用于相似度计算的Neo4j Cypher查询性能较慢

我对Neo4j/Graph数据库非常陌生，并尝试复制Cypher cookbook中的教程：

随机数据集包含100种食物和1500人，所有人都通过“时间”整数属性的ATE关系与食物相关。食物和人都有标签，并有“名称”属性，该属性由自动索引索引

neo4j-sh (?)$ dbinfo -g "Primitive count"
{
  "NumberOfNodeIdsInUse": 1600,
  "NumberOfPropertyIdsInUse": 151600,
  "NumberOfRelationshipIdsInUse": 150000,
  "NumberOfRelationshipTypeIdsInUse": 1
}

neo4j-sh (?)$ index --indexes
Node indexes:
  node_auto_index
Relationship indexes:
  relationship_auto_index

在neo4j shell中从cookbook运行修改后的查询永远不会完成（可能是因为太多节点/关系？）

有没有可能使这样的查询执行得更快，以便“实时”使用

系统和Neo4j

Windows 7（64位）、Intel Core I7-2600K、8GB RAM、SSD驱动器上的Neo4j数据库

Neo4j社区版：2.1.0-M01（也在2.0.1稳定版上测试）

neo4j社区。选项

 -Xmx2048m
 -Xms2048m

neostore.nodestore.db.mapped_memory=200M
neostore.relationshipstore.db.mapped_memory=200M
neostore.propertystore.db.mapped_memory=200M
neostore.propertystore.db.strings.mapped_memory=330M
neostore.propertystore.db.arrays.mapped_memory=330M

node_auto_indexing=true
node_keys_indexable=name
relationship_auto_indexing=true
relationship_keys_indexable=times

neo4j.属性

 -Xmx2048m
 -Xms2048m

neostore.nodestore.db.mapped_memory=200M
neostore.relationshipstore.db.mapped_memory=200M
neostore.propertystore.db.mapped_memory=200M
neostore.propertystore.db.strings.mapped_memory=330M
neostore.propertystore.db.arrays.mapped_memory=330M

node_auto_indexing=true
node_keys_indexable=name
relationship_auto_indexing=true
relationship_keys_indexable=times

（503kb压缩）

配置文件输出

ColumnFilter(symKeys=["similarity", "you", "you.name", "me", "me.name"], returnItemNames=["me.name", "you.name", "similarity"], _rows=100, _db_hits=0)
Sort(descr=["SortItem(similarity,false)"], _rows=100, _db_hits=0)
  Extract(symKeys=["me", "you", "similarity"], exprKeys=["me.name", "you.name"], _rows=100, _db_hits=200)
    ColumnFilter(symKeys=["me", "you", "  INTERNAL_AGGREGATEcb085cf5-8982-4a83-ba3d-9642de570c59"], returnItemNames=["me", "you", "similarity"], _rows=100, _db_hits=0)
      EagerAggregation(keys=["me", "you"], aggregates=["(INTERNAL_AGGREGATEcb085cf5-8982-4a83-ba3d-9642de570c59,Sum(Divide(Multiply(Subtract(Literal(1),AbsFunction(Subtract(Divide(Property(r1,times(1)),H1),Divide(Property(r2,times(1)),H2)))),Add(Property(r1,times(1)),Property(r2,times(1)))),Add(H1,H2))))"], _rows=100, _db_hits=40000)
        SimplePatternMatcher(g="(you)-['r2']-(food),(me)-['r1']-(food)", _rows=10000, _db_hits=0)
          ColumnFilter(symKeys=["me", "you", "  INTERNAL_AGGREGATE677cd11c-ae53-4d7b-8df6-732ffed28bbf", "  INTERNAL_AGGREGATEb5eb877c-de01-4e7a-9596-03cd94cfa47a"], returnItemNames=["me", "H1", "H2", "you"], _rows=100, _db_hits=0)
            EagerAggregation(keys=["me", "you"], aggregates=["(  INTERNAL_AGGREGATE677cd11c-ae53-4d7b-8df6-732ffed28bbf,Distinct(Count(r1),r1))", "(  INTERNAL_AGGREGATEb5eb877c-de01-4e7a-9596-03cd94cfa47a,Distinct(Count(r2),r2))"], _rows=100, _db_hits=0)
              SimplePatternMatcher(g="(you)-['r2']-(food),(me)-['r1']-(food)", _rows=10000, _db_hits=0)
                ColumnFilter(symKeys=["me", "food", "you", "r2"], returnItemNames=["me", "you"], _rows=100, _db_hits=0)
                  Slice(limit="Literal(100)", _rows=100, _db_hits=0)
                    Filter(pred="NOT(me == you)", _rows=100, _db_hits=0)
                      SimplePatternMatcher(g="(you)-['r2']-(food)", _rows=100, _db_hits=0)
                        ColumnFilter(symKeys=["food", "me", "r1"], returnItemNames=["me", "food"], _rows=1, _db_hits=0)
                          Filter(pred="Property(me,name(0)) == {name}", _rows=1,_db_hits=148901)
                            TraversalMatcher(start={"label": "Person", "producer": "NodeByLabel", "identifiers": ["me"]}, trail="(me)-[r1:ATE WHERE true AND true]->(food)", _rows=148901, _db_hits=148901)

