satori VS sharp

View on GitHub

I've used satori [0] on the backend with TypeScript/Deno to render JSX as an SVG (which is then rendered to a PNG).

Satori is meant for rendering Open Graph images (e.g. the little images that come up when you post a link on Twitter/Slack/Facebook), but I found that it works well for rendering arbitrary images. It supports a subset of modern CSS, including flexbox.

My use case is posting match reports for League of Legends into a Discord text channel, e.g. person X just played a match, here are their stats.

It's quite nice because there are almost zero server-side native dependencies (the one exception is the library to convert svg -> png requires some native libraries).

Here's what a match report looks like: [1]

Here's an example of what the JSX looks like: [2]

[0]: https://github.com/vercel/satori

[1]: https://github.com/shepherdjerred/glitter/blob/main/assets/p...

[2]: https://github.com/shepherdjerred/glitter/blob/main/packages...

Another way is to write HTML/CSS and use satori [0] to convert that to SVG. It's meant for Open Graph images (the images that show up when you link a site in Discord, Slack, Twitter, etc.), but it works quite well for anything.

This is obviously not as flexible as true SVG, but it is familiar to author for anyone who's written a React application. I've used it on the backend to generate match reports for League of Legends [1]

[0]: https://github.com/vercel/satori

[1]: https://github.com/shepherdjerred/glitter-boys

Install the @vercel/og package. This library is designed to convert React code into PNG images. It is built on Satori, a library that converts HTML and CSS into SVGs.

Examples are available in the Vercel OG Playground.

In this version, we no longer use Puppeteer to capture HTML and return images. Instead, we utilize the @vercel/og library, which employs Satori as its core engine. Satori is a library that converts HTML and CSS into SVG.

This is made possible thanks to the Dynamic Open Graph Image Generation feature introduced with Next.js version 13.3, and the new Metadata API. In summary, it involves generating images using code (in our case, TSX, HTML, and CSS) with the help of the libraries @vercel/og (already integrated in the App router) and Satori. Satori converts HTML and CSS to SVG, and then resvg-js converts the SVG to a PNG image. All of this in just a few milliseconds!

Here you are returning an ImageResponse instead of the Response, alternatively you can also extend the request and response web api using 'NextRequest' and 'NextRespone', to do that you can import them using import { NextResponse, NextRequest } from 'next/server';, though for this example it is not required. Now if you refresh your browser you will get an image generated by your 'route.js' at request time. Well we are almost done. You can render whatever dynamic data in your image you want and customize your image using og playground, you can even generate 'SVG' on request as the og image. For this example we will fetch a random number from random.org api, then we will use that number as an id and fetch an image from Lorem Picsum, with the same image url we will fetch the description for the image from the Alt Image Generator and generate an image on request with the image that we fetched and the description we have fetched and use it in a design to create the og image. Kind of like that.

Use Vue files to generate SVG images by Satori. The image can be generated by running ESM script or CLI.

I'm not familiar with it but looks like it's made with nodejs, node uses the same js engine used by chrome, node renders the template and converts it to plain html/css and then they use this library to convert it to png but in the library github page it says that they don't support everything so it's kinda similar to xhtml2pdf or weasyprint

Last thing left is to use our new upload function in our server action. Since I like to upload images in single format and have some more control over them, I will additionally use sharp library. For file name, I'll generate some random string using nanoid:

blur is a technique to blur images while reducing the file size surprisingly. blur works by enlarging the pixels of the image, which reduces the details of the image, and the number of colors also decreases, thus saving storage space. Sharp is a popular image processing library in Node.js, and it supports the blur function. After going through the blur function, the image size at this point is only a few KB, which is reasonable for an image placeholder in the article.

sharp

For simplicity, I will be generating PNGs with JavaScript/Node and the Sharp image library. Any library that can convert between pixel arrays and image files will make the process quite straightforward.

There is also a library that Next.js itself uses: sharp. It can be setup as Node.js service. I even played around a little: image-proxy-service

Sharp Documentation: Link

ESLint does an amazing job in detecting floating promises. I've not had it miss one, ever. When adding this to a project, I've discovered multiple accidental bugs due to a missing "await" keyword--bugs that were extremely subtle and intermittent in many cases.

The only thing it can't do is determine that you actually did handle the promise later. Which is fine. It's a LINTING RULE, and false positives are the name of the game.

What's BAD is when you accidentally miss handling a promise at all. It's an invisible error without the linting rule.

Your other comments...don't even make sense. You're going to build a Lanczos filter by hand? Or you're only going to ... compile ImageMagick to WebAssembly?!, ... an implementation which is tremendously slower (nearly unusably so for large images) than that of Sharp:

https://www.npmjs.com/package/sharp

... which is simply an import away?

No, what you're doing is called "motivated reasoning." You've concluded that Deno is the best, and you're reinterpreting all of my complaints in convoluted ways to support your predetermined conclusion.

Standard fanboy behavior. Or troll behavior. I cite Poe's Law as why it's impossible to tell the difference.