498 lines
18 KiB
Markdown
498 lines
18 KiB
Markdown
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---
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tags: [MySQL, 子查询, EXISTS, IN, 派生表, CTE]
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create time: 2026-05-16 00:00
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---
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# 子查询与派生表
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## 概述
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子查询是嵌套在另一个查询中的 SELECT 语句。它可以在 WHERE、FROM、SELECT 等多个位置出现，每种位置的语义和执行方式不同。
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## 分类
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```mermaid
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graph BT
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subgraph "标量子查询"
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S1["返回一行一列<br/>用在 SELECT / WHERE"]
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end
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subgraph "行子查询"
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S2["返回一行多列<br/>用在 ROW() 比较"]
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end
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subgraph "列子查询"
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S3["返回多行一列<br/>用在 IN / ANY / ALL"]
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end
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subgraph "表子查询（派生表）"
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S4["返回多行多列<br/>用在 FROM 子句"]
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end
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subgraph "EXISTS 子查询"
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S5["返回布尔值<br/>用于 EXISTS / NOT EXISTS"]
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end
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```
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## 标量子查询
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### 基本用法
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```sql
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-- 用法 1：在 SELECT 中调用
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SELECT
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username,
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(SELECT COUNT(*) FROM orders WHERE user_id = users.id) AS order_count
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FROM users;
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-- 副作用：这个查询其实也是"相关子查询"的典型例子
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-- 因为内层的 user_id = users.id 引用了外层表的列
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-- 每一行 users 的记录都会触发一次内层查询
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-- 用法 2：在 WHERE 中等价于常量
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SELECT * FROM products
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WHERE price > (SELECT AVG(price) FROM products);
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-- 用法 3：在 INSERT 中赋值
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INSERT INTO reports (month, order_total)
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VALUES ('2026-05', (SELECT SUM(amount) FROM orders WHERE MONTH(created_at) = 5));
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```
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> [!QUESTION] 上面用法 1 的子查询，每一行都要重新执行吗？
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> 是的——但不是因为"放在 SELECT 里"，而是因为它引用了外层的 `users.id`。
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> 这就引出了子查询最重要的分类维度：**相关 vs 无关**。
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### 相关 vs 无关子查询
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这是理解子查询性能的基石：**子查询是否引用了外层查询的列？**
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```mermaid
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flowchart LR
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UC["子查询"] --> UCX{"是否引用外层表的列？"}
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UCX -->|否：无关子查询<br/>Uncorrelated| UCN["先执行一次<br/>结果供外层复用 ✅"]
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UCX -->|是：相关子查询<br/>Correlated| UCR["对每一行外层记录<br/>都要重新执行 ⚠️"]
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style UCN fill:#00B6BC,color:#fff
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style UCR fill:#FF9F43,color:#000
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```
