Hi,
> Scenario 1:
> I would like to get all Nodes that do not have a relationship to another
> node. What is the best way to do this Gremlin?
>
> Root => NodeA => NodeB
> Root => NodeC
>
> Output should be NodeC
I don't understand the problem.
> Scenario 2:
> Root = > User -> Centre
>
> I would like to get all centres for userA, and then get all users who are
> also linked to the same centres of the userA
userA.out('hasCentre').in('hasCentre').except([userA])
> Scenario 3:
> User => Centre
> I would like to get all users that do not have a link to a Centre, and if
> this is the case, do a projection that returns a fake centre with property
> "Unknown"
g.V.ifThenElse{it.out('hasCentre').filter{it ==
aCentre}.hasNext()}{}{println "${it} has center unknown"}
If you have an index of your Users, then its more efficient to hit that index,
then iterate through all vertices (g.V). E.g.
g.idx(T.v)[[type:'User']].ifThenElse....
> Scenario 4:
>
> REFERRAL => PERSON
>
> I would like to combine a query that gets all
> Referrals that are linked to a person and project a table result (contains
> referrals and person property values (this is easy to do)
> However, I then want to do a SPLIT query, that gets referrals without the
> persons, and then MERGE both back into the table projection? So table
> projecton might look like this.
> I guess this is done with .table().it{}....cap, but what I am not sure, is
> how to do a split and merge in parallel and then get that merge projected
> into the same table.
I don't quite understand your problem, but here is an example of splitting and
merging.
g.v(1).out('knows').copySplit(_().out('knows').name,
_().out('created').name).fairMerge
This will get all the people that v[1] knows and then generate two parallel
pipeline. Each friend of v[1] is copied to each of the 2 parallel pipelines.
One pipeline will get their friend's names and one will get their created
project's names. It will then merge those two parallel pipelines into a single
stream. You can do either fairMerge or exhaustMerge depending on the merging
algorithm you want. fairMerge is more memory efficient. I need to write more
about split/merge in the Gremlin documentation.
https://github.com/tinkerpop/gremlin/wiki/Split-Merge-Pattern <-- will
work on it :)
HTH,
Marko.
http://markorodriguez.com
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