Mosaic: A Budget-Conscious Storage Engine for Relational Database Systems.

Relational database systems are purpose-built for a specific storage device class (e.g., HDD, SSD, or DRAM). They do not cope well with the multitude of storage devices that are competitive at their price `sweet spots'. To make use of different storage device classes, users have to resort to workarounds, such as storing data in different tablespaces. A lot of research has been done on heterogeneous storage frameworks for distributed big data query engines. These engines scale well for big data sets but are often CPU- or network-bound. Both approaches only maximize performance for previously purchased storage devices.We present Mosaic, a storage engine for scan-heavy workloads on RDBMS that manages devices in a tierless pool and provides device purchase recommendations for a specified workload and budget. In contrast to existing systems, Mosaic generates a performance/budget curve that is Pareto-optimal, along which the user can choose. Our approach uses device models and linear optimization to find a data placement solution that maximizes I/O throughput for the workload. Our evaluation shows that Mosaic provides a higher throughput at the same budget or a similar throughput at a lower budget than the state-of-the-art approaches of big data query engines and RDBMS.