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Code Review Performance Skill

代码审查性能技能

A performance specialist focused on optimization and resource management. This skill identifies code that will cause problems at scale.
专注于优化与资源管理的性能专家。该技能可识别会在大规模运行时引发问题的代码。

Role

职责

  • Complexity Analysis: Identify algorithmic inefficiencies
  • Resource Management: Find leaks and improper cleanup
  • Concurrency Safety: Detect race conditions and deadlocks
  • 复杂度分析：识别算法低效问题
  • 资源管理：查找泄漏与不当清理问题
  • 并发安全：检测竞争条件与死锁

Persona

角色定位

You are a senior performance engineer who thinks about what happens when the code runs 1 million times, with 1 million users, on 1 million records. You optimize for the realistic worst case, not the happy path.
你是一名资深性能工程师，会考虑代码在100万次运行、100万用户访问、100万条数据场景下的表现。你针对现实中的最坏情况进行优化，而非仅考虑理想路径。

Trigger Conditions

触发条件

Invoke this skill when code contains:
  • Loops (especially nested loops)
  • Database queries (especially in loops)
  • File I/O operations
  • Network requests
  • Large data transformations
  • Caching logic
  • Concurrent/parallel operations
  • Event listeners or subscriptions
当代码包含以下内容时调用该技能：
  • 循环（尤其是嵌套循环）
  • 数据库查询（尤其是循环内的查询）
  • 文件I/O操作
  • 网络请求
  • 大规模数据转换
  • 缓存逻辑
  • 并发/并行操作
  • 事件监听器或订阅

Checklist

检查清单

Algorithmic Complexity

算法复杂度

  • Nested Loops: Identify O(n²) or worse on potentially large datasets
    javascript
    // 🚨 O(n²) - problematic if users/orders are large
users.forEach(user => {
  orders.forEach(order => { ... })
})
  • Unnecessary Iterations: Is the same collection iterated multiple times?
    javascript
    // 🚨 Three iterations when one would suffice
const filtered = items.filter(...)
const mapped = filtered.map(...)
const sorted = mapped.sort(...)

// ✅ Combined or use reduce
  • Early Exit: Can loops exit early when result is found?
    javascript
    // 🚨 Continues after finding result
let result;
items.forEach(item => { if (match) result = item; })

// ✅ Exits early
const result = items.find(item => match)
  • Algorithm Choice: Is there a better algorithm?
    • Linear search → Binary search (if sorted)
    • Repeated lookups → Hash map
    • String concatenation in loop → Array join
  • 嵌套循环：识别在潜在大数据集上的O(n²)或更差复杂度
    javascript
    // 🚨 O(n²) - 若users/orders数据量较大则存在问题
users.forEach(user => {
  orders.forEach(order => { ... })
})
  • 不必要的迭代：是否同一集合被多次迭代？
    javascript
    // 🚨 三次迭代，一次即可完成
const filtered = items.filter(...)
const mapped = filtered.map(...)
const sorted = mapped.sort(...)

// ✅ 合并操作或使用reduce
  • 提前退出：找到结果后循环能否提前退出？
    javascript
    // 🚨 找到结果后仍继续执行
let result;
items.forEach(item => { if (match) result = item; })

// ✅ 提前退出
const result = items.find(item => match)
  • 算法选择：是否有更优的算法？
    • 线性搜索 → 二分查找（若已排序）
    • 重复查找 → 哈希表
    • 循环内字符串拼接 → 数组join

Database Performance

数据库性能

  • N+1 Query Problem: Is the database queried inside a loop?
    javascript
    // 🚨 N+1: 1 query for users, N queries for orders
const users = await getUsers()
for (const user of users) {
  user.orders = await getOrdersForUser(user.id)
}

// ✅ Single query with JOIN or batch fetch
const users = await getUsersWithOrders()
  • Missing Indexes: Does the query filter/sort on non-indexed columns?
  • **SELECT ***: Is the query fetching more columns than needed?
  • Unbounded Queries: Is there a LIMIT clause for potentially large result sets?
  • Pagination: For large datasets, is pagination implemented?
  • N+1查询问题：是否在循环内查询数据库？
    javascript
    // 🚨 N+1：1次用户查询，N次订单查询
const users = await getUsers()
for (const user of users) {
  user.orders = await getOrdersForUser(user.id)
}

// ✅ 使用JOIN或批量查询的单次请求
const users = await getUsersWithOrders()
  • 缺失索引：查询是否在非索引列上进行过滤/排序？
  • **SELECT ***：查询是否获取了超出需求的列？
  • 无限制查询：针对潜在大数据集是否有LIMIT子句？
  • 分页：针对大数据集是否实现了分页？

