Neo4j super node issue - fanning out pattern

Question

I\'m new to the Graph Database scene, looking into Neo4j and learning Cypher, we\'re trying to model a graph database, it\'s a fairly simple one, we got users, and we g

Answer 1

UPDATE - October 2020. This article is the best source on this topic, covering all aspects of super nodes

(my original answer below)

It's a good question. This isn't really an answer, but why shouldn't we be able to discuss this here? Technically I think I'm supposed to flag your question as "primarily opinion based" since you're explicitly soliciting opinions, but I think it's worth the discussion.

The boring but honest answer is that it always depends on your query patterns. Without knowing what kinds of queries you're going to issue against this data structure, there's really no way to know the "best" approach.

Supernodes are problems in other areas as well. Graph databases sometimes are very difficult to scale in some ways, because the data in them is hard to partition. If this were a relational database, we could partition vertically or horizontally. In a graph DB when you have supernodes, everything is "close" to everything else. (An Alaskan farmer likes Lady Gaga, so does a New York banker). Moreso than just graph traversal speed, supernodes are a big problem for all sorts of scalability.

Rik's suggestion boils down to encouraging you to create "sub-clusters" or "partitions" of the super-node. For certain query patterns, this might be a good idea, and I'm not knocking the idea, but I think hidden in here is the notion of a clustering strategy. How many meta nodes do you assign? How many max links per meta-node? How did you go about assigning this user to this meta node (and not some other)? Depending on your queries, those questions are going to be very hard to answer, hard to implement correctly, or both.

A different (but conceptually very similar) approach is to clone Lady Gaga about a thousand times, and duplicate her data and keep it in sync between nodes, then assert a bunch of "same as" relationships between the clones. This isn't that different than the "meta" approach, but it has the advantage that it copies Lady Gaga's data to the clone, and the "Meta" node isn't just a dumb placeholder for navigation. Most of the same problems apply though.

Here's a different suggestion though: you have a large-scale many-to-many mapping problem here. It's possible that if this is a really huge problem for you, you'd be better off breaking this out into a single relational table with two columns (from_id, to_id), each referencing a neo4j node ID. You then might have a hybrid system that's mostly graph (but with some exceptions). Lots of tradeoffs here; of course you couldn't traverse that rel in cypher at all, but it would scale and partition much better, and querying for a particular rel would probably be much faster.

One general observation here: whether we're talking about relational, graph, documents, K/V databases, or whatever -- when the databases get really big, and the performance requirements get really intense, it's almost inevitable that people end up with some kind of a hybrid solution with more than one kind of DBMS. This is because of the inescapable reality that all databases are good at some things, and not good at others. So if you need a system that's good at most everything, you're going to have to use more than one kind of database. :)

There is probably quite a bit neo4j can do to optimize in these cases, but it would seem to me that the system would need some kinds of hints on access patterns in order to do a really good job at that. Of the 2,000,000 relations present, how to the endpoints best cluster? Are older relationships more important than newer, or vice versa?