scala - 无法让 Spark 聚合器正常工作

Question

我想在 Scala Spark 中尝试聚合器，但我似乎无法让它们同时使用 select函数和 groupBy/agg函数(在我当前的实现中，agg 函数无法编译)。我的聚合器写在下面，应该是不言自明的。

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

/** Stores the number of true counts (tc) and false counts (fc) */
case class Counts(var tc: Long, var fc: Long)

/** Count the number of true and false occurances of a function */
class BooleanCounter[A](f: A => Boolean) extends Aggregator[A, Counts, Counts] with Serializable {
  // Initialize both counts to zero
  def zero: Counts = Counts(0L, 0L) 
  // Sum counts for intermediate value and new value
  def reduce(acc: Counts, other: A): Counts = { 
    if (f(other)) acc.tc += 1 else acc.fc += 1
    acc 
  }
  // Sum counts for intermediate values
  def merge(acc1: Counts, acc2: Counts): Counts = { 
    acc1.tc += acc2.tc
    acc1.fc += acc2.fc
    acc1
  }
  // Return results
  def finish(acc: Counts): Counts = acc 
  // Encoder for intermediate value type
  def bufferEncoder: Encoder[Counts] = Encoders.product[Counts]
  // Encoder for return type
  def outputEncoder: Encoder[Counts] = Encoders.product[Counts]
}

下面是我的测试代码。

val ds: Dataset[Employee] = Seq(
  Employee("John", 110),
  Employee("Paul", 100),
  Employee("George", 0), 
  Employee("Ringo", 80) 
).toDS()

val salaryCounter = new BooleanCounter[Employee]((r: Employee) => r.salary < 10).toColumn
// Usage works fine 
ds.select(salaryCounter).show()
// Causes an error
ds.groupBy($"name").agg(salaryCounter).show()

salaryCounter的第一次使用工作正常，但第二个导致以下编译错误。

java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast to Employee 

Databricks 有一个 tutorial这相当复杂，但似乎是 Spark 2.3。还有 this使用 Spark 1.6 中的实验性功能的旧教程。

Answer 1

您错误地混合了“静态类型”和“动态类型”API。要使用以前的版本，您应该调用 agg在 KeyValueGroupedDataset ，不是 RelationalGroupedDataset :

ds.groupByKey(_.name).agg(salaryCounter)