White-Box Micro-Adaptive Query Processing
Operator performance in in-memory data management systems (DMS) often suffers from micro-architectural hazards such as cache misses and branch mispredictions. While many operators have alternative implementations that are robust against such hazards, these generally perform worse when no hazards are...
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| Vydané v: | Data engineering s. 2880 - 2893 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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IEEE
19.05.2025
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| ISSN: | 2375-026X |
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| Abstract | Operator performance in in-memory data management systems (DMS) often suffers from micro-architectural hazards such as cache misses and branch mispredictions. While many operators have alternative implementations that are robust against such hazards, these generally perform worse when no hazards are encountered. Unfortunately, hazards are caused by order-dependent data characteristics that query optimizers struggle to capture (e.g., sortedness, clusteredness) making a priori hazard-conscious optimization difficult. Additionally, statically optimized plans fail to adapt when data characteristics vary within a table. To address these problems, we propose a hazardadaptive approach to query execution. Through hardwareassisted runtime profiling of low-level metrics, operators dynamically adapt to "hazardous" data. We propose an architecture for hazard-adaptive operators and integrate our approach into a DMS. We demonstrate that using hazard-adaptive operators provides a \sim \mathbf{2-20} \times speedup across several TPC-H queries. |
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| AbstractList | Operator performance in in-memory data management systems (DMS) often suffers from micro-architectural hazards such as cache misses and branch mispredictions. While many operators have alternative implementations that are robust against such hazards, these generally perform worse when no hazards are encountered. Unfortunately, hazards are caused by order-dependent data characteristics that query optimizers struggle to capture (e.g., sortedness, clusteredness) making a priori hazard-conscious optimization difficult. Additionally, statically optimized plans fail to adapt when data characteristics vary within a table. To address these problems, we propose a hazardadaptive approach to query execution. Through hardwareassisted runtime profiling of low-level metrics, operators dynamically adapt to "hazardous" data. We propose an architecture for hazard-adaptive operators and integrate our approach into a DMS. We demonstrate that using hazard-adaptive operators provides a \sim \mathbf{2-20} \times speedup across several TPC-H queries. |
| Author | Mohr-Daurat, Hubert Pirk, Holger Pearce, Jack |
| Author_xml | – sequence: 1 givenname: Jack surname: Pearce fullname: Pearce, Jack email: jack.pearce22@imperial.ac.uk organization: Imperial College London – sequence: 2 givenname: Hubert surname: Mohr-Daurat fullname: Mohr-Daurat, Hubert email: h.mohr-daurat19@imperial.ac.uk organization: Imperial College London – sequence: 3 givenname: Holger surname: Pirk fullname: Pirk, Holger email: hlgr@ic.ac.uk organization: Imperial College London |
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| Snippet | Operator performance in in-memory data management systems (DMS) often suffers from micro-architectural hazards such as cache misses and branch mispredictions.... |
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| SubjectTerms | Engines Glass box Hazards in-memory database Measurement micro-adaptivity micro-architectural hazards Microarchitecture Monitoring Optimization Pathology Query processing Runtime white-box optimization |
| Title | White-Box Micro-Adaptive Query Processing |
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