Low-Latency Sliding Window Connectivity
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913635623763968 |
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| author | Zhang, Chao Bonifati, Angela Özsu, Tamer |
| author_facet | Zhang, Chao Bonifati, Angela Özsu, Tamer |
| contents | Connectivity queries, which check whether vertices belong to the same connected component, are fundamental in graph computations. Sliding window connectivity processes these queries over sliding windows, facilitating real-time streaming graph analytics. However, existing methods struggle with low-latency processing due to the significant overhead of continuously updating index structures as edges are inserted and deleted. We introduce a novel approach that leverages spanning trees to efficiently process queries. The novelty of this method lies in its ability to maintain spanning trees efficiently as window updates occur. Notably, our approach completely eliminates the need for replacement edge searches, a traditional bottleneck in managing spanning trees during edge deletions. We also present several optimizations to maximize the potential of spanning-tree-based indexes. Our comprehensive experimental evaluation shows that index update latency in spanning trees can be reduced by up to $458\times$ while maintaining query performance, leading to an $8\times$ improvement in throughput. Our approach also significantly outperforms the state-of-the-art in both query processing and index updates. Additionally, our methods use significantly less memory and demonstrate consistent efficiency across various settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00884 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Low-Latency Sliding Window Connectivity Zhang, Chao Bonifati, Angela Özsu, Tamer Databases Connectivity queries, which check whether vertices belong to the same connected component, are fundamental in graph computations. Sliding window connectivity processes these queries over sliding windows, facilitating real-time streaming graph analytics. However, existing methods struggle with low-latency processing due to the significant overhead of continuously updating index structures as edges are inserted and deleted. We introduce a novel approach that leverages spanning trees to efficiently process queries. The novelty of this method lies in its ability to maintain spanning trees efficiently as window updates occur. Notably, our approach completely eliminates the need for replacement edge searches, a traditional bottleneck in managing spanning trees during edge deletions. We also present several optimizations to maximize the potential of spanning-tree-based indexes. Our comprehensive experimental evaluation shows that index update latency in spanning trees can be reduced by up to $458\times$ while maintaining query performance, leading to an $8\times$ improvement in throughput. Our approach also significantly outperforms the state-of-the-art in both query processing and index updates. Additionally, our methods use significantly less memory and demonstrate consistent efficiency across various settings. |
| title | Low-Latency Sliding Window Connectivity |
| topic | Databases |
| url | https://arxiv.org/abs/2410.00884 |