How to query for multiple vertices and counts of their relationships in Gremlin/Tinkerpop 3?

Question

I am using Gremlin/Tinkerpop 3 to query a graph stored in TitanDB.

The graph contains user vertices with properties, for example, "description", and edges denoting relationships between users.

I want to use Gremlin to obtain 1) users by properties and 2) the number of relationships (in this case of any kind) to some other user (eg, with id = 123). To realize this, I make use of the match operation in Gremlin 3 like so:

g.V().match('user',__.as('user').has('description',new P(CONTAINS,'developer')),
__.as('user').out().hasId(123).values('name').groupCount('a').cap('a').as('relationships'))
.select()

This query works fine, unless there are multiple user vertices returned, for example, because multiple users have the word "developer" in their description. In this case, the count in relationships is the sum of all relationships between all returned users and the user with id 123, and not, as desired, the individual count for every returned user.

Am I doing something wrong or is this maybe an error?

PS: This question is related to one I posted some time ago about a similar query in Tinkerpop 2, where I had another issue: How to select optional graph structures with Gremlin?

Answer 1

Here's the sample data I used:

graph = TinkerGraph.open()
g = graph.traversal()
v123=graph.addVertex(id,123,"description","developer","name","bob")
v124=graph.addVertex(id,124,"description","developer","name","bill")
v125=graph.addVertex(id,125,"description","developer","name","brandy")
v126=graph.addVertex(id,126,"description","developer","name","beatrice")
v124.addEdge('follows',v125)
v124.addEdge('follows',v123)
v124.addEdge('likes',v126)
v125.addEdge('follows',v123)
v125.addEdge('likes',v123)
v126.addEdge('follows',v123)
v126.addEdge('follows',v124)

My first thought, was: "Do we really need match step"? Secondarily, of course, I wanted to write this in TP3 fashion and not use a lambda/closure. I tried all manner of things in the first iteration and the closest I got was stuff like this from Daniel Kuppitz:

gremlin> g.V().as('user').local(out().hasId(123).values('name')
              .groupCount()).as('relationships').select()
==>[relationships:[:]]
==>[relationships:[bob:1]]
==>[relationships:[bob:2]]
==>[relationships:[bob:1]]

so here we used local step to restrict the traversal within local to the current element. This works, but we lost the "user" tag in the select . Why? groupCount is a ReducingBarrierStep and paths are lost after those steps.

Well, let's go back to match . I figured I could try to make the match step traverse using local :

gremlin> g.V().match('user',__.as('user').has('description','developer'),
gremlin>             __.as('user').local(out().hasId(123).values('name').groupCount()).as('relationships')).select()
==>[relationships:[:], user:v[123]]
==>[relationships:[bob:1], user:v[124]]
==>[relationships:[bob:2], user:v[125]]
==>[relationships:[bob:1], user:v[126]]

Ok - success - that's what we wanted: no lambdas and local counts. But, it still left me feeling like: "Do we really need match step"? That's when Mr. Kuppitz closed in on the final answer which makes copious use of the by step:

gremlin> g.V().has('description','developer').as("user","relationships").select().by()
              .by(out().hasId(123).values("name").groupCount())
==>[user:v[123], relationships:[:]]
==>[user:v[124], relationships:[bob:1]]
==>[user:v[125], relationships:[bob:2]]
==>[user:v[126], relationships:[bob:1]]

As you can see, by can be chained (on some steps). The first by groups by vertex and the second by processes the grouped elements with a "local" groupCount .