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> [!EXAMPLE] 对比示例
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> ```sql
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> -- 🟢 无关子查询：只执行一次
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> -- 子查询完全不依赖 users 表
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> SELECT * FROM products
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> WHERE price > (SELECT AVG(price) FROM products);
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>
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> -- 🔴 相关子查询：执行 N 次（N = users 行数）
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> -- 子查询引用了外层 u.id
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> SELECT u.username,
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> (SELECT COUNT(*) FROM orders o WHERE o.user_id = u.id) AS cnt
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> FROM users u;
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> -- 10 万用户 → 子查询执行 10 万次
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> ```
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> [!WARNING] 标量子查询的性能隐患
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> MySQL 8.0.21 之前，标量子查询**无法被物化**，会导致每次执行都重新计算（N+1 问题）。
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> ```sql
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> -- ❌ 慢：每个用户都要查一次 orders 表（相关子查询）
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> SELECT u.username,
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> (SELECT COUNT(*) FROM orders o WHERE o.user_id = u.id) AS cnt
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> FROM users u;
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> -- 如果有 10 万用户，就要查 10 万次 orders 表
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>
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> -- ✅ 换成 JOIN 或窗口函数
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> SELECT u.username, COALESCE(SUB.cnt, 0) AS cnt
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> FROM users u
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> LEFT JOIN (
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> SELECT user_id, COUNT(*) AS cnt
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> FROM orders GROUP BY user_id
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> ) SUB ON u.id = SUB.user_id;
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> ```
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## IN vs EXISTS
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这是面试经典题，也是实际中最纠结的选择。
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```sql
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-- IN 子查询
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SELECT * FROM users
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WHERE id IN (SELECT user_id FROM orders);
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-- EXISTS 子查询
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SELECT * FROM users u
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WHERE EXISTS (SELECT 1 FROM orders o WHERE o.user_id = u.id);
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```
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```mermaid
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flowchart TD
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A["Optimizer 选择策略"] --> B{"哪张表更小？"}
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B -->|"子查询结果小"<| C["转换 IN → EXISTS<br/>先跑子查询，外层仅匹配结果"]
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B -->|"子查询结果大"<| D["转换 EXISTS → IN<br/>外层先行过滤，减少子查询次数"]
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C --> E["NOT IN 永远转不成 EXISTS!<br/>NULL 值会导致语义错误"]
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D --> F["NOT EXISTS 是最安全的反查写法"]
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style E fill:#EE5A24,color:#fff
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style F fill:#00D866,color:#fff
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```
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> [!QUESTION] NOT IN 和 NOT EXISTS 有什么区别？
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> ```sql
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> -- ⚠️ 致命陷阱
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> SELECT * FROM users WHERE id NOT IN (SELECT user_id FROM orders);
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>
