Multigraph Topology for Scalable, Isolated Graph Workloads

FalcorDB's team conducted an assessment to evaluate the scalability of a multi-tenant graph database in handling access-permission workloads and its performance when transitioning from a single server to a clustered deployment. The study focused on a real-world query pattern—checking user permissions for files—and tested throughput across different hardware configurations, observing near-linear increases in query-per-second rates with each 32-core increment, thanks to minimal coordination overhead across shards. The database supports full graph isolation during queries, allowing each query to target a single graph key, which minimizes data leakage risks and maintains write integrity at the master level. FalcorDB's multigraph functionality, available in both open-source and managed versions, enables scalable read operations by adding replicas, though it does not support cross-graph queries within a single execution. Memory usage and snapshot time rise with more graphs, but query latency remains constant as each query is limited to a single graph. The platform is designed for technical teams managing complex, real-time data, enhancing accuracy in generative AI applications while reducing hallucinations in outputs.