Is there a data structure / library to do in memory olap / pivot tables in Java / Scala?

Question

Relevant questions

This question is quite relevant, but is 2 years old: In memory OLAP engine in Java

Background

I would like to create a pivot-table like matrix from a given tabular dataset, in memory

eg an age by marital status count (rows are age, columns are marital status).

• The input : List of People, with age and some Boolean property (eg married),

• The desired output : count of People, by age (row) and isMarried (column)

What I've tried (Scala)

case class Person(val age:Int, val isMarried:Boolean)

...
val people:List[Person] = ... //

val peopleByAge = people.groupBy(_.age)  //only by age
val peopleByMaritalStatus = people.groupBy(_.isMarried) //only by marital status

I managed to do it the naive way, first grouping by age, then map which is doing a count by marital status, and outputs the result, then I foldRight to aggregate

TreeMap(peopleByAge.toSeq: _*).map(x => {
    val age = x._1
    val rows = x._2
    val numMarried = rows.count(_.isMarried())
    val numNotMarried = rows.length - numMarried
    (age, numMarried, numNotMarried)
}).foldRight(List[FinalResult]())(row,list) => {
     val cumMarried = row._2+ 
        (if (list.isEmpty) 0 else list.last.cumMarried) 
     val cumNotMarried = row._3 + 
        (if (list.isEmpty) 0 else l.last.cumNotMarried) 
     list :+ new FinalResult(row._1, row._2, row._3, cumMarried,cumNotMarried) 
}.reverse

I don't like the above code, it's not efficient, hard to read, and I'm sure there is a better way.

The question(s)

How do I groupBy "both"? and how do I do a count for each subgroup, eg

How many people are exactly 30 years old and married?

Another question, is how do I do a running total, to answer the question:

How many people above 30 are married?

---

Edit:

Thank you for all the great answers.

just to clarify, I would like the output to include a "table" with the following columns

• Age (ascending)
• Num Married
• Num Not Married
• Running Total Married
• Running Total Not Married

Not only answering those specific queries, but to produce a report that will allow answering all such type of questions.

Answer 1

You can

val groups = people.groupBy(p => (p.age, p.isMarried))

and then

val thirty_and_married = groups((30, true))._2
val over_thirty_and_married_count = 
  groups.filterKeys(k => k._1 > 30 && k._2).map(_._2.length).sum

Answer 2

Here is an option that is a little more verbose, but does this in a generic fashion instead of using strict data types. You could of course use generics to make this nicer, but i think you get the idea.

/** Creates a new pivot structure by finding correlated values 
  * and performing an operation on these values
  *
  * @param accuOp the accumulator function (e.g. sum, max, etc)
  * @param xCol the "x" axis column
  * @param yCol the "y" axis column
  * @param accuCol the column to collect and perform accuOp on
  * @return a new Pivot instance that has been transformed with the accuOp function
  */
def doPivot(accuOp: List[String] => String)(xCol: String, yCol: String, accuCol: String) = {
  // create list of indexes that correlate to x, y, accuCol
  val colsIdx = List(xCol, yCol, accuCol).map(headers.getOrElse(_, 1))

  // group by x and y, sending the resulting collection of
  // accumulated values to the accuOp function for post-processing
  val data = body.groupBy(row => {
    (row(colsIdx(0)), row(colsIdx(1)))
  }).map(g => {
    (g._1, accuOp(g._2.map(_(colsIdx(2)))))
  }).toMap

  // get distinct axis values
  val xAxis = data.map(g => {g._1._1}).toList.distinct
  val yAxis = data.map(g => {g._1._2}).toList.distinct

  // create result matrix
  val newRows = yAxis.map(y => {
    xAxis.map(x => {
      data.getOrElse((x,y), "")
    })
  })

 // collect it with axis labels for results
 Pivot(List((yCol + "/" + xCol) +: xAxis) :::
   newRows.zip(yAxis).map(x=> {x._2 +: x._1}))
}

my Pivot type is pretty basic:

class Pivot(val rows: List[List[String]]) {

  val headers = rows.head.zipWithIndex.toMap
  val body    = rows.tail
  ...
}

And to test it, you could do something like this:

val marriedP = Pivot(
  List(
    List("Name", "Age", "Married"),
    List("Bill", "42", "TRUE"),
    List("Heloise", "47", "TRUE"),
    List("Thelma", "34", "FALSE"),
    List("Bridget", "47", "TRUE"),
    List("Robert", "42", "FALSE"),
    List("Eddie", "42", "TRUE")

  )
)

def accum(values: List[String]) = {
    values.map(x => {1}).sum.toString
}
println(marriedP + "\n")
println(marriedP.doPivot(accum)("Age", "Married", "Married"))

Which yields:

Name     Age      Married  
Bill     42       TRUE     
Heloise  47       TRUE     
Thelma   34       FALSE    
Bridget  47       TRUE     
Robert   42       FALSE    
Eddie    42       TRUE     

Married/Age  47           42           34           
TRUE         2            2                         
FALSE                     1            1 

The nice thing is that you can use currying to pass in any function for the values like you would in a traditional excel pivot table.

More can be found here: https://github.com/vinsonizer/pivotfun

Answer 3

I think it would be better to use the count method on List s directly

For question 1

people.count { p => p.age == 30 && p.isMarried }

For question 2

people.count { p => p.age > 30 && p.isMarried }

If you also want to actual groups of people who conform to those predicates use filter.

people.filter { p => p.age > 30 && p.isMarried }

You could probably optimise these by doing the traversal only once but is that a requirement?

Answer 4

You can group using a tuple:

val res1 = people.groupBy(p => (p.age, p.isMarried)) //or
val res2 = people.groupBy(p => (p.age, p.isMarried)).mapValues(_.size) //if you dont care about People instances

You can answer both question like that:

res2.getOrElse((30, true), 0)
res2.filter{case (k, _) => k._1 > 30 && k._2}.values.sum
res2.filterKeys(k => k._1 > 30 && k._2).values.sum // nicer with filterKeys from Rex Kerr's answer

You could answer both questions with a method count on List:

people.count(p => p.age == 30 && p.isMarried)
people.count(p => p.age > 30 && p.isMarried)

Or using filter and size:

people.filter(p => p.age == 30 && p.isMarried).size
people.filter(p => p.age > 30 && p.isMarried).size

edit: slightly cleaner version of your code:

TreeMap(peopleByAge.toSeq: _*).map {case (age, ps) =>
    val (married, notMarried) = ps.span(_.isMarried)
    (age, married.size, notMarried.size)
  }.foldLeft(List[FinalResult]()) { case (acc, (age, married, notMarried)) =>
    def prevValue(f: (FinalResult) => Int) = acc.headOption.map(f).getOrElse(0)
    new FinalResult(age, married, notMarried, prevValue(_.cumMarried) + married, prevValue(_.cumNotMarried) + notMarried) :: acc
  }.reverse