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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.04584 |
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| _version_ | 1866909568500498432 |
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| author | Grätzer, Simon Heling, Lars Klinov, Pavel |
| author_facet | Grätzer, Simon Heling, Lars Klinov, Pavel |
| contents | Stardog is a commercial Knowledge Graph platform built on top of an RDF graph database whose primary means of communication is a standardized graph query language called SPARQL. This paper describes our journey of developing a more performant query execution layer and plugging it into Stardog's query engine. The new executor, called BARQ, is based on the known principle of processing batches of tuples at a time in most critical query operators, particularly joins. In addition to presenting BARQ, the paper describes the challenges of integrating it into a mature, tightly integrated system based on the classical tuple-at-a-time Volcano model. It offers a gradual approach to overcoming the challenges that small- to medium-size engineering teams typically face. Finally, the paper presents experimental results showing that BARQ makes Stardog substantially faster on CPU-bound queries without sacrificing performance on disk-bound and OLTP-style queries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04584 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BARQ: A Vectorized SPARQL Query Execution Engine Grätzer, Simon Heling, Lars Klinov, Pavel Databases Stardog is a commercial Knowledge Graph platform built on top of an RDF graph database whose primary means of communication is a standardized graph query language called SPARQL. This paper describes our journey of developing a more performant query execution layer and plugging it into Stardog's query engine. The new executor, called BARQ, is based on the known principle of processing batches of tuples at a time in most critical query operators, particularly joins. In addition to presenting BARQ, the paper describes the challenges of integrating it into a mature, tightly integrated system based on the classical tuple-at-a-time Volcano model. It offers a gradual approach to overcoming the challenges that small- to medium-size engineering teams typically face. Finally, the paper presents experimental results showing that BARQ makes Stardog substantially faster on CPU-bound queries without sacrificing performance on disk-bound and OLTP-style queries. |
| title | BARQ: A Vectorized SPARQL Query Execution Engine |
| topic | Databases |
| url | https://arxiv.org/abs/2504.04584 |