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> -- 如果子查询返回任何 NULL 值，整个 NOT IN 结果为 UNKNOWN
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> -- 最终返回空结果集！
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>
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> -- ✅ 正确写法
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> SELECT * FROM users
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> WHERE id NOT IN (SELECT user_id FROM orders WHERE user_id IS NOT NULL);
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>
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> -- 或者直接用 NOT EXISTS（更安全）
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> SELECT * FROM users u
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> WHERE NOT EXISTS (SELECT 1 FROM orders o WHERE o.user_id = u.id);
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> ```
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### 选型决策：IN vs EXISTS vs JOIN
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实际开发中三者往往能写出等价逻辑，选法参考下表：
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| 场景 | 推荐写法 | 理由 |
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|------|----------|------|
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| 外大内小（外层百万、子查询几百） | `EXISTS` | 外层每行只需匹配一条即可停止，短路效应明显 |
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| 外小内大 | `IN` | Optimizer 会先物化子查询结果再匹配，效率高 |
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| 子查询含 `NULL` 值可能 | `EXISTS` / `NOT EXISTS` | `NOT IN` 遇到 NULL 直接返回空集（见上文陷阱） |
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| 需要返回外键表的完整列 | `JOIN` + `DISTINCT` | EXISTS 只能判断存在性，无法获取关联表字段 |
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| 只是判断"有没有" | `EXISTS` | 语义最清晰，性能也最优 |
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> [!TIP] 经验法则
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> **不确定时优先用 EXISTS**——它的语义是"是否存在"而非"值是否匹配"，即使 MySQL Optimizer 最终把 IN 和 EXISTS 优化成执行计划也一样。用 EXISTS 能让读代码的人一眼看懂意图，同时给 Optimizer 留下优化空间。
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## 派生表（Derived Table / Subquery in FROM）
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```sql
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-- 派生表：子查询作为一个虚拟表出现在 FROM 位置
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SELECT dept, avg_salary
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FROM (
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SELECT department AS dept, AVG(salary) AS avg_salary
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FROM employees
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WHERE status = 'active'
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GROUP BY department
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) AS dept_stats
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WHERE avg_salary > 15000
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ORDER BY avg_salary DESC;
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```
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### 物化（Materialization）
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MySQL 是否将子查询物化为临时表，取决于查询复杂度：
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```mermaid
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flowchart LR
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A["子查询"] --> B{"能否被推入外层？"}
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B -->|能| C["Flattening<br/>展开为 JOIN，零额外开销 ✅"]
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B -->|不能| D{"是否需要聚合？"}
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D -->|是| E["Materialization<br/>物化为临时表 ⚠️"]
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D -->|否| F["Semi-join 优化<br/>半连接优化 ✅"]
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style C fill:#00D866,color:#fff
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style F fill:#00B6BC,color:#fff
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style E fill:#FF9F43,color:#000
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```
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```sql
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-- ✅ 可以被 Flattening 优化（无额外开销）
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SELECT u.id, o.amount
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FROM users u
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JOIN (SELECT user_id, amount FROM orders) AS o ON u.id = o.user_id;
