如何过滤 SQL 结果中的 has-many-through 关系

本文介绍了如何过滤 SQL 结果中的 has-many-through 关系的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

假设我有 student、club 和 student_club 表:

Assuming I have the tables student, club, and student_club:

student {
    id
    name
}
club {
    id
    name
}
student_club {
    student_id
    club_id
}

我想知道如何查找足球 (30) 和棒球 (50) 俱乐部的所有学生.
虽然这个查询不起作用，但它是我迄今为止最接近的东西:

I want to know how to find all students in both the soccer (30) and baseball (50) club.
While this query doesn't work, it's the closest thing I have so far:

SELECT student.*
FROM   student
INNER  JOIN student_club sc ON student.id = sc.student_id
LEFT   JOIN club c ON c.id = sc.club_id
WHERE  c.id = 30 AND c.id = 50

推荐答案

我很好奇.众所周知，好奇心以杀死猫而闻名.

I was curious. And as we all know, curiosity has a reputation for killing cats.

本次测试的猫皮环境:

PostgreSQL 9.0 在 Debian Squeeze 上，具有不错的 RAM 和设置.
6000 名学生，24000 名俱乐部会员(数据从具有现实生活数据的类似数据库复制而来.)
与问题中的命名模式略有不同:student.id 是 student.stud_id 并且 club.id 是 club.club_id 在这里.
我在这个帖子中以作者的名字命名了这些查询.
我多次运行所有查询以填充缓存，然后我使用 EXPLAIN ANALYZE 从 5 个中选出了最好的.
相关索引(应该是最优的——只要我们不知道哪些俱乐部会被查询):

PostgreSQL 9.0 on Debian Squeeze with decent RAM and settings.
6.000 students, 24.000 club memberships (data copied from a similar database with real life data.)
Slight diversion from the naming schema in the question: student.id is student.stud_id and club.id is club.club_id here.
I named the queries after their author in this thread.
I ran all queries a couple of times to populate the cache, then I picked the best of 5 with EXPLAIN ANALYZE.
Relevant indexes (should be the optimum - as long as we lack fore-knowledge which clubs will be queried):

ALTER TABLE student ADD CONSTRAINT student_pkey PRIMARY KEY(stud_id );
ALTER TABLE student_club ADD CONSTRAINT sc_pkey PRIMARY KEY(stud_id, club_id);
ALTER TABLE club       ADD CONSTRAINT club_pkey PRIMARY KEY(club_id );
CREATE INDEX sc_club_id_idx ON student_club (club_id);

club_pkey 这里的大多数查询都不需要.
PostgreSQL中主键自动实现唯一索引.
最后一个索引是为了弥补多列索引在 PostgreSQL 上:

club_pkey is not required by most queries here.
Primary keys implement unique indexes automatically In PostgreSQL.
The last index is to make up for this known shortcoming of multi-column indexes on PostgreSQL:

多列 B 树索引可以与以下查询条件一起使用涉及索引列的任何子集，但索引是最当前导(最左侧)列有约束时，效率很高.

A multicolumn B-tree index can be used with query conditions that involve any subset of the index's columns, but the index is most efficient when there are constraints on the leading (leftmost) columns.

结果

EXPLAIN ANALYZE 的总运行时间.

SELECT s.stud_id, s.name
FROM   student s
JOIN   student_club sc USING (stud_id)
WHERE  sc.club_id IN (30, 50)
GROUP  BY 1,2
HAVING COUNT(*) > 1;

2)欧文1:33.217毫秒

SELECT s.stud_id, s.name
FROM   student s
JOIN   (
   SELECT stud_id
   FROM   student_club
   WHERE  club_id IN (30, 50)
   GROUP  BY 1
   HAVING COUNT(*) > 1
   ) sc USING (stud_id);

3)马丁1:31.735毫秒

SELECT s.stud_id, s.name
FROM   student s
WHERE  student_id IN (
   SELECT student_id
   FROM   student_club
   WHERE  club_id = 30

   INTERSECT
   SELECT stud_id
   FROM   student_club
   WHERE  club_id = 50
   );

4)德里克:2.287毫秒

SELECT s.stud_id,  s.name
FROM   student s
WHERE  s.stud_id IN (SELECT stud_id FROM student_club WHERE club_id = 30)
AND    s.stud_id IN (SELECT stud_id FROM student_club WHERE club_id = 50);

5)欧文2:2.181毫秒

SELECT s.stud_id,  s.name
FROM   student s
WHERE  EXISTS (SELECT * FROM student_club
               WHERE  stud_id = s.stud_id AND club_id = 30)
AND    EXISTS (SELECT * FROM student_club
               WHERE  stud_id = s.stud_id AND club_id = 50);

6)肖恩:2.043毫秒

SELECT s.stud_id, s.name
FROM   student s
JOIN   student_club x ON s.stud_id = x.stud_id
JOIN   student_club y ON s.stud_id = y.stud_id
WHERE  x.club_id = 30
AND    y.club_id = 50;

最后三个的表现几乎相同.4) 和 5) 产生相同的查询计划.

