spark sql 自定义UDAF函数

spark3.0 用户自定义函数
重写Aggregator 方法

import org.apache.spark.{SparkConf, sql}
import org.apache.spark.sql.{Encoder, Encoders, SparkSession, functions}
import org.apache.spark.sql.expressions.Aggregator

object Spark_basic {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[*]").setAppName("waj")
      val spark = SparkSession.builder().config(conf).getOrCreate()
//TODO user defined function
// 创建Dataframe
      val df = spark.read.json("datas/user.json")
      //注册临时表
    df.createTempView("user")
    // 注册udaf 函数
    spark.udf.register("mymean",functions.udaf(new MyAvgUDAF()))
    spark.sql("select mymean(age ) as mean  from user").show()


    spark.close()
  }
  case class Buff(var total:Long,var count:Long)
  class MyAvgUDAF extends Aggregator[Long,Buff,Long]{
    override def zero: Buff = new Buff(0,0L)

    override def reduce(b: Buff, a: Long): Buff = {
      b.count+=1
      b.total+=a
      b
    }

    override def merge(b1: Buff, b2: Buff): Buff = {
      b1.total=b1.total+b2.total
      b1.count=b2.count+b1.count
      b1
    }

    override def finish(reduction: Buff): Long = reduction.total/reduction.count

    override def bufferEncoder: Encoder[Buff] = Encoders.product

    override def outputEncoder: Encoder[Long] = Encoders.scalaLong
  }
}