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公司:Uber

关联话题: 优步

优步(英语:Uber,/ˈuːbər/)是一间交通网络公司,总部位于美国加利福尼亚州旧金山,以开发移动应用程序连结乘客和司机,提供载客车辆租赁及媒合共乘的分享型经济服务。乘客可以透过应用程序来预约这些载客的车辆,并且追踪车辆的位置。营运据点分布在全球785个大都市。人们可以透过网站或是手机应用程序进入平台。

优步的名称大多认为是源自于德文über,和over是同源,意思是“在…上面”。 (页面存档备份,存于互联网档案馆)

然而其营业模式在部分地区面临法律问题,其非典型的经营模式在部分地区可能会有非法营运车辆的问题,有部分国家或地区已立法将之合法化,例如美国加州及中国北京及上海。原因在于优步是将出租车行业转型成社群平台,叫车的客户透过手机APP(应用程序),就能与欲兼职司机的优步用户和与有闲置车辆的租户间三者联系,一旦交易成功即按比例抽佣金、分成给予反馈等去监管化的金融手法。

2019年5月10日,优步公司透过公开分发股票成为上市公司,但首日即跌破分发价。

据估算,优步在全球有1.1亿活跃用户,在美国有69%的市占率。优步亦在大中华区开展业务,目前优步已在香港和台湾建成主流召车服务平台,并于中国大陆通过换股方式持有该市场最大网约车出行平台滴滴出行母公司小桔科技17.7%经济权益。

How Uber ensures Apache Cassandra®’s tolerance for single-zone failure

这篇文章介绍了Uber如何将现有的Cassandra集群从单区容错转换为单区故障容错。Uber通过添加新节点环、配置节点分布和分组、复制数据到新环、切换客户端连接等步骤实现了这一目标。他们还改进了驱动程序,实现了动态切换流量而无需重新启动客户端。为了实现无缝的流量切换,Uber创建了一个微服务来发布Cassandra集群的联系信息,并要求客户端选择与其所在区域相同的初始节点和协调器节点。此外,他们还解决了多机架设置中的挑战,确保了各个机架的容量均衡。

Debugging with Production Neighbors – Powered by SLATE

这篇文章主要介绍了基于微服务架构的软件开发过程中的调试选项,包括远程调试和本地调试。通过SLATE环境中的远程调试,可以在生产基础设施上进行只读调试,但存在动态修改的限制。为了填补本地调试环境和生产环境之间的差距,SLATE Attach引入了一个SLATE代理,将测试请求重定向到开发人员的本地开发机器进行调试和开发。文章还提到了SLATE Sniffer用于通过在生产环境中监控调试问题。

Personalized Marketing at Scale: Uber’s Out-of-App Recommendation System

文章介绍了Uber在个性化营销方面的工作。他们使用层次化配置合并和覆盖不同地理区域的策略,以提供个性化的推荐和营销。除了送货业务外,Uber还计划将个性化能力扩展到其他业务领域。

Flaky Tests Overhaul at Uber

Testopedia是Uber内部的一个测试工具,用于识别和处理测试中的不稳定性问题。它可以根据测试历史记录和运行结果,自动创建问题票据并将相关信息发送到JIRA。Testopedia还支持自定义配置,用户可以自定义分组规则、票据类型、优先级和描述模板。Uber还计划将GenAI等先进技术整合到系统中,以自动生成测试修复方案,并通过AI分析故障模式,将测试失败自动分类到更具体的子类别中,提高故障排查和解决的效率。通过在Uber的各个Monorepo中推广使用Testopedia,已经显著减少了不稳定测试的数量,提高了CI的可靠性。

Modernizing Uber’s Batch Data Infrastructure with Google Cloud Platform

Uber计划将其批量数据分析和机器学习训练堆栈迁移到Google Cloud Platform(GCP)。他们将使用HiveSync和Hudi库来实现在两个区域之间保持数据湖同步,并将本地数据湖的数据复制到云端数据湖和对应的Hive Metastore。迁移后,他们将在GCP上为YARN和Presto集群提供新的IaaS,并通过现有的数据访问代理将流量路由到云端堆栈。迁移过程中可能会面临性能、成本管理、非分析/机器学习应用使用HDFS和未知挑战等问题,但他们计划通过改进开源连接器、利用云的弹性、迁移其他文件存储用例以及积极解决问题来解决这些挑战。

How Uber Accomplishes Job Counting At Scale

文章讨论了在工作粒度方面的昂贵性以及提高性能和减少存储成本的方法。通过放弃工作粒度要求,可以显著提高读取吞吐量。

DataK9: Auto-categorizing an exabyte of data at field level through AI/ML

Data categorization–the process of classifying data based on its characteristics and essence–is a foundational pillar of any privacy or security program. The effectiveness of fine-grained data…

From Predictive to Generative – How Michelangelo Accelerates Uber’s AI Journey

In the past few years, the Machine learning (ML) adoption and impact at Uber have accelerated across all business lines. Today, ML plays a key role in Uber’s business, being used to make business…

DragonCrawl: Generative AI for High-Quality Mobile Testing

Quality and testing go hand in hand, and in 2023 we took on a new and exciting challenge to change how we test our mobile applications. Specifically, we are training ML models to test our applications…

Ensuring Precision and Integrity: A Deep Dive into Uber’s Accounting Data Testing Strategies

The financial accounting services platform at Uber operates at an internet scale– approximately 1.5 billion journal entries (JEs) per day and 120 million transactions per day via ETL and data…

Improving Uber Eats Home Feed Recommendations via Debiased Relevance Predictions

Uber Eats’ mission is to make eating effortless, at any time, for anyone. The Uber Eats home feed is an important tool for fulfilling this goal, as it aims to provide a magical food browsing…

Supercharge the Way You Render Large Lists in React

Rendering large lists in React can be a challenging task for developers. As the size of the list grows, the DOM (Document Object Model) tree also grows, leading to performance issues like slow…

Uber: GC Tuning for Improved Presto Reliability

Uber uses open-source Presto to query nearly every data source, both in motion and at rest. Presto’s versatility empowers us to make intelligent, data-driven business decisions. We operate around 20…

Palette Meta Store Journey

The Uber Michelangelo feature store, called Palette, is a database of Uber-specific curated and internally crowd-sourced features that are easy to use in machine learning projects. It comes to solve…

Stopping Uber Fraudsters Through Risk Challenges

As a marketplace-based, consumer-facing app, Uber encounters a multitude of sources of fraud across its platform. In one of the most common cases of fraud, bad actors use various methods to attempt to…

DataCentral: Uber’s Big Data Observability and Chargeback Platform

Discover real-time query analytics and governance with DataCentral: Uber’s big data observability powerhouse, tackling millions of queries in petabyte-scale environments.

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