具有子图聚合的递归查询（任意深度）

时间：2015-01-21 05:16:36

标签： recursion neo4j cypher

我问了一个关于在图表中汇总数量的问题earlier。提供的两个答案运作良好，但现在我试图将Cypher查询扩展到可变深度的图表。

总结一下，我们从一堆叶子商店开始，这些叶子商店都与特定供应商相关联，这是Store节点上的一个属性。然后将库存移至其他商店，每个供应商的比例对应于他们对原始商店的贡献。

因此，对于节点B02，S2贡献了750/1250 = 60%，S3贡献了40%。然后，我们将B02 60%属于S2和40%的{​​{1}}移至600 S3，依此类推。

enter image description here

我们想知道D01中最终700个单位的百分比属于每个供应商。供应商名称相同的供应商。因此，对于上图，我们期望：

  

S1,38.09
  S2,27.61
  S3,34.28

我已使用此Cypher脚本准备了一个图表：

CREATE (A01:Store {Name: 'A01', Supplier: 'S1'})
CREATE (A02:Store {Name: 'A02', Supplier: 'S1'})
CREATE (A03:Store {Name: 'A03', Supplier: 'S2'})
CREATE (A04:Store {Name: 'A04', Supplier: 'S3'})
CREATE (A05:Store {Name: 'A05', Supplier: 'S1'})
CREATE (A06:Store {Name: 'A06', Supplier: 'S1'})
CREATE (A07:Store {Name: 'A07', Supplier: 'S2'})
CREATE (A08:Store {Name: 'A08', Supplier: 'S3'})

CREATE (B01:Store {Name: 'B01'})
CREATE (B02:Store {Name: 'B02'})
CREATE (B03:Store {Name: 'B03'})
CREATE (B04:Store {Name: 'B04'})

CREATE (C01:Store {Name: 'C01'})
CREATE (C02:Store {Name: 'C02'})

CREATE (D01:Store {Name: 'D01'})

CREATE (A01)-[:MOVE_TO {Quantity: 750}]->(B01)
CREATE (A02)-[:MOVE_TO {Quantity: 500}]->(B01)
CREATE (A03)-[:MOVE_TO {Quantity: 750}]->(B02)
CREATE (A04)-[:MOVE_TO {Quantity: 500}]->(B02)
CREATE (A05)-[:MOVE_TO {Quantity: 100}]->(B03)
CREATE (A06)-[:MOVE_TO {Quantity: 200}]->(B03)
CREATE (A07)-[:MOVE_TO {Quantity: 50}]->(B04)
CREATE (A08)-[:MOVE_TO {Quantity: 450}]->(B04)

CREATE (B01)-[:MOVE_TO {Quantity: 400}]->(C01)
CREATE (B02)-[:MOVE_TO {Quantity: 600}]->(C01)
CREATE (B03)-[:MOVE_TO {Quantity: 100}]->(C02)
CREATE (B04)-[:MOVE_TO {Quantity: 200}]->(C02)

CREATE (C01)-[:MOVE_TO {Quantity: 500}]->(D01)
CREATE (C02)-[:MOVE_TO {Quantity: 200}]->(D01)

目前的查询是：

MATCH (s:Store { Name:'D01' })
MATCH (s)<-[t:MOVE_TO]-()<-[r:MOVE_TO]-(supp)
WITH t.Quantity as total, collect(r) as movements
WITH total, movements, reduce(totalSupplier = 0, r IN movements | totalSupplier + r.Quantity) as supCount
UNWIND movements as movement
RETURN startNode(movement).Supplier as Supplier, round(100.0*movement.Quantity/supCount) as pct

我正在尝试使用递归关系，就像这样：

MATCH (s)<-[t:MOVE_TO]-()<-[r:MOVE_TO*]-(supp)

然而，它提供了到终端节点的多条路径，我需要在每个节点聚合库存。

3 个答案:

答案 0 :(得分：3)

正如我之前所说，我喜欢这个问题。我知道你已经接受了答案，但是我决定发布我的最终答案，因为它也会在没有客户努力的情况下返回百分位数（这意味着您还可以在节点上执行SET以在需要时更新数据库中的值）并且当然，如果出于任何其他原因，我可以回来:) 这是console example

