# TigerGraph: A Native MPP Graph Database

arXiv: https://arxiv.org/abs/1901.08248 · HTML: https://mlsanity.com/paper/1901.08248/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Distributed Graph Neural Network Training: A Survey](https://mlsanity.com/paper/2211.00216/) (2022)
- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch](https://mlsanity.com/paper/2406.00552/) (2024)
- [Knowledge Graphs Querying](https://mlsanity.com/paper/2305.14485/) (2023)
- [ProGS: Property Graph Shapes Language (Extended Version)](https://mlsanity.com/paper/2107.05566/) (2021)
- [LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval](https://mlsanity.com/paper/2604.18913/) (2026)

## Abstract (arXiv)

We present TigerGraph, a graph database system built from the ground up to support massively parallel computation of queries and analytics. TigerGraph's high-level query language, GSQL, is designed for compatibility with SQL, while simultaneously allowing NoSQL programmers to continue thinking in Bulk-Synchronous Processing (BSP) terms and reap the benefits of high-level specification. GSQL is sufficiently high-level to allow declarative SQL-style programming, yet sufficiently expressive to concisely specify the sophisticated iterative algorithms required by modern graph analytics and traditionally coded in general-purpose programming languages like C++ and Java. We report very strong scale-up and scale-out performance over a benchmark we published on GitHub for full reproducibility.