krishnanraman

scala> empDB.minBy(e=>(-e.tenure,-e.age,e.salary))
res1: Employee = Employee(35,6,85079,emp7435)

krishnanraman

case class Monoid(e:Set[Employee]) {     
  def plus(a:Employee,b:Employee)= {
  if(a.age > b.age) a else 
  if(b.age > a.age) b else 
  if(a.tenure > b.tenure) a else 
  if(b.tenure > a.tenure) b else 
  if (a.salary > b.salary) b else 
  if(b.salary > a.salary) a else 
  if( math.random > 0.5) a else b 
}

krishnanraman

import scalaz.Monoid

case class EmpMonoid(e:Set[Employee]) extends Monoid[Employee] {
  
  override val zero:Employee = Employee(0,0,0,"emp0")
  
  override def append(a:Employee, b: =>Employee):Employee = {
  if(a.age > b.age) a else 
  if(b.age > a.age) b else 
  if(a.tenure > b.tenure) a else 

krishnanraman

scala> val m = Map(1->"one",2->"one",3->"two")
scala> m.filter(pair=>pair._2 == "one").head._1
res32: Int = 1

krishnanraman

/**
Convert integers in the interval [0-1 BILLION] to spoken english & vice versa
examples -
six hundred ninety one million one hundred forty five thousand eight hundred twenty five is 691145825
six hundred ninety one million one hundred fifty eight thousand one hundred seventy is 691158170
six hundred ninety one million one hundred seventy thousand five hundred fifteen is 691170515
six hundred ninety one million one hundred eighty two thousand eight hundred sixty is 691182860
six hundred ninety one million one hundred ninety five thousand two hundred five is 691195205
six hundred ninety one million two hundred seven thousand five hundred fifty is 691207550

krishnanraman

def f[T](a:Map[String,T],b:Map[String,T]) = {
  val res= (a.keys++b.keys).toList.sorted.mkString(",")
  if (res.length == 0) None else Some(res)
}

krishnanraman

Goal: Process the 12 million plus records 
from: http://seer.cancer.gov/popdata/download.html
using: a Scala API atop Cascading, aka SCALDING ( Inventors: Avi Bryant, Oscar Boykin, Argyris )
to find: 
THE FASTEST GROWING COUNTY IN THE UNITED STATES over the 1969-2011 timeframe.
-----------------------------------------------------------------------------
RESULTS: Scroll to the very bottom.

First, the scalding source... 
---

krishnanraman

Goal: You are a male between the age of (20,40). 
You want to hook up with a lady, perhaps get married. 
Where should you go to maximize your chances ?

Scalding code: sexratio(2011,(20,40),people,fipspipe)

where sexratio as below:

def sexratio(baseYear:Int, agerange:(Int,Int), people:Pipe, fipspipe:Pipe) = {
    people.filter('year,'age) {

krishnanraman

Goal: Using Scalding, find the county with the highest ratio of whites to the total population.
Do the same for blacks.

Results: fastforward to the very end.
  
  /*
    Want: white people in an agegroup
  */
  race(2011,(20,40),people,fipspipe,true)

krishnanraman

RESULT:
[tw-mbp13-kraman algebird (develop)]$ scala ProportionMonoid
List(1.0, 3.0)=>List(0.25, 0.75)
List(1.0, 2.0, 5.0, 2.0, 6.0, 4.0)=>List(0.05, 0.1, 0.25, 0.1, 0.3, 0.2)

SOURCE:
case class ProportionMonoid(x:Seq[Double]) {
  private val n = x.size
  if (n < 2) throw new RuntimeException("No proportions to compute!!!")