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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.06705 |
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| _version_ | 1866908757104001024 |
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| author | de Graaf, Daan Brijder, Robert Chakraborty, Soham Fletcher, George van de Wall, Bram Yakovets, Nikolay |
| author_facet | de Graaf, Daan Brijder, Robert Chakraborty, Soham Fletcher, George van de Wall, Bram Yakovets, Nikolay |
| contents | Graph database query languages cannot express algorithms like PageRank, forcing costly data wrangling, while existing solutions such as algorithm libraries, vertex-centric APIs, and recursive CTEs lack the necessary combination of expressiveness, performance, and usability. We present GraphAlg: a domain-specific language for graph algorithms that compiles to relational algebra, enabling seamless integration with query processing pipelines. Built on linear algebra foundations, GraphAlg provides intuitive matrix operations that are amenable to aggressive optimization including sparsity analysis, loop-invariant code motion, and in-place aggregation. Our implementation in AvantGraph demonstrates significant code complexity reduction compared to SQL/Python and Pregel while achieving excellent performance on LDBC Graphalytics benchmarks. GraphAlg establishes that graph databases can serve as unified platforms for both queries and analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06705 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Algorithm Support for Graph Databases, Done Right de Graaf, Daan Brijder, Robert Chakraborty, Soham Fletcher, George van de Wall, Bram Yakovets, Nikolay Databases Graph database query languages cannot express algorithms like PageRank, forcing costly data wrangling, while existing solutions such as algorithm libraries, vertex-centric APIs, and recursive CTEs lack the necessary combination of expressiveness, performance, and usability. We present GraphAlg: a domain-specific language for graph algorithms that compiles to relational algebra, enabling seamless integration with query processing pipelines. Built on linear algebra foundations, GraphAlg provides intuitive matrix operations that are amenable to aggressive optimization including sparsity analysis, loop-invariant code motion, and in-place aggregation. Our implementation in AvantGraph demonstrates significant code complexity reduction compared to SQL/Python and Pregel while achieving excellent performance on LDBC Graphalytics benchmarks. GraphAlg establishes that graph databases can serve as unified platforms for both queries and analytics. |
| title | Algorithm Support for Graph Databases, Done Right |
| topic | Databases |
| url | https://arxiv.org/abs/2601.06705 |