BuffCut: Prioritized Buffered Streaming Graph Partitioning
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arXiv
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| Format: | Preprint |
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2026
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| author | Baumgärtner, Linus Chhabra, Adil Faraj, Marcelo Fonseca Schulz, Christian |
| author_facet | Baumgärtner, Linus Chhabra, Adil Faraj, Marcelo Fonseca Schulz, Christian |
| contents | Streaming graph partitioners enable resource-efficient and massively scalable partitioning, but one-pass assignment heuristics are highly sensitive to stream order and often yield substantially higher edge cuts than in-memory methods. We present BuffCut, a buffered streaming partitioner that narrows this quality gap, particularly when stream ordering is adversarial, by combining prioritized buffering with batch-wise multilevel assignment. BuffCut maintains a bounded priority buffer to delay poorly informed decisions and regulate the order in which nodes are considered for assignment. It incrementally constructs high-locality batches of configurable size by iteratively inserting the highest-priority nodes from the buffer into the batch, effectively recovering locality structure from the stream. Each batch is then assigned via a multilevel partitioning algorithm. Experiments on diverse real-world and synthetic graphs show that BuffCut consistently outperforms state-of-the-art buffered streaming methods. Compared to the strongest prioritized buffering baseline, BuffCut achieves 20.8% fewer edge cuts while running 2.9 times faster and using 11.3 times less memory. Against the next-best buffered method, it reduces edge cut by 15.8% with only modest overheads of 1.8 times runtime and 1.09 times memory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21248 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BuffCut: Prioritized Buffered Streaming Graph Partitioning Baumgärtner, Linus Chhabra, Adil Faraj, Marcelo Fonseca Schulz, Christian Databases Streaming graph partitioners enable resource-efficient and massively scalable partitioning, but one-pass assignment heuristics are highly sensitive to stream order and often yield substantially higher edge cuts than in-memory methods. We present BuffCut, a buffered streaming partitioner that narrows this quality gap, particularly when stream ordering is adversarial, by combining prioritized buffering with batch-wise multilevel assignment. BuffCut maintains a bounded priority buffer to delay poorly informed decisions and regulate the order in which nodes are considered for assignment. It incrementally constructs high-locality batches of configurable size by iteratively inserting the highest-priority nodes from the buffer into the batch, effectively recovering locality structure from the stream. Each batch is then assigned via a multilevel partitioning algorithm. Experiments on diverse real-world and synthetic graphs show that BuffCut consistently outperforms state-of-the-art buffered streaming methods. Compared to the strongest prioritized buffering baseline, BuffCut achieves 20.8% fewer edge cuts while running 2.9 times faster and using 11.3 times less memory. Against the next-best buffered method, it reduces edge cut by 15.8% with only modest overheads of 1.8 times runtime and 1.09 times memory. |
| title | BuffCut: Prioritized Buffered Streaming Graph Partitioning |
| topic | Databases |
| url | https://arxiv.org/abs/2602.21248 |