III: Small: Automatic Database Management System Tuning Through Large-scale Machine Learning

收集、处理和分析大量数据的能力对于能够在商业、科学和医疗应用程序中推断新知识至关重要。数据库管理系统(DBMS)是现代“大数据”应用程序的关键组件，因为它们是所有这些信息的中央存储库。但从历史上看，调优DBMS以提高性能是一项艰巨的任务，因为它们有数百个配置“旋钮”，控制系统中的所有内容，例如要使用的内存量和写入数据的频率。如果这些设置不正确，系统将无法在合理的时间内回答有关数据的问题，甚至会导致数据丢失。许多组织求助于聘请专家来配置这些旋钮，但这样做的成本高得令人望而却步。据估计，人员成本几乎占DBMS总拥有成本的50%，许多管理员将近四分之一的时间花在这些调优活动上。此外，随着数据库的规模和复杂性的增长，优化DBMS以满足新应用程序的需求已经超过了即使是最优秀的人类专家的能力。因此，这项提议的目标是通过在大规模历史性能数据收集上使用机器学习，为数据库管理系统的自动配置开发基础和相应的实用技术。我们的方法将与以前的工作不同，因为我们寻求减少通过依赖于从以前的优化工作中获得的知识来训练为每个应用程序优化DBMS的算法所需的时间量。这项工作的结果将允许任何人部署一个能够处理大量数据和更复杂的工作负载的DBMS，而不需要任何数据库管理专业知识。在数据库管理系统(DBMS)中实现良好的性能并不是一件容易的事，因为它们是具有许多可调选项的复杂系统，几乎控制其运行时操作的所有方面。正确地进行此调优对于现代大容量和高吞吐量工作负载至关重要，因为性能收益可能非常显著。因此，许多组织求助于雇佣一名昂贵的数据库管理员来手动调整他们的DBMS。但数据库的大小和复杂性现在已经超过了最优秀的人类专家的能力。因此，我们计划开发针对广泛的应用程序工作负载调优和优化DBMS配置的自动化技术。我们将探索使用机器学习来为更大的数据集扩展DBMS的基础，从而消除在充分发挥数据驱动的决策应用程序的好处方面的一个主要障碍。我们方法的关键是将任意应用程序的工作负载映射到最能代表工作负载属性的一个或多个规范基准测试的特性，然后使用该基准测试从DBMS收集性能数据。然后，这些数据被用来训练模型，使我们能够识别旋钮之间的依赖关系及其对DBMS的影响。在此基础上，模型将为应用程序选择一个接近最佳的旋钮设置。这与早期的工作不同，早期的工作侧重于孤立地优化单个DBMS安装，并且无法利用从以前的调优工作中获得的知识。我们的方法不需要用户生成大量(可能很昂贵的)实验样本数据集来得出适当的配置。有关更多信息，请参阅项目网站：http://oltpbenchmark.com

The ability to collect, process, and analyze large amounts of data is paramount for being able to extrapolate new knowledge in business, scientific, and medical applications. Database management systems (DBMSs) are the critical component of modern "Big Data" applications because they are the central repository for all of this information. But tuning a DBMS to perform well is historically a difficult task because they have hundreds of configuration "knobs" that control everything in the system, such as the amount of memory to use and how often data is written. Getting these settings wrong will prevent the system from answering questions about data in a reasonable amount of time or even cause it to lose data. Many organizations resort to hiring experts to configure these knobs, but this is prohibitively expensive. Personnel cost is estimated to be almost 50% of the total ownership cost of a DBMS, and many administrators spend nearly a quarter of their time on these tuning activities. Furthermore, as databases grow in both size and complexity, optimizing a DBMS to meet the needs of new applications has surpassed the abilities of even the best human experts. Thus, the goal of this proposal is to develop the foundation and corresponding practical techniques for the automatic configuration of DBMSs by using machine learning on large-scale collections of historical performance data. Our approach will differ from previous work in that we seek to reduce the amount of time that is needed to train the algorithms that tune the DBMS for each application by relying on knowledge gained from previous tuning efforts. The results from this work will allow anyone to deploy a DBMS that is able to handle large amounts of data and more complex workloads without any expertise in database administration.Achieving good performance in a database management system (DBMS) is non-trivial because they are complex systems with many tunable options that control nearly all aspects of their runtime operation. Getting this tuning right is critical for modern high-volume and high-throughput workloads, as the performance gains can be significant. As such, many organizations resort to hiring an expensive database administrator to manually tune their DBMS. But the size and complexity of databases have now surpassed the abilities of even the best human experts. Hence, we plan to develop automatic techniques for tuning and optimizing DBMS configurations for a broad class of application workloads. We will explore the foundations of using machine learning to scale DBMSs for larger data sets, thereby removing a major impediment in deriving the full benefits of data-driven decision making applications. The crux of our approach is to map an arbitrary application's workload to features of one or more canonical benchmarks that best represents the workload's properties, and then to collect performance data from the DBMS using that benchmark. This data is then used to train models that will allow us to identify the dependencies between knobs and their effects on the DBMS. From this, the models will select a near-optimal knob setting for the application. This differs from earlier work that focused on optimizing a single DBMS installation in isolation and are unable to leverage knowledge gained from previous tuning efforts. Our approach will not require the user to generate a large sample data set of (potentially expensive) experiments to derive the proper configuration.For further information see project web site at: http://oltpbenchmark.com