Main memory database systems aim to provide users with low latency and high throughput access to data. Most data resides in secondary storage, which is limited by the access speed of the technology. For hot content, data resides in DRAM, which has become increasingly expensive as datasets grow in size and access demand. With the emergence of low-latency storage solutions such as Flash and Intel's 3D XPoint (3DXP), there is an opportunity for these systems to give users high Quality-of-Service while reducing the cost for providers. To achieve high performance, providers must provision the server hosts for these datasets with the proper amount of DRAM and secondary storage, as well as selecting a storage technology. The growth of capacity and transaction load overtime makes it expensive to flip back-and-forth between different storage technologies and memory-storage combinations. Servers set up for one storage technology must now be reconfigured, repartitioned, and potentially replaced altogether. As more low-latency storage solutions become available, how does one decide on the right memory-storage combination, as well as selecting a storage technology, given a predicted trend in dataset growth and offered load? In this paper, we describe and make the case for using the TRaCaR ratio - the transaction rate divided by the storage capacity needed for a workload - for allowing providers to choose the most cost-effective memory-storage combination and storage technology given their predicted dataset trend and load requirement. We explore how the TRaCaR ratio can be used with 3DXP and Flash with a highly-zipfian b-tree database, and discuss potential research directions that can leverage the ratio.

中文翻译：

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TRaCaR 比率：为活动数据集服务数据库选择正确的存储技术

主存数据库系统旨在为用户提供低延迟和高吞吐量的数据访问。大多数数据驻留在二级存储中，这受到技术访问速度的限制。对于热门内容，数据驻留在 DRAM 中，随着数据集大小和访问需求的增长，DRAM 变得越来越昂贵。随着闪存和英特尔 3D XPoint (3DXP) 等低延迟存储解决方案的出现，这些系统有机会为用户提供高服务质量，同时降低供应商的成本。为了实现高性能，供应商必须为这些数据集提供适当数量的 DRAM 和二级存储的服务器主机，以及选择存储技术。容量和事务负载的增长使得在不同的存储技术和内存存储组合之间来回切换的成本很高。为一种存储技术设置的服务器现在必须重新配置、重新分区，甚至可能完全更换。随着越来越多的低延迟存储解决方案可用，考虑到数据集增长的预测趋势和提供的负载，人们如何决定正确的内存存储组合以及选择存储技术？在本文中，我们描述并说明了使用 TRaCaR 比率（事务率除以工作负载所需的存储容量）的案例，以允许提供商根据他们预测的数据集选择最具成本效益的内存存储组合和存储技术趋势和负载要求。