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我对 Neptune 中的请求性能有疑问。我有一个这样的图表：

hasId('A_id') (count: 1) -> out('has_group') (count: 12) -> out('has_class').hasLabel('C') (count: 9751) -> out('has_type ').hasLabel('D') (count: 9749) -> out('has_element') (count: 472370) -> hasLabel(Within(11 elements label)) (count: 107233)

我替换了所有的标签来简化，但图表完全是这样的。内部会大大降低查询的性能。更多细节 ：

g.V('0f7a21df-9413-4c71-99f3-242ae25356a5').out('has_group').out('has_class').hasLabel('C').out('has_type').hasLabel('D').out('has_element').count()

此请求不到 1 秒并返回 472370。

如果我像这样添加最后一个 hasLabel(whithin()) ：

g.V('0f7a21df-9413-4c71-99f3-242ae25356a5').out('has_group').out('has_class').hasLabel('C').out('has_type').hasLabel('D').out('has_element').hasLabel(P.within('element_1','element_2','element_3','element_4','element_5','element_6','element_7','element_8','element_9','element_10','element_11')).count()

时间减少到 18 秒。当我对查询进行分析时，我们可以看到 joinTime 至少占用了 inside 查询执行时间的 90%：

Optimized Traversal
===================
Neptune steps: [
    NeptuneCountGlobalStep {
        JoinGroupNode {
            PatternNode[(?1=<0f7a21df-9413-4c71-99f3-242ae25356a5>, ?5=<has_group>, ?3, ?6) . project ?1,?3 . IsEdgeIdFilter(?6) .
            ],
            {estimatedCardinality=12, expectedTotalOutput=12, indexTime=0, joinTime=1, numSearches=1, actualTotalOutput=12
            }
            PatternNode[(?3, ?9=<has_class>, ?7, ?10) . project ?3,?7 . IsEdgeIdFilter(?10) .
            ],
            {estimatedCardinality=208482, expectedTotalOutput=2000, indexTime=0, joinTime=9, numSearches=1, actualTotalOutput=10945
            }
            PatternNode[(?7, <~label>, ?8=<C>, <~>) . project ask .
            ],
            {estimatedCardinality=424296, expectedTotalOutput=2000, indexTime=5, joinTime=111, numSearches=10945, actualTotalOutput=9751
            }
            PatternNode[(?7, ?13=<has_type>, ?11, ?14) . project ?7,?11 . IsEdgeIdFilter(?14) .
            ],
            {estimatedCardinality=9675934, expectedTotalOutput=11695, indexTime=15, joinTime=95, numSearches=10, actualTotalOutput=42226
            }
            PatternNode[(?11, <~label>, ?12=<D>, <~>) . project ask .
            ],
            {estimatedCardinality=2333386, expectedTotalOutput=11695, indexTime=23, joinTime=402, numSearches=42226, actualTotalOutput=9749
            }
            PatternNode[(?11, ?17=<has_element>, ?15, ?18) . project ?11,?15 . IsEdgeIdFilter(?18) .
            ],
            {estimatedCardinality=8562896, expectedTotalOutput=556904, indexTime=18, joinTime=442, numSearches=10, actualTotalOutput=472370
            }
            PatternNode[(?15, <~label>, ?16, <~>) . project ask . ContainsFilter(?16 in (<element_1>, <element_2>, <element_3>, <element_4>, <element_5>, <element_6>, <element_7>, <element_8>, <element_9>, <element_10>, <element_11>)) .
            ],
            {estimatedCardinality=1158922, indexTime=598, joinTime=18991, numSearches=472370
            }
        }, annotations={path=[Vertex(?1):GraphStep, Vertex(?3):VertexStep, Vertex(?7):VertexStep, Vertex(?11):VertexStep, Vertex(?15):VertexStep
            ], joinStats=true, optimizationTime=1, maxVarId=19, executionTime=20799
        }
    }
]

