What makes this graph database "highly scalable, distributed"? There are difficult theoretical computer science problems that effectively limit the parallelization/distribution of generalized graph operators. To achieve high scalability you have to solve these computer science problems first. If this design offers a novel solution to the longstanding computer science problem then kudos, but nothing at the site sugges…
You could have said the same thing about generalized distributed computing as well. Much like with MapReduce, the trick is to recognize that there is an 80% solution that works quite well (based around BSP): http://kowshik.github.com/JPregel/pregel_paper.pdf http://googleresearch.blogspot.com/2009/06/large-scale-graph... The trick is not to build a generalized graph operator solution. It's to have a specialized graph…
For example, if you can reduce a trillion-edge graph analysis problem into a billion-edge graph plus some other stuff (usually materialized document structures) then you can fit that into something like Pregel. That is how almost all real-world graph analysis is done today.
But by doing so, you've lost the ability to do graph analysis on the other ~trillion edges for the sake of tractability in a narrow case. You can't do relationship analysis across the attached documents. There are many, many graph analytic problems that require a true graph that is orders of magnitude larger than what can be partitioned even after accounting for graph reduction techniques such as those used in Pregel.
The Holy Grail is still the ability to run ad hoc graph analytic queries against a massively distributed graph representation. There are no shortcuts around this for many interesting applications. Right now, we are limited to mere billions of edges for most practical purposes and all of the hacks and workarounds are designed to keep the number of true edges to around this number even when the data model is much larger.