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-- Optimizer 将其转换为普通的 JOIN
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-- ❌ 必须 Materialization（有临时表开销）
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SELECT u.id, SUB.total
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FROM users u
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JOIN (
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SELECT user_id, SUM(amount) AS total
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FROM orders GROUP BY user_id
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) AS SUB ON u.id = SUB.user_id;
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-- 子查询因为有 GROUP BY，必须先物化成临时表
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```
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> [!NOTE] 如何判断是否物化？
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> 使用 `EXPLAIN FORMAT=JSON`：
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> ```json
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> {
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> "select_type": "DERIVED",
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> "materialized_from_subquery": { ... }
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> }
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> ```
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> 如果出现 `"using_temporary_table": true`，说明使用了临时表。
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实战中更常用的方式是 `EXPLAIN` + 观察 type 和 Extra：
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```sql
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-- ❌ 物化型派生表 → Extra 出现 "Using temporary"
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EXPLAIN SELECT u.id, SUB.total
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FROM users u
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JOIN (SELECT user_id, SUM(amount) AS total FROM orders GROUP BY user_id) AS SUB ON u.id = SUB.user_id;
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-- ✅ 可 Flattening → 无临时表，Extra 干净
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EXPLAIN SELECT u.id, o.amount
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FROM users u JOIN (SELECT user_id, amount FROM orders) AS o ON u.id = o.user_id;
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```
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对比两种情况的执行计划：
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```mermaid
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graph LR
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subgraph "❌ 物化型派生表"
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T1["select_type: DERIVED"] -->|orders | T2["type: ALL<br/>Extra: Using temporary, Using filesort"]
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P1["PRIMARY → <derived2>"] -->|全表扫描| P2["type: ALL"]
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P3["PRIMARY → u"] -->|全表扫描| P4["Extra: Using where"]
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end
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subgraph "✅ 可展开型派生表"
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F1["select_type: SIMPLE"] -->|orders | F2["type: ALL<br/>Extra: NULL（无额外开销）"]
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F3["SIMPLE → u"] -->|索引扫描| F4["type: index<br/>Extra: Using index"]
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end
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style T2 fill:#FF9F43,color:#000
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style P2 fill:#EE5A24,color:#fff
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style F2 fill:#00D866,color:#fff
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style F4 fill:#00D866,color:#fff
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```
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> [!TIP] 性能优化技巧
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> - 当发现派生表产生临时表时，考虑**手动提升为 CTE**（MySQL 8.0+），有时能改变 Optimizer 行为
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> - `optimizer_switch='derived_merge=on'`（默认开启）控制是否允许 Flattening
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> - 对于超大结果集物化，注意 `tmp_table_size` / `max_heap_table_size` 限制
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### 常见陷阱与最佳实践
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| 坑 | 症状 | 解法 |
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|----|------|------|
