In large organisations, it is a common practice to isolate the cloud resources of different verticals. Amazon Web Services (AWS) Virtual Private Cloud (VPC) is a convenient way of doing so. At Grab, while our core AWS services reside in a main VPC, a number of Grab Tech Families (TFs) have their own dedicated VPC. One such example is GrabKios. Previously known as “Kudo”, GrabKios was acquired by Grab in 2017 and has always been residing in its own AWS account and dedicated VPC.

In this article, we explore how we exposed an Apache Kafka cluster across multiple Availability Zones (AZs) in Grab’s main VPC, to producers and consumers residing in the GrabKios VPC, via a VPC Endpoint Service. This design is part of Coban unified stream processing platform at Grab.

“ 要深入学习Kafka，理解Kafka的存储机制是非常重要的。本文介绍Kafka存储消息的格式以及数据文件和索引组织方式，以便更好的理解Kafka是如何工作的。”

Learn more about how to migrate your Kafka cluster from one Zookeeper cluster to another without any user impact.

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。

Uber recently launched a new capability: Ads on UberEats. With this new ability came new challenges that needed to be solved at Uber, such as systems for ad auctions, bidding, attribution, reporting, and more. This article focuses on how we leveraged open source technology to build Uber’s first “near real-time” exactly-once events processing system. We’ll dive into the details of how we achieved exactly-once processing as well as the inner workings of our event processing jobs.

Uber has one of the largest deployments of Apache Kafka in the world, processing trillions of messages and multiple petabytes of data per day. As Figure 1 shows, today we position Apache Kafka as a cornerstone of our technology stack. It empowers a large number of different workflows, including pub-sub message buses for passing event data from the rider and driver apps, streaming analytics (e.g., Apache Flink, Apache Samza), streaming database changelogs to the downstream subscribers, and ingesting all sorts of data into Uber’s Apache Hadoop data lake.

熟悉Apache Kafka的同学都知道，当Kafka集群负载到达瓶颈或者出现突发流量需要紧急扩容时，新加入集群的节点需要经过数据迁移才能均分集群压力。而数据迁移会因为数据堆积量，节点负载等因素的影响，导致迁移时间较长，甚至出现迁移不动的情况。同时数据迁移也会增大当前节点的压力，可能导致集群进一步崩溃。本文将探讨应对需要紧急扩容的技术方案。

消息队列是分布式系统中重要中间件，目前比较常见的产品有ActiveMQ，RabbitMQ，ZeroMQ，Kafka，RocketMQ，NSQ等。本文将其中对三款优秀消息中间件（nsq,kafka,rocketMq）的实现架构进行简单介绍～

本文主要总结当kafka集群流量达到 万亿级记录/天或者十万亿级记录/天 甚至更高后，我们需要具备哪些能力才能保障集群高可用、高可靠、高性能、高吞吐、安全的运行。

我的上家公司是做餐饮系统的，每天中午和晚上用餐高峰期，系统的并发量不容小觑。为了保险起见，公司规定各部门都要在吃饭的时间轮流值班，防止出现线上问题时能够及时处理。

我当时在后厨显示系统团队，该系统属于订单的下游业务。用户点完菜下单后，订单系统会通过发kafka消息给我们系统，系统读取消息后，做业务逻辑处理，持久化订单和菜品数据，然后展示到划菜客户端。这样厨师就知道哪个订单要做哪些菜，有些菜做好了，就可以通过该系统出菜。系统自动通知服务员上菜，如果服务员上完菜，修改菜品上菜状态，用户就知道哪些菜已经上了，哪些还没有上。这个系统可以大大提高后厨到用户的效率。

接下来，我跟大家一起聊聊使用kafka两年时间踩过哪些坑。

LogI-KafkaManager脱胎于滴滴内部多年的Kafka运营实践经验，是面向Kafka用户、Kafka运维人员打造的共享多租户Kafka云平台。专注于Kafka运维管控、监控告警、资源治理等核心场景，经历过大规模集群、海量大数据的考验。内部满意度高达90%的同时，还与多家知名企业达成商业化合作。

美团Kafka系统每天处理消息总量达8万亿，PageCache竞争是当前最大痛点，针对该问题我们设计了一套新型的架构，本文详细介绍了该方案的设计与实现。

本文主要介绍了kafka的一些基本概念，围绕kafka的基础架构，对生产者、消费者以及kafka的工作流程，文件存储机制、分区分配策略等进行了简要的介绍。

Uber has one of the largest deployments of Apache Kafka in the world, processing trillions of messages and multiple petabytes of data per day. As Figure 1 shows, today we position Apache Kafka as a cornerstone to Uber’s technology stack and build a complex ecosystem on top of it to empower a large number of different workflows.

kafka 中的可靠性设计介绍。

Read this deep dive on our Kubernetes infrastructure setup for Grab's stream processing framework.