Hi,
This is a floating point rounding error. There are many numbers that
floating points cannot represent accurately and if you assign 1.9 to a
float, the value that will be stored is actually closer to 1.89999998.
Since the range query internally works on doubles, which have better
precision than floats, the 1.9 from the facet definition is not precisely
1.9 but very close thanks to double precision while your float in the index
is still close to 1.89999998 and thus compares less than the double
representation of 1.9.
There are two ways to work around this issue:
- either store the imageSize field as a double so that the rounding error
will be the same at indexing and faceting time,
- or if your numbers have a finite number of digits, store them as
integers instead. For example, if your image sizes only have two digits,
you could store them all as (imageSize*10^2) which is an integer, and
change your facet definition to be `from: 190` instead of `from: 1.9`. This
requires a bit more work on client side but a great benefit is that there
won't be any more rounding issue.
On Wed, Jan 15, 2014 at 8:10 AM, Jettro Coenradie <
jettro.coenra...@gmail.com> wrote:
> Hi,
> I have a float field that I want to use in a range facet. I give one range
> to the facet like the following query.
>
> {
> "query": {
> "matchAll": {}
> },
> "facets": {
> "imageSize": {
> "range": {
> "field": "imageSize",
> "ranges": [
> {
> "from": 1.9
> }
> ]
> }
> }
> }
> }
>
> If I have a document with the value 1.9 it is not included in the results.
> If I change the from part to 1.89 however it is included. I am sure the
> type of the field is 1.9, checked it in the mapping.
>
> Any ideas what is the problem?
>
> thanks,
> Jettro
>
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Adrien Grand
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