云星数据---Apache Flink实战系列(精品版)】：Flink流处理API详解与编程实战013-Flink在流处理中常见的sink和source002

3.基于网络套接字的source（Socket-based-source）

方法原型

def socketTextStream(hostname: String, port: Int, delimiter: Char = '\n', maxRetry: Long = 0):DataStream[String]

示例程序

package code.book.stream.sinksource.scala

//0.引用必要的元素
import org.apache.flink.streaming.api.scala._
object DataSource002 {
  def main(args: Array[String]): Unit = {
    //0.创建运行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    //1.定义text1数据流，采用默认值,行分隔符为'\n'，失败重试0次
    val text1 = env.socketTextStream("qingcheng11", 9999)
    text1.print()

    //2.定义text2数据流,行分隔符为'|'，失败重试3次
    val text2 = env.socketTextStream("qingcheng11", 9998, delimiter = '|', maxRetry = 3)
    text2.print()
    //5.触发计算
    env.execute(this.getClass.getName)
  }
}

4.自定义的source（Custom-source,以kafka为例）

package code.book.stream.sinksource.scala
import java.util.Properties
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer09
import org.apache.flink.streaming.util.serialization.SimpleStringSchema
object DataSource004 {
  def main(args: Array[String]) {

    //1指定kafka数据流的相关信息
    val zkCluster = "qingcheng11,qingcheng12,qingcheng13:2181"
    val kafkaCluster = "qingcheng11:9092,qingcheng12:9092,qingcheng13:9092"
    val kafkaTopicName = "food"
    //2.创建流处理环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    //3.创建kafka数据流
    val properties = new Properties()
    properties.setProperty("bootstrap.servers", kafkaCluster)
    properties.setProperty("zookeeper.connect", zkCluster)
    properties.setProperty("group.id", kafkaTopicName)

    val kafka09 = new FlinkKafkaConsumer09[String](kafkaTopicName,
    new SimpleStringSchema(), properties)
    //4.添加数据源addSource(kafka09)
    val text = env.addSource(kafka09).setParallelism(4)
    text.print()

    //5.触发运算
    env.execute("flink-kafka-wordcunt")
  }
}