知鸦日报2022-01-20

Capacity is a key component of reliability. Uber’s services require enough resources in order to handle daily peak traffic and to support our different kinds of business units. These services are deployed across different cloud platforms and data centers (“zones”). With manual capacity management, it often results in an over-provisioned capacity, which is insufficient for resource usage. Uber built an auto-scaling service, which is able to manage and adjust resources for thousands of micro services. Currently, our auto-scaling service is based on a pure utilization metric. We recently built a new system, Capacity Recommendation Engine (CRE), with a new algorithm that relies on throughput and utilization based scaling with machine learning modeling. The model provides us with the relationship between the golden signal metrics and service capacity. With reactive prediction, CRE helps us to estimate the zonal service capacity based on linear regression modeling and peak traffic estimation. Apart from capacity, the analysis report can also tell us different zonal service characteristics and performance regression. In this article, we will deep dive into CRE’s modeling and system architecture, and present some analysis of its results.

In recent years, Identity and Access Management has gained importance within technology industries as attackers continue to target large corporations in order to gain access to private data and services. To address this issue, the Grab Identity team has been using a 6-digit PIN to authenticate a user during a sensitive transaction such as accessing a GrabPay Wallet. We also use SMS one-time passwords (OTPs) to log a user into the application.

We look at existing mechanisms that Grab uses to authenticate its users and how biometric authentication helps strengthen application security and save costs. We also look at the various technical decisions taken to ensure the robustness of this feature as well as some key learnings.

如何保证开发数据的周期，如何更快的承接需求，如何更高效的计算指标，减少人效是值得深思的地方。

背景：公司的云his静态项目代码量巨大，依赖的npm包大概有100个，打包一次大概要14分钟。利用webpack5的持久化缓存提升构建效率。

Ideas fuel innovation. Innovation drives our product toward our mission of bringing everyone the inspiration to create a life they love. The speed of innovation is determined by how quickly we can get a signal or feedback on the promise of an idea so we can learn whether to pursue or pivot. Online experimentation is often used to evaluate product ideas, but it is costly and time-consuming. Could we predict experiment outcomes without even running an experiment? Could it be done in hours instead of weeks? Could we rapidly pick only the best ideas to run an online experiment? This post will describe how Pinterest uses offline replay experimentation to predict experiment results in advance.

本文前半部分重点阐述事件驱动架构的定义和重要概念，以及架构设计的场景和原因分析，后半部分简单描述事件驱动架构在内容平台的落地。

本篇文章介绍vivo平台的建设背景、已经建成的能力、建设过程中的一些思考和探索。

使用CSS实现一种接近迷雾的遮罩效果。

百度广告业务系统建立在分布式系统之上，面向商业服务，每天发生各类接口调用PV达到百亿次，产生TB级的监控数据，对监控系统的设计也提出了巨大的挑战。分位值对接口性能的敏感度高，在性能分析中具有很大价值。

本文简要介绍了之家云多数据库管理平台产生的背景，目前支持的数据库类型及为用户提供的便利服务，并对未来发展做了规划。

本人是作者对工作中现有业务的领域驱动重构得出的心得,还有在领域驱动模型上进行需求迭代的经验讲解。

本文基于行程平台的中台化进行了一些中台化建设的思路和感想，更多是对中台的定义以及一些框架的使用。

故事发生在一个内部项目上，这个项目有 Go 和 Rust 两个部分，其中 Rust 库作为一个存储组件被 Go 的业务部分依赖着。出于种种原因，两个部分需要作为同一个进程来运行，中间有一层 C FFI 接口作为理想与现实的桥梁。

Rust 部分首先将使用到的 Library 的接口和结构使用另一个小小的 cdylib [1] shim 项目封装一下，并通过这个项目生成 Rust 库的编译产物一个动态链接对象和 C 的接口定义。再基于 C 的定义写一个 Go 的 SDK 给上层使用。单独的左边 Go 或者右边 Rust 项目都不够刺激，本文主要集中在中间那一团麻花的部分上，也是我 too young too simple 掉了很多头发的地方。