公司：Grab
Grab（前身为MyTeksi）是一间在东南亚地区提供服务的技术公司和交通网络公司，总部位于新加坡，由陈炳耀和陈慧玲于2012年在马来西亚雪兰莪州八打灵再也创立的移动应用程序。该应用连结乘客和司机，提供载客车辆租赁及即时共乘的分享型经济服务。乘客可以透过发送短信或是使用移动应用程序来预约这些载客的车辆，利用移动应用程序时还可以追踪车辆的位置。疫情期间兼开始经营外卖、送货、电子商务等等，成为全方面的生活平台。
Debugging High Latency Due to Context Leaks
Learn how the Marketplace Tech Family debugged and resolved Market-Store's high latency issues.
Building a Hyper Self-Service, Distributed Tracing and Feedback System for Rule & Machine Learning (ML) Predictions
了解信托、身份、安全和安保（TISS）团队如何利用内部构建的解决方案Archivist改进机器学习预测。
Our Journey to Continuous Delivery at Grab (Part 2)
Read more about our long awaited piece on the automation work we have made through integration and hermeticity.
How We Improved Agent Chat Efficiency with Machine Learning
随着聊天的持续增长和新的内部工具的出现，帮助我们的代理更有效率和生产力是确保为我们的用户提供更快的支持时间并进一步扩大聊天规模的关键。
从对另一个第三方工具的使用情况进行分析，以及进行一些观察，我们意识到建立一个基于模板的功能不会有帮助。我们需要提供个性化的功能，因为我们的消费者支持专家关心他们的写作风格和语气，而使用模板往往让人觉得是机器人。
我们决定建立一个机器学习模型，称为SmartChat，它通过利用几个内部数据源提供上下文建议，帮助我们的聊天专家更快地打字，从而为更多的消费者服务。
在这篇文章中，我们将解释从问题发现到设计迭代的过程，并分享该模型是如何从数据科学和软件工程角度实现的。
Democratising Fare Storage at Scale Using Event Sourcing
Read how we built Grab's single source of truth for fare storage and management. In this post, we explain how we used the Event Sourcing pattern to build our fare data store.
How Grab Leveraged Performance Marketing Automation to Improve Conversion Rates by 30%
Read to find out how Grab's Performance Marketing team leveraged on automation to improve conversion rates.
One small step closer to containerising service binaries
了解Grab如何分析和减少Golang项目的服务可执行文件的大小。
Serving driver-partners data at scale using mirror cache
Find out how a team at Grab used Mirror Cache, an in-memory local caching solution, to serve driver-partners data efficiently.
Trident - Real-time event processing at scale
Find out where the messages and rewards come from, that arrive on your Grab app. Walk through scaling and processing optimizations that achieve tremendous throughput.
Pharos - Searching Nearby Drivers on Road Network at Scale
你有没有想过，当你叫车回家时，点击确认呼叫按钮会发生什么？这个简单的动作背后，发生了很多事情，如果要把所有的事情都说出来，要花上几天几夜的时间。也许，我们可以把这个问题重新表述得更精确一些。你有没有想过Grab是如何存储和使用司机位置来为你分配司机的？如果有，你一定会发现这篇文章很有趣，因为下面会介绍这背后的工作原理。
Democratizing Fare Storage at scale using Event Sourcing
Read how we built Grab's single source of truth for fare storage and management. In this post, we explain how we used the Event Sourcing pattern to build our fare data store.
Keeping 170 libraries up to date on a large scale Android App
Learn how we maintain our libraries and prevent defect leaks in our Grab Passenger app.
Optimally scaling Kafka consumer applications
Read this deep dive on our Kubernetes infrastructure setup for Grab's stream processing framework.
Our Journey to Continuous Delivery at Grab (Part 1)
Continuous Delivery is the principle of delivering software often, everyday. Read more to find out how we implemented continuous delivery at Grab.
Uncovering the truth behind Lua and Redis data consistency
Redis does not guarantee the consistency between master and its replica nodes when Lua scripts are used. Read more to find out why and how to guarantee data consistency.
Securing and managing multi-cloud Presto Clusters with Grab’s DataGateway
This blog post discusses how Grab's DataGateway plays a key role in supporting hundreds of users in our entire Presto ecosystem - from managing user access, cluster selection, workload distribution,…