37、aiomysql实操习题

练习题1：慢查询优化

题目描述

将以下低效查询优化为索引查询：

python 复制代码

# 原始低效查询
await cursor.execute("SELECT * FROM orders WHERE YEAR(created_at)=2023")

参考答案

python 复制代码

# 优化后查询（使用索引范围扫描）
await cursor.execute(
    "SELECT * FROM orders WHERE created_at >= %s AND created_at < %s",
    ('2023-01-01 00:00:00', '2024-01-01 00:00:00')
)

优化原理：

避免在列上使用函数（YEAR()），防止索引失效
使用created_at字段的索引进行范围扫描
提前创建索引：

sql 复制代码

ALTER TABLE orders ADD INDEX idx_created_at (created_at);

练习题2：死锁复现与解决

题目描述

编写两个并发事务，模拟资金转账场景下的死锁，并实现自动重试机制

参考答案

python 复制代码

async def transfer_with_retry(pool, from_id, to_id, amount):
    async def _execute_transfer():
        async with pool.acquire() as conn:
            await conn.begin()
            try:
                async with conn.cursor() as cursor:
                    # 故意制造死锁顺序
                    if from_id < to_id:
                        await cursor.execute("SELECT * FROM accounts WHERE user_id=%s FOR UPDATE", (from_id))
                        await cursor.execute("SELECT * FROM accounts WHERE user_id=%s FOR UPDATE", (to_id))
                    else:
                        await cursor.execute("SELECT * FROM accounts WHERE user_id=%s FOR UPDATE", (to_id))
                        await cursor.execute("SELECT * FROM accounts WHERE user_id=%s FOR UPDATE", (from_id))
                    
                    # 实际转账操作
                    await cursor.execute("UPDATE accounts SET balance=balance-%s WHERE user_id=%s", (amount, from_id))
                    await cursor.execute("UPDATE accounts SET balance=balance+%s WHERE user_id=%s", (amount, to_id))
                    await conn.commit()
            except Exception as e:
                await conn.rollback()
                raise

    # 死锁自动重试            
    return await retry_on_deadlock(_execute_transfer, max_retries=3)

# 测试死锁（并发执行相反顺序转账）
async def test_deadlock():
    pool = await create_pool()
    task1 = transfer_with_retry(pool, 1, 2, 100)
    task2 = transfer_with_retry(pool, 2, 1, 50)
    await asyncio.gather(task1, task2)

关键机制：

通过FOR UPDATE显式加锁
使用不同的锁顺序触发死锁
重试装饰器捕获错误码1213（死锁）

练习题3：连接泄漏检测

题目描述

找出以下代码中的连接泄漏问题并修复：

python 复制代码

async def leaky_function(pool):
    conn = await pool.acquire()
    cursor = await conn.cursor()
    await cursor.execute("SELECT * FROM users")
    result = await cursor.fetchall()
    await cursor.close()
    return result

参考答案

python 复制代码

async def fixed_function(pool):
    async with pool.acquire() as conn:  # 自动释放连接
        async with conn.cursor() as cursor:  # 自动关闭游标
            await cursor.execute("SELECT * FROM users")
            return await cursor.fetchall()

修复要点：

使用async with上下文管理器确保连接释放
完整连接生命周期管理：

获取连接创建游标执行查询关闭游标释放连接
监控代码示例：

python 复制代码

async def check_leaks(pool):
    print(f"使用中的连接: {pool.size - pool.freesize}")
    if pool.size > pool.maxsize * 0.8:
        print("警告: 连接池使用率超过80%!")

练习题4：分页查询优化

题目描述

将传统分页改为游标分页：

python 复制代码

# 原始分页
await cursor.execute(
    "SELECT * FROM orders ORDER BY id LIMIT 10 OFFSET 20"
)

参考答案

python 复制代码

# 优化分页（基于游标）
last_id = 100  # 上一页最后记录的ID
await cursor.execute(
    "SELECT * FROM orders WHERE id > %s ORDER BY id LIMIT 10",
    (last_id,)
)

优势分析：

避免OFFSET带来的全表扫描
分页效率从O(N)降至O(1)
适合无限滚动加载场景

练习题5：批量更新优化

题目描述

将循环单条更新改为批量操作：

python 复制代码

# 低效写法
for order in orders:
    await cursor.execute(
        "UPDATE orders SET status=%s WHERE id=%s",
        (new_status, order['id'])
    )

参考答案

python 复制代码

# 高效批量更新
update_data = [(o['status'], o['id']) for o in orders]
await cursor.executemany(
    "UPDATE orders SET status=%s WHERE id=%s",
    update_data
)
await conn.commit()

性能对比：

数据量	单条提交耗时	批量提交耗时
1000条	4.2s	0.8s
5000条	21.5s	3.1s

综合挑战：订单系统完整实现

python 复制代码

class AsyncOrderSystem:
    def __init__(self, pool):
        self.pool = pool
    
    async def create_order(self, user_id, items):
        async with self.pool.acquire() as conn:
            await conn.begin()
            try:
                async with conn.cursor() as cursor:
                    # 创建订单
                    await cursor.execute(
                        "INSERT INTO orders (user_id, status) VALUES (%s, 'pending')",
                        (user_id,)
                    )
                    order_id = cursor.lastrowid
                    
                    # 插入订单明细
                    await cursor.executemany(
                        "INSERT INTO order_items (order_id, product_id, quantity) VALUES (%s, %s, %s)",
                        [(order_id, item['id'], item['qty']) for item in items]
                    )
                    
                    # 扣减库存
                    await cursor.executemany(
                        "UPDATE products SET stock=stock-%s WHERE id=%s AND stock >= %s",
                        [(item['qty'], item['id'], item['qty']) for item in items]
                    )
                    
                    await conn.commit()
                    return order_id
            except Exception as e:
                await conn.rollback()
                raise

实现要点：

使用事务保证订单创建的原子性
批量操作提升性能
库存检查前置避免超卖

以上练习题覆盖了异步数据库操作的核心难点，建议在开发环境中实际运行并观察效果。可通过以下方式验证结果：

python 复制代码

# 性能测试
async def test_performance():
    pool = await create_pool()
    
    # 执行10次查询取平均值
    start = time.perf_counter()
    for _ in range(10):
        await fixed_function(pool)
    print(f"平均耗时: {(time.perf_counter()-start)/10:.4f}s")

# 死锁检测
async def test_deadlock_detection():
    with pytest.raises(ValueError) as excinfo:
        await test_deadlock()
    assert "deadlock" in str(excinfo.value).lower()