Beyond Virtual Lists: How to Render 100K Items with 100s of Updates/sec in React

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There is generally a good understanding on how to render large (say, 100K items) datasets using virtual lists, …if they remain largely static. But what if new entries are being added or updated at a rate of hundreds per second? And what if the user should be able to filter and sort them freely? How can we stay responsive in such scenarios? In this talk we discuss how Flipper introduced map-reduce inspired FSRW transformations to handle such scenarios gracefully. By applying the techniques introduced in this talk Flipper frame rates increased at least 10-fold and we hope to open-source this approach soon.

Michel Weststrate
Michel Weststrate
27 min
22 Oct, 2021

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Video Summary and Transcription

The Talk discusses optimizing rendering of big tables using Flipper, a new version that is ten times faster with improved user interaction and richer data. It explores optimizing rendering with React, virtualization, filtering, sorting, and windowing techniques. The introduction of the Flipper Datasource packet simplifies handling updates, inserts, and removals. The performance of the Flipper data source package is excellent, even in a debug build of React, with minimal CPU usage. The Q&A session covers incremental sorting, dynamic row height, and the potential for two-dimensional virtualization in the future.

1. Optimizing Rendering of Big Tables with Flipper

Short description:

I'm Michel Vestrater from Facebook, and today I'll talk about optimizing rendering of big tables using Flipper. The original implementation was slow, especially with filters and line wrapping issues. We built a new version that is ten times faster, with improved user interaction and richer data. The performance improvement was significant, with a ten times frame drop and reduced CPU usage.

Good morning, everyone. My name is Michel Vestrater. I work at Facebook, I think, because it might have another name today. But it's good to be here and it's very good to see all your faces without also seeing your bedroom and your laundry and your empty bowl of porridge. Thanks for having me.

I'm working currently on a project called Flipper which is an open source platform which is used primarily by mobile developers. And I'm going to talk about how to optimize rendering of really big tables. And in this talk, I'll feature a little bit about Flipper. I'll find an actually useful application of bitcoins and I will show you an open source library.

So first of all, there's a tool called ADB which is used for debugging mobile applications. And if you run, for example, ADB log, it just generates a lot of data. It can easily generate up to 100 lines per second or something. And so we're working on Flipper, a tool that allows you to like inspect layouts in a mobile device and see the network requests of your React Native application and so forth and so on. However, we're going to focus in this talk on one specific feature, which is the logs. And the logs, they basically show the very same output as you just saw from ADB, except that we parsed it so it can sort a filter.

Now, the original implementation we had isn't very fluent. Like it starts a bit, it doesn't really keep up, and especially if you apply some filters, it kind of slows down. And also, it doesn't properly wrap lines, which is really annoying if you use logs. So we figured that this is kind of too slow, and actually what we detected, even in production builds, if the filter is slightly complicated, and you have 100,000 log lines in there already, then a single render pass of our React component could easily eat up to 250 milliseconds, so you end up with four frames per second, which is really annoying.

And so we figured we needed a better implementation, and we built one. And so this is the new version, which is much more smooth, but actually keeps up with the data that arrives over ADB, and even if you're filtering, it does barely affect performance. And we made the user interaction a lot better as well, so we have links that are highlighted, lines wrap automatically if you resize the window, that kind of stuff. So we made the data a lot richer, and we even added things like being able to sort on any column you want.

And so how did this affect performance? And it turns out the performance of the new implementation was ten times faster. And we saw that so on Facebook, we measure like what the performance is, how many frames we drop, what CPU load is for our users, and we saw basically a ten times frame drop, and a much slower usage of CPU. And if you look at the profile in Chrome, we can actually explain that. And so the left two sections are basically the old situation where there's a lot of CPU going on and most of it is yellow. And yellow means this is running JavaScript code, our code of Flipper. And in the new situation, there's still a lot of CPU usage but it's spent differently. It's no longer spent on the yellow part, no longer on the scripting.

2. Optimizing Rendering with React

Short description:

Now it's doing a lot of layouting and styling, and it means that it's cranking out much more frames per second. We set up a WebSocket connection to Coinbase and start listening for three different exchange rates. We introduce some state to store all the rows that arrive and apply filters and sorting functions. Then we render the rows one by one.

Now it's doing a lot of layouting and styling, and it means that it's cranking out much more frames per second. So, in short, purple is good. Yellow is bad. And so, how did we do that? And to explain it a little bit, I'm going to show you a small demo application which streams a lot of Bitcoin transactions so that we at least put all those wasted CPU cycles to good use. And I'm not as courageous as Sarah, so I just pre-recorded it. Anyway.

So let's start with the basis. What do we do? We set up a WebSocket connection to Coinbase and we start listening for three different exchange rates. And for every new message we arrive, we collect callbacks. And the data basically looks a little bit like this. Just an exchange of a certain rate. And so what happens here is that we're not really rendering at all. Sorry. I'm messing this up. So what Sarah can do with live coding, I can do with a video. Isn't that amazing? And so the idea here is that we don't render anything yet. And if you start streaming, you will see that we basically are receiving a thousand items per second. CPU isn't doing anything because we don't do anything with it. But this is our baseline. So conceptually, this is what is happening. So let's throw React into the mix.

So we have our little base components and we start to introduce some state in which we store all the rows that arrive. We also start with a base set of ten thousand rows to have something at least. And every time any row arrives, we update the state, allocate a new array and put it in there. And then if there's a filter active, we apply that to the dataset. And similarly, if the user wants to sort the sections by price, we apply a sorting function. So, then we map over all the rows and we render them out one by one. So if you run this... Oh, I think the presentation is broken when moving it over to a laptop. Anyway, that's what happens if you change it at the last moment.

QnA

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