SPARK数据框：自定义的聚合函数和向量的列

问题描述

我有两个列的数据帧， ID 类型内部和 VEC 类型矢量（ org.apache.spark.mllib.linalg.Vector ）

I have a DataFrame of two columns, ID of type Int and Vec of type Vector (org.apache.spark.mllib.linalg.Vector).

数据框看起来如下：

ID,Vec
1,[0,0,5]
1,[4,0,1]
1,[1,2,1]
2,[7,5,0]
2,[3,3,4]
3,[0,8,1]
3,[0,0,1]
3,[7,7,7]
....

我愿做一个 GROUPBY（$ID）然后由矢量求和应用在各组内的行的集合。

I would like to do a groupBy($"ID") then apply an aggregation on the rows inside each group by summing the vectors.

上面的例子中所述的期望的输出将是：

The desired output of the above example would be:

ID,SumOfVectors
1,[5,2,7]
2,[10,8,4]
3,[7,15,9]
...

可用的聚合功能将无法正常工作，例如， df.groupBy（$ID）。AGG（SUM（$VEC）将导致ClassCastException异常。

The available aggregation functions will not work, e.g. df.groupBy($"ID").agg(sum($"Vec") will lead to an ClassCastException.

如何实现自定义的聚合功能，可以让我做载体或数组或任何其他自定义操作的总和？

How to implement a custom aggregation function that allows me to do the sum of vectors or arrays or any other custom operation?

推荐答案

我个人不打扰UDAFs。有超过冗长，不完全是快速。相反，我会简单地使用 reduceByKey ：

Personally I wouldn't bother with UDAFs. There are more than verbose and not exactly fast. Instead I would simply use reduceByKey:

import org.apache.spark.sql.Row
import breeze.linalg.{DenseVector => BDV}
import org.apache.spark.mllib.linalg.{Vector, Vectors}

val rdd = sc.parallelize(Seq(
  (1, "[0,0,5]"), (1, "[4,0,1]"), (1, "[1,2,1]"),
  (2, "[7,5,0]"), (2, "[3,3,4]"), (3, "[0,8,1]"),
  (3, "[0,0,1]"), (3, "[7,7,7]")))

val df = rdd.map{case (k, v) => (k, Vectors.parse(v))}.toDF("id", "vec")

val aggregated = df
  .map{ case Row(k: Int, v: Vector) => (k, BDV(v.toDense.values))}
  .reduceByKey(_ + _)
  .mapValues(v => Vectors.dense(v.toArray))
  .toDF("id", "vec")

aggregated.show

// +---+--------------+
// | id|           vec|
// +---+--------------+
// |  1| [5.0,2.0,7.0]|
// |  2|[10.0,8.0,4.0]|
// |  3|[7.0,15.0,9.0]|
// +---+--------------+

和公正的比较简单UDAF：

And just for comparison a "simple" UDAF:

import org.apache.spark.sql.expressions.{MutableAggregationBuffer,
  UserDefinedAggregateFunction}
import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
import org.apache.spark.sql.types.{StructType, ArrayType, DoubleType}
import scala.collection.mutable.WrappedArray

class VectorSum (n: Int) extends UserDefinedAggregateFunction {
    def inputSchema = new StructType().add("v", new VectorUDT())
    def bufferSchema = new StructType().add("buff", ArrayType(DoubleType))
    def dataType = new VectorUDT()
    def deterministic = true

    def initialize(buffer: MutableAggregationBuffer) = {
      buffer.update(0, Array.fill(n)(0.0))
    }

    def update(buffer: MutableAggregationBuffer, input: Row) = {
      if (!input.isNullAt(0)) {
        val buff = buffer.getAs[WrappedArray[Double]](0)
        val v = input.getAs[Vector](0).toSparse
        for (i <- v.indices) {
          buff(i) += v(i)
        }
        buffer.update(0, buff)
      }
    }

    def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = {
      val buff1 = buffer1.getAs[WrappedArray[Double]](0)
      val buff2 = buffer2.getAs[WrappedArray[Double]](0)
      for ((x, i) <- buff2.zipWithIndex) {
        buff1(i) += x
      }
      buffer1.update(0, buff1)
    }

    def evaluate(buffer: Row) =  Vectors.dense(
      buffer.getAs[Seq[Double]](0).toArray)
}

和用法的例子：

df.groupBy($"id").agg(new VectorSum(3)($"vec") alias "vec").show

// +---+--------------+
// | id|           vec|
// +---+--------------+
// |  1| [5.0,2.0,7.0]|
// |  2|[10.0,8.0,4.0]|
// |  3|[7.0,15.0,9.0]|
// +---+--------------+

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