请求似乎很简单，而且数量并不多。内部不是这里的好方法吗？你有一些改进查询的线索吗？

编辑1：

我尝试了@saikiranboga 提供的请求，索引操作的数量更大（除以10）但加入时间仍然很高。我很困惑。

之前的索引操作数：

Index Operations
================
Query execution:
    # of statement index ops: 525563
    # of unique statement index ops: 525563
    Duplication ratio: 1.0
    # of terms materialized: 0

之后

Index Operations
================
Query execution:
    # of statement index ops: 53666
    # of unique statement index ops: 53666
    Duplication ratio: 1.0
    # of terms materialized: 0

Optimized Traversal
===================
Neptune steps: [
    NeptuneCountGlobalStep {
        JoinGroupNode {
            PatternNode[(?1=<0f7a21df-9413-4c71-99f3-242ae25356a5>, ?5=<has_group>, ?3, ?6) . project ?1,?3 . IsEdgeIdFilter(?6) .
            ],
            {estimatedCardinality=12, expectedTotalOutput=12, indexTime=0, joinTime=0, numSearches=1, actualTotalOutput=12
            }
            PatternNode[(?3, ?9=<has_class>, ?7, ?10) . project ?3,?7 . IsEdgeIdFilter(?10) .
            ],
            {estimatedCardinality=208000, expectedTotalOutput=2000, indexTime=0, joinTime=10, numSearches=1, actualTotalOutput=10945
            }
            PatternNode[(?7, <~label>, ?8=<C>, <~>) . project ask .
            ],
            {estimatedCardinality=424296, expectedTotalOutput=2000, indexTime=4, joinTime=102, numSearches=10945, actualTotalOutput=9751
            }
            PatternNode[(?7, ?13=<has_type>, ?11, ?14) . project ?7,?11 . IsEdgeIdFilter(?14) .
            ],
            {estimatedCardinality=9456689, expectedTotalOutput=11695, indexTime=13, joinTime=94, numSearches=10, actualTotalOutput=42226
            }
            PatternNode[(?11, <~label>, ?12=<D>, <~>) . project ask .
            ],
            {estimatedCardinality=2333386, expectedTotalOutput=11695, indexTime=17, joinTime=341, numSearches=42226, actualTotalOutput=9749
            }
            PatternNode[(?11, ?17=<has_element>, ?15, ?18) . project ?11,?15 . IsEdgeIdFilter(?18) .
            ],
            {estimatedCardinality=7919022, expectedTotalOutput=556904, indexTime=17, joinTime=411, numSearches=10, actualTotalOutput=472370
            }
            PatternNode[(?15, <~label>, ?16, <~>) . project ?16 . ContainsFilter(?16 in (<element_1>, <element_2>, <element_3>, <element_4>, <element_5>, <element_6>, <element_7>, <element_8>, <element_9>, <element_10>, <element_11>)) .
            ],
            {estimatedCardinality=1145096, indexTime=848, joinTime=15268, numSearches=473
            }
        }, finishers=[dedup(?15)
        ], annotations={path=[Vertex(?1):GraphStep, Vertex(?3):VertexStep, Vertex(?7):VertexStep, Vertex(?11):VertexStep, Vertex(?15):VertexStep@[element
                ], VertexLabel(?16):LabelStep
            ], joinStats=true, optimizationTime=1, maxVarId=19, executionTime=17283
        }
    }
]
4

1 回答 1

2

您的观察是正确的，最后一个模式确实是在查询中花费更多时间的原因。“询问”预测通常涉及多个索引查找并可能导致速度缓慢，这些通常由数据库优化，但在这种情况下并非如此。

您能否尝试使用重写版本的查询来获取标签并过滤它们，而不是使用 hasLabel(...) 过滤器，如下所示：

g.V('0f7a21df-9413-4c71-99f3-242ae25356a5')
.out('has_group').out('has_class').hasLabel('C')
.out('has_type').hasLabel('D')
.out('has_element').as("element")
.label().is(
  within(
    'element_1','element_2','element_3','element_4','element_5',
    'element_6','element_7','element_8','element_9','element_10','element_11'
  )
)
.dedup("element")
.count()
于 2022-02-25T06:52:51.090 回答