Hi, I’m Tony, and I’m an engineer on League. This article is a followup to my performance series, where I talk about optimisation and profiling. This will be a high level overview of how we monitor game performance in League of Legends, how we detect when a performance degradation has slipped through QA and escaped into the wild, and how we track global trends in frame times over many patches and millions of players. I hope you enjoy it!

For the past 8 years, League has been using a patching system called RADS (Riot Application Distribution System) to deliver updates. RADS is a custom patching solution based on binary deltas that we built with League in mind. While RADS has served us well, we felt we had an opportunity to improve some key areas of the patching experience. We knew we could deliver updates much more quickly and more reliably by using a fundamentally different approach to patching, so we set out to build a brand new patcher based on content-defined chunking.

Imagine you're a web developer. You have to create an app that leverages five different data sources. The project has 26 different views built in - and don’t forget to make it fast, easy to test, and reliable. I work on creating the tools that make this kind of app as easy as possible for developers to build and manage. My name is Matthew Drake and I’m a member of the Riot Data team.

So, why do Bug Blogs? Well, at Riot, we believe that a fundamental component of the development process is understanding that failures can and do happen. We make mistakes because we’re humans, and humans are far from perfect. And that’s okay! What’s important is to recognize failures for what they are, work to understand why they happened, learn from them, and make needed changes based on those learnings. That’s how we grow and that’s how we get better.

Hi, I’m Tony Albrecht, an engineer on League. I’m back with some more performance goodness. In the previous article, we tracked a slowdown reported in an LCS game and an issue with Swain that was first reported by players. For this article, we’re stepping back a little further and looking at how we can measure the aggregate performance of League over an entire region, notice a dip, and then narrow down the cause. In this case, we chased a 2ms per frame drop in performance to a single missing ASCII character in our code.

Hey there. I’m Stephen “Riot FloofyRice” Zhang and I’m a technical developer in QA on the Content Efficiency team, which focuses on building internal tools. I want to talk about our efforts to clean up one of our bigger problems at Riot: data debt.

Hi, I’m Tony Albrecht, an engineer on League. You might remember me from such articles as Profiling: Measurement and Analysis and Profiling: Optimisation where we looked at how we find and optimise performance bottlenecks in the LoL code base. In this article, we’re going to take a step back and look at how we detect and then fix real world performance issues that slip out past our QA and monitoring systems and escape into the wild to plague you, The Player.

Hi, I’m Tony Albrecht, an engineer at Riot, and I’m a performance junkie. This is the second part in a series on how to optimise your code. In Part 1, Measurement and Analysis, we learned how to find and analyse performance bottlenecks in our sample code. We surmised that the issue was due to slow memory access. In this article we’ll look at how we can reduce the cost of memory accesses and thereby speed up our program.

The League of Legends client UI resembles many traditional web applications, both in its choice of technology (JavaScript/HTML) and its range of functionality. However, the League client (the pre-game experience) is not a traditional web app - at its core it’s a game UI with high-quality visuals, which led to several interesting challenges along the way. I’m Richard Ye, a software engineer on the League of Legends client, and I’m going to talk about one of the major development challenges we encountered in the client UI: animation.

Hi, I’m Tony Albrecht, an engineer at Riot, and I like profiling and optimising things. In this article, I’m going to walk you through profiler basics and analyse some example C++ code as we profile it on a Windows machine. We’ll start at a very high level and delve deeper and deeper into the bowels of the CPU step by step. Once we identify opportunities for optimisation in this article, we’ll implement changes and explore some real-world examples from the League of Legends codebase in a future article. Let’s begin!