Quegel

Qizhen Zhang, Da Yan, James Cheng
2016 Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16  
Inspired by Google's Pregel, many distributed graph processing systems have been developed recently to process big graphs. These systems expose a vertex-centric programming interface to users, where a programmer thinks like a vertex when designing parallel graph algorithms. However, existing systems are designed for tasks where most vertices in a graph participate in the computation, and they are not suitable for processing light-workload graph queries which only access a small portion of
more » ... es. This is because their programming model can seriously under-utilize the resources in a cluster for processing graph queries. In this demonstration, we introduce a general-purpose system for querying big graphs, called Quegel, which treats queries as first-class citizens in the design of its computing model. Quegel adopts a novel superstep-sharing execution model to overcome the weaknesses of existing systems. We demonstrate it is user-friendly to write parallel graph-querying programs with Quegel's interface; and we also show that Quegel is able to achieve real-time response time in various applications, including the two applications that we plan to demonstrate: point-to-point shortest-path queries and XML keyword search.
doi:10.1145/2882903.2899398 dblp:conf/sigmod/ZhangYC16 fatcat:udpa5cv2ura3jd7353pb42o32u