知鸦日报2023-09-08

在商详页基于用户动线和VOC挖掘用户决策因子带来浏览体验提升；在物流侧洞察用户求助时间与实际物流停滞时长的关系制订表达策略带来物流产品满意度提升；在性能优化域构建主客观关联模型找到启动时长与负向反馈指标的魔法数字以明确优化目标；构建多源VOC标签体系综合运用用户行为和用户VOC洞察、落地体验优化策略，并总结出一套用户体验分析方法论。

Apache Kafka is an open-source distributed event streaming platform that is used for data streaming pipelines, integration, and ingestion at PayPal. It supports our most mission-critical applications and ingests trillions of messages per day into the platform, making it one of the most reliable platforms for handling the enormous volumes of data we process every day.

To handle the tremendous growth of PayPal’s streaming data since its introduction, Kafka needed to scale seamlessly while ensuring high availability, fault tolerance, and optimal performance. In this blog post, we will provide a high-level overview of Kafka and discuss the steps taken to achieve high performance at scale while managing operational overhead, and our key learnings and takeaways.

前段时间，通过监控观察到，我们业务中的某个服务突然出现goroutine数量、堆对象数量激增的情况，作为测试人员的我们跟着开发一步一步揭开了问题的真相，同时通过这个问题的排查也引发了我们的一些思考，如果你也有遇到过类似问题，毫无头绪的时候，欢迎阅读本文，跟我一起拨开层层迷雾，找到问题真凶。

给告警一个胶带，是“胶带”，也是“交代”。

经常看到集群的merge限流耗时比较高，所以想分析其原因、造成的影响、以及反思merge的一些优化手段。

本文主要分析了资源管理的现状与存在问题，在业务游戏化背景下，探索了符合社交直播业务发展的资源管理解决方案，并介绍了不同场景下的使用方式。

“DDD诊所”是Thoughtworks DDD社区的一项活动，通过对同事们在实施DDD过程中遇到的问题进行分析和解答，共同提高开发水平。我们将其中一些典型案例整理成文供大家参考。之后也会考虑在适当的时候将这一形式对外部开放。

作为一名研发人员，除了业务开发之外，研发提效是一个永恒的话题，而女娲正是这一话题下进行的一次全面的剖析和实践。

Note: This publication assumes you have basic familiarity with the service mesh pattern (e.g. Istio, Linkerd, Envoy — created at Lyft!)

The majority of ML models at Nextdoor are typically driven by a large number of features that are primarily either continuous or discrete in nature. The personalized features usually stem from historical aggregations or real-time summarization of interaction features, typically captured through logged tracking events. However, representing content through deep understanding using information behind it (text/image) is crucial for modeling nuanced user signals and better personalizing complex user behavior across many of our products. In the rapidly evolving field of NLP, utilizing transformer models to perform representation learning effectively and efficiently has become increasingly important for user understanding and improving their product experience.

Towards that, we have built a lot of entity embedding models spanning entities such as posts, comments, users, search queries & classifieds. We first leveraged deep understanding of content and used that to derive embeddings for meta entities like users based on their past interacted content. These powerful representations are found to be very crucial towards extracting meaningful features for some of the biggest ML ranking systems at Nextdoor such as notifications scoring and feed ranking. By making them readily available and building to scale, we can drive adoption of state-of-the-art reliably and put them in the hands of ML Engineers for rapidly building performant models across the company.

Pinterest’s mission is to bring everyone the inspiration to create a life they love. We rely on an extensive suite of AI powered products to connect over 460M users to hundreds of billions of Pins, resulting in hundreds of millions of ML inferences per second, hundreds of thousands of ML training jobs per month by just a couple of hundreds of ML engineers.

In 2021, ML was siloed at Pinterest with 10+ different ML frameworks relying on different deep learning frameworks, framework versions, and boilerplate logic to connect with our ML platform. It was a major bottleneck for ML innovation at Pinterest because the amount of engineering resources spent by each ML team to maintain their own ML stack was immense and there was limited knowledge sharing across teams.

In today’s technology-driven world, privacy is a paramount concern for app developers. Whether it’s sensitive personal information, secure transaction details, or confidential corporate data, apps often handle data that should be protected from prying eyes.

One often overlooked privacy aspect is the snapshot of your app shown in the task switcher or taken when a screenshot is triggered. Unintentional exposure of sensitive data in these snapshots can lead to dire consequences.

As Flutter developers, we have the power to minimize these risks by implementing a simple yet effective feature — the “Privacy Screen.”

原来的巨型单体服务不断被拆解成一个个微服务，在方便功能复用及高效迭代的同时，也给运维带来了不少挑战。

众所周知，在现实世界中，每一个资源都有其提供能力的最大上限，当单一资源达到最大上限后就得让多个资源同时提供其能力来满足使用方的需求。同理，在计算机世界中，单一数据库资源不能满足使用需求时，我们也会考虑使用多个数据库同时提供服务来满足需求。当使用了多个数据库来提供服务时，最为关键的点是如何让每一个数据库比较均匀的承担压力，而不至于其中的某些数据库压力过大，某些数据库没什么压力。这其中的关键点之一就是拆分键的设计。

本文是快速成长系列第三篇《自我培养和培养他人》，会从人才培养的目标、缺失、系统、实施几个方面介绍人才培养的实践经验。

本文就团队管理聊管理，一个参照点是笔者带团队时的一些经验，另一个是管理方法论体系，争取做到理论和实际相结合。