SSDs are hardware and software systems whose design involves complex and undocumented trade-offs between cost, energy consumption and performance. This complexity is hidden behind standard interfaces and a few headline specifications, such as capacity, sequential, and random performance. As a result, database system designers often assume that SSDs are interchangeable commodities and regularly use a single SSD model to evaluate database performance. Does it matter which SSD model is provisioned for a database system? If yes, how to choose the right one? These are the questions we address in this paper. We study the performance characteristics of commercial data center SSDs, highlighting the limitations of current standard metrics in capturing their true behavior. We conduct experiments on nine SSDs from major vendors, revealing significant differences in performance despite similar headline specifications. We show that the choice of SSD matters for database system performance. We propose a new benchmark, SSD-iq, which introduces four additional metrics to better characterize SSD performance, particularly for write-intensive workloads. Incidentally, our work should encourage vendors to optimize SSD controllers using more comprehensive and transparent performance criteria.

NVMe SSDs based on flash are cheap and offer high throughput. Combining several of these devices into a single server enables 10 million I/O operations per second or more. Our experiments show that existing out-of-memory database systems and storage engines achieve only a fraction of this performance. In this work, we demonstrate that it is possible to close the performance gap between hardware and software through an I/O optimized storage engine design. In a heavy out-of-memory setting, where the dataset is 10 times larger than main memory, our system can achieve more than 1 million TPC-C transactions per second.