Flink学习（九） Sink到Kafka

package com.wyh.streamingApi.sink

import java.util.Properties

import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.{FlinkKafkaConsumer011, FlinkKafkaProducer011}


//温度传感器读数样例类
case class SensorReading(id: String, timestamp: Long, temperature: Double)

object Sink2Kafka {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)

    /**
      * sensor_1,1547718199,35.80018327300259
      * sensor_6,1547718201,15.402984393403084
      * sensor_7,1547718202,6.720945201171228
      * sensor_10,1547718205,38.1010676048934444
      * sensor_1,1547718199,35.1
      * sensor_1,1547718199,31.0
      * sensor_1,1547718199,39
      */
    //Source操作
//    val inputStream = env.readTextFile("F:\flink-study\wyhFlinkSD\data\sensor.txt")

    val properties = new Properties()
    properties.setProperty("zookeeper.connect", "tuijian:2181")
    properties.setProperty("bootstrap.servers", "tuijian:9092")
    properties.setProperty("group.id", "test-consumer-group")
    properties.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
    properties.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
    properties.setProperty("auto.offset.reset", "latest") //偏移量自动重置

    val inputStream = env.addSource(new FlinkKafkaConsumer011[String]("sensor",new SimpleStringSchema(),properties))



    //Transform操作
    val dataStream: DataStream[String] = inputStream.map(data => {
      val dataArray = data.split(",")
      SensorReading(dataArray(0).trim, dataArray(1).trim.toLong, dataArray(2).trim.toDouble).toString //转成String方便序列化输出
    })

    //Sink操作
    dataStream.addSink(new FlinkKafkaProducer011[String]("tuijian:9092","sinkTest",new SimpleStringSchema()))

    dataStream.print()
    env.execute("kafka sink test")


  }

}