知鸦日报2021-08-07

本文先讲解研究纹样提炼规则时的心路历程，引出纹样的规则，然后结合“实现模型”和“心理模型”的两种设计方案是如何融合规则在设计层面的，最后用随机生成剖析用户心智。

Zippy DB: How Facebook built its general purpose key value store.

Re-ordering table columns to avoid wasting space for data type alignment.

Braintree Payments uses PostgreSQL as its primary datastore. We rely heavily on the data safety and consistency guarantees a traditional relational database offers us, but these guarantees come with certain operational difficulties. To make things even more interesting, we allow zero scheduled functional downtime for our main payments processing services.

Uber delivers efficient and reliable transportation across the global marketplace, which is powered by hundreds of services, machine learning models, and tens of thousands of datasets. While growing rapidly, we’re also committed to maintaining data quality, as it can greatly impact business operations and decisions. Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data. In the worst cases, degradations could go unnoticed, silently resulting in inconsistent behaviors.

This led us to build a consolidated data quality platform (UDQ), with the purpose of monitoring, automatically detecting, and handling data quality issues. With the goal of building and achieving data quality standards across Uber, we have supported over 2,000 critical datasets on this platform, and detected around 90% of data quality incidents. In this blog, we describe how we created data quality standards at Uber and built the integrated workflow to achieve operational excellence.

In May 2020, Pinterest launched a partnership with Shopify that allowed merchants to easily upload their catalogs to the Pinterest platform and create Product Pins and shopping ads. This vastly increased the number of shopping ads in our corpus available for our recommendation engine to choose from, when serving an ad on Pinterest. In order to continue to support this rapid growth, we leveraged a key-value (KV) store and some memory optimizations in Go to scale the size of our ad corpus by 60x.

Decorator 装饰器是 ECMAScript 的语言提案，目前还处于 stage-2 阶段，但是借助 TypeScript 或者 Babel，已经有大量的优秀开源项目深度用上它了，比如：VS Code, Angular, Nest.Js(后端 Node.js 框架), TypeORM, Mobx(5) 等等。

鉴于前端正在向多元化方向发展，场景愈加复杂，生产环境也经常会出现一些本地环境和测试环境无法复现的问题。而且，用户的具体行为和操作我们也不可预期，后端错误监控又只能监测到发起请求后的错误日志，那么错误发生时我们就只能靠用户反馈。

随着贝壳找房在产业互联网领域不断深耕，各项业务都在持续高速发展，业务功能和复杂度不断增长的同时，带来的问题是 App 启动变慢，用户体验变差，如果没有收口处理，开发团队每过一段时间都要花时间进行优化。

冷启动优化是性能优化的重要一环，其重要性毋庸置疑，启动的快慢是一个 App 给人的第一印象，对于 C 端 App 甚至会影响用户的留存。每个客户端研发对冷启动优化基本都耳熟能详，但往往知识点是零散的，不够系统和全面，以至于优化只是根据自己熟悉的部分做了几点，效果不是特别明显。我们首先要明确目标，在高目标的驱动下，传统思维方式的转变、许多近乎苛刻的优化方法和全面的防劣化手段就是题中应有之义。

本文将从优化思想到技术实现的细节讲解，让你体系化地了解启动优化过程的设计和实现。值得强调的是，本文并不会面面俱到，对一些耳熟能详的优化方法，比如+load的治理、二进制重排等，网上已经有很多资料，读者可以自行查阅。这里着重介绍对较大收益的方案系统化落地、大家容易忽视的一些优化方法以及对优化成果进行防劣化治理。

本文基于2021年GMTC全球大前端技术大会"移动技术新趋势"专题下主题分享《VR 及 3D 技术在 Web 端架构设计与实践》整理而来。内容与当日分享基本无异，仅以文字的形式呈现。

VR 看房是 VR 及 3D 技术落地的场景之一，其特点是通过手机终端就能让人真正的置身其中，用自己直觉的空间感去感受整个房屋特征。本次分享将介绍贝壳如视前端团队是如何基于 VR 3D 模型进行前端架构设计的。除此之外，还将分享如视是如何基于 VR 看房能力探索新的业务形式以及面临的技术挑战。