Towards effective and efficient free space management
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM Transactions on Database Systems (TODS)
Efficient Bulk Deletes in Relational Databases
Proceedings of the 17th International Conference on Data Engineering
Dynamic Storage Allocation: A Survey and Critical Review
IWMM '95 Proceedings of the International Workshop on Memory Management
The automatic improvement of locality in storage systems
ACM Transactions on Computer Systems (TOCS)
Design of flash-based DBMS: an in-page logging approach
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Design tradeoffs for SSD performance
ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
| Hi-index | 0.00 |
Space allocation is a fundamental operation performed by a database management system (DBMS) when it inserts a record into a table. A good space allocation algorithm quickly locates and reserves enough space for a record, places it closer to its related records, and utilizes the available space. Satisfying these conflicting requirements is challenging and trade-offs are carefully balanced by well-chosen heuristics. As a DBMS evolves over time, especially a commercial DBMS, its space allocation algorithm gets more sophisticated and complex and relies on many heuristics. Technological changes, new applications, and greater data volumes render many legacy heuristics ineffective. These factors hinder understanding of space allocation behavior under many workload conditions and make it difficult to enhance the algorithm without causing performance regressions for some of the workloads. To facilitate research and study the performance of a table space allocation algorithm of a modern DBMS in real-world workload scenarios, we build an extensible simulation framework. We analyze algorithm behavior and make surprising observations. We use the findings to further improve the existing algorithm by proposing algorithm enhancements and showing their benefits with respect to key performance metrics. In conclusion, the proposed framework has been effective in research to understand the performance, improve the space allocation algorithms, and to guide the developers of a commercial DBMS.