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| Ming-Syan Chen, Philip S. Yu, "Optimal Design of Multiple Hash Tables for Concurrency Control," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 3, pp. 384-390, May-June, 1997. | |||
| BibTex | x | ||
| @article{ 10.1109/69.599928, author = {Ming-Syan Chen and Philip S. Yu}, title = {Optimal Design of Multiple Hash Tables for Concurrency Control}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {9}, number = {3}, issn = {1041-4347}, year = {1997}, pages = {384-390}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.599928}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Knowledge and Data Engineering TI - Optimal Design of Multiple Hash Tables for Concurrency Control IS - 3 SN - 1041-4347 SP384 EP390 EPD - 384-390 A1 - Ming-Syan Chen, A1 - Philip S. Yu, PY - 1997 KW - Concurrency control KW - hash algorithms KW - lock contentions KW - multiple hash tables. VL - 9 JA - IEEE Transactions on Knowledge and Data Engineering ER - | |||
Abstract—In this paper, we propose the approach of using multiple hash tables for lock requests with different data access patterns to minimize the number of false contentions in a data sharing environment. We first derive some theoretical results on using multiple hash tables. Then, in light of these derivations, a two-step procedure to design multiple hash tables is developed. In the first step, data items are partitioned into a given number of groups. Each group of data items is associated with the use of a hash table in such a way that lock requests to data items in the same group will be hashed into the same hash table. In the second step, given an aggregate hash table size, the hash table size for each individual data group is optimally determined so as to minimize the number of false contentions. Some design examples and remarks on the proposed method are given. It is observed from real database systems that different data sets usually have their distinct data access patterns, thus resulting in an environment where this approach can offer significant performance improvement.
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