《MySQL 分区表：优化大表查询的 4 个场景》（451）
# 📊 MySQL 分区表：优化大表查询的 4 个场景
当数据量达到千万级甚至更大时，MySQL 单表查询性能会显著下降 😫。分区表（Partitioning）通过将大表拆分为多个物理子表，可以大幅提升查询效率 🚀。以下是 4 个适合使用分区表的典型场景：
## 1️⃣ **时间序列数据（按日期/月份分区）** 📅
日志、订单等时间敏感数据非常适合按时间分区。例如：
```sql
PARTITION BY RANGE (YEAR(create_time)) (
PARTITION p2022 VALUES LESS THAN (2023),
PARTITION p2023 VALUES LESS THAN (2024)
)
```
查询时MySQL只需扫描特定时间分区，避免全表扫描 ⏳。
## 2️⃣ **冷热数据分离（按访问频率分区）** 🔥❄️
将高频访问的"热数据"和低频的"冷数据"分开存储：
```sql
PARTITION BY LIST (is_hot) (
PARTITION hot VALUES IN (1),
PARTITION cold VALUES IN (0)
)
```
SSD存放热数据，HDD存放冷数据，兼顾性能与成本 💰。
## 3️⃣ **地域数据查询（按地区分区）** 🌍
电商、物流等业务可按地区分区：
```sql
PARTITION BY LIST COLUMNS(region) (
PARTITION east VALUES IN ('shanghai','hangzhou'),
PARTITION south VALUES IN ('guangzhou','shenzhen')
)
```
查询特定区域数据时效率提升3-5倍 📈。
## 4️⃣ **定期归档历史数据（动态管理分区）** 🗃️
通过`ALTER TABLE ... DROP PARTITION`快速删除过期数据：
```sql
ALTER TABLE logs DROP PARTITION p2020; -- 秒级删除2020年数据
```
比DELETE语句更高效，且不会产生碎片 🧹。
## 💡 注意事项
- 分区键应出现在WHERE条件中，否则所有分区都会被扫描
- 避免过多分区（一般不超过100个）
- 事务和唯一约束在分区表中有特殊限制
合理使用分区表，能让你的大数据查询飞起来 ✈️！
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