的链接

它会返回一个包含商店名称的行，从所有供应商处移除的总和以及每个供应商的百分位数

MATCH p =s<-[:MOVE_TO*]-sup
WHERE HAS (sup.Supplier) AND NOT HAS (s.Supplier)
WITH s,sup,reduce(totalSupplier = 0, r IN relationships(p)| totalSupplier + r.Quantity) AS TotalAmountMoved
WITH sum(TotalAmountMoved) AS sumMoved, collect(DISTINCT ([sup.Supplier, TotalAmountMoved])) AS MyDataPart1,s
WITH reduce(b=[], c IN MyDataPart1| b +[{ Supplier: c[0], Quantity: c[1], Percentile: ((c[1]*1.00))/(sumMoved*1.00)*100.00 }]) AS MyData, s, sumMoved
RETURN s.Name, sumMoved, MyData

答案 1 :(得分：2)

我无法通过纯密码的解决方案来思考，因为我认为你不能在密码中做这样的递归。您可以使用cypher以简单的方式返回树中的所有数据，以便您可以使用自己喜欢的编程语言计算它。像这样：

MATCH path=(source:Store)-[move:MOVE_TO*]->(target:Store {Name: 'D01'})
WHERE source.Supplier IS NOT NULL
RETURN
  source.Supplier,
  reduce(a=[], move IN relationships(path)| a + [{id: ID(move), Quantity: move.Quantity}])

这将返回每条路径上每个关系的ID和数量。然后你可以处理该客户端（可能首先将其转换为嵌套数据结构？）

答案 2 :(得分：2)

此查询为符合问题中描述的模型的任意图形生成正确的结果。 （当Store x将商品移至Store y时，系统会假定所移动商品的Supplier百分比与Store x相同。）

但是，此解决方案不仅包含单个Cypher查询（因为这可能无法实现）。相反，它涉及多个查询，其中一个必须迭代，直到计算级联通过Store个节点的整个图形。该迭代查询将清楚地告诉您何时停止迭代。需要其他Cypher查询：为迭代准备图表，报告＆＃34; end＆＃34;的供应商百分比。节点，并清理图形（以便它恢复到下面步骤1之前的方式）。

这些查询可能会进一步优化。

以下是必需的步骤：

  1. 为迭代查询准备图形（为所有起始pcts节点初始化临时Store数组）。这包括创建单个Suppliers节点，该节点具有包含所有供应商名称的数组。这用于建立临时pcts数组元素的顺序，并将这些元素映射回正确的供应商名称。

    MATCH (store:Store)
WHERE HAS (store.Supplier)
WITH COLLECT(store) AS stores, COLLECT(DISTINCT store.Supplier) AS csup
CREATE (sups:Suppliers { names: csup })
WITH stores, sups
UNWIND stores AS store
SET store.pcts =
  EXTRACT(i IN RANGE(0,LENGTH(sups.names)-1,1) |
    CASE WHEN store.Supplier = sups.names[i] THEN 1.0 ELSE 0.0 END)
RETURN store.Name, store.Supplier, store.pcts;

    以下是问题数据的结果：

    +---------------------------------------------+
| store.Name | store.Supplier | store.pcts    |
+---------------------------------------------+
| "A01"      | "S1"           | [1.0,0.0,0.0] |
| "A02"      | "S1"           | [1.0,0.0,0.0] |
| "A03"      | "S2"           | [0.0,1.0,0.0] |
| "A04"      | "S3"           | [0.0,0.0,1.0] |
| "A05"      | "S1"           | [1.0,0.0,0.0] |
| "A06"      | "S1"           | [1.0,0.0,0.0] |
| "A07"      | "S2"           | [0.0,1.0,0.0] |
| "A08"      | "S3"           | [0.0,0.0,1.0] |
+---------------------------------------------+
8 rows
83 ms
Nodes created: 1
Properties set: 9

