  In this study, we present the first results of a complete implementation of
the LDBC SNB benchmark -- interactive short, interactive complex, and business
intelligence -- in two native graph database systems---Neo4j and TigerGraph. In
addition to thoroughly evaluating the performance of all of the 46 queries in
the benchmark on four scale factors -- SF-1, SF-10, SF-100, and SF-1000 -- and
three computing architectures -- on premise and in the cloud -- we also measure
the bulk loading time and storage size. Our results show that TigerGraph is
consistently outperforming Neo4j on the majority of the queries---by two or
more orders of magnitude (100X factor) on certain interactive complex and
business intelligence queries. The gap increases with the size of the data
since only TigerGraph is able to scale to SF-1000---Neo4j finishes only 12 of
the 25 business intelligence queries in reasonable time. Nonetheless, Neo4j is
generally faster at bulk loading graph data up to SF-100. A key to our study is
the active involvement of the vendors in the tuning of their platforms. In
order to encourage reproducibility, we make all the code, scripts, and
configuration parameters publicly available online.
