Abstract
In a database system, if the degree of concurrency is high, using a concurrency control algorithm alone will reduce the performance of the system, and the process of selecting a concurrency control algorithm will virtually improve the knowledge threshold of users. In order to overcome this limitation, this paper proposes a hybrid concurrency control algorithm, which is called cluster based concurrency control algorithm. It creatively puts forward the concept of transaction working set, uses the minimum hash algorithm to calculate the Jaccard similarity between different transaction working sets to measure the conflict rate between different transactions, and uses this as a standard to place transactions in different clusters, Transactions in the same cluster adopt pessimistic concurrency control algorithm, and transactions in different clusters adopt optimistic concurrency control algorithm. The clustering based concurrency control algorithm combines the traditional pessimistic concurrency control algorithm with the optimistic concurrency control algorithm to obtain the advantages of the two algorithms and alleviate the performance bottleneck of the two algorithms. Finally, through simple experiments, it is proved that the concurrency control algorithm based on clustering is indeed better than the traditional pessimistic and optimistic concurrency control algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wu, W., Li, B., Chen, L., Gao, J., Zhang, C.: A review for weighted minhash algorithms. IEEE Trans. Knowl. Data Eng. 34(6), 2553–2573 (2020)
Sun, L.: An improved apriori algorithm based on support weight matrix for data mining in transaction database. J. Ambient. Intell. Humaniz. Comput. 11(2), 495–501 (2019). https://doi.org/10.1007/s12652-019-01222-4
Msb, P., Cv, G. R., Vangipuram, R., Cheruvu, A.: Similarity association pattern mining in transaction databases. In International Conference on Data Science, E-learning and Information Systems 2021, pp. 180–184. ACM, Petra (2021)
Stit, O., Riffi, J., Yahyaouy, A., Tairi, H.: Comparative study of different association rule methods. In: 5th International Congress on Information Science and Technology (CiSt), pp. 323–327. IEEE, Marrakesh (2018)
Priya, N., Punithavathy, E.: A review on database and transaction models in different cloud application architectures. In: Proceedings of Second International Conference on Sustainable Expert Systems, pp. 809–822. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7657-4_65
Yan, X., et al.: Carousel: low-latency transaction processing for globally-distributed data. In: Proceedings of the 2018 International Conference on Management of Data, pp. 231–243. ACM, Houston (2018)
Hu, H., Zhou, X., Zhu, T., Qian, W., Zhou, A.: In-memory transaction processing: efficiency and scalability considerations. Knowl. Inf. Syst. 61(3), 1209–1240 (2019). https://doi.org/10.1007/s10115-019-01340-7
Zhang, C., Li, Y., Zhang, R., Qian, W., Zhou, A.: Benchmarking for transaction processing database systems in big data era. In: Zheng, C., Zhan, J. (eds.) Bench 2018. LNCS, vol. 11459, pp. 147–158. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32813-9_13
Özsu, M.T., Valduriez, P.: Principles of Distributed Database Systems. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-26253-2
Singh, A.A., Nawab, F.: WedgeDB: transaction processing for edge databases. In: Proceedings of the ACM Symposium on Cloud Computing, p. 482. ACM, California (2019)
Sadoghi, M., Blanas, S.: Transaction concepts. In: Transaction Processing on Modern Hardware. Springer, Cham (2019). https://doi.org/10.1007/978-3-031-01870-1_2
Redis Homepage. https://redis.io/. Accessed 1 July 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shen, P., Qian, H. (2022). Research on Concurrency Control in Database Systems. In: Liu, Q., Liu, X., Cheng, J., Shen, T., Tian, Y. (eds) Proceedings of the 12th International Conference on Computer Engineering and Networks. CENet 2022. Lecture Notes in Electrical Engineering, vol 961. Springer, Singapore. https://doi.org/10.1007/978-981-19-6901-0_128
Download citation
DOI: https://doi.org/10.1007/978-981-19-6901-0_128
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6900-3
Online ISBN: 978-981-19-6901-0
eBook Packages: Computer ScienceComputer Science (R0)