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.

翻译：随着智能数据服务的快速崛起，现代企业日益需要高效、多模态且成本低廉的数据分析基础设施。然而，在字节跳动的生产环境中，现有系统存在诸多不足，例如多模态存储的I/O效率低下、查询优化不够灵活（如未能针对多模态访问模式进行优化），以及资源解耦导致的性能退化（如远程存储中数据局部性的丧失）。为应对这些挑战，我们提出了ByteHouse（https://bytehouse.cloud），一种专为实时多模态数据分析设计的云原生数据仓库。其存储层集成了统一表引擎，提供两层逻辑抽象与物理一致布局；支持固态硬盘驱动的集群级缓存（CrossCache），实现计算节点间的共享缓存；以及虚拟文件系统（NexusFS），支持计算节点上的高效本地访问。计算层支持分析、批处理和增量执行模式，并对混合查询（如基于层级向量索引的运行时过滤）进行了定制优化。控制层协调全局元数据与事务，并具备一个由历史执行轨迹与人工智能辅助计划选择增强的高效优化器。在内部与标准工作负载上的评估表明，ByteHouse相比现有系统实现了显著的效率提升。