Accelerating Distributed Knowledge Graph System Based on Non-Volatile Memory

Yuhang Li, Weiming Li, Yangyang Wang, Yanfeng Chai, Yunpeng Chai

Abstract: Deploying large scale knowledge graphs on distributed systems has become an industry trend for their high scalability and availability. There are some distributed graph databases that prefer to adopt the NoSQL data models like the key-value store as their storage engines for its scalability and practicability. Therefore, an upper-level graph query language (GQL) statement will be translated into a group of the native and hybrid kinds of key-value (KV) operations. To accelerate the KV operations generated form upper-level knowledge graph queries, we propose a high performance knowledge graph system with a non-volatile memory based queries booster (KGB). KGB mainly contains a neighbors queries auxiliary index for reducing KVs searching cost, a fast Raft algorithm for efficient KVs operations, and a KV tuning mechanism to acquire extra performance promotion for knowledge graph application scenarios. Experiments show that KGB can effectively reduce the latency and achieve higher performance promotion for knowledge graph system.