Relevant bibliographies by topics / SQL query performance tuning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'SQL query performance tuning'

Author: Grafiati
Published: 5 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "SQL query performance tuning"

1

Zhao, De Yu. "Research on Improving Oracle Query Performance in MES." Applied Mechanics and Materials 201-202 (October 2012): 39–42. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.39.

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The tuning for Oracle database system is vital to the normal running of the whole system, but it is a complicated work. SQL statement tuning is a very critical aspect of database performance tuning. It is an inherently complex activity requiring a high level of expertise in several domains: query optimization, to improve the execution plan selected by the query optimizer, access design to identify missing access structures and SQL design to restructure and simplify the text of a badly written SQL statement. In this paper, the author analyzes the execution procedure of oracle optimizer, and researches how to improve the oracle database query performance in MES.
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Souza, Ana Paula Dos Santos, Bruno Fidelis Campos, Carla Glênia Guedes Dias, et al. "Tuning em Banco de Dados Data base Tuning." Cadernos UniFOA 4, no. 10 (2017): 19. http://dx.doi.org/10.47385/cadunifoa.v4i10.965.

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Devido ao grande volume de dados que são gerados pelas Empresas que utilizam Sistemas de Informação, é fundamental o papel do Banco de Dados (BD). Geralmente os dados precisam ser acessados a todo instante, logo, a disponibilidade dos resultados nem sempre são satisfatórias. Nesse contexto, entra a questão do desempenho ao se obter informações de um BD e como otimizá-las. Muitos problemas de performance não estão relacionados a infraestrutura, sistemas operacionais ou mesmo ao hardware. Pode-se encontrar problemas de perda de performance dentro do próprio BD, sendo a consulta a principal causadora desses problemas. Ajustar e otimizar uma consulta e o próprio BD tornam-se fatores importantes, podendo-se ter um ganho de performance aceitável, visto que cada consulta é tratada de forma diferente, dependendo do Sistema Gerenciador de Banco de Dados (SGBD). Este artigo avalia como melhorar o desempenho de consultas Transact-Structured Query Language (T-SQL) em um ambiente Microsoft SQL Server 2005, sugerindo possíveis alterações que possam levar a um ganho de performance considerável.
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Souza, Ana Paula dos Santos, Bruno Fidelis Campos, Carla Glênia Guedes Dias, et al. "Tuning em Banco de Dados Data base Tuning." Cadernos UniFOA 4, no. 10 (2017): 19–25. http://dx.doi.org/10.47385/cadunifoa.v4.n10.965.

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Devido ao grande volume de dados que são gerados pelas Empresas que utilizam Sistemas de Informação, é fundamental o papel do Banco de Dados (BD). Geralmente os dados precisam ser acessados a todo instante, logo, a disponibilidade dos resultados nem sempre são satisfatórias. Nesse contexto, entra a questão do desempenho ao se obter informações de um BD e como otimizá-las. Muitos problemas de performance não estão relacionados a infraestrutura, sistemas operacionais ou mesmo ao hardware. Pode-se encontrar problemas de perda de performance dentro do próprio BD, sendo a consulta a principal causadora desses problemas. Ajustar e otimizar uma consulta e o próprio BD tornam-se fatores importantes, podendo-se ter um ganho de performance aceitável, visto que cada consulta é tratada de forma diferente, dependendo do Sistema Gerenciador de Banco de Dados (SGBD). Este artigo avalia como melhorar o desempenho de consultas Transact-Structured Query Language (T-SQL) em um ambiente Microsoft SQL Server 2005, sugerindo possíveis alterações que possam levar a um ganho de performance considerável.
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Muhammad, Qasim Memon, He Jingsha, Memon Aasma, Gulzar Rana Khurram, and Salman Pathan Muhammad. "Query Processing for Time Efficient Data Retrieval." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 784–88. https://doi.org/10.11591/ijeecs.v9.i3.pp784-788.

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In database management system (DBMS) retrieving data through structure query language is an essential aspect to find better execution plan for performance. In this paper, we incorporated database objects to optimize query execution time and its cost by vanishing poorly SQL statements. We proposed a method of evolving and inserting database constraints as database objects embedded with queries either to add them for the sake of transactions required by user to detect those queries for the betterment of performance. We took analysis on several databases while processing queries itself and assimilate real time database workload with the bunch of transactions are invoked in comparison with tuning approaches. These database objects are coded in procedural language environment pertaining rules to make it worth and are merged into queries offering improved execution plan.
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AzraJabeen, Mohamed Ali. "SQL Server Optimization-Best Practices for Maximizing Performance." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 4 (2020): 1–10. https://doi.org/10.5281/zenodo.14535769.

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This paper explores the best practices for SQL Server optimization, offering a comprehensive guide to enhance the performance of database systems.In the data-driven world of today, sustaining high efficiency and responsiveness requires that SQL Server databases operate at their best.By addressing key aspects such as query tuning, indexing strategies, and resource management, it presents effective techniques to minimize latency and improve execution speed. It also highlights the importance of proper configuration, efficient use of memory, and effective database maintenance practices. Through these best practices, database administrators and developers can ensure that SQL Server operates at peak performance, supporting faster queries, reduced downtime, and seamless scalability.This paper serves as an invaluable resource for anyone seeking to optimize their SQL Server environment, ensuring better performance and reliability in real-world applications.
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Öztürk, Emir. "Improving Text-to-Sql Conversion for Low-Resource Languages Using Large Language Models." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14, no. 1 (2025): 163–78. https://doi.org/10.17798/bitlisfen.1561298.

