Question

我是格雷姆林的新手。这是一个示例图

<?xml version='1.0' ?>
<graphml xmlns='http://graphml.graphdrawing.org/xmlns'>
    <key id='labelV'  for='node' attr.name='labelV'  attr.type='string'></key>
    <key id='type'    for='node' attr.name='type'    attr.type='string'></key>
    <key id='pick'    for='node' attr.name='pick'    attr.type='string'></key>
    <key id='match'   for='node' attr.name='match'   attr.type='string'></key>
    <key id='pot'  for='node' attr.name='pot'  attr.type='double'></key>
    <key id='stake'   for='node' attr.name='stake'   attr.type='double'></key>
    <key id='labelE'  for='edge' attr.name='labelE'  attr.type='string'></key>

<graph id='routes' edgedefault='directed'>
    <!-- vertices -->
    <node id='1'>
        <data key='labelV'>transaction</data>
        <data key='type'>transaction</data>
        <data key='match'>M1</data>
        <data key='pos'>H1</data>
        <data key='pick'>M1H1</data>
        <data key='pot'>100.0</data>
        <data key='stake'>10.0</data>
    </node>
    <node id='2'>
        <data key='labelV'>transaction</data>
        <data key='type'>transaction</data>
        <data key='match'>M1</data>
        <data key='pos'>D1</data>
        <data key='pick'>M1D1</data>
        <data key='pot'>50.0</data>
        <data key='stake'>5.0</data>
    </node>
    <node id='3'>
        <data key='labelV'>transaction</data>
        <data key='type'>transaction</data>
        <data key='match'>M1</data>
        <data key='pos'>A1</data>
        <data key='pick'>M1A1</data>
        <data key='pot'>150.0</data>
        <data key='stake'>15.0</data>
    </node>
    <node id='4'>
        <data key='labelV'>transaction</data>
        <data key='type'>transaction</data>
        <data key='match'>M2</data>
        <data key='pos'>A2</data>
        <data key='pick'>M2A2</data>
        <data key='pot'>75.0</data>
        <data key='stake'>10.0</data>
    </node>
    <node id='5'>
        <data key='labelV'>transaction</data>
        <data key='type'>transaction</data>
        <data key='match'>M3</data>
        <data key='pos'>A3</data>
        <data key='pick'>M3A3</data>
        <data key='pot'>70.0</data>
        <data key='stake'>7.0</data>
    </node>
    <node id='6'>
        <data key='labelV'>transaction</data>
        <data key='type'>transaction</data>
        <data key='match'>M1</data>
        <data key='pos'>H1</data>
        <data key='pick'>M1H1:M2D2</data>
        <data key='pot'>60.0</data>
        <data key='stake'>6.0</data>
    </node>

    <!-- edges -->
    <edge id='20' source='1' target='4'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='21' source='1' target='5'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='22' source='1' target='6'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='23' source='2' target='4'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='24' source='2' target='5'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='25' source='3' target='4'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='26' source='3' target='5'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='27' source='4' target='1'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='28' source='4' target='2'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='29' source='4' target='3'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='30' source='4' target='5'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='31' source='5' target='1'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='32' source='5' target='2'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='33' source='5' target='3'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='34' source='5' target='4'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='35' source='5' target='6'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='36' source='6' target='1'>
        <data key='labelE'>compatible</data>
    </edge>
    <edge id='37' source='6' target='5'>
        <data key='labelE'>compatible</data>
    </edge>
</graph>
</graphml>

我不知道如何使用graphML添加具有多个值的属性，因此这是向V（6）添加附加值的代码。

g.V(6).property(set, 'match', "M2")

