Goodbye, Slow-Motion Tortoise: A Deep Dive into SSR Streaming

In the early days of the web, we were used to waiting. But today, modern users demand instant feedback. Yet, many of our applications still suffer from what I call the “slow-motion tortoise 🐢” running in the browser. Have you ever experienced a UI like that?

Vibe coding might get you to a working demo, but real engineering is what gets you to production. And in production, traditional Server-Side Rendering (SSR) has a massive, all-or-nothing problem. We call it the buffering problem.

Let’s talk about why traditional SSR is holding your web apps back and how Streaming SSR changes the game entirely.

Note: If you are a visual learner, I have recorded a full masterclass video covering this exact architecture over on my YouTube channel, tapaScript. You can watch it right here:

https://youtu.be/zQoqgClU278

The Problem with Traditional SSR

In traditional SSR, the server is a perfectionist.

Imagine a server connected to two databases: a Weather DB and an Analytics DB. It needs to serve data from both to the UI. The weather data API is blazing fast, but the analytics API is slow.

Here is the fatal flaw of traditional SSR: it blocks rendering until the full HTML response is prepared. The server refuses to send a single byte of HTML to the browser until every database query and API call has resolved.

Even if your header and weather components are ready to go, that slow analytics API will force the server to withhold everything. Your Time to First Byte (TTFB) and First Contentful Paint (FCP) metrics are entirely at the mercy of your slowest API. The user is left staring at a blank, white screen.

The Uncanny Valley of Hydration

The traditional SSR process is monolithic:

The server generates the full HTML.
It sends it to the browser, which paints it.
The JavaScript bundle loads to enable interactivity.

This creates a network waterfall and introduces the “uncanny valley of hydration.” Because React receives the entire HTML at once, it has to hydrate the entire component tree in a single, blocking task.

If a user tries to click a menu button while the main thread is busy sprinkling JavaScript over the DOM, the page feels dead. This leads to user frustration, high bounce rates, and the feeling of a broken app.

We need to fix this. Enter Streaming.

The Hero of the Story: Chunked Transfer Encoding

The magic behind streaming is an HTTP/1.1 mechanism called Chunked Transfer Encoding.
Instead of waiting to generate and send the entire response at once, this mechanism allows the server to send data in a series of discrete chunks. It eliminates the need to calculate the total response size upfront, keeping the connection open so data can stream from the server to the client.

The Fluid System under the Hood

With Streaming SSR, the server instantly sends a static shell — your layouts, navigation, headers, and CSS. It also sends skeleton placeholders wrapped in React Suspense boundaries.

Your users immediately see the structure of the page. Then, as asynchronous data resolves on the server, it doesn't just send raw HTML. It sends a script tag. The script tag actually carries the logic to replace the skeleton with the actual data.

<!-- 1. Sent immediately in the initial static shell -->
<div id="skeleton-1">
  <div class="spinner">Loading...</div>
</div>

<!-- 2. Streamed later once the async data resolves on the server -->
<div id="loaded-data-d1" hidden>
  <div>Actual Weather and Analytics Data</div>
</div>

<!-- 3. The script tag that executes immediately upon arrival to swap them -->
<script>
  // It finds the skeleton and replaces it with the newly loaded data
  const skeleton = document.getElementById('skeleton-1');
  const loadedData = document.getElementById('loaded-data-d1');
  
  skeleton.replaceWith(loadedData.cloneNode(true));
  loadedData.remove(); // Cleans up the hidden data div
</script>

Traditional SSR is like waiting for a bucket to fill before you can pour water on your field. Streaming SSR is like opening the tap and letting the water flow directly to the field immediately.

Because this happens at the Suspense boundary level, the rest of the UI remains fully functional. This unlocks selective hydration. If a user clicks on a "Reviews" tab while "Product Details" are still loading, React prioritizes hydrating the exact region the user is interacting with. Clicks are never dropped, and the uncanny valley is gone.

How It Works in Modern Frameworks

Streaming is supported in React 18 and 19 for both Node.js and Edge environments:

Node.js: renderToPipeableStream allows you to flush a pipeable object to the response. You rely on two signals: onShellReady (for normal users, flushing the shell instantly) and onAllReady (for SEO bots that need the full HTML upfront).

import { renderToPipeableStream } from 'react-dom/server';
import App from './App';

app.use('/', (req, res) => {
  // Logic to detect if the request is from an SEO crawler or a real user
  const isBot = checkIfBot(req.headers['user-agent']); 

  const { pipe } = renderToPipeableStream(<App />, {
    onShellReady() {
      // For normal users: flash the static shell immediately
      if (!isBot) {
        res.setHeader('content-type', 'text/html');
        pipe(res);
      }
    },
    onAllReady() {
      // For crawlers/social bots: wait for the entire HTML tree to resolve
      if (isBot) {
        res.setHeader('content-type', 'text/html');
        pipe(res);
      }
    },
    onError(error) {
      console.error('Streaming error encountered:', error);
    }
  });
});

Edge (Vercel, Cloudflare, etc.): renderToReadableStream is promise-based, resolving a stream once the shell is ready.

import { renderToReadableStream } from 'react-dom/server';
import App from './App';

export default async function fetch(request: Request) {
  try {
    // This returns a Promise that resolves when the static shell is ready
    const stream = await renderToReadableStream(<App />, {
      onError(error) {
        // Logs errors that occur mid-stream after the shell has been sent
        console.error('Streaming error caught on the Edge:', error);
      },
    });

    // The stream is returned as a standard Web Response
    return new Response(stream, {
      headers: { 'Content-Type': 'text/html' },
    });
  } catch (error) {
    // A fallback in case an error occurs before the shell can even be generated
    console.error('Fatal error before shell was ready:', error);
    return new Response('<h1>Internal Server Error</h1>', {
      status: 500,
      headers: { 'Content-Type': 'text/html' },
    });
  }
}

But as a developer, you rarely touch these internal APIs directly. It is frameworks like Next.js and TanStack Start that use them internally to provide streaming solutions.

