# To Float Or Not To Float

They say money makes the work go 'round. And these days computers are a big part of how the money moves. From accounting to checkbook management to online purchases, it's all about the Benjamins. Unfortunately, computers are really bad with money.

Which is odd, because what computers are really good at is arithmetic. They can add, subtract, multiply, and, except for some Pentium 4s, divide. Pretty much everything else they do is just doing that really quickly in complex patterns. So why are they so bad with money?

It comes down to the fact that computers do everything in base 2, and, since they only use non-negative exponents, the only thing that you can represent exactly are integers. Unfortunately, there are an infinite number of real numbers between any two integers. And almost all the programming languages we use attempt to model them with approximations. Floats, Doubles, Longs, Long Longs, and when we eventually start talking about Double Plus Long Longs, are just more and more accurate attempts.

But no matter how much precision you have, it's still an approximation. Your typical Float has 7 digits of precision. When you're dealing with money that can be a problem. Not for the kind of transactions that people typically deal with, but when the numbers get large, things happen.

We ran into this when we were working on Falcon 4.0. We modeled the entire Korean peninsula to handle a dynamic simulation of the air-land battle. Everything worked fine below the DMZ, but as the front line moved north the land part of the battle ground to a halt. Our tanks and infantry literally couldn't move. It turned out that the combination of velocity and simulation step size meant that each time we advanced the simulation that forward distance the vehicles traveled was less than the minimum quantum of a floating point number that far from the origin. So nothing moved. To keep things moving we ended up moving the origin of the grid from the southwest corner of the map to the center. Then things could move at both extremes.

The same thing can (and will, if you're not careful) happen with money. If you have over \$1,000,000 and you try to calculate interest over time then your interest calculations are going to be wrong. And the more money you have, the more inaccurate they'll be. You can switch from floats to longs to long longs, but it just buys you time, not a real solution.

So what can you do? One option might be to model pennies instead of dollars. Then you're dealing with integers instead of approximations, but that doesn't really work either. Your percentages are still non-integers. So you multiply them but some factor to make them whole numbers. Which works for a while, until someone wants to use pennies per thousand dollars instead of pennies per hundred dollars for a mill levy.

SQL has a solution. The Decimal type. Which makes sense since the early databases were all about tracking money, Instead of approximating, you specify how many digits before and after the decimal you want and it does the right thing in the background. But it's slow, and only works in SQL, so it's not a general solution. Java's BigDecimal works the same way.

So, before you go stuffing your dollars and cents into a Float, think about your boundary cases. Maybe a double or long long is enough. Maybe you can switch to integers, or maybe you need to find a Decimal library for your language. It depends.