在执行apache spark示例时，出现以下错误
org.apache.spark.SparkException：作业由于阶段故障而中止：任务不可序列化：java.io.NotSerializableException：org.apache.spark.sql.TypedColumn
执行最后一行时出现错误。
import org.apache.spark.sql.{Encoder, Encoders, SparkSession}
import org.apache.spark.sql.expressions.Aggregator
case class Employee(name: String, salary: Long)
case class Average(var sum: Long, var count: Long)
object MyAverage extends Aggregator [Employee, Average, Double] {
def zero: Average = Average(0L,0L)
def reduce (buffer: Average, employee: Employee): Average = {
buffer.sum += buffer.sum + employee.salary
buffer.count += buffer.count + 1
buffer
}
def merge (b1: Average, b2: Average): Average = {
b1.sum = b1.sum + b2.sum
b1.count = b1.count + b2.count
b1
}
def finish (reduction: Average) : Double = reduction.sum/reduction.count
def bufferEncoder: Encoder[Average] = Encoders.product
def outputEncoder: Encoder[Double] = Encoders.scalaDouble
}
val ds = spark.read.json("FileStore/tables/employee.json").as[Employee]
ds.show()
val averageSalary= MyAverage.toColumn.name("average_salary")
val result = ds.select(averageSalary)
result.show()