| Issue |
E3S Web Conf.
Volume 460, 2023
International Scientific Conference on Biotechnology and Food Technology (BFT-2023)
|
|
|---|---|---|
| Article Number | 04005 | |
| Number of page(s) | 7 | |
| Section | IoT, Big Data and AI in Food Industry | |
| DOI | https://doi.org/10.1051/e3sconf/202346004005 | |
| Published online | 11 December 2023 | |
Achieving new SQL query performance levels through parallel execution in SQL Server
1 Kazan National Research Technical University named after A. N. Tupolev – KAI, Kazan, Russia
2 Kazan State Power Engineering University, Kazan, Russia
3 Kazan State Agrarian University, Kazan, Russia
* Corresponding author: marat_nu1@mail.ru
This article provides an in-depth look at implementing parallel SQL query processing using the Microsoft SQL Server database management system. It examines how parallelism can significantly accelerate query execution by leveraging multi-core processors and clustered environments. The article explores SQL Server's sophisticated parallel processing capabilities including automatic query parallelization, intra-query parallelism techniques like parallel joins and parallel data aggregation, as well as inter-query parallelism for concurrent query execution. It covers key considerations around effective parallelization such as managing concurrency and locks, handling data skew, resource governance, and monitoring. Challenges like debugging parallel plans and potential bottlenecks from excessive parallelism are also discussed along with mitigation strategies. Real-world examples demonstrate how judicious application of parallel processing helps optimize complex analytics workloads involving massive datasets. The insights presented provide guidance to database developers and administrators looking to enable parallel SQL query execution in SQL Server environments for substantial performance gains and scalability.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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