知鸦日报2021-10-09

没有规模效应的事物，统统都会被地球淘汰。

叶军从ToB产品的第一性原理出发，以“ToB产品如何自传播”为题，结合钉钉上千行百业的数字化实践，分享钉钉这款现象级产品对于ToB的理解。

面对不同原因的数据异常，从监控角度我们怎么去管控呢？

Payments Team将讲述FAS水平扩展方案是如何设计实现的，以达到高容错、低风险和高性能的无缝扩展目标。

A deep dive on how we crafted an order of magnitude change in our spend (10x reduction compared to baseline growth) over the last two years with iterative understanding and changes in Slack’s Continuous Integration (CI) infrastructure.

How innovation in technology and people processes have enabled iOS developers to remain productive in a large codebase.

Consolidating long-running, lightweight tasks for improved resource utilization.

When it comes to the prediction of continuous variables, the first thing that comes to our mind is always the regression model. For instance, linear regression is the most commonly used regression model, and it has the benefits of simple implementation and high interpretability. On the other hand, random forest regression can handle missing data and is adaptive to interactions and nonlinearity. While these algorithms all work well for continuous target variables in different scenarios analytically, they provide less information on the predicted numbers’ confidence level, especially in real-world applications.

In this article, we will explore an unconventional framework to predict continuous variables with given confidence scores. Instead of framing the prediction as a regression problem, we twist the problem into a classification problem. This framework also allows us to have more visibility on the predicted results and can be adjustable to different confidence levels. The article will use revenue estimation as an example. Given a variety of business attributes for many businesses, we will illustrate how we can predict the revenue for each business given a specific confidence level.

Many impactful business problems need to be translated into corresponding machine learning tasks. For example, we must be able to recognize fraudulent credit card transactions to prevent loss for both our merchants and consumers. Credit card fraud detection is often translated to a classification task in machine learning.

Recommending the right financial products to the right customers is another important problem. One way to tackle product recommendation is by computing a propensity score (i.e. the likelihood of adopting a product for a user). Effective product recommendations and promotional strategies are often built based on product propensity scores. Propensity modeling, which determines the best product-customer pairs, is often translated into a classification task.

When our customers make purchases with credit or debit cards in their PayPal wallet, we send the payment details through card networks to card issuing banks for payment authorization. During this process, payments can be declined by the issuing banks for various reasons, such as the card: exceeding its credit limit, not having enough balance, being reported as stolen, or outdated information(see here for more information regarding issuer declines causes).

Apart from these reasons, issuers also consider many other factors when deciding to approve or decline a payment request, such as: what type of card is being used for the payment? What merchandise is being purchased? What time is the payment request submitted? What is the card’s approval and decline history from issuers?

身为前端工程师，势必听过这个问题：“Vue 与 React 有什么区别？” 的确，Vue和React都是目前最流行、生态最好的框架，整体的功能也非常相似。

对此，我们组织了一次 diff 主题分享，采用奇葩说的模式，针对同一功能讨论下 vue 与react 的异同。本月主题将围绕以下角度带大家更深刻的认识vue和react框架: 组件及逻辑复用，diff算法，编译构建及运行，渲染。

vue与react都推崇组件式的开发理念，那么我们第一篇从组件开始，话不多说，直接进入本篇的正题：vue 组件及逻辑复用的源码解析。

如何实现安全的数据流通，保护数据隐私并发挥数据的价值，支持多方的联合计算，是各大数据平台亟需解决的问题。而隐私计算技术旨在实现“数据可用不可见”的目标，具有广阔的应用前景。

AST 的遍历是怎么实现的。

前一段时间，我们对 webview 做了一期性能优化，在优化过程中，我们追求的是页面要足够「快」「稳」。那怎么定量地衡量这个「快」「稳」呢？性能指标帮助我们从数据化角度了解页面性能现状，性能瓶颈以及优化完成后，衡量优化效果。

本文基于自动化营销的业务高扩展性实践，分析介绍工厂模式、模板方法模式、策略模式以及状态模式这四种设计模式在营销策略引擎项目开发中的应用场景，同时进一步介绍了状态机和工作流引擎涵盖多种设计模式的系统组件应用，并分享了实践过程中的思考和总结。

WebFlux 是一款优秀的反应式框架，借助Reactor 实现了完全异步非阻塞的交互方式。

How did we experiment with a UX copy assessment to get the right name for a new resource in our platform.

本系列文章将讲解好大夫在线如何探索SRE落地的。

探索内核调试中，对于正在运行的内核，通过 systemtap 获取关键数据结构的值的通用方法。

使用 JuiceFS 客户端 mount 远端对象存储到本地机器路径，通过编写 ClickHouse 的存储策略，如同使用多卷存储一样使用远端对象存储。

Controller，是Apache Kafka的核心组件非常重要。它的主要作用是在Apache Zookeeper的帮助下管理和协调控制整个Kafka集群。

在整个Kafka集群中，如果Controller故障异常，有可能会影响到生产和消费。所以，我们需要对其状态、选举、日志等做全面的监控。

在达达，有大量的业务性质的配置（后文简称“业务配置”）被存放在研发配置中心，占到了配置中心所有配置的 20%左右，占比不高，但却给系统运维带来了巨大的成本。本文将介绍如何从0到1，构建达达快送自动化业务配置中心。

首先，为什么会出现业务配置添加到研发配置中心中的情况？在设计时，我们一般会从三点进行考虑。

• 投入产出比，在产品设计阶段，为一个不常用的功能，或是只是短期存在的业务去研发一个配置管理页面，其投入产出比相对较低。
• 研发资源不足，需求较大时，受到了研发资源的限制，将需求进行拆分为多期，先保证核心流程，二期或后续再完成配置页面相关的研发工作。
• 需求的紧急程度，业务需求倒排，本次需求要完成上线。评估后，要达成该上线目标，只能做出妥协，将业务配置临时添加到研发配置中心，后续再完成配置页面相关的研发工作。

达达从成立至今，已经7年。7年间，不断有业务配置被以研发配置的形式添加到研发配置中心。7年间，公司业务不断的增长，系统复杂度也不断上升，目前，公司在业务配置的维护上投入的资源巨大，已成为公司的一大痛点。

我们动用 perf，来追踪打开句柄的系统调用。

本文旨在介绍云测如何设计、技术实施、与实际业务结合的落地，提供云测在c端测试应用的实践干货。

“直男”叙事星座之“直”，并非耿直，而是直击本质。