---

您多次执行相同的匹配。这个效果更好吗

EXPORT name="Florida Goyette"
MATCH (me:Person { name: {name}})-[r1:ATE]->(food)<-[r2:ATE]-(you:Person)
WITH me,r1,r2,count(DISTINCT r1) AS H1,count(DISTINCT r2) AS H2,you
LIMIT 100 
RETURN SUM((1-ABS(r1.times/H1-r2.times/H2))*(r1.times+r2.times)/(H1+H2)) AS similarity;

导出名称=“佛罗里达戈耶特”

---

MATCH（me:Person{name:{name}}）-[r1:ATE]>（食物）您使用的索引类型错误。使用创建标签索引

CREATE INDEX ON :Person(name)

使用检查模式索引和约束

neo4j外壳

schema
schema ls -l :User 

或

neo4j浏览器

:schema 
:schema ls -l :User

可能需要对查询进行优化，但从这里开始

windows上的内存映射位于堆内。因此，将堆大小增加到4G

您不需要旧的自动索引，但需要像jjaderberg所述的新模式索引

这个返回多少行

---

MATCH（me:Person{name:{name}）-[r1:ATE]->（food）（food）（food）（food）no.
1行361023 ms
设置适当的索引后，它现在在1400ms内返回。但是结果是不准确的。你能分享结果吗？是的，当前的版本会找到所有路径和过滤器。谢谢。我误读了索引文档并使用了“遗留”索引。我已经将-Xmx和-Xms设置为3072m（目前在我的笔记本电脑上）。Windows“资源管理器”显示了neo4j-community.exe的提交3.4GB和工作集1.3GB，我按照@jjaderberg的建议更改了索引。第一次查询在700ms内返回149900行（第一次运行后）。第二个查询以140588ms的速度返回149000行（在第一次运行之后）。我无法使您的第三个查询正常工作，我得到语法异常
（SyntaxException:无效输入“”。：应为空白）a+（1-ABS（r[0]。times/H1-r[1]。times/H2））*“
在：r[0]中指出的错误。我创建的times是您想要的吗？编辑：
:schema 
:schema ls -l :User

MATCH (me:Person { name: {name}})-[r1:ATE]->(food)<-[r2:ATE]-(you:Person) RETURN count(*)

MATCH (me:Person { name: {name}})-[r1:ATE]->(food)<-[r2:ATE]-(you:Person)
WITH me,count(DISTINCT r1) AS H1,count(DISTINCT r2) AS H2,you
MATCH (me)-[r1:ATE]->(food)<-[r2:ATE]-(you)
RETURN COUNT(*)

MATCH (me:Person { name: {name}})-[r1:ATE]->(food)<-[r2:ATE]-(you:Person)
WITH me,
     collect([r1,r2]) as rels, 
     count(DISTINCT r1) AS H1,
     count(DISTINCT r2) AS H2,
     you

RETURN me,you, 
       reduce(a=0,r in rels | 
              a + (1-ABS(r[0].times/H1-r[1].times/H2))*
                  (r[0].times+r[1].times)
                  /(H1+H2) as similarity