TigerGraph, the only scalable graph database for the enterprise, announced the results of the first comprehensive graph data management benchmark study using nearly 5TB of raw data on a cluster of machines – and the performance numbers prove graph can scale with real data, in real time. The company used the Linked Data Benchmark Council Social Network Benchmark (LDBC SNB), recognized as the reference standard for evaluating graph technology performance with intensive analytical and transactional workloads. TigerGraph is the industry’s first vendor to report LDBC benchmark results at this scale. TigerGraph is able to run deep-link OLAP queries on a graph of almost nine billion vertices (entities) and more than 60 billion edges (relationships), returning results in under a minute.
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“This benchmark and these results are significant, both for TigerGraph and the overall market. While TigerGraph has multiple customers in production with 10X data size and number of entities/relationships, this is the first public benchmark report where anyone can download the data, queries, and perform the benchmark. No other graph database vendor or relational database vendor has demonstrated equivalent analytical capabilities or performance numbers,” said Dr. Yu Xu, CEO and founder, TigerGraph. “If there was lingering uncertainty about graph’s ability to scale to accommodate large data volumes in record time, these results should eliminate those doubts. Graph is the engine that enables us to answer high-value business questions with complex real data, in real time, at scale. TigerGraph’s ongoing work in advanced graph analytics has been validated by market recognition, innovative customer applications and continued product evolution – and these benchmark results confirm the company’s position as a clear market leader, succeeding where other vendors have failed.”
Historically, enterprises in multiple industries – from financial services to healthcare – have struggled with numerous graph-related challenges as they work to unlock real value from connected data. These challenges include an inability to support large data volumes, slow query performance, and lack of flexibility with existing BI tools. TigerGraph has addressed these pain points with the world’s fastest and most scalable graph platform, providing massive scalability of data volumes, fast deep-link analysis for real-time performance and an offering that is delivered as a service and on-prem. TigerGraph’s proven technology connects data silos for deeper, wider and operational analytics at scale.
For this latest benchmark, TigerGraph’s performance was measured using the LDBC SNB Benchmark scale-factor 10K dataset (4.8TB raw data, 8.86B vertices, 61.77B edges,) on a distributed cluster. The implementation uses GSQL, a query language developed by TigerGraph. The queries were compiled and loaded into the database as stored procedures. TigerGraph’s performance was tested with three types of queries: IS Workload (all queries answered in one to three seconds), IC Workload (all queries answered in three to nine seconds) and BI Workload (the majority of OLAP-style iterative and/or deep-link graph queries were answered in under one minute). Each query was performed three times, and the median of the elapsed times presented as the final latency time. Each query was performed on clusters of 24 machines, 18 machines and 12 machines, respectively.
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