Redis/Memcached缓存与Etag缓存的差异及前者使用必要性咨询

Great question—this is a super common point of confusion when working with HTTP caching vs. server-side caches. Let’s break this down step by step.

First, A Quick Refresher on ETag #

ETag is an HTTP-level caching mechanism focused on reducing bandwidth waste between clients and your server. Here’s how it works in practice:

• When your server sends a resource (like book data), it includes an ETag header—a unique hash of the resource’s content.
• The client stores this ETag alongside the cached resource on their end.
• On the next request for the same resource, the client sends an If-None-Match header with the stored ETag.
• Your server checks if the current resource’s ETag matches the one sent:
  • If it does, send a tiny 304 Not Modified response (no need to re-send the full resource body).
  • If not, send the updated resource with a new ETag.

So Why Use Redis/Memcached Then? #

ETag is handy, but it has critical limitations that make dedicated caching tools like Redis or Memcached essential for many apps:

• No escape from server-side work: Even with ETag, your server still has to generate or fetch the resource every single time to compute the ETag and compare it. If that resource comes from an expensive database query or an external API (like the Google Books API in your example), you’re still wasting CPU, database resources, or API call limits on every request. Redis/Memcached lets you store the fully computed resource directly, so your server can skip that heavy work entirely.
• Dependent on client behavior: ETag relies on the client (browser, mobile app, etc.) to properly cache the resource and send the If-None-Match header. Not all clients handle caching perfectly—some might ignore headers, clear their cache early, or be configured to bypass caching entirely. Dedicated caches are controlled by your server, so you’re not at the mercy of client settings.
• No cross-client caching: ETag caches are per-client. If 1000 different users request the same book, each will trigger a full resource fetch until they get their own ETag. With Redis, the first request caches the book, and all subsequent users get the cached version instantly—no repeated work.
• Far more flexibility: Tools like Redis let you set precise expiration times (as in your code with setex for 1 hour), cache partial resources, store complex data structures, or even cache non-HTTP data (like raw database query results) that ETag can’t touch.

Core Differences Between ETag and Redis/Memcached Caching #

Let’s boil down the key distinctions:

• Layer of operation:
  • ETag works at the HTTP protocol layer (client-server request/response cycle).
  • Redis/Memcached operate at the application/server layer—they store raw data your app can access directly, no HTTP headers required.
• What gets cached:
  • ETag caches the resource on the client and only validates if it’s changed (doesn’t skip server-side computation unless a 304 is sent).
  • Redis/Memcached cache the pre-computed resource data on your server (or a dedicated cache server), so your app can return it instantly without re-generating it.
• Control:
  • ETag control is limited to HTTP headers (you can set strong/weak ETags, but that’s about it).
  • Redis/Memcached give you full control: set custom TTLs, delete entries manually, share cache across multiple server instances, cache non-JSON data, etc.
• Primary use case:
  • Use ETag when you want to cut down on bandwidth usage (since 304 responses are tiny) and rely on client-side caching.
  • Use Redis/Memcached when you want to reduce server load (skip expensive computations) and have a centralized cache for all clients.

Example: How Your Redis Cache Code Adds Value #

Looking at your snippet, let’s see the practical impact:

const getBook = (req, res) => { 
  let isbn = req.query.isbn; 
  let url = `https://www.googleapis.com/books/v1/volumes?q=isbn:${isbn}`; 
  return axios.get(url) 
    .then(response => { 
      let book = response.data.items; 
      // Cache the book data for 1 hour
      client.setex(isbn, 3600, JSON.stringify(book)); 
      res.send(book); 
    }) 
    .catch(err => { 
      res.send('The book you are looking for is not found !!!'); 
    }); 
}; 

const getCache = (req, res) => { 
  let isbn = req.query.isbn; 
  // Check Redis first
  client.get(isbn, (err, result) => { 
    if (result) { 
      // Return cached data instantly—no API call needed!
      res.send(result); 
    } else { 
      // Fetch from API and cache if not found
      getBook(req, res); 
    } 
  }); 
}; 

app.get('/book', getCache);

Without Redis, every request would hit the Google Books API, even if the book data hasn’t changed. With Redis:

1. The first request fetches from the API and caches the result.
2. All requests for the same ISBN in the next hour get the cached data immediately—no API call, no wait time, no extra load on your server or the external API.

ETag could help reduce bandwidth here by letting clients skip re-downloading the book data, but your server would still have to call the Google Books API every time to generate and compare the ETag. Redis skips that entire step for cached requests.

内容的提问来源于stack exchange，提问作者sravan ganji