Dealing with supernodes in ArangoDB

About a year ago I wrote about data modeling in ArangoDB. The main take away is to avoid the temptation to make unnecessary vertices (and all the attendant edges), since the traversing high degree vertices (a vertex with lots of edges pointing at it) is an expensive process.

Graphs are cool and it’s easy to forget that ArangoDB is a great document store. Treating is as such means “Embedding“, to borrow a term from MongoDB,  which lets you keep your traversals fast.

But while good data modeling can prevent you from creating some high-degree vertices, you will run into them eventually, and ArangoDB’s new “vertex-centric” indexes is a feature that is there for exactly those moments.

First we can try and get a sense of the impact these high-degree vertices have on a traversal. To do that I wrote a script that would generate a star graph starting with three “seed” vertices, a start, middle, and end.

The goal was to walk across the middle vertex to get from start to end while changing the number of vertices connected to the middle.

// the seed vertices
let seedVertices = [
  {name: 'start', _key: '1', type: "seed"},
  {name: 'middle', _key: '2', type: "seed"},
  {name: 'end', _key: '3', type: "seed"}

With just 10 vertices, this is nowhere near deserving the name “supernode”, but it’s pretty clear why these things are called star-graphs.

A baby “super node”.

Next we crank up the number of vertices so we can see the effect on traversal time.


By the time we get to a vertex surrounded by 100,000 to 1,000,000 other vertices we are starting to get into full-blown supernode territory. You can see that by the time we get to sorting through a million incident edges ArangoDB is up to 4.3 seconds to traverse across that middle vertex.

A “vertex-centric” index is one that include either _to or _from plus some other edge attribute. In this case I’ve added a type attribute which I’ll combine with _to to make my index. (Note that if I was allowing “any” as a direction I would need a second index that combines type and _from)


ArangoDB calculates the path and offers it to you as you do a traversal. You can access the path by declaring a variable to receive it. In the query below p contains the path. If we use the ALL array comparison operator on p.edges to say that all the edges in the path should have type of “seed”, that should be enough to get Arango to use are new index.

    FOR v,e,p IN 1..2 ANY 'vertices/1' edges
      FILTER p.edges[*].type ALL == 'seed' && == 'end'
        RETURN v

The selectivity score shown by explain doesn’t leave you very hopeful that Arango will use the index…

Indexes used:
 By   Type   Collection   Unique   Sparse   Selectivity   Fields               Ranges
  2   hash   edges        false    false         0.00 %   [ `type`, `_to` ]    base INBOUND
  2   edge   edges        false    false        50.00 %   [ `_from`, `_to` ]   base OUTBOUND

but having our query execute in 0.2 milliseconds instead of 4.3 seconds is a pretty good indication it’s working.


Back to modeling

For me, this little experiment has underscored the importance of good data modeling. You don’t have to worry about the number of edges if you don’t create a vertex in the first place. If you are conservative about what you are willing to make a vertex, and make good use of indexes you can see that ArangoDB is going to be able to gracefully handle some pretty hefty data.

With vertex-centric indexes, and other features like the new Smartgraphs, ArangoDB has gone from being a great document database with a trick up it’s sleeve (Joins!) to being a really solid graph database and there are new features landing regularly. I’m curious to see where they go next.