Common practice how to deal with Integer overflows? - scala

Question

What's the common practice to deal with Integer overflows like 999999*999999 (result > Integer.MAX_VALUE) from an Application Development Team point of view?
One could just make BigInt mandatory and prohibit the use of Integer, but is that a good/bad idea?

Answer 1

If it is extremely important that the integer not overflow, you can define your own overflow-catching operations, e.g.:
def +?+(i: Int, j: Int) = {
val ans = i.toLong + j.toLong
if (ans < Int.MinValue || ans > Int.MaxValue) {
throw new ArithmeticException("Int out of bounds")
}
ans.toInt
}
You may be able to use the enrich-your-library pattern to turn this into operators; if the JVM manages to do escape analysis properly, you won't get too much of a penalty for it:
class SafePlusInt(i: Int) {
def +?+(j: Int) = { /* as before, except without i param */ }
}
implicit def int_can_be_safe(i: Int) = new SafePlusInt(i)
For example:
scala> 1000000000 +?+ 1000000000
res0: Int = 2000000000
scala> 2000000000 +?+ 2000000000
java.lang.ArithmeticException: Int out of bounds
at SafePlusInt.$plus$qmark$plus(<console>:12)
...
If it is not extremely important, then standard unit testing and code reviews and such should catch the problem in the large majority of cases. Using BigInt is possible, but will slow your arithmetic down by a factor of 100 or so, and won't help you when you have to use an existing method that takes an Int.

Answer 2

By far the most common practice regarding integer overflows is that programmers are expected to know that the issue exists, to watch for cases where they might happen, and to make the appropriate checks or rearrange the math so that overflows won't happen, things like doing a * (b / c) rather than (a * b) / c . If the project uses unit test, they will include cases to try and force overflows to happen.
I have never worked on or seen code from a team that required more than that, so I'm going to say that's good enough for almost all software.
The one embedded application I've seen that actually, honest-to-spaghetti-monster NEEDED to prevent overflows, they did it by proving that overflows weren't possible in each line where it looked like they might happen.

Answer 3

If you're using Scala (and based on the tag I'm assuming you are), one very generic solution is to write your library code against the scala.math.Integral type class:
def naturals[A](implicit f: Integral[A]) =
Stream.iterate(f.one)(f.plus(_, f.one))
You can also use context bounds and Integral.Implicits for nicer syntax:
import scala.math.Integral.Implicits._
def squares[A: Integral] = naturals.map(n => n * n)
Now you can use these methods with either Int or Long or BigInt as needed, since instances of Integral exist for all of them:
scala> squares[Int].take(10).toList
res0: List[Int] = List(1, 4, 9, 16, 25, 36, 49, 64, 81, 100)
scala> squares[Long].take(10).toList
res0: List[Long] = List(1, 4, 9, 16, 25, 36, 49, 64, 81, 100)
scala> squares[BigInt].take(10).toList
res1: List[BigInt] = List(1, 4, 9, 16, 25, 36, 49, 64, 81, 100)
No need to change the library code: just use Long or BigInt where overflow is a concern and Int otherwise.
You will pay some penalty in terms of performance, but the genericity and the ability to defer the Int-or-BigInt decision may be worth it.

Answer 4

In addition to simple mindfulness, as noted by #mjfgates, there are a couple of practices that I always use when dealing with scaled-decimal (non-floating-point) real-world quantities. This may not be on point for your particular application - apologies in advance if not.
First, if there are multiple units of measure in use, values must always clearly identify what they are. This can be by naming convention, or by using a separate class for each unit of measure. I've always just used names - a suffix on every variable name. In addition to eliminating errors from confusion over the units, it encourages thinking about overflow because the measures are less likely to be thought of as just numbers.
Second, my most frequent source of overflow concern is usually rescaling - converting from one measure to another - when it requires a lot of significant digits. For example, the conversion factor from cm to inches is 0.393700787402. In order to avoid both overflow and loss of significant digits, you need to be careful to multiply and divide in the right order. I haven't done this in a long time, but I believe what you want is something like:
Add to Rational.scala, from The Book:
def rescale(i:Int) : Int = {
(i * (numer/denom)) + (i/denom * (numer % denom))
Then you get as results (shortened from a specs2 test):
val InchesToCm = new Rational(1000000000,393700787)
InchesToCm.rescale(393700787) must_== 1000000000
InchesToCm.rescale(1) must_== 2
This doesn't round, or deal with negative scaling factors.
A production implementation may want to factor out numer/denom and numer % denom.