Performance optimizations in Reagent – part 2

Have I mentioned that it’s a nightmare to install React Devtools in Electron? Turns out, it’s worth it. But it’s still a pain. If you want to try, you’ll need to first install the extension in a chrome-based browser (like Brave, the one I use) and then install it by code. At Chlorine, what I did was to copy the whole extension folder to a devtools directory and then changed the script to load electron to the script below (see the BrowserWindow.addDevToolsExtension):

const path = require('path')
const {app, BrowserWindow} = require('electron')

app.on('ready', () => {
  let browser = new BrowserWindow({
    width: 900, height: 600,
    webPreferences: {nodeIntegration: true}})
  browser.loadURL(path.join('file://', __dirname, '/index.html'))


Originally, the React Devtools was installed at ~/.config/BraveSoftware/Brave-Browser/Default/Extensions/fmkadmapgofadopljbjfkapdkoienihi/4.21.0_0 (tilde is my home folder). If you’re trying to install on Atom editor, you’ll have to open developer tools and issue the following script:

r = require('remote')
r.BrowserWindow.addDevToolsExtension('./devtools') // or the right directory

This command will fail, but somehow when you reload Atom, the React Devtools will be there. There’s only one feature that I use – highlight which elements were updated. And that’s where our performance tuning starts again.

The magical wonders of programming with pure data

I’ll start with a disclaimer – if you’re developing in ClojureScript because of performance, you’re doing the wrong thing. Although a Clojure code can be really close to Java performance, ClojureScript simply can’t match JS performance – specially because in Javascript, the fundamental data structures like arrays, sets, and objects are implemented in a faster language, and as of today, no “user space” data structure can get even close the “native” ones. And, let’s not forget that JS is slower than Java.

So, if you want to beat JS performance in ClojureScript, you have to make use of what ClojureScript offers you – the REPL, the fast experimentation, and the wonderful experience of programming with data. So, this is a tale of a huge performance improvement that I’m currently working in Chlorine.

Rendering EDN

In Chlorine and Clover, when you evaluate something, the result will be rendered in a tree-like way at the console. This works quite well for Clojure, because UNREPL makes structures that are too big small, and you can query for more data. So far, so good.

In ClojureScript, Clojerl, Babashka, etc, things are not so good. Big data structures will be rendered fully on the view. They can lock or crash your editor, they can occupy all your memory, etc. The reason for that is that tree structures are hard – when you’re rendering the “parent”, you don’t know what the children will be. Currently, in Chlorine, rendering (range 80000) locks my editor for 4 seconds until it calculates everything, layouts all data, etc… and I wanted to change this.

Reagent vs Helix vs React

I was always intrigued on how much performance hit I get when I use Reagent’s Hiccup data structure instead of React objects – after all, there must be some performance problems, right? After all, that’s the promise of Helix – to not pay this performance hit because you’re closer to what React wants.

So, first things first: profiling Javascript inside Electron IS A NIGHTMARE. I decided to install React Devtools extension on Electron (inside the Atom editor) but that got me into so much trouble, false-positives, wrong profiles that I ended up deciding against it and simply used the “performance” tab to do my profiling. There, I could see batching.cljs hogging my performance, so the next move was easy – move away from Reagent and use Helix.

Except… that Helix uses some macros and I ended up doing the code in React. My testcase was simple: render a vector of 80000 elements and see how it would perform, obviously without all the bells and whistles that Chlorine offers today (otherwise this experiment would be waaay longer). And that’s where things get surprising: with Reagent, I was hitting 1800ms of scripting, and about 120ms of rendering. With React… 1650ms of scripting, and about 200ms of rendering. I decided to do more benchmarks and probably because of OS caching, warm-up, or whatever, the results got even closer, with Reagent sometimes performing better than React – but still too slow.

Clojure, reflection, and performance/memory issues

Right now, I’m working in a game project in Clojure. I don’t really know how it will turn out, but for now I’m just trying to learn a better way of making games.

While working in this project, I found out that my game was consuming a lot of memory. I’m using play-clj library, and I know that it creates a lot of small objects for each render cycle, so that was my first guess.

So, I plugged in a VisualVM in my running game to understand what was happening. In the beginning, nothing seemed to make sense: the heap grew, then was released, the correct and normal cycle of any Java application. Then, I tried a memory profiling and a memory dump. Then, things became interesting.

There were a lot of float[] objects popping up, as I would expect – play-clj uses floats to position elements on the screen, and all the time I found myself trying to coerce doubles to floats. But there was something even stranger there was consuming a lot of memory: instances of java.lang.Method.

For those who don’t know, Clojure interoperability with Java relies on reflection when it can’t resolve a type. To resolve a type means that Clojure can be certain that, at run time, that a specific identifier will be a specific type. So, for the following code:

(ns example.core)

(defn sum-abs [a b]
  (Math/abs (/ a (float b))))

(defn only-abs [a]
  (Math/abs a))

The first method call will use reflection because it knows that the result of a sum will always be a float. The second one has no idea if it will be called with a number or not, so it relies on reflection. It may seem strange, as we’re calling Math/abs, but remember that in Java we can have different methods with the same name, differing only on type signature.

So, to resolve the type, we’ll need type hints. But first, we can test if our code is using reflection using lein check.