知鸦日报2024-10-02

As Southeast Asia’s leading super app, Grab serves millions of users across multiple countries every day. Our services range from ride-hailing and food delivery to digital payments and much more. The backbone of our operations? Machine Learning (ML) models. They power our real-time decision-making capabilities, enabling us to provide a seamless and personalised experience to our users. Whether it’s determining the most efficient route for a ride, suggesting a food outlet based on a user’s preference, or detecting fraudulent transactions, ML models are at the forefront.

However, serving these ML models at Grab’s scale is no small feat. It requires a robust, efficient, and scalable model serving platform, which is where our ML model serving platform, Catwalk, comes in.

Catwalk has evolved over time, adapting to the growing needs of our business and the ever-changing tech landscape. It has been a journey of continuous learning and improvement, with each step bringing new challenges and opportunities.

The Rule Executor Service at Myntra stands out as a pivotal tool in the domain of rule engine. While, at a glance, one might wonder what sets it apart from the myriad of rule engines available today, the difference is profound. In Myntra ecosystem, supporting numerous pricing use cases necessitated building a capability that is engineered to handle vast volumes of rules, processing millions of facts against similar scale in terms of rules for multiple tenants with high reliability, performance, availability and consistency. In order for the system to be performant and use as a broker for all Myntra systems preference was given to scalability, and ability to persist and evaluate large large number of rules with low latency evaluation window.

本文旨在探讨和分享多种预加载技术及其在提升网站性能、优化用户体验方面的应用。

最近，我在 emoji-picker-element 上遇到了一个性能问题：在一个接近 20k 个自定义表情符号的 Fediverse 实例上，打开表情符号选择器时，页面至少冻结了一秒钟，之后会卡顿一段时间。

与 Slack、Discord 等类似，在 Mastodon 或 Fediverse 的不同服务器上也可以有自己的自定义表情符号。对于 20k 大小的自定义表情符号虽然不常见，但也并非不会出现。