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| **缺少别名** | `Every derived table must have its own alias` | 派生表必须起别名：`FROM (...) AS t` |
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| **自引用冲突** | `Table 'xxx' is specified twice` | UPDATE/DELETE 中的派生表需用嵌套包裹 |
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| **NULL 传播** | NOT IN 返回空集 | 改用 `NOT EXISTS` 或加 `IS NOT NULL` 过滤 |
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| **隐式类型转换** | 索引失效，全表扫描 | 确保比较列类型一致（如 `VARCHAR` vs `INT`）|
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| **大结果集物化** | `Using temporary; Using filesort` | 用 JOIN 重写或拆分为多次查询 |
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## 实战：性能排查 Checklist
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遇到慢的子查询，按以下顺序逐项检查：
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```mermaid
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flowchart TD
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S["慢查询：含子查询"] --> C1{"EXPLAIN 看了吗？"}
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C1 -->|没看| STOP["🛑 先跑 EXPLAIN<br/>别盲猜，数据说话"]
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C1 -->|看了| C2{"Extra 有 Using temporary 吗？"}
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C2 -->|"是：有临时表"| D1["派生表物化了 → 考虑改 JOIN 或提升为 CTE"]
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C2 -->|"否"| C3{"有 Using filesort 吗？"}
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C3 -->|"是"| D2["排序开销大 → 检查是否有可用索引"]
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C3 -->|"否"| C4{"子查询是否在 SELECT 列表中？"}
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C4 -->|"是：标量子查询"| D3["N+1 问题 → 改写为 LEFT JOIN + GROUP BY"]
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C4 -->|"否"| C5{"用了 NOT IN 吗？"}
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C5 -->|"是"| D4["NULL 陷阱 → 换 NOT EXISTS"]
|
|||
|
|
C5 -->|"否"| C6{"关联列类型一致吗？"}
|
|||
|
|
|
|||
|
|
C6 -->|"不一致"| D5["隐式类型转换导致索引失效 → 统一类型"]
|
|||
|
|
C6 -->|"一致"| DONE["✅ 执行计划合理，可能是业务逻辑本身复杂"]
|
|||
|
|
|
|||
|
|
style STOP fill:#EE5A24,color:#fff
|
|||
|
|
style D1 fill:#FF9F43,color:#000
|
|||
|
|
style D2 fill:#FF9F43,color:#000
|
|||
|
|
style D3 fill:#FF9F43,color:#000
|
|||
|
|
style D4 fill:#EE5A24,color:#fff
|
|||
|
|
style D5 fill:#FF9F43,color:#000
|
|||
|
|
style DONE fill:#00D866,color:#fff
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
> 核心原则：**先看 EXPLAIN，再动手改**。很多情况下不是子查询的问题，而是缺了索引或类型不匹配。
|
|||
|
|
|
|||
|
|
## ANY / ALL / SOME
|
|||
|
|
|
|||
|
|
```sql
|
|||
|
|
-- ANY：子查询返回的值中，任意一个满足即可
|
|||
|
|
SELECT product_name, price
|
|||
|
|
FROM products
|
|||
|
|
WHERE price > ANY (SELECT price FROM products WHERE category = 'premium');
|
|||
|
|
-- 只要比任意一件 premium 产品便宜就行
|
|||
|
|
|
|||
|
|
-- ALL：必须大于子查询返回的所有值
|
|||
|
|
SELECT product_name, price
|
|||
|
|
FROM products
|
|||
|
|
WHERE price > ALL (SELECT price FROM products WHERE category = 'premium');
|
|||
|
|
-- 必须比所有 premium 产品都贵（即最大值之上）
|
|||
|
|
|
|||
|
|
-- SOME 等价于 ANY（同义词）
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
```mermaid
|
|||
|
|
graph TB
|
|||
|
|
P["Products: 10, 50, 100, 200"]
|
|||
|
|
|
|||
|
|
ANY -->|"price > ANY(...)"<| A1["> 10 OR > 50 OR > 100 OR > 200"]
|
|||
|
|
A1 --> R1["结果: 所有 > 10 的商品"]
|
|||
|
|
|
|||
|
|
ALL -->|"price > ALL(...)"<| A2["> 10 AND > 50 AND > 100 AND > 200"]
|
|||
|
|
A2 --> R2["结果: 只有 > 200 的商品"]
|
|||
|
|
|
|||
|
|
style R1 fill:#00B6BC,color:#fff
|
|||
|
|
style R2 fill:#C44569,color:#fff
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
## UPDATE / DELETE 中的子查询
|
|||
|
|
|
|||
|
|
子查询不仅限于 SELECT，UPDATE 和 DELETE 同样可以使用。
|
|||
|
|
|
|||
|
|
```sql
|
|||
|
|
-- UPDATE：用子查询结果更新列
|
|||
|
|
UPDATE employees e
|
|||
|
|
SET salary = (
|
|||
|
|
SELECT avg_salary FROM (
|
|||
|
|
SELECT AVG(salary) AS avg_salary
|
|||
|
|
FROM employees WHERE department = e.department
|
|||
|
|
) tmp
|
|||
|
|
)
|
|||
|
|
WHERE dept_level = 'manager';
|
|||
|
|
-- ⚠️ MySQL 要求嵌套一层：不能直接在 UPDATE 中引用被更新的表
|
|||
|
|
-- 所以要用派生表包裹一下（tmp）来绕过这个限制
|
|||
|
|
|
|||
|
|
-- DELETE：条件过滤删除记录
|
|||
|
|
DELETE FROM sessions
|
|||
|
|
WHERE user_id IN (
|
|||
|
|
SELECT id FROM users WHERE status = 'banned'
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
-- 关联删除：保留每个分组中最新的一条
|
|||
|
|
DELETE t1 FROM logs t1
|
|||
|
|
INNER JOIN logs t2
|
|||
|
|
ON t1.category = t2.category
|
|||
|
|
AND t1.created_at < t2.created_at;
|
|||
|
|
-- 虽然这不是子查询写法，但效果等价于上面的子查询逻辑
|
|||
|
|
