January 2018
Beginner to intermediate
234 pages
7h 8m
Chinese
Content preview from Kafka权威指南
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12
｜
第
1
章
而且解析
XML
文件非常耗费计算资源。要想更改所创建的活动类型，需要在前端应用和
离线处理程序之间做大量的协调工作。即使是这样，在更改数据结构时，仍然经常出现系
统崩溃现象。而且批处理时间以小时计算，无法用它完成实时的任务。
监控和用户活动跟踪无法使用同一个后端服务。监控服务太过笨重，数据格式不适用于活
动跟踪，而且无法在活动跟踪中使用轮询拉取模型。另一方面，把跟踪服务用在度量指标
上也过于脆弱，批处理模型不适用于实时的监控和告警。不过，好在数据间存在很多共
性，信息（比如特定类型的用户活动对应用程序性能的影响）之间的关联度还是很高的。
特定类型用户活动数量的下降说明相关应用程序存在问题，不过批处理的长时间延迟意味
着无法对这类问题作出及时的反馈。
最开始，我们调研了一些现成的开源解决方案，希望能够找到一个系统，可以实时访问
数据，并通过横向扩展来处理大量的消息。我们使用
ActiveMQ
创建了一个原型系统，但
它当时还无法满足横向扩展的需求。
LinkedIn
不得不使用这种脆弱的解决方案，虽然
ActiveMQ
有很多缺陷会导致
broker
暂停服务。客户端的连接因此被阻塞，处理用户请求
的能力也受到影响。于是我们最后决定构建自己的基础设施。
1.5.2
Kafka
的诞生
LinkedIn
的开发团队由
Jay Kreps
领导。
Jay Kreps
是
LinkedIn
的首席工程师，之前负责分
布式键值存储系统
Voldemort
的开发。初建团队成员还包括
Neha Narkhede
，不久之后，
Jun Rao
也加入了进来。他们一起着手创建一个消息系统，可以同时满足上述的两种需求， ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.