  Join optimization has been dominated by Selinger-style, pairwise optimizers
for decades. But, Selinger-style algorithms are asymptotically suboptimal for
applications in graphic analytics. This suboptimality is one of the reasons
that many have advocated supplementing relational engines with specialized
graph processing engines. Recently, new join algorithms have been discovered
that achieve optimal worst-case run times for any join or even so-called beyond
worst-case (or instance optimal) run time guarantees for specialized classes of
joins. These new algorithms match or improve on those used in specialized
graph-processing systems. This paper asks can these new join algorithms allow
relational engines to close the performance gap with graph engines?
  We examine this question for graph-pattern queries or join queries. We find
that classical relational databases like Postgres and MonetDB or newer graph
databases/stores like Virtuoso and Neo4j may be orders of magnitude slower than
these new approaches compared to a fully featured RDBMS, LogicBlox, using these
new ideas. Our results demonstrate that an RDBMS with such new algorithms can
perform as well as specialized engines like GraphLab -- while retaining a
high-level interface. We hope this adds to the ongoing debate of the role of
graph accelerators, new graph systems, and relational systems in modern
workloads.
