SQL 查询优化助手

v1.0.0

Use this skill when you need to write, review, optimize, or debug SQL queries. Covers query construction, performance tuning, index strategy, window function...

0· 169· 1 versions· 2 current· 2 all-time· Updated 1d ago· MIT-0
byHjs102468@goldath

Install

openclaw skills install sql-assistant

SQL 查询优化助手

核心工作流

Step 1 — 理解需求

收集上下文:

  • 数据库类型(PostgreSQL / MySQL / SQLite / SQL Server)
  • 表结构(DDL 或列描述)
  • 业务目标(查什么、过滤条件、聚合逻辑)
  • 数据量级(小表 <10万 / 中表 <1000万 / 大表 >1000万)
  • 性能问题描述(慢查询?错误结果?)

Step 2 — 查询构建

基础查询框架

SELECT
  col1,
  col2,
  agg_func(col3) AS alias
FROM table_name t
JOIN other_table o ON t.id = o.fk_id
WHERE condition
GROUP BY col1, col2
HAVING agg_condition
ORDER BY alias DESC
LIMIT 100;

CTE 模式(复杂逻辑拆分)

WITH base_data AS (
  SELECT user_id, COUNT(*) AS order_count
  FROM orders
  WHERE created_at >= '2026-01-01'
  GROUP BY user_id
),
ranked AS (
  SELECT *, RANK() OVER (ORDER BY order_count DESC) AS rk
  FROM base_data
)
SELECT * FROM ranked WHERE rk <= 10;

Step 3 — 性能优化策略

索引策略

-- 单列索引
CREATE INDEX idx_orders_user ON orders(user_id);

-- 复合索引(遵循最左前缀原则)
CREATE INDEX idx_orders_user_date ON orders(user_id, created_at);

-- 覆盖索引(避免回表)
CREATE INDEX idx_orders_cover ON orders(user_id, created_at, status, amount);

EXPLAIN 分析

EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)
SELECT * FROM orders WHERE user_id = 42;

关注指标:

  • Seq Scan → 考虑加索引
  • rows 估算偏差大 → 需要 ANALYZE
  • cost 高 → 优化 JOIN 顺序或添加索引
  • Buffers: shared hit/read → 缓存命中率

Step 4 — 常见优化模式

分页优化(大表 OFFSET 慢)

-- ❌ 慢:OFFSET 需扫描丢弃前N行
SELECT * FROM orders ORDER BY id LIMIT 20 OFFSET 100000;

-- ✅ 快:游标分页
SELECT * FROM orders WHERE id > :last_seen_id ORDER BY id LIMIT 20;

IN 子查询优化

-- ❌ 可能慢
SELECT * FROM users WHERE id IN (SELECT user_id FROM premium_members);

-- ✅ 用 EXISTS 或 JOIN
SELECT u.* FROM users u
JOIN premium_members pm ON u.id = pm.user_id;

避免函数破坏索引

-- ❌ 函数包装列,索引失效
WHERE YEAR(created_at) = 2026

-- ✅ 范围条件,索引有效
WHERE created_at >= '2026-01-01' AND created_at < '2027-01-01'

Step 5 — 窗口函数常用模式

-- 分组内排名
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC)

-- 累计求和
SUM(amount) OVER (PARTITION BY user_id ORDER BY created_at)

-- 环比计算
LAG(revenue, 1) OVER (ORDER BY month) AS prev_month_revenue

-- 移动平均
AVG(score) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)

Step 6 — 查询审查清单

  • SELECT 只取需要的列(避免 SELECT *)
  • WHERE 条件列有索引
  • JOIN 条件有索引
  • 大表分页用游标而非 OFFSET
  • 聚合前先 WHERE 过滤(减少聚合数据量)
  • 复杂逻辑用 CTE 而非嵌套子查询
  • 无 N+1 查询问题

反模式速查

反模式修复方式
SELECT *显式列出需要的列
OFFSET 大分页改用游标/keyset 分页
WHERE 列用函数改用范围条件
隐式类型转换确保参数类型匹配
无 LIMIT 的全表扫描加 LIMIT 或索引过滤
OR 替代 UNION改用 UNION ALL

Version tags

latestvk97d2myyxeatv7sb0r5nx1yehn84fja0