Brave’s answer, which it argues massively improves browser performance, is found in Rust, the Mozilla-hatched programming language that was in part created by Eich.
As ZDNet reported in June, developers of Chromium-based browsers like Opera, Brave and Vivaldi, didn’t support Google’s plans to cripple ad-blockers outlined under its Manifest version 3 proposal.
Brave now claims to have delivered a “69x average improvement” in its ad-blocking tech using Rust in place of C++. The improvements can be experienced in its experimental developer and nightly channel releases.
Eich told ZDNet earlier this month that Brave intended to support webRequest for all extensions in Brave, against Google’s Chromium plans to heavily restrict it while offering a knee-capped replacement.
We therefore rebuilt our ad-blocker taking inspiration from uBlock Origin and Ghostery’s ad-blocker approach. This focuses on a tokenization approach specific to ad-block rule matching against URLs and rule evaluation optimised to the different kinds of rules. We implemented the new engine in Rust as a memory-safe, performant language compilable down to native code and suitable to run within the native browser core as well as being packaged in a standalone Node.js module. Overall, we found that:
• The new algorithm with optimised set of rules is 69x faster on average than the current engine.
• For the popular filter list combination of EasyList and EasyPrivacy it achieves class-leading performance of spending only 5.7μs on average per request.
• An additional benefit of having the blocker built into the core of the browser is even less work duplicating what the browser already does, e.g. for URL parsing. With this information already available, our browser-focused API provides still better performance, cutting average request processing time down to 4.6μs.
• The new engine already supports more of the filter rule syntax that has evolved beyond the original specification, which will allow us to handle web compatibility issues better and faster.
For performance measurements in this study we used a 2018 MacBook Pro laptop with 2.6 GHz Intel Core i7 CPU and 32GB RAM.
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