January 2018
Beginner to intermediate
234 pages
7h 8m
Chinese
Content preview from Kafka权威指南
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流式处理
｜
205
理。与其他流式处理框架不一样的是，
Streams
通过使用新的键和分区将事件写到新的主
题来实现重新分区，并启动新的任务从新主题上读取和处理事件。重新分区的步骤是将拓
扑拆分成两个子拓扑，每个子拓扑都有自己的任务集，如图
11-13
所示。第二个任务集依
赖第一个任务集，因为它们处理的是第一个子拓扑的结果。不过，它们仍然可以独立地并
行执行，因为第一个任务集以自己的速率将数据写到一个主题上，而第二个任务集也以自
己的速率从这个主题读取和处理事件。两个任务集之间不需要通信，也没有共享资源，而
且它们也不需要运行在相同的线程里或相同的服务器上。这是
Kafka
提供的最有用的特性
之一——减少管道各个部分之间的依赖。
主题
分区1
分区2
分区1
分区2
任务1
任务2
任务3
任务4
重分区主题
图 11-13：处理主题分区事件的两组任务
11.5.3
从故障中存活下来
Streams
的伸缩模型不仅允许伸缩应用，还能优雅地处理故障。首先，包括本地状态在内
的所有数据被保存到有高可用性的
Kafka
上。如果应用程序出现故障需要重启，可以从
Kafka
上找到上一次处理的数据在流中的位置，并从这个位置开始继续处理。如果本地状
态丢失（比如可能需要将服务器替换掉），应用程序可以从保存在
Kafka
上的变更日志重
新创建本地状态。
Streams
还利用了消费者的协调机制来实现任务的高可用性。如果一个任务失败，只要还
有其他线程或者应用程序实例可用，就可以使用另一个线程来重启该任务。这类似于消费
者群组的故障处理，如果一个消费者失效，就把分区分配给其他活跃的消费者。 ...
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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.