Journal of East China Normal University(Natural Science) >
A memory allocation strategy for learned index based on huge pages
Received date: 2021-09-09
Online published: 2023-03-23
In the era of big data and with the continuous expansion of data, there are significant challenges with efficient access to data. Hence, designing an efficient index structure is of great significance. ALEX (updatable adaptive learned index) is a learned index that uses a machine learning model to replace the traditional B-tree index structure. Although it offers good time and space performance, it suffers from frequent page faults. In order to solve this problem and further improve the performance of ALEX, a memory pre-allocation strategy based on huge pages is proposed, on the basis of ALEX, that can help reduce the rate of memory page faults and improve the overall performance of ALEX. In the memory allocation phase, the pre-allocation strategy is adopted, and the memory free phase adopts a delayed release strategy. Experiments on the Longitudes dataset show that this strategy offers good performance.
Key words: learned index; huge pages; data access
Jialin GUAN , Yan ZHU , Tingliang WU , Yan CHEN , Jingwei ZHANG . A memory allocation strategy for learned index based on huge pages[J]. Journal of East China Normal University(Natural Science), 2023 , 2023(2) : 73 -81 . DOI: 10.3969/j.issn.1000-5641.2023.02.009
1 | GARCIA-MOLINA H, ULLMAN J D, WIDOM J. Database System Implementation [M]. Upper Saddle River, NJ, USA: Prentice Hall, 2000: 47-95. |
2 | ALEXIOU K, KOSSMANN D, LARSON P ?. Adaptive range filters for cold data: Avoiding trips to siberia. Proceedings of the VLDB Endowment, 2013, 6 (14): 1714- 1725. |
3 | KIM C, CHHUGANI J, SATISH N, et al. FAST: Fast architecture sensitive tree search on modern CPUs and GPUs [C]// Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. ACM, 2010: 339-350. |
4 | FAN B, ANDERSEN D G, KAMINSKY M, et al. Cuckoo filter: Practically better than bloom [C]// Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies. ACM, 2014: 75-88. |
5 | GRAEFE G, LARSON P A. B-tree indexes and CPU caches [C]// Proceedings 17th International Conference on Data Engineering. IEEE, 2001: 349-358. |
6 | RICHTER S, ALVAREZ V, DITTRICH J. A seven-dimensional analysis of hashing methods and its implications on query processing. Proceedings of the VLDB Endowment, 2015, 9 (3): 96- 107. |
7 | ZHANG H, ANDERSEN D G, PAVLO A, et al. Reducing the storage overhead of main-memory OLTP databases with hybrid indexes [C]// Proceedings of the 2016 International Conference on Management of Data. ACM, 2016: 1567-1581. |
8 | 李国良, 周煊赫, 孙佶, 等. 基于机器学习的数据库技术综述. 计算机学报, 2020, 43 (11): 2019- 2049. |
9 | KRASKA T, BEUTEL A, CHI E H, et al. The case for learned index structures [C]// Proceedings of the 2018 International Conference on Management of Data. ACM, 2018: 489-504. |
10 | DING J L, MINHAS U F, YU J, et al. ALEX: An updatable adaptive learned index [C]// Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. ACM, 2020: 969-984. DOI: 10.1145/3318464.3389711. |
11 | LI X, LI J, WANG X. ASLM: Adaptive single layer model for learned index [C]// International Conference on Database Systems for Advanced Applications. Cham: Springer, 2019: 80-95. |
12 | WU J C, ZHANG Y, CHEN S M, et al. Updatable learned index with precise positions. Proceedings of the VLDB Endowment, 2021, 14 (8): 1276- 1288. |
/
〈 |
|
〉 |