知鸦日报2023-11-01

产品经理与用户体验设计师在工作中确实是有所交叉的，都需要围绕着产品与需求展开工作。但他们其实并不一样，工作的侧重点与工作内容多会有一定的差别。

Modern compute platforms are foundational to accelerating innovation and running applications more efficiently. At Pinterest, we are evolving our compute platform to provide an application-centric and fully managed compute API for the 90th percentile of use cases. This will accelerate innovation through platform agility, scalability, and a reduced cost of keeping systems up to date, and will improve efficiency by running our users’ applications on Kubernetes-based compute. We refer to this next generation compute platform as PinCompute, and our multi-year vision is for PinCompute to run the most mission critical applications and services at Pinterest.

PinCompute aligns with the Platform as a Service (PaaS) cloud computing model, in that it abstracts away the undifferentiated heavy lifting of managing infrastructure and Kubernetes and enables users to focus on the unique aspects of their applications. PinCompute evolves Pinterest architecture with cloud-native principles, including containers, microservices, and service mesh, reduces the cost of keeping systems up to date by providing and managing immutable infrastructure, operating system upgrades, and graviton instances, and delivers costs savings by applying enhanced scheduling capabilities to large multi-tenant Kubernetes clusters, including oversubscription, bin packing, resource tiering, and trough usage.

In this article, we discuss the PinCompute primitives, architecture, control plane and data plane capabilities, and showcase the value that PinCompute has delivered for innovation and efficiency at Pinterest.

This is the third and final post in our series on Meta’s Systematic Code and Asset Removal Framework (SCARF). SCARF contains a combination of subsystems that analyze code and data usage throughout Meta to aid in the safe and efficient removal of deprecated products. In our first post on automating product deprecation, we discussed the complexities of product deprecations and introduced SCARF’s workflow management tools that guide engineers through a coordinated process to safely deprecate products. In the second post on automating dead code cleanup, we discussed SCARF’s dead code subsystem and its ability to analyze both static and dynamic usage of code to automatically generate change requests to remove unused code. Throughout this series, we have referred to the example of the deprecation of a photo sharing app called Moments, which Meta launched in 2015 and eventually shut down in 2019.

In this post, we introduce the subsystem responsible for automating the identification and safe removal of unused data types at Meta. This process can be unexpectedly difficult, because large software systems are inevitably interconnected. Moments relied on several pieces of shared Facebook functionality and infrastructure, and deleting it was more complicated than simply turning off servers and removing data tables.

财务开发好陌生，是什么？好了，现在假如你五行属商家，并且就在淘宝上卖东西。当消费者买了你的东西，淘宝是不是需要给你结算这笔交易订单的钱，另外淘宝是不是还要收你一笔服务费？那财务开发的一部分职责，就是主要和作为商家的你打钱扣钱打交道的。

自动装配是 SpringBoot 的核心功能，主要是让开发者尽可能少的关注一些基础化的 Bean 的配置，实际上完成的工作是自动将 Bean 装载到 Ioc 容器中。

在深入探讨优化实践之前，让我们先了解一下 CLS（累计布局偏移）及其优化的重要性。

通过对端口的定义、原理、分类、利用方法进行分析并结合vivo互联网在端口安全体系建设的实践，分别从工具，管理及系统平台建设的角度对端口风险治理体系进行详细拆解。

弹性云的发展过程中，宿主机的生命周期管理是一个重要的问题。

SpringBoost是基于Spring框架开发出来的功能更强大的Java程序开发框架，其最主要的特点是：能使程序开发者快速搭建一套开发环境。SpringBoot能将主流的开发框架（例如SpringMVC,Dubbo,Mybatis,Redis等），做到像Maven导入Jar包一样的简洁快速，做到开箱即用。其中最关键的技术就是SpringBoot定制的各种Starter，通Maven引入Starter就能快速搭建开发环境。

以前单独使用SpringMVC Web编程框架时，我们需要单独配置DispatcherServlet和Tomcat，使用SpringBoot之后，我们只需要引入SpringBoot-Starter-Web就能直接开始编写Controller等Web相关的代码，这就是SpringBoot为们提供的开箱即用的便捷能力，下面就以SpringBoot-Starter-Web来说明SpringBoot自动配置的关键原理。

2022年12月19日，Twitch上出现了一个名为“vedal987”的新直播频道。该频道没有真人主播，只有一个可爱的二次元女孩形象在屏幕上移动和说话。她自称为Neurosama，是一位人工智能VTuber。

从那天起，她开始了自己的直播生涯，并迅速获得了大量关注和支持。与传统基于动作捕捉的中之人范式的虚拟主播不同的是，Neuro完全是由人工智能驱动的，准确来说是由背后大语言模型驱动的。

本文将介绍两款AI代码工具在研发流程中的使用场景、使用技巧以及提效能力分析。

作为线上定位问题和排查故障的重要手段，日志在可观测领域有着不可替代的作用。因此，日志系统需要追求稳定性、性能、成本、易用性、可扩展性等关键点。

目前我司的日志系统是基于ELK的，支持云主机、容器日志采集和特殊分类日志的综合采集等功能。但是随着公司的业务发展，日志应用场景逐渐遇到了一些瓶颈：

1. 数据增长和处理需求增加：业务的不断扩张和数据量的增加，原有的日志系统无法满足现有的数据处理需求。数据处理速度变慢，存储空间不足等问题。
2. 数据质量和可靠性要求提高：日志数据对于公司业务和运维至关重要，因此数据质量和可靠性要求越来越高。原有的日志系统存在日志丢失、日志收集慢等问题，需要进行改进。

现状：目前总共运行 8个 ES 集群，机器数量100+， Logstash 机器 50+，需要的硬件和维护成本很高，通过扩容的方法去满足业务场景，ES集群会太大会变动不稳定，创建独立集群，也需要更高成本，两者都会使得成本和维护工作量剧增。

鉴于这些问题，去年下半年开始探索新的日志系统架构，以彻底解决上面的问题。

我们有一个flink任务，消费的kafka的数据，写入到es，非常简单的逻辑，但是出现了数据丢失的情况。

往左调一点烫死，往右调一点冻死。