Grasper

Abstract

The property graph (PG) model is one of the most general graph data model and has been widely adopted in many graph analytics and processing systems. However, existing systems suffer from poor performance in terms of both latency and throughput for processing online analytical workloads on PGs due to their design defects such as expensive interactions with external databases, low parallelism, and high network overheads. In this paper, we propose Grasper, a high performance distributed system for OLAP on property graphs. Grasper adopts RDMA-aware system designs to reduce the network communication cost. We propose a novel query execution model, called Expert Model, which supports adaptive parallelism control at the fine-grained query operation level and allows tailored optimizations for different categories of query operators, thus achieving high parallelism and good load balancing. Experimental results show that Grasper achieves low latency and high throughput on a broad range of online analytical workloads.