Let's see them in action:

With Next.js

In Next.js, streaming is automatic via the App Router. You simply use Suspense anywhere in your component hierarchy. Next.js handles the heavy lifting, replacing your fallbacks with content as it streams in. (If you want a deep dive on this, check out my "15 Days of React Design Patterns" course on the tapaScript YouTube channel).

import { Suspense } from 'react';

// 1. A slow Server Component fetching asynchronous data
async function SlowAnalytics() {
  // Simulating a slow Analytics API response
  await new Promise(resolve => setTimeout(resolve, 3000));
  
  return (
    <div className="p-4 border rounded shadow-md">
      <h3>Analytics Data</h3>
      <p>Views: 1,200 | Clicks: 340</p>
    </div>
  );
}

// 2. The Skeleton Fallback (Your static placeholder)
function AnalyticsSkeleton() {
  return (
    <div className="p-4 border rounded shadow-md animate-pulse bg-gray-100">
      <div className="h-6 bg-gray-300 rounded w-1/3 mb-2"></div>
      <div className="h-4 bg-gray-300 rounded w-1/2"></div>
    </div>
  );
}

// 3. The Page combining the static shell and streaming boundaries
export default function DashboardPage() {
  return (
    <main className="p-8">
      {/* This static shell renders instantly */}
      <h1 className="text-2xl font-bold mb-6">Dashboard</h1>
      
      {/* The Suspense boundary catches the slow component and streams the skeleton first */}
      <Suspense fallback={<AnalyticsSkeleton />}>
        <SlowAnalytics />
      </Suspense>
    </main>
  );
}

With TanStack Start

While Next.js focuses on streaming UI, TanStack Start focuses on streaming typed data through server functions.
Using JavaScript async generators, you can stream actual TypeScript-based objects. Instead of the client receiving a black-box HTML swap, it consumes a stream of typed chunks via a for await...of loop. You get full autocomplete and type safety for every chunk that arrives. It’s explicit, linear, and incredible for data-heavy applications.

import { createServerFn } from '@tanstack/start';

// 1. The Server Function using an async generator
export const streamAnalyticsData = createServerFn(
  'GET',
  async function* () {
    const dataChunks = [
      { id: 1, metric: 'views', value: 1200 },
      { id: 2, metric: 'clicks', value: 340 },
      { id: 3, metric: 'conversions', value: 12 }
    ];

    for (const chunk of dataChunks) {
      // Simulate slow asynchronous database fetching
      await new Promise(resolve => setTimeout(resolve, 500));
      
      // Yield strictly typed chunks to the stream
      yield chunk; 
    }
  }
);

// 2. Consuming the stream on the Client Side
async function loadAnalytics() {
  const stream = await streamAnalyticsData();
  
  // The real win: full autocomplete and type safety for every chunk
  for await (const chunk of stream) {
    console.log(`Metric: ${chunk.metric}, Value: ${chunk.value}`); 
    // Update your UI state with the new chunk here
  }
}

The Catch: Error Handling in a Stream

There is one major gotcha. The moment the server sends the first chunk of the static shell, it logs an HTTP 200 OK status code. You cannot change an HTTP status code mid-stream. If a component deep down the tree throws an exception or hits a 404, you can't go back and change the header to a 400 or 500.

This is why you must wrap your Suspense boundaries in an Error Boundary. If a stream fails midway, the Error Boundary catches it on the client side and displays the appropriate fallback error UI, keeping the rest of the application stable.

The Impact on Web Vitals

Streaming isn't just a cool developer trick; it directly improves your Core Web Vitals:

TTFB (Time to First Byte): Drops significantly because the static shell is sent instantly.
FCP (First Contentful Paint): Improves dramatically because the UI is decoupled from slow data fetching.
INP (Interaction to Next Paint): Selective hydration yields to the browser, making the page highly responsive to user inputs.
CLS (Cumulative Layout Shift): Warning! You must ensure your Suspense fallbacks (skeletons) match the exact size of the incoming data; otherwise, your layout will shift when the data arrives.

Wrapping Up

Streaming SSR bridges the gap between fast initial loads and rich, data-driven applications. It respects the user’s time and provides a drastically better experience.

If you want a visual summary to keep at your desk, I’ve put together a cheat sheet comparing Traditional SSR and Streaming SSR. You can grab it here.

Did you learn something new today? Let me know in the comments, and if you found this helpful, consider sharing it and connect with me for more fundamental engineering deep dives:

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See you soon with my next article. Until then, please take care of yourself and keep learning.

Goodbye, Slow-Motion Tortoise: A Deep Dive into SSR Streaming

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The Problem with Traditional SSR

The Uncanny Valley of Hydration

The Hero of the Story: Chunked Transfer Encoding

The Fluid System under the Hood

How It Works in Modern Frameworks

With Next.js

With TanStack Start

The Catch: Error Handling in a Stream

The Impact on Web Vitals

Wrapping Up

Command Palette

Comments (1)

More from this blog

The Problem with Traditional SSR

The Uncanny Valley of Hydration

The Hero of the Story: Chunked Transfer Encoding

The Fluid System under the Hood

How It Works in Modern Frameworks

With Next.js

With TanStack Start

The Catch: Error Handling in a Stream

The Impact on Web Vitals

Wrapping Up