1.reduce简介

按照指定的方式，把每个元素进行累计执行。比如实现累加计算

 

示例：

import keyByNameTest.StockPrice
import org.apache.flink.api.scala.createTypeInformation
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment

object reduceTest {

  //defined the dataSource's data type
  case class StockPrice(stockId:String, timestamp: Long, price:Double)

  def main(args: Array[String]): Unit = {

    //create env
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    //create ds

    val pricesList = List(StockPrice("stock1", 10, 1), StockPrice("stock1", 11, 2), StockPrice("stock2", 10, 666), StockPrice("stock3", 10, 888.23))

    val ds = env.fromCollection(pricesList)

    //transformation

//update the stock's new time, and accumulate the price
    val reducedDs = ds.keyBy(0).reduce((t1, t2) => StockPrice(t1.stockId, t2.timestamp, t1.price + t2.price))

    reducedDs.print()

    env.execute()

  }

}


输出结果：

自定义reduce func

核心步骤：

1.继承 ReduceFunction 类

2.重写reduce 方法

示例：

import org.apache.flink.api.common.functions.ReduceFunction
import org.apache.flink.api.scala.createTypeInformation
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment


object myReduceTest {


  //defined the dataSource's data type
  case class StockPrice(stockId:String, timestamp: Long, price:Double)

  //define my reduce func
  update the stock's new time, and accumulate the price
  class MyReduceFunc extends ReduceFunction[StockPrice] {
    override def reduce(t: StockPrice, t1: StockPrice): StockPrice = {

      //update the stock's new time, and accumulate the price
      StockPrice(t.stockId, t1.timestamp, t.price + t1.price)

    }
  }

  def main(args: Array[String]): Unit = {

    //create env
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    //create ds
    val pricesList = List(StockPrice("stock1", 10, 1), StockPrice("stock1", 11, 2), StockPrice("stock2", 10, 666), StockPrice("stock3", 10, 888.23))

    val ds = env.fromCollection(pricesList)

    //transformation
    val keyByedDs = ds.keyBy(0)

      //use my reduce func
    val myReducedDs = keyByedDs.reduce(new MyReduceFunc)

    myReducedDs.print()

    env.execute()
  }



}

输出结果：