Flink ADD Multi Source

import java.util.Properties

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

object MultiDataStreamExample {
 def main(args: Array[String]): Unit = {
  //设置环境
  val env: StreamExecutionEnvironment = StreamExecutionEnvironment.createLocalEnvironment()
  //设置数据源
  val valueDeserializer: SimpleStringSchema = new SimpleStringSchema;
  val kafkaProps = new Properties()
  kafkaProps.setProperty("bootstrap.servers", "node01:9092,node02:9092,node-3:9092")
  kafkaProps.setProperty("group.id", "test")
  val helloStream: FlinkKafkaConsumer011[String] = new FlinkKafkaConsumer011[String]("hello", valueDeserializer, kafkaProps)
  val hello1Stream: FlinkKafkaConsumer011[String] = new FlinkKafkaConsumer011[String]("hello1", valueDeserializer, kafkaProps)
  val hello2Stream: FlinkKafkaConsumer011[String] = new FlinkKafkaConsumer011[String]("hello2", valueDeserializer, kafkaProps)

  val s1: DataStream[String] = env.addSource(helloStream)
  val s2: DataStream[String] = env.addSource(hello1Stream)
  val s3: DataStream[String] = env.addSource(hello2Stream)

  val allStream: DataStream[String] = s1.union(s2).union(s3)
  //计算逻辑
  val flatMapDataStream: DataStream[String] = allStream.flatMap(_.split(" "))
  val mapDataStream: DataStream[(String, Int)] = flatMapDataStream.map((_, 1))
  val keyedStream: KeyedStream[(String, Int), String] = mapDataStream.keyBy(_._1)
  val reduceDataStream: DataStream[(String, Int)] = keyedStream.reduce((x, y) => {
   print(x._2)
   (x._1, x._2 + y._2)
  })
  //输出结果
  reduceDataStream.addSink(x => {
   print(x)
  })
  //提交任务
  env.execute("word count")
 }
}