[
https://issues.apache.org/jira/browse/TINKERPOP-2342?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Stephen Mallette closed TINKERPOP-2342.
---------------------------------------
Resolution: Not A Problem
I can't remember when this was fixed, but it is working now at least as of
3.5.1:
{code}
gremlin> g.V().repeat(outE().has("dist", 10).inV().outE().inV().sideEffect{i =
0}).times(1).path()
==>[v[0],e[1][0-link->0],v[0],e[1][0-link->0],v[0]]
{code}
> FilterRankingStrategy interferes with IncidentToAdjacentStrategy
> ----------------------------------------------------------------
>
> Key: TINKERPOP-2342
> URL: https://issues.apache.org/jira/browse/TINKERPOP-2342
> Project: TinkerPop
> Issue Type: Bug
> Components: process
> Affects Versions: 3.4.4
> Reporter: Florian G
> Priority: Minor
> Attachments: explain.txt
>
>
> The implementation of {{IncidentToAdjacentStrategy}} replaces steps from V to
> E followed by a step from E to V by a single step from V to V. In order to
> avoid touching parts of the query which may not be reordered, this strategy
> marks certain steps with a special label.
> In some cases however, these labels are pushed to other steps by the
> FilterRankingStrategy. As a consequence, the IncidentToAdjacentStrategy does
> not behave as expected anymore.
> See the following example:
> Assume a graph consisting of a single Vertex {{v}} connected to itself by an
> edge with the property {{dist = 10}}.
> {code:java}
> g.V().repeat(outE().has("dist", 10).inV().outE().inV().sideEffect{i =
> 0}).times(1).path()
> {code}
> - The {{sideEffect}} step just sets a dummy variable and is necessary to
> cause the problem.
> - The {{has}} step has no semantic effect of filtering but is necessary to
> cause the problem as well.
> - The {{path}} step shows how the output differs from the expected output.
> One would expect a result like:
> {code:java}
> path[v[1], e[2][1->label->1], v[1], e[2][1->label->1], v[1]]
> {code}
> But the actual result is:
> {code:java}
> path[v[1], e[2][1->label->1], v[1], v[1]]
> {code}
> Let's call {{explain()}} on the above query to see why this happens:
> {code:java}
> ==>Traversal Explanation
> =================================================================================================================================================================================================================================================================================================
> Original Traversal [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge), HasStep([dist.eq(10)]),
> EdgeVertexStep(IN), VertexStep(OUT,edge), EdgeVertexStep(IN),
> LambdaSideEffectStep(lambda), RepeatEndStep],until(loops(1)),emit(false)),
> PathStep]
> ConnectiveStrategy [D] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge), HasStep([dist.eq(10)]), EdgeVertexStep(IN),
> VertexStep(OUT,edge), EdgeVertexStep(IN), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> EarlyLimitStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge), HasStep([dist.eq(10)]), EdgeVertexStep(IN),
> VertexStep(OUT,edge), EdgeVertexStep(IN), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> MatchPredicateStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge), HasStep([dist.eq(10)]), EdgeVertexStep(IN),
> VertexStep(OUT,edge), EdgeVertexStep(IN), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> FilterRankingStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge), HasStep([dist.eq(10)]), EdgeVertexStep(IN),
> VertexStep(OUT,edge), EdgeVertexStep(IN), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> InlineFilterStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge), HasStep([dist.eq(10)]), EdgeVertexStep(IN),
> VertexStep(OUT,edge), EdgeVertexStep(IN), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> RepeatUnrollStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge), HasStep([dist.eq(10)]), EdgeVertexStep(IN),
