https://blog.csdn.net/weixin_43236925/article/details/146382981
清晰易懂的 PHP 安装与配置教程
12.6 查找每组行中含有最大或最小值的行
sql
mysql> set @max_price = (select max(price) from painting);
Query OK, 0 rows affected (0.01 sec)
mysql> select artist.name, painting.title, painting.price
-> from artist inner join painting
-> on painting.a_id = artist.a_id
-> where painting.price = @max_price;
+----------+-----------+-------+
| name | title | price |
+----------+-----------+-------+
| Da Vinci | Mona Lisa | 87 |
+----------+-----------+-------+
1 row in set (0.00 sec)
mysql> create table tmpp select max(price) as max_price from painting;
Query OK, 1 row affected (0.07 sec)
Records: 1 Duplicates: 0 Warnings: 0
mysql> select artist.name, painting.title, painting.price
-> from artist inner join painting inner join tmpp
-> on painting.a_id = artist.a_id
-> and painting.price = tmpp.max_price;
+----------+-----------+-------+
| name | title | price |
+----------+-----------+-------+
| Da Vinci | Mona Lisa | 87 |
+----------+-----------+-------+
1 row in set (0.00 sec)
mysql> create table tmpp1
-> select a_id, max(price) as max_price from painting group by a_id;
Query OK, 3 rows affected (0.04 sec)
Records: 3 Duplicates: 0 Warnings: 0
mysql> select artist.name, painting.title, painting.price
-> from artist inner join painting inner join tmpp1
-> on painting.a_id = artist.a_id
-> and painting.a_id = tmpp1.a_id
-> and painting.price = tmpp1.max_price;
+----------+-------------------+-------+
| name | title | price |
+----------+-------------------+-------+
| Da Vinci | Mona Lisa | 87 |
| Van Gogh | The Potato Eaters | 67 |
| Renoir | Les Deux Soeurs | 64 |
+----------+-------------------+-------+
3 rows in set (0.00 sec)
mysql> select artist.name, painting.title, painting.price
-> from artist inner join painting inner join
-> (select a_id, max(price) as max_price from painting group by a_id)
-> as tmpp1
-> on painting.a_id = artist.a_id
-> and painting.a_id = tmpp1.a_id
-> and painting.price = tmpp1.max_price;
+----------+-------------------+-------+
| name | title | price |
+----------+-------------------+-------+
| Da Vinci | Mona Lisa | 87 |
| Van Gogh | The Potato Eaters | 67 |
| Renoir | Les Deux Soeurs | 64 |
+----------+-------------------+-------+
3 rows in set (0.00 sec)
mysql> select p1.a_id, p1.title, p1.price
-> from painting as p1 left join painting as p2
-> on p1.a_id = p2.a_id and p1.price < p2.price
-> where p2.a_id is null;
+------+-------------------+-------+
| a_id | title | price |
+------+-------------------+-------+
| 1 | Mona Lisa | 87 |
| 3 | The Potato Eaters | 67 |
| 4 | Les Deux Soeurs | 64 |
+------+-------------------+-------+
3 rows in set (0.00 sec)
mysql> select artist.name, p1.title, p1.price
-> from painting as p1 left join painting as p2
-> on p1.a_id = p2.a_id and p1.price < p2.price
-> inner join artist on p1.a_id = artist.a_id
-> where p2.a_id is null;
+----------+-------------------+-------+
| name | title | price |
+----------+-------------------+-------+
| Da Vinci | Mona Lisa | 87 |
| Van Gogh | The Potato Eaters | 67 |
| Renoir | Les Deux Soeurs | 64 |
+----------+-------------------+-------+
3 rows in set (0.00 sec)
mysql> DROP TABLE IF EXISTS driver_log;
Query OK, 0 rows affected (0.05 sec)
mysql> #@ _CREATE_TABLE_
mysql> CREATE TABLE driver_log
-> (
-> rec_id INT UNSIGNED NOT NULL AUTO_INCREMENT,
-> name VARCHAR(20) NOT NULL,
-> trav_date DATE NOT NULL,
-> miles INT NOT NULL,
-> PRIMARY KEY (rec_id)