目标是通过获取每个节点及其连接的顶点，并根据其匹配值将该节点的顶点分组（将两个节点分组意味着它们至少有一个相交的{ {1}}值），取每个组的最大值，然后对该节点求和。最后，以取所有节点的最大值。我不知道这是否应该完成，但这是我尝试过的：

pot

预期结果：对于每个顶点，我们应该具有如下的BulkSet：

match

以及每个顶点的最大g.E().hasLabel('compatible').outV().dedup().as('s'). //1. get all edges with comptible label local( //2. for each node union( //3. get the node and its first level connected vertices identity(), out() ) .as('compat') .values('match') //4. get the values of the match property .as('match') .local( //5. for each key in the list select('compat') //6. get all vertices in the sub graph that have this value in their match list .has('match',within(identity())) //7 .order().by("pot", decr).limit(1) //8. get the one with the highest pot value ) .dedup() //9. remove duplicates .values('pot') .sum() //10. sum the values for this node ).max() //11. get the max for all nodes的映射：

V(5)==>[M1:[v[2],v[3],v[6],v[1]],M2:[v[4],v[6]],M3:[v[5]]]
V(6)==>[M1:[v[6],v[1]],M3:[v[5]]]
V(1)==>[M1:[v[1],v[6]],M2:[v[4], v[6]],M3:[v[5]]]
V(2)==>[M1:[v[2]],M2:[v[4]],M3:[v[5]]]
V(3)==>[M1:[v[3]],M2:[v[4]],M3:[v[5]]]
V(4)==>[M1:[v[1],v[2],v[3]],M2:[v[4]],M3:[v[5]]]

最终输出应为295。

第一个问题是在7点。如果我传递诸如pot之类的静态值，我确实获得了数据，但使用V(5) ==> [M1:v[3],M2:v[4],M3:v[5]], Sum(pot) ==> 150 + 75 + 70 = 295 V(6) ==> [M1:v[1],M3:v[5]], Sum(pot) ==> 100 + 70 = 170 V(1) ==> [M1:v[1],M2:[v[4]],M3:v[5]], Sum(pot) ==> 100 + 75 + 70 = 295 V(2) ==> [M1:v[2],M2:v[4],M3:v[5]], Sum(pot) ==> 50 + 75 + 70 = 195 V(3) ==> [M1:v[3],M2:v[4],M3:v[5]], Sum(pot) ==> 150 + 75 + 70 = 295 V(4) ==> [M1:v[3],M2:v[4],M3:v[5]], Sum(pot) ==> 150 + 75 + 70 = 295得到了一个空列表。

第二个问题是在8。我得到了多个节点，而不仅仅是具有最大值的节点。

Answer 1

在上面的示例中为我工作：

g.V().hasLabel('transaction').local(
union(identity(), out('compatible')).as('v')
    .values('match')
    .group()
    .by()
    .by(select('v').values('pot').max()).select(values).unfold().sum()
).max()

Answer 2

我认为您的预期结果不太正确（M2应该是v [6]批量设置的一部分）。也就是说，这就是我认为解决方案的样子：

gremlin> g.V().hasLabel("transaction").
           map(union(identity(), out("compatible")).as("v").
           values("match").
           group().
             by().
             by(select("v").order().by("pot", decr)))
==>[M1:v[1],M2:v[4],M3:v[5]]
==>[M1:v[2],M2:v[4],M3:v[5]]
==>[M1:v[3],M2:v[4],M3:v[5]]
==>[M1:v[3],M2:v[4],M3:v[5]]
==>[M1:v[3],M2:v[4],M3:v[5]]
==>[M1:v[1],M2:v[6],M3:v[5]]

gremlin> g.V().hasLabel("transaction").
           map(union(identity(), out("compatible")).as("v").
           values("match").
           group().
             by().
             by(select("v").order().by("pot", decr))).
           map(select(values).unfold().values("pot").fold())
==>[100.0,75.0,70.0]
==>[50.0,75.0,70.0]
==>[150.0,75.0,70.0]
==>[150.0,75.0,70.0]
==>[150.0,75.0,70.0]
==>[100.0,60.0,70.0]

gremlin> g.V().hasLabel("transaction").
           map(union(identity(), out("compatible")).as("v").
           values("match").
           group().
             by().
             by(select("v").order().by("pot", decr))).
           map(select(values).unfold().values("pot").sum()).
           max()
==>295.0

在内部步骤中使用identity（）时，没有返回值，但是在硬编码时会返回值

2 个答案: