SumProduct in Spark DataFrame

Question

I want to create essentially a sumproduct across columns in a Spark DataFrame. I have a DataFrame that looks like this:

id    val1   val2   val3   val4
123   10     5      7      5

I also have a Map that looks like:

val coefficents = Map("val1" -> 1, "val2" -> 2, "val3" -> 3, "val4" -> 4)

I want to take the value in each column of the DataFrame, multiply it by the corresponding value from the map, and return the result in a new column so essentially:

(10*1) + (5*2) + (7*3) + (5*4) = 61

I tried this:

val myDF1 = myDF.withColumn("mySum", {var a:Double = 0.0; for ((k,v) <- coefficients) a + (col(k).cast(DoubleType)*coefficients(k));a})

but got an error that the "+" method was overloaded. Even if I solved that, I'm not sure this would work. Any ideas? I could always dynamically build a SQL query as text string and do it that way but I was hoping for something a little more eloquent.

Any ideas are appreciated.

Answer 1

Problem with your code is that you try to add a Column to Double. cast(DoubleType) affects only a type of stored value, not a type of column itself. Since Double doesn't provide *(x: org.apache.spark.sql.Column): org.apache.spark.sql.Column method everything fails.

To make it work you can for example do something like this:

import org.apache.spark.sql.Column
import org.apache.spark.sql.functions.{col, lit}

val df = sc.parallelize(Seq(
    (123, 10, 5, 7, 5), (456,  1, 1, 1, 1)
)).toDF("k", "val1", "val2", "val3", "val4")

val coefficients = Map("val1" -> 1, "val2" -> 2, "val3" -> 3, "val4" -> 4)

val dotProduct: Column = coefficients
  // To be explicit you can replace
  // col(k) * v with col(k) * lit(v)
  // but it is not required here
  // since we use * f Column.* method not Int.*
  .map{ case (k, v) => col(k) * v }  // * -> Column.*
  .reduce(_ + _)  // + -> Column.+

df.withColumn("mySum", dotProduct).show
// +---+----+----+----+----+-----+
// |  k|val1|val2|val3|val4|mySum|
// +---+----+----+----+----+-----+
// |123|  10|   5|   7|   5|   61|
// |456|   1|   1|   1|   1|   10|
// +---+----+----+----+----+-----+

Answer 2

It looks like the issue is that you aren't actually doing anything with a

for((k, v) <- coefficients) a + ...

You probably meant a += ...

---

Also, some advice for cleaning up the block of code inside the withColumn call:

You don't need to call coefficients(k) because you've already got its value in v from for((k,v) <- coefficients)

Scala is pretty good at making one-liners, but it's kinda cheating if you have to put semicolons in that one line :P I'd suggest breaking up the sum calculation section into one line per expression.

The sum expression could be rewritten as a fold which avoids using a var (idiomatic Scala usually avoids vars), e.g.

import org.apache.spark.sql.functions.lit

coefficients.foldLeft(lit(0.0)){ 
  case (sumSoFar, (k,v)) => col(k).cast(DoubleType) * v + sumSoFar
}

Answer 3

I'm not sure if this is possible through the DataFrame API since you are only able to work with columns and not any predefined closures (e.g. your parameter map).

I've outlined a way below using the underlying RDD of the DataFrame:

import org.apache.spark.sql.types._
import org.apache.spark.sql.Row

// Initializing your input example.
val df1 = sc.parallelize(Seq((123, 10, 5, 7, 5))).toDF("id", "val1", "val2", "val3", "val4")

// Return column names as an array
val names = df1.columns

// Grab underlying RDD and zip elements with column names
val rdd1 = df1.rdd.map(row => (0 until row.length).map(row.getInt(_)).zip(names))

// Tack on accumulated total to the existing row
val rdd2 = rdd0.map { seq => Row.fromSeq(seq.map(_._1) :+ seq.map { case (value: Int, name: String) => value * coefficents.getOrElse(name, 0) }.sum) }

// Create output schema (with total)
val totalSchema = StructType(df1.schema.fields :+ StructField("total", IntegerType))

// Apply schema to create output dataframe
val df2 = sqlContext.createDataFrame(rdd1, totalSchema)

// Show output:
df2.show()
...
+---+----+----+----+----+-----+
| id|val1|val2|val3|val4|total|
+---+----+----+----+----+-----+
|123|  10|   5|   7|   5|   61|
+---+----+----+----+----+-----+