As enterprise data grows more interconnected and time-sensitive, property graph databases are becoming foundational to solving complex business problems—from fraud detection and cybersecurity to supply chain visibility and customer journey analysis. But choosing the right graph database remains a challenge. Traditional benchmarks fail to capture the unique demands of pattern-based graph queries with constraints on the properties. That’s where the Temporal Triangles benchmark comes in.
Developed to reflect real-world analytic needs, Temporal Triangles focuses on a deceptively simple pattern: a three-node cycle where events unfold in time-ordered steps. Whether it’s money flowing through a shell network, data moving through a compromised system, or customer referrals looping back during a campaign, the pattern represents cause-and-effect within a constrained time window. Its broad applicability across domains makes it a strategic benchmark for evaluating how well a graph database handles meaningful, time-sensitive insights—not just raw traversal speed.
From financial services to security operations centers, the triangle pattern appears wherever causality, feedback, and sequence matter. By surfacing this common signal across industries, Temporal Triangles provides a clear and relevant test of database capabilities.
Stress-Testing for the Real World
Unlike synthetic tests that prioritize theory over practice, the Temporal Triangles benchmark is grounded in realism. It uses RMAT-generated datasets to simulate actual data topologies—dense hubs, long tails, and power-law distributions—across a wide range of scalable sizes, from thousands to billions of edges. The benchmark evaluates each system’s ability to find temporal triangles under varying time constraints, while measuring performance across query latency, throughput, and resource usage.
This allows organizations to assess how graph platforms perform under increasing data loads, tighter time windows, or constrained compute environments. And because the benchmark supports multiple implementations—Cypher for Neo4j and Rocketgraph, GSQL for TigerGraph, Gremlin for TinkerPop—it enables apples-to-apples comparisons across major technologies.
The result isn’t just a number; it’s an operational insight. Which platform gives you results faster? Which scales most predictably? Which gets you more answers per dollar or per gigabyte of memory? For teams navigating cloud costs or architecting for future growth, Temporal Triangles offers practical clarity.
From Evaluation to Advantage
Temporal Triangles isn’t just a tool for testing—it’s a blueprint for better decision-making. It helps technical leaders evaluate current systems, compare alternatives, and forecast infrastructure needs. It also highlights where optimization matters most—whether that’s better temporal indexing, smarter query structure, or balanced CPU/memory allocation.
At Rocketgraph, we see this benchmark as part of a larger mission: to help enterprises and government agencies find insights that matter, fast. Our platform was built to meet these demands, and Temporal Triangles helps validate that performance in a transparent, vendor-neutral way.
Download the paper to explore the full benchmark, its design, and implementation across platforms—and see how it can guide your next strategic data investment.