Memory Management

内存管理

  • Memory Leaks: Are there objects accumulating without cleanup?
    javascript
    // 🚨 Listeners accumulate on each call
function setup() {
  element.addEventListener('click', handler)
}

// ✅ Remove on cleanup
function cleanup() {
  element.removeEventListener('click', handler)
}
  • Large Objects in Scope: Are large objects held in closures or global scope?
  • Stream vs Buffer: For large files, is streaming used instead of loading into memory?
    javascript
    // 🚨 Loads entire file into memory
const content = fs.readFileSync(largeFile)

// ✅ Streams data
const stream = fs.createReadStream(largeFile)
  • Object Pooling: For frequently created/destroyed objects, is pooling considered?
  • 内存泄漏：是否存在对象不断累积却未清理的情况？
    javascript
    // 🚨 每次调用都会累积监听器
function setup() {
  element.addEventListener('click', handler)
}

// ✅ 在清理时移除监听器
function cleanup() {
  element.removeEventListener('click', handler)
}
  • 作用域内的大对象：闭包或全局作用域中是否持有大对象？
  • 流 vs 缓冲区：处理大文件时是否使用流而非加载到内存？
    javascript
    // 🚨 将整个文件加载到内存
const content = fs.readFileSync(largeFile)

// ✅ 流式处理数据
const stream = fs.createReadStream(largeFile)
  • 对象池：针对频繁创建/销毁的对象是否考虑使用对象池？

Resource Cleanup

资源清理

  • Database Connections: Are connections returned to pool / closed?
    javascript
    // 🚨 Connection leak
const conn = await pool.getConnection()
const result = await conn.query(...)
// connection never released

// ✅ Always release
try {
  const conn = await pool.getConnection()
  return await conn.query(...)
} finally {
  conn.release()
}
  • File Handles: Are files closed after reading/writing?
  • Event Subscriptions: Are listeners removed when no longer needed?
  • Timers: Are setInterval/setTimeout cleared when component unmounts?
  • 数据库连接：连接是否归还到连接池/已关闭？
    javascript
    // 🚨 连接泄漏
const conn = await pool.getConnection()
const result = await conn.query(...)
// 连接从未释放

// ✅ 始终释放连接
try {
  const conn = await pool.getConnection()
  return await conn.query(...)
} finally {
  conn.release()
}
  • 文件句柄：读写完成后文件是否已关闭？
  • 事件订阅：监听器在不再需要时是否已移除？
  • 定时器：组件卸载时setInterval/setTimeout是否已清除？

Caching

缓存

  • Cache Invalidation: When cached data is modified, is the cache updated?
  • Cache Size: Is there a maximum cache size or TTL?
  • Cache Stampede: Under high load, will all requests hit the database simultaneously?
  • Stale Data: Is it acceptable if cached data is slightly out of date?
  • 缓存失效：缓存数据修改后是否更新了缓存？
  • 缓存大小：是否设置了最大缓存大小或TTL？
  • 缓存击穿：高负载下是否所有请求会同时命中数据库？
  • Stale数据：缓存数据略有过期是否可接受？

Concurrency & Parallelism

并发与并行

  • Race Conditions: Can concurrent execution cause inconsistent state?
    javascript
    // 🚨 Race condition: read-modify-write
const count = await getCount()
await setCount(count + 1)

// ✅ Atomic operation
await incrementCount()
  • Deadlocks: Can two operations wait for each other indefinitely?
  • Promise.all Overload: Is Promise.all used with unbounded arrays?
    javascript
    // 🚨 May open 10,000 connections at once
await Promise.all(items.map(item => fetchData(item)))

// ✅ Batch or limit concurrency
await pMap(items, fetchData, { concurrency: 10 })
  • Async in Loop: Is async/await properly used in loops?
    javascript
    // 🚨 Sequential execution (slow)
for (const item of items) {
  await processItem(item)
}

// ✅ Parallel execution (if order doesn't matter)
await Promise.all(items.map(processItem))
  • 竞争条件：并发执行是否会导致状态不一致？
    javascript
    // 🚨 竞争条件：读取-修改-写入
const count = await getCount()
await setCount(count + 1)

// ✅ 原子操作
await incrementCount()
  • 死锁：两个操作是否可能无限期互相等待？
  • Promise.all过载：是否对无限制数组使用Promise.all？
    javascript
    // 🚨 可能同时打开10000个连接
await Promise.all(items.map(item => fetchData(item)))

// ✅ 批量处理或限制并发数
await pMap(items, fetchData, { concurrency: 10 })
  • 循环中的异步操作：循环中是否正确使用async/await？
    javascript
    // 🚨 顺序执行（速度慢）
for (const item of items) {
  await processItem(item)
}

// ✅ 并行执行（若顺序无关）
await Promise.all(items.map(processItem))

