Towards efficient concurrent scans on flash disks

Flash disk, also known as Solid State Disk (SSD), is widely considered by the database community as a next-generation storage media which will completely or to a large extent replace magnetic disk in data-intensive applications. However, the vast differences on the I/O characteristics between SSD and magnetic disk imply that a considerable part of the existing database techniques need to be modified to gain the best efficiency on flash storage. In this paper, we study the problem of large-scale concurrent disk scans which are frequently used in the decision support systems. We demonstrate that the conventional sharing techniques of mutiple concurrent scans are not suitable for SSDs as they are designed to exploit the characteristics of hard disk drivers (HDD). To leverage the fast random reads on SSD, we propose a new framework named Semi-Sharing Scan (S³) in this paper. S³ shares the readings between scans of similar speeds to save the bandwidth utilization. Meanwhile, it compensates the faster scans by executing random I/O requests separately, in order to reduce single scan latency. By utilizing techniques called group splitting and I/O scheduling, S³ aims at achieving good performance for concurrent scans on various workloads. We implement the S³ framework on a PostgreSQL database deployed on an enterprise SSD drive. Experiments demonstrate that S³ outperforms the conventional schemes in both bandwidth utilization and single scan latency.