Neo4j自我网络（查询优化）

Question

我想查询给定节点的自我网络。我当前的代码耗时将近1秒。在具有42k节点和200万边缘的数据库上返回4k行。这似乎非常慢。我该如何优化呢？

Topic.name上有唯一的约束/索引。

查询：

match (n:Topic)-[e]-(m: Topic) 
where n.name = 'raman'
with collect(m.name) as ms, n
match (m1:Topic)-[e]-(m2:Topic)
where (m1.name in ms or m1.name = n.name) and (m2.name in ms or m2.name = n.name) and m1.name < m2.name
return m1.name, m2.name, e.weight;

执行配置文件：

Query executed in 842ms. Query type: READ_ONLY.
Profile:
----------> NodeUniqueIndexSeek@neo4j[records: 1; dbHits: 2] {identifiers: [n]; arguments: {Order="n.name ASC", PageCacheHitRatio=0.0, Details="UNIQUE n:Topic(name) WHERE name = $autostring_0, cache[n.name]", PageCacheMisses=0, EstimatedRows=1.0, DbHits=2, Rows=1, PageCacheHits=0}}
---------> Expand(All)@neo4j[records: 179; dbHits: 180] {identifiers: [n, e, m]; arguments: {Order="n.name ASC", PageCacheHitRatio=0.0, Details="(n)-[e]-(m)", PageCacheMisses=0, EstimatedRows=95.69165595671438, DbHits=180, Rows=179, PageCacheHits=0}}
--------> Filter@neo4j[records: 179; dbHits: 179] {identifiers: [n, e, m]; arguments: {Order="n.name ASC", PageCacheHitRatio=0.0, Details="m:Topic", PageCacheMisses=0, EstimatedRows=95.69165595671438, DbHits=179, Rows=179, PageCacheHits=0}}
-------> EagerAggregation@neo4j[records: 1; dbHits: 179] {identifiers: [n, ms]; arguments: {PageCacheHitRatio=0.0, Details="n, collect(m.name) AS ms", Memory=11448, PageCacheMisses=0, EstimatedRows=9.782211199760225, DbHits=179, Rows=1, PageCacheHits=0}}
----------> NodeUniqueIndexSeek@neo4j[records: 179; dbHits: 358] {identifiers: [n, ms, m1]; arguments: {PageCacheHitRatio=0.0, Details="UNIQUE m1:Topic(name) WHERE name IN ms, cache[m1.name]", PageCacheMisses=0, EstimatedRows=244.48469730942983, DbHits=358, Rows=179, PageCacheHits=0}}
----------> NodeUniqueIndexSeek@neo4j[records: 1; dbHits: 3] {identifiers: [n, ms, m1]; arguments: {PageCacheHitRatio=0.0, Details="UNIQUE m1:Topic(name) WHERE name = cache[n.name], cache[m1.name]", PageCacheMisses=0, EstimatedRows=9.782211199760225, DbHits=3, Rows=1, PageCacheHits=0}}
---------> Union@neo4j[records: 180; dbHits: 0] {identifiers: [n, ms, m1]; arguments: {PageCacheHitRatio=0.0, PageCacheMisses=0, EstimatedRows=254.26690850919005, DbHits=0, Rows=180, PageCacheHits=0}}
--------> Distinct@neo4j[records: 180; dbHits: 0] {identifiers: [n, ms, m1]; arguments: {PageCacheHitRatio=0.0, Details="n, ms, m1", Memory=2143856, PageCacheMisses=0, EstimatedRows=254.2610272318913, DbHits=0, Rows=180, PageCacheHits=0}}
----------> NodeUniqueIndexSeek@neo4j[records: 32220; dbHits: 64440] {identifiers: [n, ms, m2]; arguments: {PageCacheHitRatio=0.0, Details="UNIQUE m2:Topic(name) WHERE name IN ms, cache[m2.name]", PageCacheMisses=0, EstimatedRows=244.48469730942983, DbHits=64440, Rows=32220, PageCacheHits=0}}
