How Much RAM Is Your UseMemo Using? Let’s Profile It!

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Memoize all the things!, is what most React guides will tell you, explaining how its memory cost is negligible compared to its performance benefits, but has anyone actually ever measured it? What about doing it in a complex React application with several thousands components? Turns out getting that answer is not simple at all, and requires delving into the complex world of Chrome Memory Profiling. This talk will explore the basics of heap profiling, moving on to more advanced tools and techniques for analysing memory usage in your React application, including how to profile it in your CI pipeline and answer your own optimisation questions by writing custom heap analyzers.

Giulio Zausa‮
Giulio Zausa‮
20 min
12 Dec, 2023

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Video Summary and Transcription

Memory usage is often overlooked in web applications, but excessive memory usage impacts performance and user experience. It's important to analyze memory usage and optimize it, considering concepts like count versus size and shallow versus retain memory. The Chrome Memory Profiler and Memlab are useful tools for analyzing memory usage. By optimizing React components and using tools like Memlab, memory usage can be reduced significantly. React hooks can be expensive for large-scale projects, and memory analysis is a challenging task.

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1. Memory Usage in Web Applications

Short description:

Memory usage is often overlooked in web applications. We optimized a complex React app, but our memory usage became very high. Browsers limit memory usage, and excessive memory usage impacts performance and user experience.

Memory usage is a commonly overlooked part of modern web applications, with frameworks like React trading it for performance and ease of development. The reason why we do this is that normally web applications are simple enough. And memory in clients is plentiful. But is this always the case? And what do we do when it's not?

Hi, I'm Giulio Zauza, I'm a software engineer. And I wanted to bring you some lessons I learned the hard way while optimizing a complex React application. In fact I'm working on a complex web application called Flux. It's a browser-based electronics CAD tool which enables quick and collaborative electrical circuit and PCB design. Under its hood, it's a big-type script application that uses WebGL to render complex documents, and it's built using React, 3.js and Reactive Fiber.

We're working hard on performance as we wanted the application to be snappy and responsive but also scalable. We need to be able to support giant documents with thousands of components, each one made of dozens of complex shapes. Originally, we thought that frozen time during interactions and FPS were the things to optimize for, but we soon realized that that was actually just a part of the picture. When doing those kind of optimization, sometimes you trade memory usage for performance, which is a thing that in our case backfired. In fact, we built a rendering system using lots of memorization whenever it was possible, and we used caches to prevent re-renders, and we used a lot of pre-computations. This initially actually started making performance better, but at a certain point, it actually made things way worse. The memory usage was very, very high when opening larger projects and went over the gigabyte mark very easily. We did those optimizations because we followed what is still considered a best practice for performance with React, that is using memorization as much as possible. In fact, there is even a famous article that advocates for using React memorization for every known symbol value. What we found, though, is that using this strategy can become harmful as it will affect your memory usage in ways that are even unexpected.

Well, but you might ask, why do we care about memory usage? Especially now that clients have more RAM than ever. Well, there are three ways in which memory usage impacts your application negatively. The first is that browsers heavily limit your memory usage. Desktop Chrome, for example, has an hard memory limit around four gigabytes per tab. The moment you go over that limit, the browser will just kill your tab without any way for you to recover from it. And this limit gets even lower on mobile devices such as Safari, iOS, for example. The second reason is that the more memory usage waits on the garbage collector. Even if you're trying to optimize for speed alone, you will probably see a lot of entries in your time profiles related to garbage collection activities such as my major and minor GC. That is happening when too many memory allocations are being done in a short amount of time and the browser is forced to pause your application to take care of them. And the third reason is that using too much memory worsens the user experience. Many users are using their device for multiple things at once. And this means that if your web app is holding entire gigabytes of memory, you will make the user experience of everything else running on their client significantly worse.

2. Analyzing Memory Usage and Tooling

Short description:

It's important to keep an eye on memory consumption and optimize it. We'll focus on identifying memory usage and making distinctions. Transient memory usage can be hard to catch and may cause app crashes. Count versus size and shallow versus retain memory are important concepts. Different allocation types in JavaScript VM and JS code also take up space. Let's explore the available tooling for analyzing memory usage.

And because of those reasons, I would say that probably regardless of the type of application that you're building, it's always a good idea to keep an eye on memory consumption of your app and optimize it when needed, especially now that Chrome is starting to tell users the memory consumption of single tabs when you hover on the title of the tab. Imagine running a simple to-do list app and seeing half a gigabyte occupied by it. That's not a good impression, I would say.

So suppose that you are in one of those situations and you want to make things better for your application. How do you go about it? Well, the approach I commonly follow runs on three points. The first is that you need to identify what is taking up so much memory in your app. And then once you've found what is taking up too much RAM, you can use some strategies to optimize it. And lastly, you want to try to prevent those things from happening again in the future. And you can use automated memory testing in your CI pipeline for this. In this talk, we'll focus on the first point only, as the other two points are also very big and implementation-dependent.

When it comes to analyzing memory usage, I think it's useful to introduce some terms and make some distinctions first. The first one is about transient versus static memory usage. We call static memory usage the set of memory allocation that stays somewhat stable throughout the execution of the app, and it's the one that you would expect to find while taking a heap snapshot when your app is at the steady state. Transient memory usage, instead, it's when your app allocates a lot of memory at once and to release it shortly after, creating a peak in memory usage. This could be very hard to catch with only heap snapshot, and a very big peak could crush your app.

Another important distinction is count versus size. As you can have single units of allocation that occupies a lot of memory. But you can also have many smaller allocations, which alone are small enough. But together they fill up your RAM. In the second case, it can become more difficult to find them out and optimize them. We then have two terms that come up often in memory profiles, which are shallow and retain memory. As JavaScript relies on using nested data structures and pointers, there is a distinction between the size of an object itself versus the size that that object is pointing to. For example, we can have an array of 10 elements, each one being a different string with a million characters each. Because of their size, the string will occupy in total around 10 megabytes, whereas the array will just occupy a few dozen bytes. Since the array is containing and retaining those strings though, we will say that the retained size of that array would be 10 megabytes anyway, as it's the reason why the string has been kept in memory anyway.

The last thing that I think it's important to introduce are the different allocation types inside the JavaScript Visual Machine. Chrome, for example, makes the distinction between objects, arrays, strings, typed arrays, and it's also important to notice how even your JS code takes up space in memory. And by browsing the outputs of a memory snapshot, you can learn some very interesting things, like the fact that the JS function on its own can take up space as they were objects, since they need to keep track of their closures. So it's better avoiding creating functions in loops for this reason.

Okay, now that we established some terms, let's look at the tooling that there is available for analyzing memory usage.

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