ByteHouse: ByteDance's Cloud-Native Data Warehouse for Real-Time Multimodal Data Analytics
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912981167636480 |
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| author | Han, Yuxing Lin, Yu Dong, Yifeng Zhou, Xuanhe Peng, Xindong Tian, Xinhui You, Zhiyuan Guo, Yingzhong Chen, Xi Qu, Weiping Meng, Tao Gao, Dayue Wang, Haoyu Wei, Liuxi Zhang, Huanchen Wu, Fan |
| author_facet | Han, Yuxing Lin, Yu Dong, Yifeng Zhou, Xuanhe Peng, Xindong Tian, Xinhui You, Zhiyuan Guo, Yingzhong Chen, Xi Qu, Weiping Meng, Tao Gao, Dayue Wang, Haoyu Wei, Liuxi Zhang, Huanchen Wu, Fan |
| contents | With the rapid rise of intelligent data services, modern enterprises increasingly require efficient, multimodal, and cost-effective data analytics infrastructures. However, in ByteDance's production environments, existing systems fall short due to limitations such as I/O-inefficient multimodal storage, inflexible query optimization (e.g., failing to optimize multimodal access patterns), and performance degradation caused by resource disaggregation (e.g., loss of data locality in remote storage). To address these challenges, we introduce ByteHouse (https://bytehouse.cloud), a cloud-native data warehouse designed for real-time multimodal data analytics. The storage layer integrates a unified table engine that provides a two-tier logical abstraction and physically consistent layout, SSD-backed cluster-scale cache (CrossCache) that supports shared caching across compute nodes, and virtual file system (NexusFS) that enable efficient local access on compute nodes. The compute layer supports analytical, batch, and incremental execution modes, with tailored optimizations for hybrid queries (e.g., runtime filtering over tiered vector indexes). The control layer coordinates global metadata and transactions, and features an effective optimizer enhanced by historical execution traces and AI-assisted plan selection. Evaluations on internal and standard workloads show that ByteHouse achieves significant efficiency improvement over existing systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08226 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ByteHouse: ByteDance's Cloud-Native Data Warehouse for Real-Time Multimodal Data Analytics Han, Yuxing Lin, Yu Dong, Yifeng Zhou, Xuanhe Peng, Xindong Tian, Xinhui You, Zhiyuan Guo, Yingzhong Chen, Xi Qu, Weiping Meng, Tao Gao, Dayue Wang, Haoyu Wei, Liuxi Zhang, Huanchen Wu, Fan Databases With the rapid rise of intelligent data services, modern enterprises increasingly require efficient, multimodal, and cost-effective data analytics infrastructures. However, in ByteDance's production environments, existing systems fall short due to limitations such as I/O-inefficient multimodal storage, inflexible query optimization (e.g., failing to optimize multimodal access patterns), and performance degradation caused by resource disaggregation (e.g., loss of data locality in remote storage). To address these challenges, we introduce ByteHouse (https://bytehouse.cloud), a cloud-native data warehouse designed for real-time multimodal data analytics. The storage layer integrates a unified table engine that provides a two-tier logical abstraction and physically consistent layout, SSD-backed cluster-scale cache (CrossCache) that supports shared caching across compute nodes, and virtual file system (NexusFS) that enable efficient local access on compute nodes. The compute layer supports analytical, batch, and incremental execution modes, with tailored optimizations for hybrid queries (e.g., runtime filtering over tiered vector indexes). The control layer coordinates global metadata and transactions, and features an effective optimizer enhanced by historical execution traces and AI-assisted plan selection. Evaluations on internal and standard workloads show that ByteHouse achieves significant efficiency improvement over existing systems. |
| title | ByteHouse: ByteDance's Cloud-Native Data Warehouse for Real-Time Multimodal Data Analytics |
| topic | Databases |
| url | https://arxiv.org/abs/2602.08226 |