In a graph database, I have graphs like:
v1: Protein{prefName: 'QP1'}
-- r1: part_of{evidence: 'ns:testdb'}
--> v2: Protcmplx{prefName: 'P12 Complex'}
ev: EvidenceType{ iri = "ns:testdb", label = "Test Database" }
I'd like to write a Gremlin query to fetch instances of the part_of relationship and return v1 and v2's prefName, along with the evidence's label. So far I've tried this:
g.V().hasLabel( containing('Protein') ).as('p')
.outE().hasLabel( 'is_part_of' ).as('pr')
.inV().hasLabel( containing('Protcmplx') ).as('cpx')
.V().hasLabel( containing('EvidenceType') ).as('ev')
.has( 'iri', eq( select('pr').by('evidence') ) )
.select( 'p', 'cpx', 'ev', 'pr' )
.by('prefName')
.by('prefName')
.by('label')
.by('evidence')
.limit(100)
But it takes a lot of time for a few thousand nodes+edeges, and eventually, it doesn't return anything. I'm sure the values are there and I think the problem is with has( 'iri', ... ), but I can't figure out how to match an edge property with another property in an unconnected vertex.
The graph is modelled this way, cause the LPG model doesn't allow for hyper-edges (linking >2 vertices).
I've found a way using where() and by(), but it is quite slow (11secs to get 100 tuples from a few thousands nodes+edges):
g.V().hasLabel ( containing ( 'Protcmplx' ) ).as ( 'cpx' )
.inE().hasLabel ( 'is_part_of' ).limit ( 10 ).as ( 'pr' )
.outV ().hasLabel ( containing ( 'Protein' ) ).as ( 'p' )
.V().hasLabel ( containing ( 'EvidenceType' ) ).as ( 'ev' )
.where ( 'ev', eq ( 'pr' ) ).by ( 'iri' ).by ( 'evidence' )
.select ( 'p', 'cpx', 'ev' )
.by ( 'prefName' )
.by ( 'prefName' )
.by ( 'label' )
Any help with optimisation would be welcome!
EDIT: following a suggestion from the comments (thanks!), I've rewritten the solution a bit (it's still slow) and used .profile() at the end, obtaining this:
Traversal Metrics
Step Count Traversers Time (ms) % Dur
=============================================================================================================
GraphStep(vertex,[]) 123591 123591 507.179 9.09
HasStep([~label.containing(Protcmplx)])@[cpx] 10 10 34.313 0.61
VertexStep(IN,[is_part_of],edge)@[pr] 13 13 5.089 0.09
RangeGlobalStep(0,10) 10 10 0.094 0.00
EdgeVertexStep(OUT) 10 10 1.618 0.03
HasStep([~label.containing(Protein)])@[p] 10 10 0.065 0.00
GraphStep(vertex,[]) 1738360 1738360 4574.578 81.99
HasStep([~label.containing(EvidenceType)])@[ev] 510 510 447.546 8.02
WherePredicateStep(ev,eq(pr),[value(iri), value... 10 10 6.747 0.12
NoOpBarrierStep(2500) 10 10 1.444 0.03
SelectStep(last,[p, cpx, ev],[value(prefName), ... 10 10 0.154 0.00
NoOpBarrierStep(2500) 10 8 0.785 0.01
>TOTAL - - 5579.617 -
So, the problem seems to be that the second V() picks up all the vertexes before the filters from the former traversal (on the where) can be applied. However, I can't find a way to avoid this. Does Gremlin have subqueries?
EDIT/2: inspired by the suggestion in the comments to use two separated queries (thanks!), I've tried this:
evLabels = [:]
g.V().hasLabel ( containing ( 'Protcmplx' ) ).as ( 'cpx' )
// Trying to put the limit early-on
.inE().hasLabel ( 'is_part_of' ).limit ( 100 ).as ( 'pr' )
.outV ().hasLabel ( containing ( 'Protein' ) ).as ( 'p' )
.select ( 'p', 'cpx', 'pr' )
.by ( 'prefName' )
.by ( 'prefName' )
.by ( map{
pr = it.get()
evIri = pr.values ( 'evidence' ).next ();
lbl = evLabels [ evIri ];
if ( lbl != null ) return lbl;
lbl = g.V().hasLabel ( containing ( 'EvidenceType' ) )
.has ( 'iri', evIri )
.values ( 'label' ).next ();
evLabels [ evIri ] = lbl == null ? "" : lbl;
return lbl;
})
Which avoids a full cartesian product join by accumulating sub-query results into a map. This is much faster than the original query (like <1s for 100 edges), but not very simple to read, I'm sure there is a better way to write the same.