-> );
Query OK, 0 rows affected (0.05 sec)
mysql> #@ _CREATE_TABLE_
mysql>
mysql> INSERT INTO driver_log (name,trav_date,miles)
-> VALUES
-> ('Ben','2006-08-30',152),
-> ('Suzi','2006-08-29',391),
-> ('Henry','2006-08-29',300),
-> ('Henry','2006-08-27',96),
-> ('Ben','2006-08-29',131),
-> ('Henry','2006-08-26',115),
-> ('Suzi','2006-09-02',502),
-> ('Henry','2006-09-01',197),
-> ('Ben','2006-09-02',79),
-> ('Henry','2006-08-30',203)
-> ;
Query OK, 10 rows affected (0.01 sec)
Records: 10 Duplicates: 0 Warnings: 0
mysql>
mysql> SELECT * FROM driver_log;
+--------+-------+------------+-------+
| rec_id | name | trav_date | miles |
+--------+-------+------------+-------+
| 1 | Ben | 2006-08-30 | 152 |
| 2 | Suzi | 2006-08-29 | 391 |
| 3 | Henry | 2006-08-29 | 300 |
| 4 | Henry | 2006-08-27 | 96 |
| 5 | Ben | 2006-08-29 | 131 |
| 6 | Henry | 2006-08-26 | 115 |
| 7 | Suzi | 2006-09-02 | 502 |
| 8 | Henry | 2006-09-01 | 197 |
| 9 | Ben | 2006-09-02 | 79 |
| 10 | Henry | 2006-08-30 | 203 |
+--------+-------+------------+-------+
10 rows in set (0.00 sec)
mysql> select name, trav_date, miles
-> from driver_log
-> order by name, trav_date;
+-------+------------+-------+
| name | trav_date | miles |
+-------+------------+-------+
| Ben | 2006-08-29 | 131 |
| Ben | 2006-08-30 | 152 |
| Ben | 2006-09-02 | 79 |
| Henry | 2006-08-26 | 115 |
| Henry | 2006-08-27 | 96 |
| Henry | 2006-08-29 | 300 |
| Henry | 2006-08-30 | 203 |
| Henry | 2006-09-01 | 197 |
| Suzi | 2006-08-29 | 391 |
| Suzi | 2006-09-02 | 502 |
+-------+------------+-------+
10 rows in set (0.00 sec)
mysql> create table tmpp2
-> select name, max(trav_date) as trav_date
-> from driver_log group by name;
Query OK, 3 rows affected (0.04 sec)
Records: 3 Duplicates: 0 Warnings: 0
1. 第一条语句：用外部表tmpp2进行内连接
sql
SELECT driver_log.name, driver_log.trav_date, driver_log.miles
FROM driver_log
INNER JOIN tmpp2 -- tmpp2是一个已存在的外部表
ON driver_log.name = tmpp2.name
AND driver_log.trav_date = tmpp2.trav_date
ORDER BY driver_log.name;
• tmpp2的性质：这里的tmpp2是一个预先存在的外部表（可能是手动创建的临时表或永久表），里面存储了一些name和trav_date的数据。
• 连接逻辑：只返回driver_log中与tmpp2表中完全匹配（name和trav_date都相同）的记录。
• 适用场景：当你需要筛选driver_log中符合某个预设条件（即tmpp2表中定义的特定name和trav_date组合）的记录时使用。例如：tmpp2可能存储了 "需要重点检查的司机和日期"。
2. 第二条语句：用子查询动态生成tmpp2
sql
SELECT driver_log.name, driver_log.trav_date, driver_log.miles
FROM driver_log
INNER JOIN (
-- 子查询：动态生成每个司机最新的出行日期
SELECT name, max(trav_date) as trav_date
FROM driver_log
GROUP BY name
) AS tmpp2
ON driver_log.name = tmpp2.name
AND driver_log.trav_date = tmpp2.trav_date
ORDER BY driver_log.name;
• tmpp2的性质：这里的tmpp2是动态生成的子查询结果，逻辑是 "对driver_log按司机分组，获取每个司机最新的出行日期（max(trav_date)）"。
• 连接逻辑：只返回driver_log中每个司机的最新出行记录（因为tmpp2存储的是每个司机的最大日期）。
• 适用场景：需要从driver_log中筛选每个司机的 "最新一条记录" 时使用（例如：查询每个司机最近一次出行的里程）。
核心区别总结
维度 第一条语句（外部表tmpp2） 第二条语句（子查询生成tmpp2）
tmpp2的来源 外部预设表（内容固定，非动态生成） 子查询动态生成（内容由driver_log数据决定）
连接的目的 筛选符合预设条件（tmpp2中的name+date）的记录 筛选每个司机的最新出行记录（max(trav_date)）
结果的决定因素 依赖tmpp2表的预设内容 依赖driver_log自身的最大日期数据
灵活性 低（tmpp2内容变更需手动修改） 高（自动适应driver_log数据变化）12.7 计算队伍排名
sql
mysql> select team , wins, losses from standings1
-> order by wins-losses desc;
+-------------+------+--------+
| team | wins | losses |
+-------------+------+--------+
| Winnipeg | 37 | 20 |
| Crookston | 31 | 25 |
| Fargo | 30 | 26 |
| Grand Forks | 28 | 26 |
| Devils Lake | 19 | 31 |
| Cavalier | 15 | 32 |
+-------------+------+--------+
6 rows in set (0.01 sec)
mysql> set @w1_diff = (select max(wins-losses) from standings1);
