Flink 基本转换算子（Transformation） Map Filter FlatMap_flink map 实体类转换

自定义源算子

import org.apache.flink.streaming.api.functions.source.SourceFunction

import java.util.Calendar
import scala.util.Random

/**
 * DATE:2022/10/4 0:03
 * AUTHOR：GX
 */
case class Event(user:String,url:String,timestamp:Long)
class ClickSource extends SourceFunction[Event]{
  //ParallelSourceFunction[Event] 算子可以设置并行度
  //SourceFunction[Event] 并行度必须是 1
  //标志位
  var running = true

  override def run(sourceContext: SourceFunction.SourceContext[Event]): Unit = {
    //随机数生成器
    val random = new Random()
    //定义随机数范围
    val users = Array("Mary","Alice","Bob","Cary","Leborn")
    val urls = Array("./home","./cart","./fav","./prod?id=1","./prod?id=2","./prod?id=3")

    //用标志位作为循环判断条件，不停的发送数据
    while (running) {
      //随机生成一个event
      val event = Event(users(random.nextInt(users.length)),
        urls(random.nextInt(urls.length)),
        Calendar.getInstance.getTimeInMillis)
      //调用ctx的方法向下游发送数据
      sourceContext.collect(event)
      //每隔1秒发送一条数据
      Thread.sleep(1000)
    }
  }


  override def cancel(): Unit = running = false
}

Flink程序

import org.apache.flink.api.common.functions.{FilterFunction, FlatMapFunction, MapFunction}
import org.apache.flink.streaming.api.scala.{StreamExecutionEnvironment, createTypeInformation}
import org.apache.flink.util.Collector

/**
 * DATE:2022/10/4 12:01
 * AUTHOR：GX
 */
object Transformastion {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)

    //读取自定义数据源
    val stream = env.addSource(new ClickSource)
    //注意！！！对于一个非并行的算子而言，并行度必须是 1
    //若想要并行处理，需要实现一个ParallelSourceFunction（自定义源算子继承ParallelSourceFunction），算子并行度才可以设置多个
    //提取每次点击事件的用户名\
    //1.使用匿名函数
    stream.map(_.user).print("1")
    //2.实现MapFunction接口
    stream.map(new UserExtractor).print("2")
    //3.使用匿名函数
    stream.filter(_.user == "Mary").print("3")
    //4.实现FilterFunction接口
    stream.filter(new UserFilter).print("4")
    //5.测试flatMap
    stream.flatMap(new UserFlatMap).print("5")

    env.execute()
  }
  class UserExtractor extends MapFunction[Event,String] {//[输入数据类型，输出数据类型]
    override def map(t: Event): String = t.user
  }
  class UserFilter extends FilterFunction[Event] {
    override def filter(t: Event): Boolean = t.user == "Leborn"
  }
  class UserFlatMap extends FlatMapFunction[Event,String] {
    override def flatMap(value: Event, out: Collector[String]): Unit = {
      //如果当前数据是Mary的点击事件，那么就直接输出User
      if (value.user == "Mary") {
        out.collect(value.user)
      }
      //如果当前数据是Leborn的点击事件，那么就直接输出User和Url
      else if (value.user == "Leborn") {
        out.collect(value.user)
        out.collect(value.url)
      }
    }
  }
}