SparkSQL---UDAF

package sqlspark.Day04

import java.lang

import org.apache.spark.sql.{DataFrame, Dataset, SparkSession}

object UdafText05 {
  def main(args: Array[String]): Unit = {
    //创建sparksession
    val spark: SparkSession = SparkSession.builder().appName("ipdemo2").master("local[*]").getOrCreate()
    //2.创建分布式的数据集合
    val number: Dataset[lang.Long] = spark.range(1, 11)

    //转换为df
    val numberDF: DataFrame = number.toDF()
   //number.show()
    //3.注册为视图
    numberDF.createTempView("v_number")
    //注册UDAF函数
    spark.udf.register("geo",new GemMean05_1())
    //sq
    spark.sql("select geo(id) result from v_number").show()
    spark.stop()
  }
}

package sqlspark.Day04

import org.apache.spark.sql.Row
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types.{DataType, DataTypes, StructField, StructType}

class GemMean05_1 extends UserDefinedAggregateFunction {
  //运算输入数据的类型
  override def inputSchema: StructType = StructType(List(
    StructField("value", DataTypes.DoubleType)

  ))

  //产生中间结果的数据类型
  override def bufferSchema: StructType = StructType(List(
    StructField("product", DataTypes.DoubleType),
      StructField("counts", DataTypes.LongType)
  ))

  //最终结果的数据类型
  override def dataType: DataType = DataTypes.DoubleType

  //数据一致性
  override def deterministic: Boolean = true

  //分区运算的初始值
  override def initialize(buffer: MutableAggregationBuffer): Unit = {
    //buffer(0) 存放的分区预算数据数量的初始值
    //buffer(0) = 0L
    buffer(0) = 1.0
    //buffer(1) 存放的是分区运算乘积的初始值
   // buffer(1) = 1.0
    buffer(1) = 0L
  }

  //没计算一条数据,更新中间结果
  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
    //累加计算个数
   // buffer(0) =  buffer.getLong(0) + 1L
    buffer(1) = buffer.getLong(1) + 1L
    //累乘 数字
   // buffer(1) =buffer.getDouble(0) * input.getDouble(0)
    buffer(0) = buffer.getDouble(0) * input.getDouble(0)

  }

  //合并 将各个分区的计算结果累加和累乘
  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
    buffer1(0) = buffer1.getDouble(0) * buffer2.getDouble(0)
    buffer1(1) = buffer1.getLong(1) + buffer2.getLong(1)
  }

  //最终计算
  override def evaluate(buffer: Row): Any = {
    math.pow(buffer.getDouble(0), 1.toDouble / buffer.getLong(1))
  }
}