When a global property is used and the index is global, and the property has its own unique index in each label, the query is abnormal.

[MiracleDx]

this is a demo about schema.

m = graph.openManagement()

v1 = m.makeVertexLabel('v1').make()
v2 = m.makeVertexLabel('v2').make()
v3 = m.makeVertexLabel('v3').make()

cp0 = m.makePropertyKey('cp0').dataType(String.class).make()
cp1 = m.makePropertyKey('cp1').dataType(String.class).make()
cp2 = m.makePropertyKey('cp2').dataType(String.class).make()

cp0_c = m.buildIndex('cp0_c', Vertex.class).addKey(cp0).buildCompositeIndex()
cp1_c = m.buildIndex('cp1_c', Vertex.class).addKey(cp1).unique().buildCompositeIndex()
cp2_c = m.buildIndex('cp2_c', Vertex.class).addKey(cp2).buildCompositeIndex()
cp2_c_v3 = m.buildIndex('cp2_c_v3', Vertex.class).addKey(cp2).indexOnly(v3).unique().buildCompositeIndex()

m.setConsistency(cp0, ConsistencyModifier.LOCK) 
m.setConsistency(cp1, ConsistencyModifier.LOCK) 
m.setConsistency(cp2, ConsistencyModifier.LOCK) 
m.setConsistency(cp0_c, ConsistencyModifier.LOCK) 
m.setConsistency(cp1_c, ConsistencyModifier.LOCK) 
m.setConsistency(cp2_c, ConsistencyModifier.LOCK)
m.setConsistency(cp2_c_v3, ConsistencyModifier.LOCK) 

m.commit()


------------------------------------------------------------------------------------------------
Vertex Label Name              | Partitioned | Static                                             |
---------------------------------------------------------------------------------------------------
v3                             | false       | false                                              |
v1                             | false       | false                                              |
v2                             | false       | false                                              |
---------------------------------------------------------------------------------------------------
Edge Label Name                | Directed    | Unidirected | Multiplicity                         |
---------------------------------------------------------------------------------------------------
v1_rt1_v2                      | true        | false       | MULTI                                |
v2_rt1_v3                      | true        | false       | MULTI                                |
---------------------------------------------------------------------------------------------------
Property Key Name              | Cardinality | Data Type                                          |
---------------------------------------------------------------------------------------------------
cp0                            | SINGLE      | class java.lang.String                             |
cp1                            | SINGLE      | class java.lang.String                             |
cp2                            | SINGLE      | class java.lang.String                             |
---------------------------------------------------------------------------------------------------
Graph Index (Vertex)           | Type        | Unique    | Backing        | Key:           Status |
---------------------------------------------------------------------------------------------------
cp0_c                          | Composite   | false     | internalindex  | cp0:          ENABLED |
cp2_c                          | Composite   | false     | internalindex  | cp2:          ENABLED |
cp1_c                          | Composite   | true      | internalindex  | cp1:          ENABLED |
cp2_c_v3                       | Composite   | true      | internalindex  | cp2:          ENABLED |
---------------------------------------------------------------------------------------------------
Graph Index (Edge)             | Type        | Unique    | Backing        | Key:           Status |
---------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------
Relation Index (VCI)           | Type        | Direction | Sort Key       | Order    |     Status |
---------------------------------------------------------------------------------------------------

This is data and gremlin query

gremlin> g.V().elementMap()
==>{cp0=v1, cp1=2, cp2=cp2, id=81924096, label=v1}
==>{cp0=v2, cp1=6, cp2=cp2, id=81928192, label=v2}
==>{cp0=v2, cp1=4, cp2=cp2, id=12440, label=v2}
==>{cp0=v3, cp1=9, cp2=v3_cp2_3, id=16536, label=v3}
==>{cp0=v3, cp1=8, cp2=v3_cp2_2, id=40972488, label=v3}
==>{cp0=v1, cp1=1, cp2=cp2, id=40964304, label=v1}
==>{cp0=v1, cp1=3, cp2=cp2, id=12504, label=v1}
==>{cp0=v3, cp1=7, cp2=v3_cp2_1, id=16600, label=v3}
==>{cp0=v2, cp1=5, cp2=cp2, id=40964328, label=v2}
gremlin> g.E().elementMap()
==>{id=2pkjcw-1crx1c-8v85-1cs074, label=v1_rt1_v2, IN={id=81928192, label=v2}, OUT={id=81924096, label=v1}}

gremlin>  g.V().has('cp1','2').out('v1_rt1_v2').has('cp2','cp2').elementMap()
gremlin>  g.V().has('cp1','2').out('v1_rt1_v2').elementMap()
==>{cp0=v2, cp1=6, cp2=cp2, id=81928192, label=v2}


gremlin>  g.V().has('cp1','2').out('v1_rt1_v2').has('cp2','cp2').explain()
==>Traversal Explanation
============================================================================================================================================================================================================
Original Traversal                                     [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]

ConnectiveStrategy                               [D]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
IdentityRemovalStrategy                          [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
MatchPredicateStrategy                           [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
FilterRankingStrategy                            [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
ByModulatorOptimizationStrategy                  [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
InlineFilterStrategy                             [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
IncidentToAdjacentStrategy                       [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
RepeatUnrollStrategy                             [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
PathRetractionStrategy                           [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
AdjacentToIncidentStrategy                       [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
CountStrategy                                    [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
EarlyLimitStrategy                               [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
LazyBarrierStrategy                              [O]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
AdjacentVertexHasIdOptimizerStrategy             [P]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
AdjacentVertexIsOptimizerStrategy                [P]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],vertex), HasStep([cp2.eq(cp2)])]
AdjacentVertexHasUniquePropertyOptimizerStrategy [P]   [GraphStep(vertex,[]), HasStep([cp1.eq(2)]), VertexStep(OUT,[v1_rt1_v2],edge), HasStep([~adjacent.eq(40964304)]), EdgeVertexStep(IN)]

The problem is ~adjacent.eq(40964304), it will be returned a random vertex when has step matched

• Version: 1.0.0
• Storage Backend: hbase
• Mixed Index Backend: elasticsearch
• Expected Behavior: Each label has its own unique configuration in effect
• Current Behavior: gremlin will return random result when has("") step matched in global index