November 2022
Beginner to intermediate
346 pages
11h
Chinese
Content preview from Kafka权威指南（第2版）
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流式处理
｜
301
即使是一个简单的应用程序也会有不简单的拓扑。拓扑由处理器组成，处理器是拓扑图中
的节点（在图中用椭圆表示）。大部分处理器实现了一个数据操作——过滤、映射、聚合
等。数据源处理器从主题读取数据，并将数据传给其他组件，数据池处理器从处理器接收
数据，并将数据生成到主题上。拓扑总是从一个或多个数据源处理器开始，并以一个或多
个数据池处理器结束。
14.5.2
优化拓扑
默认情况下，在执行使用
DSL API
构建的应用程序时，
Streams
会将每个
DSL
方法独立映
射到一个底层的等价对象。因为每个
DSL
方法都是独立计算的，所以错失了优化整体拓扑
的机会。
Streams
应用程序的执行分为
3
个步骤。
1.
通过创建
KStream
对象和
KTable
对象并对它们执行
DSL
操作（比如过滤和连接）来定
义逻辑拓扑。
2.
调用
StreamsBuilder.build()
，从逻辑拓扑生成物理拓扑。
3.
调用
KafkaStreams.start()
执行拓扑，这是读取、处理和生成数据的步骤。
在第
2
个步骤中，也就是从逻辑拓扑生成物理拓扑这一步，可以对执行计划进行整体优化。
目前，
Kafka
只提供了一部分优化
，主要与重用主题有关。可以通过将
StreamsConfig.
TOPOLOGY_OPTIMIZATION
设置成
StreamsConfig.OPTIMIZE
并调用
build(props)
来启用这些优
化。如果只调用
build()
但没有传入配置，则仍然无法启用优化。建议对启用了优化和没
有启用优化的应用程序进行测试，比较执行时间和写入 ...
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