RisGraph: A Real-Time Streaming System for Evolving Graphs to Support Sub-millisecond Per-update Analysis at Millions Ops/s
Summary: RisGraph provides real-time streaming for evolving graphs with sub-millisecond per-update analysis at millions of updates per second. It exploits Indexed Adjacency Lists, sparse arrays, Hybrid Parallel Mode, and a safe/unsafe update classifier to enable localized access and inter-update parallelism for high-throughput, no-batching per-update analytics (P999 ≤ 20 ms) on graphs with hundreds of millions of vertices and billions of edges. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Guanyu Feng (Beijing Institute of Technology; Tsinghua University)
- 2. Zixuan Ma (Beijing Institute of Technology; Tsinghua University)
- 3. Daixuan Li (Beijing Institute of Technology; Tsinghua University)
- 4. Shengqi Chen (Beijing Institute of Technology; Tsinghua University)
- 5. Xiaowei Zhu (Beijing Institute of Technology; Tsinghua University)
- 6. Wentao Han (Beijing Institute of Technology; Tsinghua University)
- 7. Wenguang Chen (Beijing Institute of Technology; Tsinghua University)
BibTeX Citation
@inproceedings{feng_sigmod21,
title = {{RisGraph: A Real-Time Streaming System for Evolving Graphs to Support Sub-millisecond Per-update Analysis at Millions Ops/s}},
author = {Feng, Guanyu and Ma, Zixuan and Li, Daixuan and Chen, Shengqi and Zhu, Xiaowei and Han, Wentao and Chen, Wenguang},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457263},
url = {https://dl.acm.org/doi/10.1145/3448016.3457263},
year = {2021}
}
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