Data cache management using frequency-based replacement
SIGMETRICS '90 Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems
The LRU-K page replacement algorithm for database disk buffering
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
IEEE Transactions on Computers
2Q: A Low Overhead High Performance Buffer Management Replacement Algorithm
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Redbrick Vista: Aggregate Computation and Management
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Design of flash-based DBMS: an in-page logging approach
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Cooperative scans: dynamic bandwidth sharing in a DBMS
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A case for flash memory ssd in enterprise database applications
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Query processing techniques for solid state drives
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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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.