> Unfortunately, most interesting graph analytic problems do not fit into that model because the "graph-like" aspect is still limited to problem sizes that fit

> Unfortunately, most interesting graph analytic problems do not fit into that model because the "graph-like" aspect is still limited to problem sizes that fit conventional algorithms. "interesting" != "useful" Again, the same thing has been true with distributed computing in general. MapReduce & Hadoop are pretty much the antithesis of where distributed computing research for the last ~20 years had been working, because MapReduce solves what is nearly an "embarrassingly parallel" problem. > 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. There are even more distributed computing algorithms that don't fit in to MapReduce terribly well (and really, it's not that algorithms don't work with MapReduce/Pregel, it's that they don't work well), but it is still quite useful. Turns out, the reason it's the Holy Grail is that it is just flat out hard to do (provably so). While what Titan/Pregel do isn't nearly is difficult, it is surprisingly difficult to do at massive scale, so just doing the simple stuff they do is quite useful and game changing.