从协作扫描到预测性缓冲区管理
From Cooperative Scans to Predictive Buffer Management
摘要：
在分析应用程序中，数据库系统通常需要在多个并发扫描命中同一个表的情况下维持工作负载。协作扫描（CScans）框架，它引入了一个主动缓冲区管理器（ABM）组件到数据库体系结构中，一直是最有效的和精心设计的响应这个问题，最初是在X100的研究原型。我们现在报告的经验，整合合作扫描到其工业实力的继任者，矢量数据库产品。在此实现过程中，我们发明了一种更简单的并发扫描缓冲区管理优化，称为预测缓冲区管理（PBM）。PBM是基于这样的观察，即在具有长时间运行扫描的工作负载中，缓冲区管理器具有关于不久的将来的工作负载的相当多的信息，使得理想OPT算法的近似变得可行。在对合成基准以及TPC-H吞吐量运行的评估中，我们比较了朴素缓冲区管理（LRU）与CScans、PBM和OPT的益处;表明PBM实现了接近协作扫描的益处，同时产生了低得多的架构影响。
英文摘要：
In analytical applications, database systems often need to sustain workloads with multiple concurrent scans hitting the same table. The Cooperative Scans (CScans) framework, which introduces an Active Buffer Manager (ABM) component into the database architecture, has been the most effective and elaborate response to this problem, and was initially developed in the X100 research prototype. We now report on the the experiences of integrating Cooperative Scans into its industrial-strength successor, the Vectorwise database product. During this implementation we invented a simpler optimization of concurrent scan buffer management, called Predictive Buffer Management (PBM). PBM is based on the observation that in a workload with long-running scans, the buffer manager has quite a bit of information on the workload in the immediate future, such that an approximation of the ideal OPT algorithm becomes feasible. In the evaluation on both synthetic benchmarks as well as a TPC-H throughput run we compare the benefits of naive buffer management (LRU) versus CScans, PBM and OPT; showing that PBM achieves benefits close to Cooperative Scans, while incurring much lower architectural impact.
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