Query OK, 0 rows affected (0.00 sec)
mysql> select team, wins as w, losses as L,
-> wins/(wins+losses) as pct,
-> (@w1_diff - (wins-losses)) / 2 as gb
-> from standings1
-> order by wins-losses desc, pct desc;
+-------------+------+------+--------+---------+
| team | w | L | pct | gb |
+-------------+------+------+--------+---------+
| Winnipeg | 37 | 20 | 0.6491 | 0.0000 |
| Crookston | 31 | 25 | 0.5536 | 5.5000 |
| Fargo | 30 | 26 | 0.5357 | 6.5000 |
| Grand Forks | 28 | 26 | 0.5185 | 7.5000 |
| Devils Lake | 19 | 31 | 0.3800 | 14.5000 |
| Cavalier | 15 | 32 | 0.3191 | 17.0000 |
+-------------+------+------+--------+---------+
6 rows in set (0.00 sec)
mysql> CREATE TABLE firstplace (
-> half VARCHAR(20) NOT NULL,
-> division VARCHAR(50) NOT NULL,
-> w1_diff INT NOT NULL,
-> PRIMARY KEY (half, division)
-> );
Query OK, 0 rows affected (0.03 sec)
mysql> SELECT
-> w1.half,
-> w1.division,
-> w1.team,
-> w1.wins AS w,
-> w1.losses AS L,
-> TRUNCATE(w1.wins / (w1.wins + w1.losses), 3) AS pct,
-> IF(
-> fp.w1_diff = w1.wins - w1.losses,
-> '-',
-> TRUNCATE((fp.w1_diff - (w1.wins - w1.losses)) / 2, 1)
-> ) AS gb
-> FROM standings2 AS w1
-> INNER JOIN firstplace AS fp
-> ON w1.half = fp.half AND w1.division = fp.division
-> ORDER BY
-> w1.half,
-> w1.division,
-> w1.wins - w1.losses DESC,
-> pct DESC;
Empty set (0.01 sec)
mysql> SELECT
-> w1.half,
-> w1.division,
-> w1.team,
-> w1.wins AS w,
-> w1.losses AS l,
-> TRUNCATE(w1.wins / (w1.wins + w1.losses), 3) AS pct,
-> -- 计算与榜首的差距（gb）
-> IF(
-> w1.wins - w1.losses = fp.max_diff, -- 若当前球队是榜首
-> '-', -- 榜首的gb为'-'
-> TRUNCATE((fp.max_diff - (w1.wins - w1.losses)) / 2, 1) -- 其他球队的gb
-> ) AS gb
-> FROM standings2 AS w1
-> -- 子查询获取每个分区、半程的最大胜负差（即榜首球队的胜负差）
-> INNER JOIN (
-> SELECT
-> half,
-> division,
-> MAX(wins - losses) AS max_diff -- 最大胜负差 = 榜首球队的胜负差
-> FROM standings2
-> GROUP BY half, division
-> ) AS fp
-> ON w1.half = fp.half AND w1.division = fp.division
-> -- 排序：按半程、分区，再按胜负差（降序）、胜率（降序）
-> ORDER BY
-> w1.half,
-> w1.division,
-> (w1.wins - w1.losses) DESC,
-> pct DESC;
+------+----------+-----------------+------+------+-------+------+
| half | division | team | w | l | pct | gb |
+------+----------+-----------------+------+------+-------+------+
| 1 | Eastern | St. Paul | 24 | 18 | 0.571 | - |
| 1 | Eastern | Thunder Bay | 18 | 24 | 0.428 | 6.0 |
| 1 | Eastern | Duluth-Superior | 17 | 24 | 0.414 | 6.5 |
| 1 | Eastern | Madison | 15 | 27 | 0.357 | 9.0 |
| 1 | Western | Winnipeg | 29 | 12 | 0.707 | - |
| 1 | Western | Sioux City | 28 | 14 | 0.666 | 1.5 |
| 1 | Western | Fargo-Moorhead | 21 | 21 | 0.500 | 8.5 |
| 1 | Western | Sioux Falls | 15 | 27 | 0.357 | 14.5 |
| 2 | Eastern | Duluth-Superior | 22 | 20 | 0.523 | - |
| 2 | Eastern | St. Paul | 21 | 21 | 0.500 | 1.0 |
| 2 | Eastern | Madison | 19 | 23 | 0.452 | 3.0 |
| 2 | Eastern | Thunder Bay | 18 | 24 | 0.428 | 4.0 |
| 2 | Western | Fargo-Moorhead | 26 | 16 | 0.619 | - |
| 2 | Western | Winnipeg | 24 | 18 | 0.571 | 2.0 |
| 2 | Western | Sioux City | 22 | 20 | 0.523 | 4.0 |
| 2 | Western | Sioux Falls | 16 | 26 | 0.380 | 10.0 |
+------+----------+-----------------+------+------+-------+------+
16 rows in set (0.01 sec)
关键修改说明
用子查询替代firstplace表：
通过 SELECT half, division, MAX(wins - losses) AS max_diff FROM standings2 GROUP BY half, division 动态计算每个分区、半程的榜首球队胜负差（无需手动创建firstplace表），避免了 "表不存在" 的错误。
gb字段计算逻辑：
若球队的胜负差（wins - losses）等于榜首的最大胜负差（max_diff），则gb为'-'（表示该队是榜首）。
否则，gb为（榜首胜负差 - 该队胜负差）/ 2（这是体育排名中计算 "场次差距" 的标准公式）。