The last three perform pretty much the same. 4) and 5) result in the same query plan.

花哨的 SQL，但性能跟不上:

Fancy SQL, but the performance can't keep up:

SELECT s.stud_id,  s.name
FROM   student AS s
WHERE  NOT EXISTS (
   SELECT *
   FROM   club AS c 
   WHERE  c.club_id IN (30, 50)
   AND    NOT EXISTS (
      SELECT *
      FROM   student_club AS sc 
      WHERE  sc.stud_id = s.stud_id
      AND    sc.club_id = c.club_id  
      )
   );

8)超立方体2:147.497毫秒

SELECT s.stud_id,  s.name
FROM   student AS s
WHERE  NOT EXISTS (
   SELECT *
   FROM  (
      SELECT 30 AS club_id  
      UNION  ALL
      SELECT 50
      ) AS c
   WHERE NOT EXISTS (
      SELECT *
      FROM   student_club AS sc 
      WHERE  sc.stud_id = s.stud_id
      AND    sc.club_id = c.club_id  
      )
   );

正如预期的那样，这两者的表现几乎相同.查询计划导致表扫描，计划程序在这里找不到使用索引的方法.

As expected, those two perform almost the same. Query plan results in table scans, the planner doesn't find a way to use the indexes here.

WITH RECURSIVE two AS (
   SELECT 1::int AS level
        , stud_id
   FROM   student_club sc1
   WHERE  sc1.club_id = 30
   UNION
   SELECT two.level + 1 AS level
        , sc2.stud_id
   FROM   student_club sc2
   JOIN   two USING (stud_id)
   WHERE  sc2.club_id = 50
   AND    two.level = 1
   )
SELECT s.stud_id, s.student
FROM   student s
JOIN   two USING (studid)
WHERE  two.level > 1;

花哨的 SQL，CTE 的出色性能.非常奇特的查询计划.

Fancy SQL, decent performance for a CTE. Very exotic query plan.

WITH sc AS (
   SELECT stud_id
   FROM   student_club
   WHERE  club_id IN (30,50)
   GROUP  BY stud_id
   HAVING COUNT(*) > 1
   )
SELECT s.*
FROM   student s
JOIN   sc USING (stud_id);

查询 2) 的 CTE 变体.令人惊讶的是，它可能会导致使用完全相同的数据的查询计划略有不同.我在 student 上发现了一个顺序扫描，其中子查询变体使用了索引.

CTE variant of query 2). Surprisingly, it can result in a slightly different query plan with the exact same data. I found a sequential scan on student, where the subquery-variant used the index.

另一个后期添加的超立方体.真是太神奇了，有多少种方法.

Another late addition ypercube. It is positively amazing, how many ways there are.

SELECT s.stud_id, s.student
FROM   student s
JOIN   student_club sc USING (stud_id)
WHERE  sc.club_id = 10                 -- member in 1st club ...
AND    NOT EXISTS (
   SELECT *
   FROM  (SELECT 14 AS club_id) AS c  -- can't be excluded for missing the 2nd
   WHERE  NOT EXISTS (
      SELECT *
      FROM   student_club AS d
      WHERE  d.stud_id = sc.stud_id
      AND    d.club_id = c.club_id
      )
   );

12)欧文3:2.377毫秒

ypercube 的 11) 实际上只是这个更简单的变体的令人费解的反向方法，它仍然缺失.执行速度几乎与顶级猫科动物一样快.

12)erwin3:2.377ms

ypercube's 11) is actually just the mind-twisting reverse approach of this simpler variant, that was also still missing. Performs almost as fast as the top cats.

SELECT s.*
FROM   student s
JOIN   student_club x USING (stud_id)
WHERE  sc.club_id = 10                 -- member in 1st club ...
AND    EXISTS (                        -- ... and membership in 2nd exists
   SELECT *
   FROM   student_club AS y
   WHERE  y.stud_id = s.stud_id
   AND    y.club_id = 14
   );

13)欧文4:2.375毫秒

难以置信，但这是另一个真正的新变体.我看到了超过两个成员的潜力，但它也跻身于只有两个成员的顶级猫之列.

13)erwin4:2.375ms

Hard to believe, but here's another, genuinely new variant. I see potential for more than two memberships, but it also ranks among the top cats with just two.

SELECT s.*
FROM   student AS s
WHERE  EXISTS (
   SELECT *
   FROM   student_club AS x
   JOIN   student_club AS y USING (stud_id)
   WHERE  x.stud_id = s.stud_id
   AND    x.club_id = 14
   AND    y.club_id = 10
   );

俱乐部会员的动态数量

换句话说:不同数量的过滤器.这个问题要求恰好两个俱乐部会员资格.但是许多用例必须为不同的数量做准备.见:

Dynamic number of club memberships

In other words: varying number of filters. This question asked for exactly two club memberships. But many use cases have to prepare for a varying number. See:

在 WHERE 子句中多次使用同一列

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