Abstract: Graph data is becoming more widespread across various applications due to its features to represent multiple entity types and rich associated relationships.For users of graph databases,efficient graph query service is crucial to ensure system performance.As the amount of data grows,the single-machine graph database is difficult to meet the demand of storing all the data in memory,while distributed graph databases,which spread the data across the memory of multiple machines,face challenges in scalability and resource utilization rate.A new solution to these challenges is the introduction of RDMA-based remote memory systems.These systems separate the tasks of computing and storing graph data,offering a more flexible way to use memory.Yet,a big challenge in current solutions is how to ensure the performance of graph query when using remote memory.This study takes a close look at the challenges that come up when building remote memory graph databases on general-purpose far memory platforms,which use remote memory transparently.It suggests a new approach,where the design of the remote memory graph database is aware of how it’s being used.This design method creates a storage model that understands the different types of property graph data and how it’s accessed.Specifically,the study explains how to make sure the data is arranged and accessed in the best way.Experimental results show that when the local memory is limited,the awareness method outperforms the transparent approach,giving a 12x improvement in graph query performance.

Key words: Graph query, Graph database, Graph storage, Remote memory, Property graph model