Web Performance Optimization & Lighthouse · 课时

API 响应缓存与压缩

通过内存缓存和分布式缓存层、智能缓存失效以及缩减有效载荷，加快后端响应，让服务器为每个请求执行更少的工作。

API 响应缓存与压缩 是 CoddyKit 上的免费 Web Performance Optimization & Lighthouse 课时。这是第 4 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Web Performance Optimization & Lighthouse 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Web Performance Optimization & Lighthouse 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Why Cache on the Backend?

Recomputing the same response for every request wastes CPU and database time. Caching stores computed results so repeat requests return instantly, cutting both latency and load.

Layers of Caching

In-process memory fastest, but per-instance.
Distributed cache (Redis/Memcached) shared across servers.
HTTP/CDN cache at the edge.

A Simple Cache-Aside Pattern

The most common pattern: check the cache, return on hit, otherwise compute, store, and return. This is called cache-aside.

async function getUser(id) {
  const hit = await redis.get('user:' + id);
  if (hit) return JSON.parse(hit);
  const user = await db.findUser(id);
  await redis.set('user:' + id, JSON.stringify(user), 'EX', 300);
  return user;
}

Choosing a TTL

A time to live balances freshness against hit rate. Volatile data needs short TTLs; reference data can live much longer. Always set some expiry to avoid stale buildup.

Invalidation Strategies

The hard part of caching is invalidation. On writes, either delete the affected keys or update them (write-through). Stale data here is a common production bug.

async function updateUser(id, data) {
  await db.update(id, data);
  await redis.del('user:' + id);
}

HTTP Caching Headers

For cacheable API responses, set Cache-Control so browsers and CDNs can reuse them, removing the request entirely on a hit.

res.set('Cache-Control', 'public, max-age=60, stale-while-revalidate=300');

Conditional Requests

ETag and If-None-Match let the server reply 304 Not Modified with no body when data is unchanged, saving bandwidth.

res.set('ETag', hashOf(payload));
// next time: if If-None-Match matches, send 304

Shrinking the Payload

Return only the fields clients need, paginate large lists, and avoid over-fetching. Smaller payloads serialize faster and transfer quicker.

Compressing Responses

Enable gzip or Brotli on JSON responses. Combined with caching, this minimizes both compute and transfer per request.

const compression = require('compression');
app.use(compression());

Avoiding Stampedes

When a hot key expires, many requests may hit the database at once (a cache stampede). Mitigate with locks, request coalescing, or stale-while-revalidate.

Strategy Summary

Cache-aside with sensible TTLs.
Invalidate on writes.
Use Cache-Control and ETags.
Trim and compress payloads.
Guard against stampedes.

Quick Check

After a user updates their profile, the API keeps returning the old data for several minutes. What is the most likely cause?

Recap

You learned to cut backend work with layered caching (cache-aside, TTLs, invalidation on writes), HTTP caching via Cache-Control and ETags, payload trimming, and compression, while guarding against cache stampedes.

常见问题解答

「API 响应缓存与压缩」课时是免费的吗？

是的 — 「API 响应缓存与压缩」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Web Performance Optimization & Lighthouse 课程的其余内容，请升级到 CoddyKit PRO。 Web Performance Optimization & Lighthouse 课程共包含 4 节课。

「API 响应缓存与压缩」这节课中我会学到什么？

通过内存缓存和分布式缓存层、智能缓存失效以及缩减有效载荷，加快后端响应，让服务器为每个请求执行更少的工作。你通过在浏览器中直接运行的动手代码来练习 Web Performance Optimization & Lighthouse，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Web Performance Optimization & Lighthouse 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Web Performance Optimization & Lighthouse 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 4 节课，共 4 节。

「API 响应缓存与压缩」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Web Performance Optimization & Lighthouse 课中编写并运行代码吗？

能。每节 Web Performance Optimization & Lighthouse 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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