  We study the classic subgraph enumeration problem under distributed settings.
Existing solutions either suffer from severe memory crisis or rely on large
indexes, which makes them impractical for very large graphs. Most of them
follow a synchronous model where the performance is often bottlenecked by the
machine with the worst performance. Motivated by this, in this paper, we
propose RADS, a Robust Asynchronous Distributed Subgraph enumeration system.
RADS first identifies results that can be found using single-machine
algorithms. This strategy not only improves the overall performance but also
reduces network communication and memory cost. Moreover, RADS employs a novel
region-grouped multi-round expand verify & filter framework which does not need
to shuffle and exchange the intermediate results, nor does it need to replicate
a large part of the data graph in each machine. This feature not only reduces
network communication cost and memory usage, but also allows us to adopt simple
strategies for memory control and load balancing, making it more robust.
Several heuristics are also used in RADS to further improve the performance.
Our experiments verified the superiority of RADS to state-of-the-art subgraph
enumeration approaches.
