ByteHouse: ByteDance's Cloud-Native Data Warehouse for Real-Time Multimodal Data Analytics
Yuxing Han, Yu Lin, Yifeng Dong, Xuanhe Zhou, Xindong Peng, Xinhui Tian, Zhiyuan You, Yingzhong Guo, Xi Chen, Weiping Qu, Tao Meng, Dayue Gao, Haoyu Wang, Liuxi Wei, Huanchen Zhang, Fan Wu

TL;DR
ByteHouse is a cloud-native data warehouse optimized for real-time multimodal data analytics, addressing limitations of existing systems through integrated storage, caching, and AI-enhanced query optimization.
Contribution
It introduces a unified storage engine, shared caching, and an AI-assisted optimizer tailored for real-time multimodal analytics in a cloud-native environment.
Findings
Significant efficiency improvements over existing systems
Effective handling of multimodal data access patterns
Enhanced query optimization with AI assistance
Abstract
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…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Cloud Computing and Resource Management · Distributed systems and fault tolerance
