetime DEFAULT NULL, PRIMARY KEY (`ids`) ) ENGINE=InnoDB DEFAULT CHARSET=utf8 | +------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 1 row in set (0.00 sec)
表記錄數：
mysql> select * from log_ytt_horizontal; +------------+---------------------+---------------------+ | ids | start_time | end_time | +------------+---------------------+---------------------+ | 5130000001 | 2014-05-20 21:56:42 | 2014-05-21 03:27:34 | | 5220000001 | 2014-05-20 21:56:42 | 2014-05-21 03:27:45 | | 5310000001 | 2014-05-20 21:56:42 | 2014-05-21 03:27:33 | | 5400000001 | 2014-05-20 21:56:42 | 2014-05-21 03:27:53 | | 5490000001 | 2014-05-20 21:56:42 | 2014-05-21 03:27:55 | | 5580000001 | 2014-05-20 21:56:42 | 2014-05-21 03:20:24 | | 5670000001 | 2014-05-20 21:56:42 | 2014-05-21 03:19:58 | | 5760000001 | 2014-05-20 21:56:42 | 2014-05-21 03:19:07 | | 5850000001 | 2014-05-20 21:56:42 | 2014-05-21 03:20:18 | | 5940000001 | 2014-05-20 21:56:42 | 2014-05-21 03:20:00 | | 6030000001 | 2014-05-20 21:56:42 | 2014-05-21 03:20:00 | | 6120000001 | 2014-05-20 21:56:42 | 2014-05-21 03:20:09 | | 6210000001 | 2014-05-20 21:56:42 | 2014-05-21 03:25:45 | | 6300000001 | 2014-05-20 21:56:42 | 2014-05-21 03:28:13 | | 6390000001 | 2014-05-20 21:56:42 | 2014-05-21 03:25:37 | | 6480000001 | 2014-05-20 21:56:42 | 2014-05-21 03:25:05 | | 6570000001 | 2014-05-20 21:56:42 | 2014-05-21 03:27:34 | | 6660000001 | 2014-05-20 21:56:42 | 2014-05-21 03:28:07 | | 6750000001 | 2014-05-20 21:56:42 | 2014-05-21 03:27:17 | | 6840000001 | 2014-05-20 21:56:42 | 2014-05-21 03:19:17 | | 6930000001 | 2014-05-20 21:56:42 | 2014-05-21 03:19:59 | | 7020000001 | 2014-05-20 21:56:42 | 2014-05-21 03:19:32 | | 7110000001 | 2014-05-20 21:56:42 | 2014-05-21 03:19:46 | | 7200000001 | 2014-05-20 21:56:42 | 2014-05-21 03:19:45 | | 7290000001 | 2014-05-20 21:56:42 | 2014-05-21 03:20:02 | | 7380000001 | 2014-05-20 21:56:42 | 2014-05-21 03:19:48 | | 7470000001 | 2014-05-20 21:56:42 | 2014-05-21 03:26:14 | | 7560000001 | 2014-05-20 21:56:42 | 2014-05-21 03:27:45 | | 7650000001 | 2014-05-20 21:56:42 | 2014-05-21 03:28:37 | | 7740000001 | 2014-05-20 21:56:42 | 2014-05-21 03:28:26 | +------------+---------------------+---------------------+ 30 rows in set (0.00 sec)
如果對這種稍微冗餘一些的表來進行查詢，那麼對錶的訪問以及CPU的資源佔用都達到了最低。
mysql> select sec_to_time(avg(timestampdiff(second,start_time,end_time))) as 'Running' from log_ytt_horizontal; +---------------+ | Running | +---------------+ | 05:27:08.8333 | +---------------+ 1 row in set (0.00 sec)