I see the use case for building deep nested docs from JDBC river (and other
data sources).

In JDBC river, there are open questions about nested result sets, which is
similar.

I have to think more about multiple SQL statements and create "merge
points" to construct bigger JSON from them in a natural way...

Jörg


On Tue, Apr 22, 2014 at 10:05 PM, jrizzi1 <[email protected]> wrote:

> Thank you for that gist, it really helps to see a full setup like that from
> start to finish
>
> I had attempted originally a parent-child relationship for this data by
> using _parent in separate river(s), but decided to go to a  single index
> because when I attempted to return child information you can only return
> parent properties not children properties as well in the hits, and wasnt
> sure how to additionally query to get child property data
>
>
> but reality is I more than likely need to switch to this setup to retain
> all
> of my data, which is the least of all evils
>
>
>
> [email protected] wrote
> > Is this parent/child example sketching the challenge you are facing?
> >
> > https://gist.github.com/jprante/11191387
> >
> > Jörg
> >
> >
> > On Tue, Apr 22, 2014 at 9:05 PM,
>
> > joergprante@
>
> >  <
>
> > joergprante@
>
> >> wrote:
> >
> >> Have you tried parent/child ?
> >>
> >> The idea is to execute has_parent queries on address type to find parent
> >> ID
> >>
> http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-has-parent-query.html
> >>
> >> I can prepare an example ...
> >>
> >> Jörg
> >>
> >>
> >> On Tue, Apr 22, 2014 at 8:53 PM, jrizzi1 &lt;
>
> > jrizzi1@
>
> > &gt; wrote:
> >>
> >>> Hi Jorg,
> >>>
> >>> I wanted a single ES document, i have a primary table that has unique
> >>> id's
> >>> and the names is a 1:N relation, and the address is  a 1:N relation
> >>>
> >>> reason being is we will need to search on names and addresses to try to
> >>> find
> >>> the unique ID for the individual so we can do further processing
> >>>
> >>>
> >>> how could I search over several indicies and merge those results back
> >>> together to give the one unique id that best matches? my initial test
> of
> >>> splitting these apart into different indicies is showing addresses and
> >>> entities littered in the same result set, i havent any idea how to get
> a
> >>> commonality between them
> >>>
> >>>
> >>> the addresses and names dont really have unique identifiers of their
> >>> own,
> >>> they are sequenced by the primary ID, example: if the primary table ID
> >>> is
> >>> '1001', and he has three addresses then the unqiue ID for those rows
> >>> would
> >>> be id='1001', sequence='1', id='1001', sequence='2', ... etc
> >>>
> >>>
> >>>
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