----------> NodeUniqueIndexSeek@neo4j[records: 180; dbHits: 540] {identifiers: [n, ms, m2]; arguments: {PageCacheHitRatio=0.0, Details="UNIQUE m2:Topic(name) WHERE name = cache[n.name], cache[m2.name]", PageCacheMisses=0, EstimatedRows=9.782211199760225, DbHits=540, Rows=180, PageCacheHits=0}}
---------> Union@neo4j[records: 32400; dbHits: 0] {identifiers: [n, ms, m2]; arguments: {PageCacheHitRatio=0.0, PageCacheMisses=0, EstimatedRows=254.26690850919005, DbHits=0, Rows=32400, PageCacheHits=0}}
--------> Distinct@neo4j[records: 32400; dbHits: 0] {identifiers: [n, ms, m2]; arguments: {PageCacheHitRatio=0.0, Details="n, ms, m2", Memory=2143856, PageCacheMisses=0, EstimatedRows=254.2610272318913, DbHits=0, Rows=32400, PageCacheHits=0}}
-------> CartesianProduct@neo4j[records: 32400; dbHits: 0] {identifiers: [n, ms, m1, m2]; arguments: {PageCacheHitRatio=0.0, PageCacheMisses=0, EstimatedRows=6608.799242711218, DbHits=0, Rows=32400, PageCacheHits=0}}
------> Apply@neo4j[records: 32400; dbHits: 0] {identifiers: [n, ms, m1, m2]; arguments: {PageCacheHitRatio=0.0, PageCacheMisses=0, EstimatedRows=0.45638084942956914, DbHits=0, Rows=32400, PageCacheHits=0}}
-----> CacheProperties@neo4j[records: 32400; dbHits: 0] {identifiers: [n, ms, m1, m2]; arguments: {PageCacheHitRatio=0.0, Details="cache[m2.name], cache[m1.name]", PageCacheMisses=0, EstimatedRows=0.45638084942956914, DbHits=0, Rows=32400, PageCacheHits=0}}
----> Expand(Into)@neo4j[records: 7848; dbHits: 5431444] {identifiers: [e, m2, n, m1, ms]; arguments: {PageCacheHitRatio=0.0, Details="(m1)-[e]-(m2)", Memory=2688168, PageCacheMisses=0, EstimatedRows=15.21306094348127, DbHits=5431444, Rows=7848, PageCacheHits=0}}
---> Filter@neo4j[records: 3924; dbHits: 0] {identifiers: [e, m2, n, m1, ms]; arguments: {PageCacheHitRatio=0.0, Details="cache[m1.name] < cache[m2.name]", PageCacheMisses=0, EstimatedRows=0.45638084942956914, DbHits=0, Rows=3924, PageCacheHits=0}}
--> Projection@neo4j[records: 3924; dbHits: 3924] {identifiers: [e, m2, n, `e.weight`, m1, `m2.name`, ms, `m1.name`]; arguments: {PageCacheHitRatio=0.0, Details="cache[m1.name] AS `m1.name`, cache[m2.name] AS `m2.name`, e.weight AS `e.weight`", PageCacheMisses=0, EstimatedRows=0.45638084942956914, DbHits=3924, Rows=3924, PageCacheHits=0}}
-> ProduceResults@neo4j[records: 3924; dbHits: 0] {identifiers: [e, m2, n, `e.weight`, m1, `m2.name`, ms, `m1.name`]; arguments: {PageCacheHitRatio=0.0, GlobalMemory=6986840, planner-impl="IDP", runtime="INTERPRETED", runtime-impl="INTERPRETED", version="CYPHER 4.1", DbHits=0, Details="`m1.name`, `m2.name`, `e.weight`", planner-version="4.1", runtime-version="4.1", PageCacheMisses=0, EstimatedRows=0.45638084942956914, planner="COST", Rows=3924, PageCacheHits=0}}

谢谢！

Answer 1

根据您对建立自我网络的评论，我会尝试这样的事情：

MATCH (n:Topic)
WHERE n.name = 'raman'
MATCH p=(n)-[*1..2]-(m:Topic)
WHERE (n)--(m)
WITH p
UNWIND relationships(p) as rel
WITH collect(distinct rel) as rels
UNWIND rels as r
RETURN startNode(r).name, endNode(r).name,r.weight