The TRaCaR Ratio: Selecting the Right Storage Technology for Active Dataset-Serving Databases

TL;DR

This paper introduces the TRaCaR ratio, a metric for selecting cost-effective storage solutions for active databases based on workload and dataset growth predictions, especially considering emerging low-latency storage technologies.

Contribution

It proposes the TRaCaR ratio as a novel metric to guide storage technology and memory provisioning decisions in database systems with evolving storage options.

Findings

TRaCaR ratio effectively guides storage technology selection.

Application of TRaCaR with 3DXP and Flash shows promising results.

Highlights future research directions leveraging the ratio.

Abstract

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.