Why would you use a string in JSON to represent a decimal number
I'll be a bit contrarian and say that 7.47 is perfectly safe in JSON, even for financial amounts, and that "7.47" isn't any safer.
First, let me address some misconceptions from this thread:
So, parsing 1.7 into an in-memory floating point number, then printing out the number will likely return something like 1.69 - not 1.7.
That is not true, especially in the context of IEEE 754 double precision format that was mentioned in that answer. 1.7 converts into an exact double 1.6999999999999999555910790149937383830547332763671875 and when that value is "printed" for display, it will always be 1.7, and never 1.69, 1.699999999999 or 1.70000000001. It is 1.7 "exactly".
Learn more here.
7.47 may actually be 7.4699999923423423423 when converted to float
7.47 already is a float, with an exact double value 7.46999999999999975131004248396493494510650634765625. It will not be "converted" to any other float.
a simple system that simply truncates the extra digits off will result in 7.46 and now you've lost a penny somewhere
IEEE rounds, not truncates. And it would not convert to any other number than 7.47 in the first place.
is the JSON number actually a float? As I understand it's a language independent number, and you could parse a JSON number straight into a java BigDecimal or other arbitrary precision format in any language if so inclined.
It is recommended that JSON numbers are interpreted as doubles (IEEE 754 double-precision format). I haven't seen a parser that wouldn't be doing that.
And no, BigDecimal(7.47) is not the right way to do it – it will actually create a BigDecimal representing the exact double of 7.47, which is 7.46999999999999975131004248396493494510650634765625. To get the expected behavior, BigDecimal("7.47") should be used.
Overall, I don't see any fundamental issue with {"price": 7.47}. It will be converted into a double on virtually all platforms, and the semantics of IEEE 754 guarantee that it will be "printed" as 7.47 exactly and always.
Of course floating point rounding errors can happen on further calculations with that value, see e.g. 0.1 + 0.2 == 0.30000000000000004, but I don't see how strings in JSON make this better. If "7.47" arrives as a string and should be part of some calculation, it will need to be converted to some numeric data type anyway, probably float :).
It's worth noting that strings also have disadvantages, e.g., they cannot be passed to Intl.NumberFormat, they are not a "pure" data type, e.g., the dot is a formatting decision.
I'm not strongly against strings, they seem fine to me as well but I don't see anything wrong on {"price": 7.47} either.
The main reason to transfer numeric values in JSON as strings is to eliminate any loss of precision or ambiguity in transfer.
It's true that the JSON spec does not specify a precision for numeric values. This does not mean that JSON numbers have infinite precision. It means that numeric precision is not specified, which means JSON implementations are free to choose whatever numeric precision is convenient to their implementation or goals. It is this variability that can be a pain if your application has specific precision requirements.
Loss of precision generally isn't apparent in the JSON encoding of the numeric value (1.7 is nice and succinct) but manifests in the JSON parsing and intermediate representations on the receiving end. A JSON parsing function would quite reasonably parse 1.7 into an IEEE double precision floating point number. However, finite length / finite precision decimal representations will always run into numbers whose decimal expansions cannot be represented as a finite sequence of digits:
Irrational numbers (like pi and e)
1.7 has a finite representation in base 10 notation, but in binary (base 2) notation, 1.7 cannot be encoded exactly. Even with a near infinite number of binary digits, you'll only get closer to 1.7, but you'll never get to 1.7 exactly.
So, parsing 1.7 into an in-memory floating point number, then printing out the number will likely return something like 1.69 - not 1.7.
Consumers of the JSON 1.7 value could use more sophisticated techniques to parse and retain the value in memory, such as using a fixed-point data type or a "string int" data type with arbitrary precision, but this will not entirely eliminate the specter of loss of precision in conversion for some numbers. And the reality is, very few JSON parsers bother with such extreme measures, as the benefits for most situations are low and the memory and CPU costs are high.
So if you are wanting to send a precise numeric value to a consumer and you don't want automatic conversion of the value into the typical internal numeric representation, your best bet is to ship the numeric value out as a string and tell the consumer exactly how that string should be processed if and when numeric operations need to be performed on it.
For example: In some JSON producers (JRuby, for one), BigInteger values automatically output to JSON as strings, largely because the range and precision of BigInteger is so much larger than the IEEE double precision float. Reducing the BigInteger value to double in order to output as a JSON numeric will often lose significant digits.
Also, the JSON spec (http://www.json.org/) explicitly states that NaNs and Infinities (INFs) are invalid for JSON numeric values. If you need to express these fringe elements, you cannot use JSON number. You have to use a string or object structure.
Finally, there is another aspect which can lead to choosing to send numeric data as strings: control of display formatting. Leading zeros and trailing zeros are insignificant to the numeric value. If you send JSON number value 2.10 or 004, after conversion to internal numeric form they will be displayed as 2.1 and 4.
If you are sending data that will be directly displayed to the user, you probably want your money figures to line up nicely on the screen, decimal aligned. One way to do that is to make the client responsible for formatting the data for display. Another way to do it is to have the server format the data for display. Simpler for the client to display stuff on screen perhaps, but this can make extracting the numeric value from the string difficult if the client also needs to make computations on the values.
The reason I'm doing it is that the SoftwareAG parser tries to "guess" the java type from the value it receives.
So when it receives
"jackpot":{
"growth":200,
"percentage":66.67
}
The first value (growth) will become a java.lang.Long and the second (percentage) will become a java.lang.Double
Now when the second object in this jackpot-array has this
"jackpot":{
"growth":50.50,
"percentage":65
}
I have a problem.
When I exchange these values as Strings, I have complete control and can cast/convert the values to whatever I want.