  Pioneered by Google's Pregel, many distributed systems have been developed
for large-scale graph analytics. These systems expose the user-friendly "think
like a vertex" programming interface to users, and exhibit good horizontal
scalability. However, these systems are designed for tasks where the majority
of graph vertices participate in computation, but are not suitable for
processing light-workload graph queries where only a small fraction of vertices
need to be accessed. The programming paradigm adopted by these systems can
seriously under-utilize the resources in a cluster for graph query processing.
In this work, we develop a new open-source system, called Quegel, for querying
big graphs, which treats queries as first-class citizens in the design of its
computing model. Users only need to specify the Pregel-like algorithm for a
generic query, and Quegel processes light-workload graph queries on demand
using a novel superstep-sharing execution model to effectively utilize the
cluster resources. Quegel further provides a convenient interface for
constructing graph indexes, which significantly improve query performance but
are not supported by existing graph-parallel systems. Our experiments verified
that Quegel is highly efficient in answering various types of graph queries and
is up to orders of magnitude faster than existing systems.
