In Scala 2.8, what type to use to store an in-memory mutable data table?

Question

Each time a function is called, if it's result for a given set of argument values is not yet memoized I'd like to put the result into an in-memory table. One column is meant to store a result, others to store arguments values.

How do I best implement this? Arguments are of diverse types, including some enums.

In C# I'd generally use DataTable. Is there an equivalent in Scala?

Answer 1

You could use a mutable.Map[TupleN[A1, A2, ..., AN], R] , or if memory is a concern, a WeakHashMap[1]. The definitions below (built on the memoization code from michid's blog) allow you to easily memoize functions with multiple arguments. For example:

import Memoize._

def reallySlowFn(i: Int, s: String): Int = {
   Thread.sleep(3000)
   i + s.length
}

val memoizedSlowFn = memoize(reallySlowFn _)
memoizedSlowFn(1, "abc") // returns 4 after about 3 seconds
memoizedSlowFn(1, "abc") // returns 4 almost instantly

Definitions:

/**
 * A memoized unary function.
 *
 * @param f A unary function to memoize
 * @param [T] the argument type
 * @param [R] the return type
 */
class Memoize1[-T, +R](f: T => R) extends (T => R) {
   import scala.collection.mutable
   // map that stores (argument, result) pairs
   private[this] val vals = mutable.Map.empty[T, R]

   // Given an argument x, 
   //   If vals contains x return vals(x).
   //   Otherwise, update vals so that vals(x) == f(x) and return f(x).
   def apply(x: T): R = vals getOrElseUpdate (x, f(x))
}

object Memoize {
   /**
    * Memoize a unary (single-argument) function.
    *
    * @param f the unary function to memoize
    */
   def memoize[T, R](f: T => R): (T => R) = new Memoize1(f)

   /**
    * Memoize a binary (two-argument) function.
    * 
    * @param f the binary function to memoize
    * 
    * This works by turning a function that takes two arguments of type
    * T1 and T2 into a function that takes a single argument of type 
    * (T1, T2), memoizing that "tupled" function, then "untupling" the
    * memoized function.
    */
   def memoize[T1, T2, R](f: (T1, T2) => R): ((T1, T2) => R) = 
      Function.untupled(memoize(f.tupled))

   /**
    * Memoize a ternary (three-argument) function.
    *
    * @param f the ternary function to memoize
    */
   def memoize[T1, T2, T3, R](f: (T1, T2, T3) => R): ((T1, T2, T3) => R) =
      Function.untupled(memoize(f.tupled))

   // ... more memoize methods for higher-arity functions ...

   /**
    * Fixed-point combinator (for memoizing recursive functions).
    */
   def Y[T, R](f: (T => R) => T => R): (T => R) = {
      lazy val yf: (T => R) = memoize(f(yf)(_))
      yf
   }
}

The fixed-point combinator (Memoize1.Y) makes it possible to memoize recursive functions:

val fib: BigInt => BigInt = {                         
   def fibRec(f: BigInt => BigInt)(n: BigInt): BigInt = {
      if (n == 0) 1 
      else if (n == 1) 1 
      else (f(n-1) + f(n-2))                           
   }                                                     
   Memoize.Y(fibRec)
}

[1] WeakHashMap does not work well as a cache. See http://www.codeinstructions.com/2008/09/weakhashmap-is-not-cache-understanding.html and this related question.

Answer 2

The version suggested by anovstrup using a mutable Map is basically the same as in C#, and therefore easy to use.

But if you want you can also use a more functional style as well. It uses immutable maps, which act as a kind of accumalator. Having Tuples (instead of Int in the example) as keys works exactly as in the mutable case.

def fib(n:Int) = fibM(n, Map(0->1, 1->1))._1

def fibM(n:Int, m:Map[Int,Int]):(Int,Map[Int,Int]) = m.get(n) match {
   case Some(f) => (f, m)
   case None => val (f_1,m1) = fibM(n-1,m)
                val (f_2,m2) = fibM(n-2,m1)
                val f = f_1+f_2
                (f, m2 + (n -> f))   
}

Of course this is a little bit more complicated, but a useful technique to know (note that the code above aims for clarity, not for speed).

Answer 3

Being a newbie in this subject, I could fully understand none of the examples given (but would like to thank anyway). Respectfully, I'd present my own solution for the case some one comes here having a same level and same problem. I think my code can be crystal clear for anybody having just the very-very basic Scala knowledge.

def MyFunction(dt : DateTime, param : Int) : Double
{
  val argsTuple = (dt, param)
  if(Memo.contains(argsTuple)) Memo(argsTuple) else Memoize(dt, param, MyRawFunction(dt, param))
}

def MyRawFunction(dt : DateTime, param : Int) : Double
{
  1.0 // A heavy calculation/querying here
}

def Memoize(dt : DateTime, param : Int, result : Double) : Double
{
  Memo += (dt, param) -> result
  result
}

val Memo = new  scala.collection.mutable.HashMap[(DateTime, Int), Double]

Works perfectly. I'd appreciate critique If I've missed something.