When matching a pattern using Cypher, the number of possible paths to evaluate often correlates with query execution time.

When there is a supernode in the path (a node with a high number of relationships whose type are included in your MATCH pattern), or simply enough nodes with many relationships, the number of possible paths can explode, slowing down your query. Traversal like this through supernodes can be expensive.

Sometimes when traversing certain kinds of patterns, you may know from your modeling that if at all possible, a relationship between two specific kinds of nodes ought to be traversed in a certain direction instead of its opposite for best performance, and this is often the case with supernodes.

For example, if we had a social graph of :People who :LIKE musical artists, it’s very likely that some of those artists, perhaps many, are supernodes. If we were looking at (:Person)-[:LIKES]→(:Artist {name:'Britney Spears'}), we could reasonably assume that a :Person likely has relatively few :LIKES relationships, perhaps less than 100, but that a popular :Artist like Britney Spears may have millions of :LIKES relationships pointing her way.

Traversing through Britney Spears is going to be costly as it could multiply the number of possible paths by the number of :LIKES relationships, blowing up the execution of the query. However, traversing only to Britney Spears is likely going to be relatively cheap.

In cases where a query has the potential for multiple starting places, and if the planner isn’t providing an efficient plan in light of bad traversals through supernodes, you can use a join hint in the query to prevent traversal through a node.

Instead, multiple starting points are used, and from each one expansion is performed until reaching the node specified in the join hint. Then a hash join is used to find the common nodes that were matched to from both directions, and the fully matched path is realized.

If I’m trying to find out artists my friends and I like in common (and how many likes are in common) I might have a query like this:

MATCH (me:Person {name:'Me'})-[:FRIENDS_WITH]-(friend)-[:LIKES]->(a:Artist)<-[:LIKES]-(me)
RETURN a, count(a) as likesInCommon

If the planner decides to use only a single starting point for this query and doesn’t recognize a potential supernode issue, it’s possible that it may choose to plan expansion through the :Artist node, which could be very costly:

image

As the Cypher query planner has the ability to rearrange the ordering of adjacent MATCH patterns (and across some WITH clauses), simply reordering my pattern isn’t enough. I can use a join hint to ensure we only traverse to the node in question, but not through it or away from it.

MATCH (me:Person {name:'Me'})-[:FRIENDS_WITH]-(friend)-[:LIKES]->(a:Artist)<-[:LIKES]-(me)
USING JOIN on a
RETURN a, count(a) as likesInCommon

From the query plan we can see that from the same starting point we traverse two different paths toward a, but not through it, and perform a node hash join to unify the paths on the common artist nodes.

image

Details


Author:
Andrew Bowman
Applicable versions:
3.1, 3.2, 3.3, 3.4, 3.5
Keywords:
cypherneo4j-3.1neo4j-3.2neo4j-3.3neo4j-3.4neo4j-3.5