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我正在尝试在 Spark（2.0.1，Scala 2.11）上创建一个 UDAF，如下所示。这本质上是聚合元组并输出Map 

import org.apache.spark.sql.expressions._
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions.udf
import org.apache.spark.sql.{Row, Column}

class mySumToMap[K, V](keyType: DataType, valueType: DataType) extends UserDefinedAggregateFunction {
  override def inputSchema = new StructType()
    .add("a_key", keyType)
    .add("a_value", valueType)

  override def bufferSchema = new StructType()
    .add("buffer_map", MapType(keyType, valueType))

  override def dataType = MapType(keyType, valueType)

  override def deterministic = true 

  override def initialize(buffer: MutableAggregationBuffer) = {
    buffer(0) = Map[K, V]()
  }

  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {

    // input :: 0 = a_key (k), 1 = a_value
    if ( !(input.isNullAt(0)) ) {

      val a_map = buffer(0).asInstanceOf[Map[K, V]]
      val k = input.getAs[K](0)  // get the value of position 0 of the input as string (a_key)

      // I've split these on purpose to show that return values are all of type V
      val new_v1: V = a_map.getOrElse(k, 0.asInstanceOf[V])
      val new_v2: V = input.getAs[V](1)
      val new_v: V = new_v1 + new_v2

      buffer(0) = if (new_v != 0) a_map + (k -> new_v) else a_map - k
    }
  }

  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = {
    val map1: Map[K, V] = buffer1(0).asInstanceOf[Map[K, V]]
    val map2: Map[K, V] = buffer2(0).asInstanceOf[Map[K, V]]

    buffer1(0) = map1 ++ map2.map{ case (k,v) => k -> (v + map1.getOrElse(k, 0.asInstanceOf[V])) }
  }

  override def evaluate(buffer: Row) = buffer(0).asInstanceOf[Map[K, V]]

}

但是当我编译它时，我看到以下错误：

<console>:74: error: type mismatch;
 found   : V
 required: String
             val new_v: V = new_v1 + new_v2
                                     ^
<console>:84: error: type mismatch;
 found   : V
 required: String
           buffer1(0) = map1 ++ map2.map{ case (k,v) => k -> (v + map1.getOrElse(k, 0.asInstanceOf[V])) }

我究竟做错了什么？

编辑：对于将其标记为Spark UDAF 副本的人 - 使用泛型作为输入类型？- 这不是该问题的重复，因为该问题不处理Map数据类型。对于使用 Map 数据类型所面临的问题，上面的代码非常具体和完整。

4

1 回答 1

2

将类型限制为具有Numeric[_] 

class mySumToMap[K, V: Numeric](keyType: DataType, valueType: DataType) 
  extends UserDefinedAggregateFunction {
    ...

用于Implicitly在运行时获取它：

val n = implicitly[Numeric[V]]

并使用它的plus方法代替+an zeroin place of0

buffer1(0) = map1 ++ map2.map{ 
  case (k,v) => k -> n.plus(v,  map1.getOrElse(k, n.zero))
}

要支持更广泛的类型，您可以使用：cats Monoid

import cats._
import cats.implicits._

并调整代码：

class mySumToMap[K, V: Monoid](keyType: DataType, valueType: DataType) 
  extends UserDefinedAggregateFunction {
    ...

然后：

override def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = {
  val map1: Map[K, V] = buffer1.getMap[K, V](0)
  val map2: Map[K, V] = buffer2.getMap[K, V](0)

  val m = implicitly[Monoid[Map[K, V]]]

  buffer1(0) = m.combine(map1, map2)
}
于 2017-09-29T11:38:34.167 回答