A general rule is that joins are faster in most cases (99%). The more data tables have, the subqueries are slower. The less data tables have, the subqueries have equivalent speed as joins. The subqueries are simpler, easier to understand, and easier to read. Most of the web and app frameworks and their "ORM"s and "Active record"s generate queries with subqueries, because with subqueries are easier to split responsibility, maintain code, etc. For smaller web sites or apps subqueries are OK, but for larger web sites and apps you will often have to rewrite generated queries to join queries, especial if a query uses many subqueries in the query.

有人说“一些RDBMS可以将子查询重写为连接，或将连接重写为子查询，当它认为其中一个比另一个快时”，但这句话适用于简单的情况，当然不适用于带有子查询的复杂查询，这实际上会导致性能问题。

子查询能够动态地计算聚合函数。 例如，找到这本书的最低价格，并得到所有以这个价格出售的书。 1)使用子查询:

SELECT titles, price
FROM Books, Orders
WHERE price = 
(SELECT MIN(price)
 FROM Orders) AND (Books.ID=Orders.ID);

2)使用join

SELECT MIN(price)
     FROM Orders;
-----------------
2.99

SELECT titles, price
FROM Books b
INNER JOIN  Orders o
ON b.ID = o.ID
WHERE o.price = 2.99;

我认为在引用的答案中没有强调的是重复的问题和可能由特定(使用)案例引起的有问题的结果。

(尽管马塞洛·坎托斯提到过)

我将引用斯坦福大学Lagunita SQL课程的例子。

学生表

+------+--------+------+--------+
| sID  | sName  | GPA  | sizeHS |
+------+--------+------+--------+
|  123 | Amy    |  3.9 |   1000 |
|  234 | Bob    |  3.6 |   1500 |
|  345 | Craig  |  3.5 |    500 |
|  456 | Doris  |  3.9 |   1000 |
|  567 | Edward |  2.9 |   2000 |
|  678 | Fay    |  3.8 |    200 |
|  789 | Gary   |  3.4 |    800 |
|  987 | Helen  |  3.7 |    800 |
|  876 | Irene  |  3.9 |    400 |
|  765 | Jay    |  2.9 |   1500 |
|  654 | Amy    |  3.9 |   1000 |
|  543 | Craig  |  3.4 |   2000 |
+------+--------+------+--------+

应用表

(向特定大学及专业申请)

+------+----------+----------------+----------+
| sID  | cName    | major          | decision |
+------+----------+----------------+----------+
|  123 | Stanford | CS             | Y        |
|  123 | Stanford | EE             | N        |
|  123 | Berkeley | CS             | Y        |
|  123 | Cornell  | EE             | Y        |
|  234 | Berkeley | biology        | N        |
|  345 | MIT      | bioengineering | Y        |
|  345 | Cornell  | bioengineering | N        |
|  345 | Cornell  | CS             | Y        |
|  345 | Cornell  | EE             | N        |
|  678 | Stanford | history        | Y        |
|  987 | Stanford | CS             | Y        |
|  987 | Berkeley | CS             | Y        |
|  876 | Stanford | CS             | N        |
|  876 | MIT      | biology        | Y        |
|  876 | MIT      | marine biology | N        |
|  765 | Stanford | history        | Y        |
|  765 | Cornell  | history        | N        |
|  765 | Cornell  | psychology     | Y        |
|  543 | MIT      | CS             | N        |
+------+----------+----------------+----------+

让我们试着找出申请计算机科学专业的学生的平均绩点(不论大学)

使用子查询:

select GPA from Student where sID in (select sID from Apply where major = 'CS');

+------+
| GPA  |
+------+
|  3.9 |
|  3.5 |
|  3.7 |
|  3.9 |
|  3.4 |
+------+

这个结果集的平均值是:

select avg(GPA) from Student where sID in (select sID from Apply where major = 'CS');

+--------------------+
| avg(GPA)           |
+--------------------+
| 3.6800000000000006 |
+--------------------+

使用连接:

select GPA from Student, Apply where Student.sID = Apply.sID and Apply.major = 'CS';

+------+
| GPA  |
+------+
|  3.9 |
|  3.9 |
|  3.5 |
|  3.7 |
|  3.7 |
|  3.9 |
|  3.4 |
+------+

该结果集的平均值:

select avg(GPA) from Student, Apply where Student.sID = Apply.sID and Apply.major = 'CS';

+-------------------+
| avg(GPA)          |
+-------------------+
| 3.714285714285714 |
+-------------------+

It is obvious that the second attempt yields misleading results in our use case, given that it counts duplicates for the computation of the average value. It is also evident that usage of distinct with the join - based statement will not eliminate the problem, given that it will erroneously keep one out of three occurrences of the 3.9 score. The correct case is to account for TWO (2) occurrences of the 3.9 score given that we actually have TWO (2) students with that score that comply with our query criteria.

在某些情况下，除了性能问题，子查询似乎是最安全的方法。

根据我的观察，就像两种情况，如果一个表的记录少于10万条，那么连接将工作得很快。

但是如果一个表有超过100,000条记录，那么子查询是最好的结果。

我有一个表，其中有500,000条记录，我在查询下面创建了它，它的结果时间是

SELECT * 
FROM crv.workorder_details wd 
inner join  crv.workorder wr on wr.workorder_id = wd.workorder_id;

结果:13.3秒

select * 
from crv.workorder_details 
where workorder_id in (select workorder_id from crv.workorder)

结果:1.65秒

这取决于几个因素，包括正在运行的特定查询、数据库中的数据量。子查询首先运行内部查询，然后从结果集中再次过滤出实际结果。而在join中运行并产生结果。

最好的策略是同时测试连接解决方案和子查询解决方案，以获得优化的解决方案。

在大多数情况下，join比子查询快，子查询比子查询快的情况非常罕见。

在join中，RDBMS可以为您的查询创建一个更好的执行计划，并可以预测应该加载哪些数据来处理并节省时间，不像子查询，它将运行所有的查询并加载所有的数据来进行处理。

子查询的好处是它们比join更可读:这就是为什么大多数新学习SQL的人更喜欢它们;这是简单的方法;但是当涉及到性能时，join在大多数情况下更好，尽管它们也不难读。