The new Meme order: changing the game with simple browser caching

Memes are the lingua franca of the web. What began as a niche concept has evolved into a ubiquitous means of communication, with millions of meme images shared daily across every social media platform. But this explosive growth has come at a cost: the bandwidth demands of serving the same meme images over and over again are increasingly straining the web‘s infrastructure. It‘s time for a new approach.

The problem: redundant meme transfers

While the text of a meme may change with each incarnation, the underlying image often remains the same. This means that the same few kilobytes of meme image data are being transferred millions of times unnecessarily. For users in areas with poor connectivity and limited data plans, this is highly inefficient.

Just how inefficient? Let‘s look at some data. According to the HTTP Archive, the average image payload on websites is now nearly 1MB. While not all of this is memes, of course, memes are certainly contributing to the bloat. An analysis of popular meme generator sites shows that the median meme image size is around 50KB.

Now, consider that the top meme templates are used to generate hundreds of thousands of meme instances. If each of these instances includes the full 50KB image, that‘s a massive amount of redundant data transfer. Even with the web‘s increasing support for image formats like WebP that offer better compression, we‘re still talking about a significant performance tax.

A solution: leverage browser caching

Fortunately, we have a powerful tool at our disposal to mitigate this problem: the browser cache. By intelligently leveraging browser caching, we can drastically reduce the data transfer and load times for memes.

Here‘s how it works: rather than transmitting the entire meme image every time, we first cache the most frequently used meme templates in the user‘s browser. Then, for subsequent meme requests, we send only the unique text and a reference to the cached image. The browser can then instantly hydrate the meme by combining the cached image with the dynamically loaded text.

From a technical perspective, this approach involves a few key components:

1. A backend meme API that serves meme data in a format optimized for caching
2. Intelligent client-side caching logic to prefetch and store popular meme templates
3. Dynamic meme rendering on the client using techniques like HTML5 Canvas

Let‘s dive into each of these in more detail.

Backend API design

The first step is designing a backend API for serving memes that‘s optimized for caching. Rather than just sending complete meme image files, this API should provide the meme data in a more granular format.

A sample response payload might look like:

{
  "id": "abc123",
  "template": {
    "id": "badluckbrian",
    "url": "https://api.meme.com/templates/badluckbrian.jpg",
    "width": 600,
    "height": 600
  },
  "topText": "Lost an hour of work",
  "bottomText": "Forgot to save my document"
}

Here, the meme‘s immutable image data is separated from its dynamic text content. The template object contains a URL reference to the base meme image, which can be cached by the client independently of the text.

On the backend, this meme metadata could be stored in a database using a simple schema like:

CREATE TABLE memes (
  id TEXT PRIMARY KEY,
  template_id TEXT REFERENCES templates(id),
  top_text TEXT,
  bottom_text TEXT
);

CREATE TABLE templates (
  id TEXT PRIMARY KEY,
  url TEXT UNIQUE,
  width INTEGER,
  height INTEGER 
);

An API server (built with Node.js/Express, Python/Flask, Go, etc.) would expose RESTful endpoints for querying and creating memes backed by this database. The API response payloads would follow the structure outlined above, with the goal of enabling granular caching on the client.

Client-side caching and rendering

With the backend API in place, the next step is implementing intelligent caching and rendering logic on the client. The goal is to maximize cache hits for meme template images while still allowing the text content to be dynamically updated.

The core flow might look something like this:

1. On page load, the client queries the backend API for the most popular meme templates
2. The client prefetches and caches these template images using the browser‘s Cache API
3. When a new meme is requested, the client first checks if the template image is already cached
4. If cached, the client instantly renders the meme by hydrating the cached image with the dynamic text from the API
5. If not cached, the client requests the template image from the backend, caches it, and then renders

Here‘s some sample code illustrating this flow in JavaScript:

// Fetch and cache popular meme templates on page load
async function prefetchTemplates() {
  const response = await fetch(‘/api/templates/popular‘);
  const templates = await response.json();

  for (const template of templates) {
    const cache = await caches.open(‘meme-templates‘);
    cache.add(new Request(template.url));
  }
}

// Render a meme given its API data
async function renderMeme(meme) {
  const cache = await caches.open(‘meme-templates‘);
  const cachedResponse = await cache.match(meme.template.url);

  if (cachedResponse) {
    // Render from cache instantly
    const imageBlob = await cachedResponse.blob();
    renderMemeFromBlob(imageBlob, meme);
  } else {
    // Fetch, cache and render
    const response = await fetch(meme.template.url);
    const imageBlob = await response.blob();
    cache.put(meme.template.url, new Response(imageBlob));
    renderMemeFromBlob(imageBlob, meme);
  }
}

