Money VS 0.30000000000000004

Many other options are available to customize this for your Ruby on Rails app. Check out the full details in the money and money-rails guides.

For now I just try to use better objects, avoiding primitive datatypes in internal code (using the null object pattern or an option/maybe class can go a long way), and using gems that do the same, such as dry-rb and money.

Java has to have a modern money library by now? Ruby has one => https://github.com/RubyMoney/money

Make sure to refer to the docs for more of the provided currency attributes, such as iso_code (which returns the international three-digit code of that currency) and decimal_mark (the char between the value and the fraction of the money data), among others.

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Although it would be good to move in the direction of using a BigDecimal equivalent by default when ingesting unknown data.

There's no such thing as precision for floats. Floating-point calculations are always inaccurate: read this: https://0.30000000000000004.com/

Edit: This specific example even has its own website: https://0.30000000000000004.com/

In everyday math, we know adding 0.6 + 0.3 equals 0.9, right? But when we turn to computers it results in 0.8999999999999999. Surprisingly, this doesn’t just happen only in JavaScript; it’s the same in many programming languages like Python, Java, C too. Also, it’s not just about this specific calculation. There are many more decimal calculations showing similar not-quite-right answers.

Surprisingly common values like 0.1 don't have a precise representation in binary for most formats, including standard floating point number formats. See https://0.30000000000000004.com/ for more detail than you can shake a stick at.

Also if the local tax code states using 5 decimal places for intermediate values when you will introduce “errors” using formats that give greater precision as well as those that give less precision. Having work on mortgage and pension calculations I can state that the (very) small errors seen at individual steps because of this can balloon significantly through repeated calculations.

Furthmore, the name floating point gives away the other issue. Floating point numbers are accurate to a given number of significant figures not decimal places. For large numbers any decimal places you have in the result are at best an estimate, and as above any rounding errors at each stage can compound into a much larger error by the end of a calculation.

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