Toward Implementing an Agent-based Distributed Graph Database System

Graph database (DB) systems are increasing their popularity in big-data analysis and machine learning particularly in the areas of e-commerce recommendation, fraud detection, and social-media analytics. Speed-up and spatial scalability of their DB transactions are pursued with various techniques such as index-free access to graph components in Neo4j, graph sharding over a cluster system in ArangoDB, and graph DB construction over distributed memory in AnzoGraph. However, these techniques have their respective challenges: difficulty in expanding an index-free graph over distributed memory, slow-down in accessing distributed disks, and a bottleneck incurred by repetitive master-to-worker distributions of query pipelines.As a solution to these problems, we are applying multi-agent technologies to distributed graph DB construction: multiple user processes over a cluster system maintain portion of distributed graph in their cache space; their cache contents are synchronized through a software-snooped write-back and write-update protocol; and a DB user from any cluster node dispatches an agent that handles an independent graph query through navigating over distributed graph. To follow current trends in graph DB standardization, we adopt the Cypher language whose queries are translated into agent code.This paper presents a new distributed hash-map implementation and its application to our graph DB system; differentiates it from Hazelcast from the viewpoints of its memory coherency and access speed; describes our translator that generates agent code from Cypher queries; and examines graph DB creation and traversal performance of agents in comparison with Neo4j and ArangoDB.