> VertexStep(OUT,edge), EdgeVertexStep(IN), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> IncidentToAdjacentStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge),
> HasStep([dist.eq(10)])@[~gremlin.incidentToAdjacent], EdgeVertexStep(IN),
> VertexStep(OUT,vertex), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> CountStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge),
> HasStep([dist.eq(10)])@[~gremlin.incidentToAdjacent], EdgeVertexStep(IN),
> VertexStep(OUT,vertex), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> PathRetractionStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge),
> HasStep([dist.eq(10)])@[~gremlin.incidentToAdjacent], EdgeVertexStep(IN),
> VertexStep(OUT,vertex), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> AdjacentToIncidentStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge),
> HasStep([dist.eq(10)])@[~gremlin.incidentToAdjacent], EdgeVertexStep(IN),
> VertexStep(OUT,vertex), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> LazyBarrierStrategy [O] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge),
> HasStep([dist.eq(10)])@[~gremlin.incidentToAdjacent], EdgeVertexStep(IN),
> VertexStep(OUT,vertex), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> AdjacentVertexFilterOptimizerStrategy [P] [GraphStep(vertex,[]),
> RepeatStep([VertexStep(OUT,edge),
> HasStep([dist.eq(10)])@[~gremlin.incidentToAdjacent], EdgeVertexStep(IN),
> VertexStep(OUT,vertex), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> JanusGraphLocalQueryOptimizerStrategy [P] [GraphStep(vertex,[]),
> RepeatStep([JanusGraphVertexStep([dist.eq(10)])@[~gremlin.incidentToAdjacent],
> EdgeVertexStep(IN), JanusGraphVertexStep(OUT,vertex),
> LambdaSideEffectStep(lambda), RepeatEndStep],until(loops(1)),emit(false)),
> PathStep]
> JanusGraphStepStrategy [P] [JanusGraphStep(vertex,[]),
> RepeatStep([JanusGraphVertexStep([dist.eq(10)])@[~gremlin.incidentToAdjacent],
> EdgeVertexStep(IN), JanusGraphVertexStep(OUT,vertex),
> LambdaSideEffectStep(lambda), RepeatEndStep],until(loops(1)),emit(false)),
> PathStep]
> JanusGraphIoRegistrationStrategy [P] [JanusGraphStep(vertex,[]),
> RepeatStep([JanusGraphVertexStep([dist.eq(10)])@[~gremlin.incidentToAdjacent],
> EdgeVertexStep(IN), JanusGraphVertexStep(OUT,vertex),
> LambdaSideEffectStep(lambda), RepeatEndStep],until(loops(1)),emit(false)),
> PathStep]
> ProfileStrategy [F] [JanusGraphStep(vertex,[]),
> RepeatStep([JanusGraphVertexStep([dist.eq(10)])@[~gremlin.incidentToAdjacent],
> EdgeVertexStep(IN), JanusGraphVertexStep(OUT,vertex),
> LambdaSideEffectStep(lambda), RepeatEndStep],until(loops(1)),emit(false)),
> PathStep]
> StandardVerificationStrategy [V] [JanusGraphStep(vertex,[]),
> RepeatStep([JanusGraphVertexStep([dist.eq(10)]), EdgeVertexStep(IN),
> JanusGraphVertexStep(OUT,vertex), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> Final Traversal [JanusGraphStep(vertex,[]),
> RepeatStep([JanusGraphVertexStep([dist.eq(10)]), EdgeVertexStep(IN),
> JanusGraphVertexStep(OUT,vertex), LambdaSideEffectStep(lambda),
> RepeatEndStep],until(loops(1)),emit(false)), PathStep]
> {code}
> As one can see, after the IncidentToAdjacentStrategy call, there exists a
> hidden label on the HasStep. This label was originally placed on the
> preceeding VertexStep by the IncidentToAdjacentStrategy in order to avoid
> optimizations on the inner part of the query. However, an intermediate call
> of the FilterRankingStrategy pushes this label one step to the right, which
> causes the IncidentToAdjacentStrategy to "forget" it's restrictions.
> Consequently, the query is in optimized by mistake.
> Just for comparison: If the sideEffect step is removed, the query returns the
> expected result.
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