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DBDesigner: A customizable physical design tool for Vertica Analytic Database

机译:DBDesigner:Vertica Analytic Database的可自定义物理设计工具

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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.
机译:在本文中,我们介绍了Vertica的可定制物理设计工具,称为DBDesigner(DBD),该工具可以针对各种场景和应用程序进行优化设计。对于给定的工作负载和空间预算,DBD自动推荐一种物理设计,该设计可以优化查询性能,存储占用空间,容错能力和恢复能力,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,可将数据实际组织为投影。投影是来自一个或多个表的属性子集,其中元组按一个或多个属性排序,在集群节点上被复制或分段(分布)。投影设计所涉及的主要挑战是选择合适的列集,排序顺序,群集数据分布和列编码。为了在查询性能和存储空间之间达成理想的折衷,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预测数量和优化查询以实现所需的平衡。为了查询随时间变化的工作负载,DBD还以一种全面的增量设计模式进行操作。此外,DBD使用户可以根据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。我们提供了完整的物理设计算法,详细描述了如何使用优化程序的成本和收益模型有效地探索和评估投影候选。我们的实验结果表明,DBD可以生成满足各种客户使用情况的良好物理设计。

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