构建基于OceanBase的分布式近实时分析处理数据库系统
Building an OceanBase-based Distributed Nearly Real-time Analytical Processing Database System
AI总结：
OceanBase Mercury是一款专为千万字节级数据设计的创新OLAP系统，通过自适应列式存储、差分刷新机制和多态向量化引擎，在查询延迟上显著优于专用OLAP引擎，实现高效分析与操作的平衡。
摘要：
随着数据库系统在高效管理大规模数据集的同时提供实时事务处理和高级分析能力的需求日益增长，现代数据基础设施中这一能力变得至关重要。虽然传统OLAP系统往往无法满足这些双重要求，新兴的实时分析处理系统仍面临持续的挑战，例如数据冗余过多、复杂的跨系统同步以及时间效率低下。本文介绍了OceanBase Mercury作为创新的OLAP系统，专为千万字节级数据设计。该系统具有分布式、多租户架构，确保了企业级的基本要求，包括持续可用性和弹性扩展性。我们的技术贡献包括三个关键组件：（1）一种自适应的列式存储格式，具有混合数据布局优化；（2）具有时间一致性保证的材料化视图差分刷新机制；（3）支持三种不同数据格式的多态向量化引擎。在真实世界的工作负载下进行的实证评估表明，OceanBase Mercury在查询延迟方面比专用OLAP引擎快1.3倍到3.1倍，同时保持亚秒延迟，使其成为一种突破性的AP解决方案，在大数据环境中有效平衡分析深度与操作敏捷性。
英文摘要：
The growing demand for database systems capable of efficiently managing massive datasets while delivering real-time transaction processing and advanced analytical capabilities has become critical in modern data infrastructure. While traditional OLAP systems often fail to meet these dual requirements, emerging real-time analytical processing systems still face persistent challenges, such as excessive data redundancy, complex cross-system synchronization, and suboptimal temporal efficiency. This paper introduces OceanBase Mercury as an innovative OLAP system designed for petabyte-scale data. The system features a distributed, multi-tenant architecture that ensures essential enterprise-grade requirements, including continuous availability and elastic scalability. Our technical contributions include three key components: (1) an adaptive columnar storage format with hybrid data layout optimization, (2) a differential refresh mechanism for materialized views with temporal consistency guarantees, and (3) a polymorphic vectorization engine supporting three distinct data formats. Empirical evaluations under real-world workloads demonstrate that OceanBase Mercury outperforms specialized OLAP engines by 1.3X to 3.1X speedup in query latency while maintaining sub-second latency, positioning it as a groundbreaking AP solution that effectively balances analytical depth with operational agility in big data environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