  2. 迭代查询（重复运行，直到返回0行）

    MATCH p=(s1:Store)-[m:MOVE_TO]->(s2:Store)
WHERE HAS(s1.pcts) AND NOT HAS(s2.pcts)
SET s2.pcts = EXTRACT(i IN RANGE(1,LENGTH(s1.pcts),1) | 0)
WITH s2, COLLECT(p) AS ps
WITH s2, ps, REDUCE(s=0, p IN ps | s + HEAD(RELATIONSHIPS(p)).Quantity) AS total
FOREACH(p IN ps |
  SET HEAD(RELATIONSHIPS(p)).pcts = EXTRACT(parentPct IN HEAD(NODES(p)).pcts | parentPct * HEAD(RELATIONSHIPS(p)).Quantity / total)
)
FOREACH(p IN ps |
  SET s2.pcts = EXTRACT(i IN RANGE(0,LENGTH(s2.pcts)-1,1) | s2.pcts[i] + HEAD(RELATIONSHIPS(p)).pcts[i])
)
RETURN s2.Name, s2.pcts, total, EXTRACT(p IN ps | HEAD(RELATIONSHIPS(p)).pcts) AS rel_pcts;

    迭代1结果：

    +-----------------------------------------------------------------------------------------------+
| s2.Name | s2.pcts       | total | rel_pcts                                                    |
+-----------------------------------------------------------------------------------------------+
| "B04"   | [0.0,0.1,0.9] | 500   | [[0.0,0.1,0.0],[0.0,0.0,0.9]]                               |
| "B01"   | [1.0,0.0,0.0] | 1250  | [[0.6,0.0,0.0],[0.4,0.0,0.0]]                               |
| "B03"   | [1.0,0.0,0.0] | 300   | [[0.3333333333333333,0.0,0.0],[0.6666666666666666,0.0,0.0]] |
| "B02"   | [0.0,0.6,0.4] | 1250  | [[0.0,0.6,0.0],[0.0,0.0,0.4]]                               |
+-----------------------------------------------------------------------------------------------+
4 rows
288 ms
Properties set: 24

    迭代2结果：

    +-------------------------------------------------------------------------------------------------------------------------------+
| s2.Name | s2.pcts                                      | total | rel_pcts                                                     |
+-------------------------------------------------------------------------------------------------------------------------------+
| "C02"   | [0.3333333333333333,0.06666666666666667,0.6] | 300   | [[0.3333333333333333,0.0,0.0],[0.0,0.06666666666666667,0.6]] |
| "C01"   | [0.4,0.36,0.24]                              | 1000  | [[0.4,0.0,0.0],[0.0,0.36,0.24]]                              |
+-------------------------------------------------------------------------------------------------------------------------------+
2 rows
193 ms
Properties set: 12

    迭代3结果：

    +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| s2.Name | s2.pcts                                                       | total | rel_pcts                                                                                                                    |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "D01"   | [0.38095238095238093,0.27619047619047615,0.34285714285714286] | 700   | [[0.2857142857142857,0.2571428571428571,0.17142857142857143],[0.09523809523809522,0.01904761904761905,0.17142857142857143]] |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row
40 ms
Properties set: 6

    迭代4结果：

    +--------------------------------------+
| s2.Name | s2.pcts | total | rel_pcts |
+--------------------------------------+
+--------------------------------------+
0 rows
69 ms

  3. 列出结束Supplier节点的非零Store百分比。

    MATCH (store:Store), (sups:Suppliers)
WHERE NOT (store:Store)-[:MOVE_TO]->(:Store) AND HAS(store.pcts)
RETURN store.Name, [i IN RANGE(0,LENGTH(sups.names)-1,1) WHERE store.pcts[i] > 0 | {supplier: sups.names[i], pct: store.pcts[i] * 100}] AS pcts;

    结果：

    +----------------------------------------------------------------------------------------------------------------------------------+
| store.Name | pcts                                                                                                                |
+----------------------------------------------------------------------------------------------------------------------------------+
| "D01"      | [{supplier=S1, pct=38.095238095238095},{supplier=S2, pct=27.619047619047617},{supplier=S3, pct=34.285714285714285}] |
+----------------------------------------------------------------------------------------------------------------------------------+
1 row
293 ms

  4. 清理（删除所有临时pcts道具和Suppliers节点）。

    MATCH (s:Store), (sups:Suppliers)
OPTIONAL MATCH (s)-[m:MOVE_TO]-()
REMOVE m.pcts, s.pcts
DELETE sups;

    结果：

    0 rows
203 ms
+-------------------+
| No data returned. |
+-------------------+
Properties set: 29
Nodes deleted: 1
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