基于非易失性内存的知识图谱系统优化研究

Research on Optimizing Knowledge Graph System with Non-Volatile Memory

分布式系统的高扩展性和高可用性使得在其上构建大规模知识图谱已经成为产业发展趋势。新兴的分布式图数据库更推崇采用NoSQL等数据模型,如键值存储作为其存储引擎,以进一步提高其可扩展性和实用性。在这种情况下,上层的图查询语言的语句会被翻译成一组混合的键值操作。为了加速查询翻译生成的键值操作,提出了基于非易失性内存查询性能加速(knowledge graph booster,KGB)的知识图谱系统。KGB主要包含面向邻域查询加速的NVM辅助索引,用于降低键值存储的读取成本;快速响应的改进Raft算法,用于实现高效的键值存取操作;以及面向键值存储引擎的调优机制,为知识图谱存储系统获得额外的性能提升。通过实验表明,KGB能有效降低知识图谱系统的平均延迟和尾延迟的影响,实现更高的性能提升。

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 lan-guage(GQL)statement will be translated into a group of the native and hybrid kinds of key-value(KV)operations.To accel-erate the KV operations generated from upper-level knowledge graph queries,a high-performance knowledge graph system with a non-volatile memory-based queries booster(KGB)is proposed.KGB mainly contains a neighbor 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 effec-tively reduce the average and the tail latency,achieving higher performance promotion for the knowledge graph system.