LHGstore: An In-Memory Learned Graph Storage for Fast Updates and Analytics
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914387600605184 |
|---|---|
| author | Qiao, Pengpeng Zhang, Zhiwei Wang, Xinzhou Li, Zhetao Cao, Xiaochun Cao, Yang |
| author_facet | Qiao, Pengpeng Zhang, Zhiwei Wang, Xinzhou Li, Zhetao Cao, Xiaochun Cao, Yang |
| contents | Various real-world applications rely on in-memory dynamic graphs that must efficiently handle frequent updates while supporting low-latency analytics on evolving structures. Achieving both objectives remains challenging due to the trade-off between update efficiency and traversal locality, particularly under highly skewed degree distributions. This motivates the design of graph indexing schemes optimized for in-memory graph management on modern multi-core CPUs. We present LHGstore, a degree-aware Learned Hierarchical Graph storage that, for the first time, integrates learned indexing into graph management. LHGstore designs a two-level hierarchy that decouples vertex and edge access and further organizes each vertex's edges using data structures adaptive to its degree. Lightweight arrays are used for low-degree vertices to maximize traversal locality, while learned indexes are applied to high-degree vertices to improve update throughput. Extensive experiments show that LHGstore achieves 5.9-28.2$\times$ higher throughput and significantly faster analytics than SOTA in-memory graph storage systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11596 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LHGstore: An In-Memory Learned Graph Storage for Fast Updates and Analytics Qiao, Pengpeng Zhang, Zhiwei Wang, Xinzhou Li, Zhetao Cao, Xiaochun Cao, Yang Databases Various real-world applications rely on in-memory dynamic graphs that must efficiently handle frequent updates while supporting low-latency analytics on evolving structures. Achieving both objectives remains challenging due to the trade-off between update efficiency and traversal locality, particularly under highly skewed degree distributions. This motivates the design of graph indexing schemes optimized for in-memory graph management on modern multi-core CPUs. We present LHGstore, a degree-aware Learned Hierarchical Graph storage that, for the first time, integrates learned indexing into graph management. LHGstore designs a two-level hierarchy that decouples vertex and edge access and further organizes each vertex's edges using data structures adaptive to its degree. Lightweight arrays are used for low-degree vertices to maximize traversal locality, while learned indexes are applied to high-degree vertices to improve update throughput. Extensive experiments show that LHGstore achieves 5.9-28.2$\times$ higher throughput and significantly faster analytics than SOTA in-memory graph storage systems. |
| title | LHGstore: An In-Memory Learned Graph Storage for Fast Updates and Analytics |
| topic | Databases |
| url | https://arxiv.org/abs/2603.11596 |