SparkSQL自定义强类型聚合函数

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package com.cjs import org.apache.spark.sql.{Encoder,Encoders} import org.apache.spark.sql.expressions.Aggregator //定义输入数据类型
case class Employee(name:String,salary:Long) //定义聚合缓冲器类型
case class Average(var sum:Long,var count:Long) //继承Aggregator类时需要指定泛型类型，依次为：传入聚合缓冲器的数据类型、聚合缓冲器的类型、返回结果的类型
object MyAggregator extends Aggregator[Employee,Average,Double]{ //类似于初始化聚合缓冲器
    override def zero: Average = Average(0L,0L) //根据传入的参数进行运算操作，最后更新buffer缓冲器，并返回
    override def reduce(buffer: Average,a: Employee): Average = { buffer.sum += a.salary buffer.count +=1 buffer } //b1为主缓冲器，b2为分布式架构中各个节点的缓冲器，对b1和b2的数据进行运算，并返回b1
    override def merge(b1: Average,b2: Average): Average = { b1.sum += b2.sum b1.count += b2.count b1 } //使用主缓冲器的数据进行运算，返回一个运算结果
    override def finish(reduction: Average): Double = { reduction.sum.toDouble/reduction.count } //指定中间值的编码器类型
    override def bufferEncoder: Encoder[Average] = { Encoders.product } //指定最终输出值的编码器类型
    override def outputEncoder: Encoder[Double] = { Encoders.scalaDouble } }

package com.cjs import org.apache.log4j.{Level,Logger} import org.apache.spark.SparkConf import org.apache.spark.sql.SparkSession object TestMyAggregator { case class Emp(name:String,salary:Long) def main(args: Array[String]): Unit = { Logger.getLogger("org.apache.spark").setLevel(Level.ERROR) val conf = new SparkConf() .set("spark.sql.warehouse.dir","file:///e:/tmp/spark-warehouse") .set("spark.some.config.option","some-value") val ss = SparkSession.builder() .config(conf) .appName("test_myAggregator") .master("local[2]") .getOrCreate() val path = "E:IntelliJ IdeasparkSql_practicesrcmainscalacomcjsemployee.txt" val sc = ss.sparkContext import ss.implicits._ val empRDD = sc.textFile(path).map(_.split(",")).map(value=>Emp(value(0),value(1).toLong)) val ds = empRDD.toDF().as[Employee] println("DS结构：") ds.printSchema() println("DS数据") ds.show() val averSalary = MyAggregator.toColumn.name("aver_salary")  //转换成Column
        val result = ds.select(averSalary) println("平均工资：") result.show() println("DS使用select：") ds.select($"name",$"salary").show() } }