标题： M2：用于多模型工作负载的具有专用存储引擎的分析系统 显示英文标题

标题： M2: An Analytic System with Specialized Storage Engines for Multi-Model Workloads

摘要： 现代数据分析工作负载越来越需要同时处理多种数据模型。 两种主要方法满足这一需求：多语言持久化和多模型数据库系统。 多语言持久化使用协调程序来管理多个独立的数据库系统，但由于其物理上分散的架构，导致通信成本较高。 同时，现有的多模型数据库系统依赖于针对特定数据模型优化的单一存储引擎，导致在不同数据模型上的处理效率低下。 为了解决这些限制，我们提出了M2，一个具有集成存储引擎的多模型分析系统。 M2将所有数据模型视为第一类实体，生成包含跨模型操作的查询计划。 为了有效结合不同模型的数据，该系统引入了一种称为多阶段哈希连接的专用跨模型连接算法。 我们的评估表明，M2在多模型分析中的性能比现有方法快达188倍，证实了我们所提出技术的有效性。 显示英文摘要

摘要： Modern data analytic workloads increasingly require handling multiple data models simultaneously. Two primary approaches meet this need: polyglot persistence and multi-model database systems. Polyglot persistence employs a coordinator program to manage several independent database systems but suffers from high communication costs due to its physically disaggregated architecture. Meanwhile, existing multi-model database systems rely on a single storage engine optimized for a specific data model, resulting in inefficient processing across diverse data models. To address these limitations, we present M2, a multi-model analytic system with integrated storage engines. M2 treats all data models as first-class entities, composing query plans that incorporate operations across models. To effectively combine data from different models, the system introduces a specialized inter-model join algorithm called multi-stage hash join. Our evaluation demonstrates that M2 outperforms existing approaches by up to 188x speedup on multi-model analytics, confirming the effectiveness of our proposed techniques.