-- 实际场景中更推荐 JOIN 写法，性能更好
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
## CTE：子查询的现代写法（MySQL 8.0+）
|
|||
|
|
|
|||
|
|
`WITH` 子句（Common Table Expression）是 MySQL 8.0 引入的新语法，本质上是给派生表起了一个「有名字的、可复用的」外壳。
|
|||
|
|
|
|||
|
|
```sql
|
|||
|
|
-- 传统派生表写法
|
|||
|
|
SELECT dept, avg_salary
|
|||
|
|
FROM (
|
|||
|
|
SELECT department AS dept, AVG(salary) AS avg_salary
|
|||
|
|
FROM employees WHERE status = 'active'
|
|||
|
|
GROUP BY department
|
|||
|
|
) AS dt
|
|||
|
|
WHERE avg_salary > 15000;
|
|||
|
|
|
|||
|
|
-- ✅ 等价 CTE 写法
|
|||
|
|
WITH dept_stats AS (
|
|||
|
|
SELECT department AS dept, AVG(salary) AS avg_salary
|
|||
|
|
FROM employees WHERE status = 'active'
|
|||
|
|
GROUP BY department
|
|||
|
|
)
|
|||
|
|
SELECT dept, avg_salary
|
|||
|
|
FROM dept_stats
|
|||
|
|
WHERE avg_salary > 15000;
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
```mermaid
|
|||
|
|
flowchart LR
|
|||
|
|
subgraph "传统派生表"
|
|||
|
|
D1["FROM (...)"] -->|可读性差| D2["重复写的逻辑难以复用"]
|
|||
|
|
D3["嵌套深时层级混乱 😵"]
|
|||
|
|
end
|
|||
|
|
|
|||
|
|
subgraph "CTE 写法"
|
|||
|
|
C1["WITH name AS (...)"] -->|语义清晰| C2["可在主查询中多次引用"]
|
|||
|
|
C3["链式 CTE 层层递进 🧩"]
|
|||
|
|
end
|
|||
|
|
|
|||
|
|
D1 -.->|"功能等价"| C1
|
|||
|
|
D2 -.->|"可读性提升"| C2
|
|||
|
|
D3 -.-> "|简化复杂查询|" C3
|
|||
|
|
|
|||
|
|
style C1 fill:#00D866,color:#fff
|
|||
|
|
style C2 fill:#00B6BC,color:#fff
|
|||
|
|
style C3 fill:#00B6BC,color:#fff
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
### 递归 CTE
|
|||
|
|
|
|||
|
|
这是子查询体系无法做到的能力——自我引用的 CTE 可以遍历树形结构。
|
|||
|
|
|
|||
|
|
```sql
|
|||
|
|
-- 递归 CTE：生成从 1 到 10 的序列
|
|||
|
|
WITH RECURSIVE nums AS (
|
|||
|
|
SELECT 1 AS n -- 锚点成员（递归起点）
|
|||
|
|
UNION ALL
|
|||
|
|
SELECT n + 1 FROM nums WHERE n < 10 -- 递归成员
|
|||
|
|
)
|
|||
|
|
SELECT * FROM nums;
|
|||
|
|
|
|||
|
|
-- 实战：查询组织的完整汇报线
|
|||
|
|
WITH RECURSIVE org_chain AS (
|
|||
|
|
SELECT id, name, manager_id, 1 AS level
|
|||
|
|
FROM employees WHERE manager_id IS NULL -- 根节点：CEO
|
|||
|
|
UNION ALL
|
|||
|
|
SELECT e.id, e.name, e.manager_id, oc.level + 1
|
|||
|
|
FROM employees e
|
|||
|
|
INNER JOIN org_chain oc ON e.manager_id = oc.id -- 递归：逐级下钻
|
|||
|
|
)
|
|||
|
|
SELECT * FROM org_chain ORDER BY level, name;
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
> [!QUESTION] 为什么叫「自我引用」？递归 CTE 到底怎么执行的？
|
|||
|
|
> `org_chain` 这个名字在定义内部就被引用了——`FROM employees e INNER JOIN org_chain oc`。
|
|||
|
|
> 第一轮：只执行锚点成员，得到根节点（CEO）；
|
|||
|
|
> 第二轮：用第一轮的结果去 JOIN employees，得到 CEO 的直接下属；
|
|||
|
|
> 第三轮：用第二轮的结果去 JOIN，得到下属的下属……
|
|||
|
|
> 直到某一轮 JOIN 结果为空，递归自动终止。
|
|||
|
|
> **关键：没有终止条件 → 无限循环 → 触发 `cte_max_recursion_depth` 报错。**
|
|||
|
|
|
|||
|
|
> [!WARNING] 递归 CTE 的注意事项
|
|||
|
|
> - MySQL 默认递归深度上限为 **1000**（`cte_max_recursion_depth`），超出会报错
|
|||
|
|
> - 务必设置合理的终止条件，否则会无限递归直到达到上限
|
|||
|
|
> - 递归部分不能直接用 `LIMIT` 来截断结果
|
|||
|
|
|
|||
|
|
> [!TIP] 何时用 CTE vs 派生表？
|
|||
|
|
> | 场景 | 推荐 |
|
|||
|
|
> |------|------|
|
|||
|
|
> | 只需引用一次，逻辑简单 | 派生表（更轻量） |
|
|||
|
|
> | 需要多次引用同一段逻辑 | CTE（DRY 原则） |
|
|||
|
|
> | 需要嵌套多层 | CTE（可读性碾压） |
|
|||
|
|
> | 需要递归查询（树形结构） | 只能 CTE |
|
|||
|
|
> | 兼容 MySQL 5.7 | 只能派生表 |
|
|||
|
|
|
|||
|
|
## 总结：核心要点回顾
|
|||
|
|
|
|||
|
|
| 主题 | 一句话 |
|
|||
|
|
|------|--------|
|
|||
|
|
| 标量子查询 | MySQL 8.0.21 之前不可物化，百万行数据必踩 N+1 陷阱，优先改写为 JOIN |
|
|||
|
|
| 相关 vs 无关 | 相关子查询引用了外层列，每行都要重新执行——性能杀手 |
|
|||
|
|
| IN vs EXISTS | 语义上 IN 看值、EXISTS 看存在性；不确定时选 EXISTS，更安全的默认选项 |
|
|||
|
|
| NOT IN 陷阱 | 子查询出现 NULL 即返回空集，生产中几乎永远该用 `NOT EXISTS` 替代 |
|
|||
|
|
| 派生表物化 | 有 GROUP BY / LIMIT / UNION 等操作的子查询无法被展开，必然产生临时表开销 |
|
|||
|
|
| ANY / ALL | 对应 SQL 的 OR / AND 累加，ALL 在空子查询结果时恒返回 TRUE（反直觉，需注意） |
|
|||
|
|
| CTE | MySQL 8.0+ 现代写法，可读性和可维护性优于匿名派生表，唯一支持递归的子查询形式 |
|
|||
|
|
|
|||
|
|
> [!TIP] 核心心法
|
|||
|
|
> **子查询不是万能的——它首先是为了表达清晰，其次才是性能。**
|
|||
|
|
> 写完后务必跑 `EXPLAIN`，确认没有意料之外的临时表或多表扫描。当数据量上去后，能改写成 JOIN 的子查询就尽量改写，因为 JOIN 的执行路径对 Optimizer 更加透明。
|
|||
|
|
|
|||
|
|
> [!TIP] 学习路径建议
|
|||
|
|
> 如果你刚接触子查询，建议按这个顺序学习：
|
|||
|
|
> 1. **标量子查询** → 先理解「子查询就是一次独立的 SELECT」
|
|||
|
|
> 2. **相关 vs 无关** → 理解执行次数差异（本文核心概念）
|
|||
|
|
> 3. **IN vs EXISTS** → 掌握两种过滤范式，重点理解 NOT IN 陷阱
|
|||
|
|
> 4. **派生表** → 子查询作为虚拟表，理解物化与 Flattening
|
|||
|
|
> 5. **CTE** → 用现代语法重写派生表，最终挑战递归 CTE
|
|||
|
|
>
|
|||
|
|
> 每一步都要跑 `EXPLAIN` 看执行计划——**看懂 EXPLAIN 比背语法重要十倍**。
|
|||
|
|
|
|||
|
|
## 关联笔记
|
|||
|
|
|
|||
|
|
- [[hhs/MySQL/02-SQL核心/08-DQL SELECT 全解析]] — SELECT 执行顺序与子查询的关系
|
|||
|
|
- [[hhs/MySQL/02-SQL核心/09-JOIN 原理与优化]] — EXISTS 子查询常可重写为 JOIN
|