AI-driven SQL query optimization techniques
Juopperi, Tatiana (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024060320029
https://urn.fi/URN:NBN:fi:amk-2024060320029
Tiivistelmä
The subject of the research is AI-driven SQL query optimization techniques. The study aims to explore the role and capabilities of Artificial Intelligence in SQL query optimization as one of the methods of tuning and optimization.
The research involves both quantitative and qualitative methods. This choice ensures a compre-hensive understanding of AI-driven SQL query optimization techniques. While direct contact with specific professionals was not established, information was collected from publicly available sources such as academic publications, industry blogs, forums, and documentation.
The research first explored traditional SQL query optimization methods and their limitations. Then, it explored available AI techniques which can be used to improve query performance. The study included creating and processing SQL queries and execution plan data. The thesis explores how well AI models perform compared to traditional optimization methods in terms of query speed and quality. Additionally, were taken attention of risks with which face users if used both methods.
Key findings highlight the role of indexes in traditional SQL query optimization and importance of query execution plans. Additionally, AI-driven techniques such as ChatGPT 3.5 SQL query opti-mization, cost optimization, and AI-driven detection of SQL injection risks. A review of existing AI tools for SQL query optimization is also provided.
The research sheds light on the implications of AI-driven SQL query optimization for database administrators and developers, highlighting potential challenges and opportunities in integrating these techniques into existing systems.
The research involves both quantitative and qualitative methods. This choice ensures a compre-hensive understanding of AI-driven SQL query optimization techniques. While direct contact with specific professionals was not established, information was collected from publicly available sources such as academic publications, industry blogs, forums, and documentation.
The research first explored traditional SQL query optimization methods and their limitations. Then, it explored available AI techniques which can be used to improve query performance. The study included creating and processing SQL queries and execution plan data. The thesis explores how well AI models perform compared to traditional optimization methods in terms of query speed and quality. Additionally, were taken attention of risks with which face users if used both methods.
Key findings highlight the role of indexes in traditional SQL query optimization and importance of query execution plans. Additionally, AI-driven techniques such as ChatGPT 3.5 SQL query opti-mization, cost optimization, and AI-driven detection of SQL injection risks. A review of existing AI tools for SQL query optimization is also provided.
The research sheds light on the implications of AI-driven SQL query optimization for database administrators and developers, highlighting potential challenges and opportunities in integrating these techniques into existing systems.