DBDesigner:为Vertica分析数据库定制的物理设计工具

R. Varadarajan, V. Bharathan, A. Cary, J. Dave, Sreenath Bodagala
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引用次数: 20

摘要

在本文中,我们介绍了Vertica的可定制物理设计工具,称为DBDesigner (DBD),它可以针对各种场景和应用程序生成优化的设计。对于给定的工作负载和空间预算,DBD会自动推荐能够优化查询性能、存储占用、容错和恢复的物理设计,以满足不同的客户需求。Vertica是一个分布式的、大规模并行的柱状数据库,它将数据物理地组织成投影。投影是来自一个或多个表的属性子集,这些表的元组按一个或多个属性排序,在集群节点上复制或分段(分布)。投影设计的关键挑战是选择合适的列集、排序顺序、集群数据分布和列编码。为了在查询性能和存储占用之间实现预期的平衡,DBD在三种不同的设计策略下运行:(a)负载优化、(b)查询优化或(c)平衡。这些策略间接控制建议的投影和优化查询的数量,以实现所需的平衡。为了满足随时间变化的查询工作负载,DBD还以全面和增量的设计模式运行。此外,DBD允许用户基于对数据和查询工作负载的深入了解来覆盖投影设计的特定功能。我们提出了完整的物理设计算法,详细描述了如何使用优化器的成本和效益模型有效地探索和评估投影候选对象。我们的实验结果表明,DBD产生了满足各种客户用例的良好物理设计。
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DBDesigner: A customizable physical design tool for Vertica Analytic Database
In this paper, we present Vertica's customizable physical design tool, called the DBDesigner (DBD), that produces designs optimized for various scenarios and applications. For a given workload and space budget, DBD automatically recommends a physical design that optimizes query performance, storage footprint, fault tolerance and recovery to meet different customer requirements. Vertica is a distributed, massively parallel columnar database that physically organizes data into projections. Projections are attribute subsets from one or more tables with tuples sorted by one or more attributes, that are replicated or segmented (distributed) on cluster nodes. The key challenges involved in projection design are picking appropriate column sets, sort orders, cluster data distributions and column encodings. To achieve the desired trade-off between query performance and storage footprint, DBD operates under three different design policies: (a) load-optimized, (b) query-optimized or (c) balanced. These policies indirectly control the number of projections proposed and queries optimized to achieve the desired balance. To cater to query workloads that evolve over time, DBD also operates in a comprehensive and incremental design mode. In addition, DBD lets users override specific features of projection design based on their intimate knowledge about the data and query workloads. We present the complete physical design algorithm, describing in detail how projection candidates are efficiently explored and evaluated using optimizer's cost and benefit model. Our experimental results show that DBD produces good physical designs that satisfy a variety of customer use cases.
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