// Hydrate a meme image with dynamic text
function renderMemeFromBlob(imageBlob, meme) {
  const imageURL = URL.createObjectURL(imageBlob);
  const image = new Image();
  image.onload = () => {
    const canvas = document.createElement(‘canvas‘);
    canvas.width = meme.template.width;
    canvas.height = meme.template.height;

    const ctx = canvas.getContext(‘2d‘);
    ctx.drawImage(image, 0, 0);
    ctx.fillStyle = ‘white‘;
    ctx.strokeStyle = ‘black‘;
    ctx.lineWidth = 2;
    ctx.textAlign = ‘center‘;
    ctx.font = ‘36px Impact‘;

    ctx.fillText(meme.topText, canvas.width / 2, 50);
    ctx.strokeText(meme.topText, canvas.width / 2, 50);
    ctx.fillText(meme.bottomText, canvas.width / 2, canvas.height - 20);
    ctx.strokeText(meme.bottomText, canvas.width / 2, canvas.height - 20);

    document.body.appendChild(canvas);
  };
  image.src = imageURL; 
}

// Usage
prefetchTemplates()
  .then(() => renderMeme(myMemeFromAPI));

In this example, the client first prefetches and caches the most popular meme templates on page load using the Cache API. Then, when a meme needs to be rendered, it first checks if the template image is cached. If so, it instantly hydrates the cached image with the meme text using HTML Canvas. If not, it fetches the image from the backend, caches it for future use, and then renders.

Of course, this is just one possible implementation. The specific techniques used may vary depending on the frameworks and libraries in play (React, Vue, Angular, etc.). But the core concept remains the same: leverage browser caching to minimize redundant meme data transfer.

Performance and scalability analysis

So just how effective is this meme caching approach in practice? Let‘s do some napkin math to estimate the potential performance improvements and data savings.

Recall that the median meme image size is around 50KB. Let‘s assume a typical meme is viewed 100,000 times. Without caching, this would result in 5GB of total data transfer (100,000 views x 50KB per view).

Now, let‘s assume we implement the caching strategy described above, and achieve a 90% cache hit rate (i.e. 9 out of 10 meme views are served from the cache). In this scenario, we only need to transfer the full 50KB image 10,000 times, resulting in just 500MB of data transfer. The remaining 90,000 views can be served from cache, requiring only the transfer of the small meme text payload (less than 1KB per view).

Putting it all together, we have:

• Without caching: 5GB data transfer
• With caching (90% hit rate): 0.59GB data transfer
  • 500MB for 10,000 uncached image transfers
  • 90MB for 90,000 cached text-only transfers

That‘s nearly a 90% reduction in data transfer by implementing simple meme caching!

From a performance perspective, the gains are just as impressive. By serving the bulk of meme views from cache, we eliminate the latency of redundant image downloads. This means near-instant loads for the vast majority of meme traffic. Even for uncached views, the use of granular template image URLs allows the browser cache to stay maximally effective.

At scale, this adds up to massive performance wins. Imagine a meme platform serving hundreds of millions of meme views daily. Shaving just 100ms of load time off each of these views equates to years of collective time saved. In a web ecosystem where user attention is measured in milliseconds, this is an immense competitive advantage.

Further optimizations and extensions

While the basic meme caching approach described here is already quite effective, there are many potential optimizations and extensions to consider.

On the backend, techniques like edge caching and serverless functions could further improve the performance and scalability of meme serving. Rather than always hitting a central API server, popular memes could be served from CDN edge nodes close to the user. Serverless platforms like AWS Lambda or Google Cloud Functions are well-suited for the task of generating dynamic meme data on the fly.

On the client side, more sophisticated cache invalidation strategies could be employed to keep cached meme images fresh. For example, a hash of the meme template URL and text could be used as a cache key, allowing for automatic cache busting when the text changes. Service workers could be used to enable offline meme access and intelligent prefetching based on user behavior.

From an infrastructure perspective, a decentralized meme cache powered by blockchain technology is an intriguing possibility. By storing meme images and metadata in a distributed manner (e.g. on IPFS), we could create a more resilient and censorship-resistant meme ecosystem. Tokenized incentives (paid in MemeCoins?) could encourage participants to contribute storage and bandwidth to the network.

Looking further ahead, advances in machine learning will likely revolutionize meme generation and consumption. Generative language models like GPT-3 can already produce surprisingly coherent meme text; in the future, these models could be used to automatically generate entire memes from scratch based on current events and user preferences. Meme recommendation algorithms powered by deep learning will surface the most relevant content for each user.

The possibilities are endless, but one thing is clear: memes are here to stay as a dominant medium of online communication. As web developers, it‘s our responsibility to create the infrastructure to make meme transfer as efficient and scalable as possible. By leveraging browser caching and other performance optimization techniques, we can usher in a new era of lightning-fast, infinitely shareable memes – the new meme order.

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"The future is cached." –– anon, HackerNews comment