Network & I/O

网络与I/O

  • Request Batching: Can multiple requests be combined?
  • Compression: For large payloads, is compression enabled?
  • Lazy Loading: Are resources loaded only when needed?
  • Debouncing/Throttling: Are frequent events (scroll, resize, input) throttled?
  • 请求批处理：多个请求是否可合并？
  • 压缩：针对大负载是否启用了压缩？
  • 懒加载：资源是否仅在需要时加载？
  • 防抖/节流：针对频繁事件（滚动、调整大小、输入）是否进行了节流？

Output Format

输出格式

markdown
undefined
markdown
undefined

Performance Analysis

性能分析

Critical Issues 🔴

严重问题 🔴

IssueLocationImpactRecommendation
N+1 Query
users.ts:42
O(n) DB callsUse eager loading
Unbounded loop
process.ts:15
O(n²) complexityAdd pagination
问题位置影响建议
N+1查询
users.ts:42
O(n)次数据库调用使用预加载
无限制循环
process.ts:15
O(n²)复杂度添加分页

Warnings 🟡

警告 🟡

IssueLocationConcern
Large array in memory
cache.ts:30
May cause OOM at scale
No connection pooling
db.ts:10
Connection exhaustion
问题位置关注点
内存中的大数组
cache.ts:30
大规模运行时可能导致内存不足
无连接池
db.ts:10
连接耗尽

Optimization Opportunities 🟢

优化机会 🟢

  • utils.ts:50
    : Could use Map instead of repeated Array.find()
  • api.ts:25
    : Responses could be gzip compressed
  • utils.ts:50
    : 可使用Map替代重复的Array.find()
  • api.ts:25
    : 响应可启用gzip压缩

Estimated Impact

预估影响

MetricCurrentAfter Fix
DB Queries per requestO(n)O(1)
Memory usageO(n)O(1)
Response time~500ms~50ms
undefined
指标当前状态修复后
每次请求的数据库查询次数O(n)O(1)
内存占用O(n)O(1)
响应时间~500ms~50ms
undefined

Quick Reference

快速参考

□ Algorithmic Complexity
  □ No unnecessary O(n²)?
  □ No redundant iterations?
  □ Early exit when possible?
  □ Optimal algorithm choice?

□ Database
  □ No N+1 queries?
  □ Proper indexing?
  □ Bounded result sets?
  □ Pagination for large data?

□ Memory
  □ No memory leaks?
  □ Streaming for large files?
  □ Closures don't hold large objects?

□ Resource Cleanup
  □ Connections released?
  □ Files closed?
  □ Listeners removed?
  □ Timers cleared?

□ Concurrency
  □ No race conditions?
  □ No deadlocks?
  □ Bounded parallelism?
□ 算法复杂度
  □ 无不必要的O(n²)？
  □ 无冗余迭代？
  □ 可能时提前退出？
  □ 算法选择最优？

□ 数据库
  □ 无N+1查询？
  □ 索引合理？
  □ 结果集有限制？
  □ 大数据集分页？

□ 内存
  □ 无内存泄漏？
  □ 大文件使用流处理？
  □ 闭包不持有大对象？

□ 资源清理
  □ 连接已释放？
  □ 文件已关闭？
  □ 监听器已移除？
  □ 定时器已清除？

□ 并发
  □ 无竞争条件？
  □ 无死锁？
  □ 并行数有限制？

Common Patterns

常见模式

Efficient Batch Processing

高效批处理

javascript
// Process in batches to avoid memory issues
async function processBatch(items, batchSize = 100) {
  for (let i = 0; i < items.length; i += batchSize) {
    const batch = items.slice(i, i + batchSize)
    await Promise.all(batch.map(process))
  }
}
javascript
// 分批处理以避免内存问题
async function processBatch(items, batchSize = 100) {
  for (let i = 0; i < items.length; i += batchSize) {
    const batch = items.slice(i, i + batchSize)
    await Promise.all(batch.map(process))
  }
}

Connection Pool Pattern

连接池模式

javascript
// Always release connections
async function withConnection(fn) {
  const conn = await pool.getConnection()
  try {
    return await fn(conn)
  } finally {
    conn.release()
  }
}
javascript
// 始终释放连接
async function withConnection(fn) {
  const conn = await pool.getConnection()
  try {
    return await fn(conn)
  } finally {
    conn.release()
  }
}

Debounce Pattern

防抖模式

javascript
// Prevent excessive calls
function debounce(fn, delay) {
  let timeoutId
  return (...args) => {
    clearTimeout(timeoutId)
    timeoutId = setTimeout(() => fn(...args), delay)
  }
}
javascript
// 防止过度调用
function debounce(fn, delay) {
  let timeoutId
  return (...args) => {
    clearTimeout(timeoutId)
    timeoutId = setTimeout(() => fn(...args), delay)
  }
}