流(stream)是Kafka Streams提供的最重要的抽象,它代表的是一个无限的、不断更新的数据集。一个流就是由一个有序的、可重放的、支持故障转移的不可变的数据记录(data record)序列,其中每个数据记录被定义为一个键值对。Kafka流的基本结构如图所示。 Kafka流基本结构 一个流处理器(stream processor)是处理拓扑中的...
Initially, Source processors receive data from one or more Kafka topics and deserialize it before forwarding them to the Stream Processor node which contains the main transformation or computation logic. Finally, Sink Processor writes the processed data into one or more output Kafka Topic or to a ...
"stream-processing-service");//设置servers地址props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092");StreamsBuilder builder = new StreamsBuilder();//构建流KStream<String, String> userActions = builder.stream("TopicA");//对流进行处理KTable<String, Long> user...
在kafka中,流处理持续获取输入topic的数据,进行处理加工,然后写入输出topic。例如,一个零售APP,接收销售和出货的输入流,统计数量或调整价格后输出。 For example, a retail application might take in input streams of sales and shipments, and output a stream of reorders and price adjustments computed off this...
就像Kafka的topic一样,Kafka Streams应用由若干stream partition组成。stream partition定义为一个有序的、可重放的、容错的、不可修改的的数据队列,每条记录的形式为key-value pair。 Kafka Streams应用通过若干拓扑(topology)定义计算逻辑。拓扑包括: 点:流处理器(stream processor),例如map、filter、join、aggregate等算...
KStreamBuilder builder=newKStreamBuilder();//builder.stream("my-topic").mapValues(value -> value.toString()+"gyw").to("my-topics");ProcessorSupplier p=newProcessorSupplier() { @OverridepublicProcessor get() {try{returnFactory.getProcessor(); ...
KafkaMusic (Interactive Queries) Interactive Queries, State Stores, REST API Java 7+ Example MapFunction DSL, stateless transformations, map() Java 8+ Example MixAndMatch DSL + Processor API Integrating DSL and Processor API Java 8+ Example PassThrough DSL, stream(), to() Java 7+ Example...
processor, 从上一个node中接收数据并处理数据,可以继续传给下一个processor,也可以传给sink sink,从processor中获取数据并写入topics, 构建好了Topology之后,传给KafkaStreams,启动后就能按照这个Topology运行了。 StreamsBuilder,high-level DSL strem构建器,stream()返回KStream,table()返回KTable, ...
However, with their processor topologies independently, each thread can execute one or more tasks. For example, below image describes one stream thread running two stream tasks. Test where you you stand in Kafka ...
publicclassSimpleStreamProcessor{publicstaticvoidmain(String[]args){Propertiesprops=KafkaStreamsConfig.createProperties();StreamsBuilderbuilder=newStreamsBuilder();KStream<String,String>sourceStream=builder.stream("input-topic");sourceStream.mapValues(value->"Processed: "+value).to("output-topic");Kafka...