Re: [netmod] Justification for decimal64 over string for floating point values in geo location data?

[Juergen Schoenwaelder <[e-mail]>]

Precision often means different things to different people. Here is my
take:

- Floating point numbers have almost always rounding errors. And
  floating point numbers use binary fractions, a decimal fraction like
  1.0 has no precise representation as a binary fraction. Type 0.1 +
  0.2 into python or haskell or any other language that gives you bare
  floating point numbers and enjoy the result.

- Fixed precision decimal numbers do not have rounding errors since
  they are essentially scaled integers and hence they are precise as
  long as calculations stay within the range.

- Floating point numbers can cover a large number space (from very
  tiny to really big), fixed precision decimal numbers are much more
  restrictive.

- In XML and JSON, numbers are rendered in strings that likely do not
  look much different if its a decimal64 or a float or ... If you really
  care about size, use a binary encoding like CBOR.

/js

On Tue, Jul 07, 2020 at 07:06:20AM -0400, Christian Hopps wrote:
> I received feedback in my YANG doctor review (thanks Mahesh) regarding the use of decimal64 for most of the values in the geo location grouping (https://tools.ietf.org/html/draft-ietf-netmod-geo-location-04). In my comparison sections I note that some precision (at the very extremes) may be lost when converting from other geo location formats that use string (or double for w3c) to decimal64. Given that mention of loss of extreme precision, the reviewer was asking if some justification for the decimal64 should be given in the document.
> 
> What are the advantages to using decimal64 for floating point numbers vs using a string with a pattern "[0-9]+(\.[0-9]+)?" (convert that to yang pattern language). The advantage of using a string is that the precision of the value is not restricted by the model. Does the YANG decimal64 values have a concise binary format that can be more efficiently transported or stored in binary form? If so is this the only advantage, and is it enough of one to limit the precision in the model?
> 
> It's definitely worth noting that the precision of the decimal64 values seem vastly adequate for geo location data (e.g., for Cartesian coordinates and height values which are measured in meters the fractional digits is 6 which means the surface could be up to 9 billion kilometers large (or away from for height) and the precision is to the micrometer. For ellipsoidal coordinates there are 12 fractional digits for the degrees.
> 
> Thanks,
> Chris.



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Juergen Schoenwaelder           Jacobs University Bremen gGmbH
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