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Accurate text-to-SQL conversion remains a challenge, particularly for low-resource languages like Turkish. This study explores the effectiveness of large language models (LLMs) in translating Turkish natural language queries into SQL, introducing a two-stage fine-tuning approach to enhance performance. Three widely used LLMs Llama2, Llama3, and Phi3 are fine-tuned under two different training strategies, direct SQL fine-tuning and sequential fine-tuning, where models are first trained on Turkish instruction data before SQL fine-tuning. A total of six model configurations are evaluated using execution accuracy and logical form accuracy. The results indicate that Phi3 models outperform both Llama-based models and previously reported methods, achieving execution accuracy of up to 99.95% and logical form accuracy of 99.95%, exceeding the best scores in the literature by 5–10%. The study highlights the effectiveness of instruction-based fine-tuning in improving SQL query generation. It provides a detailed comparison of Llama-based and Phi-based models in text-to-SQL tasks, introduces a structured fine-tuning methodology designed for low-resource languages, and presents empirical evidence demonstrating the positive impact of strategic data augmentation on model performance. These findings contribute to the advancement of natural language interfaces for databases, particularly in languages with limited NLP resources. The scripts and models used during the training and testing phases of the study are publicly available at https://github.com/emirozturk/TT2SQL.
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Martani, Marlene, Hanny Juwitasary, and Arya Nata Gani Putra. "Analisis Alat Bantu Tuning Fisikal Basis Data pada Sql Server 2008." ComTech: Computer, Mathematics and Engineering Applications 5, no. 1 (2014): 334. http://dx.doi.org/10.21512/comtech.v5i1.2628.

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Nowadays every company has been faced with a business competition that requires the company to survive and be superior to its competitors. One strategy used by many companies is to use information technology to run their business processes. The use of information technology would require a storage which commonly referred to as a database to store and process data into useful information for the company. However, it was found that the greater the amount of data in the database, then the speed of the resulting process will decrease because the time needed to access the data will be much longer. The long process of data can cause a decrease in the company’s performance and the length of time needed to make decisions so that this can be a challenge to achieve the company’s competitive advantage. In this study performed an analysis of technique to improve the performance of the database system used by the company to perform tuning on SQL Server 2008 database physically. The purpose of this study is to improve the performance of the database used by speeding up the time it takes when doing query processing. The research methodology used was the method of analysis such as literature studies, analysis of the process and the workings of tuning tools that already exist in SQL Server 2008, and evaluation of applications that have been created, and also tuning methods that include query optimization and create index. The results obtained from this study is an evaluation of the physical application tuning tools that can integrate database functionality of other tuning tools such as SQL Profiler and Database Tuning Advisor.
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Vishnupriya, S. Devarajulu. "Key Solutions to Optimize Database SQL Queries." Journal of Scientific and Engineering Research 6, no. 12 (2019): 311–14. https://doi.org/10.5281/zenodo.13753398.

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Optimizing SQL Queries is crucial for enhancing the performance and efficiency of database-driven applications. This article explores key solutions to the performance issues in SQL queries, with code samples and detailed explanations. Best practices such as using indexes, avoiding unnecessary columns in SELECT statements, using schema names with object names, and optimizing joins and subqueries and other solutions are discussed. By following these optimization techniques, developers can provide more efficient database with improved application performance.
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Peng, Yuchen, Ke Chen, Lidan Shou, Dawei Jiang, and Gang Chen. "AQUA: Automatic Collaborative Query Processing in Analytical Database." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4006–9. http://dx.doi.org/10.14778/3611540.3611607.

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Data analysts nowadays are keen to have analytical capabilities involving deep learning (DL). Collaborative queries, which employ relational operations to process structured data and DL models to process unstructured data, provide a powerful facility for DL-based in-database analysis. The classical approach to support collaborative queries in relational databases is to integrate DL models with user-defined functions (UDFs) in a general-purpose language (e.g., C++) to process unstructured data. This approach suffers from suboptimal performance as the opaque UDFs preclude the generation of an optimal query plan. A recent work, DL2SQL, addresses the problem of collaborative query optimization by first converting DL computations into SQL subqueries and then using a classical relational query optimizer to optimize the entire collaborative query. However, the DL2SQL approach compromises usability by requiring data analysts to manually manage DL-related data and tune query performance. To this end, this paper introduces AQUA, an analytical database designed for efficient collaborative query processing. Built on DL2SQL, AQUA automates translations from collaborative queries into SQL queries. To enhance usability, AQUA introduces two techniques: 1) a declarative scheme for DL-related data management, and 2) DL-specific optimizations for collaborative query processing, eliminating the burden of manual data management and performance tuning from the data analysts. We demonstrate the key contributions of AQUA via a web APP that allows the audience to perform collaborative queries on the CIFAR-10 dataset.
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Siddiqui, Tarique, and Wentao Wu. "ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges." ACM SIGMOD Record 52, no. 4 (2023): 19–30. http://dx.doi.org/10.1145/3641832.3641836.

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The increasing scale and complexity of workloads in modern cloud services highlight a crucial challenge in automated index tuning: recommending high-quality indexes while ensuring scalability. This is further complicated by the need for these automated solutions to minimize query performance regressions in production deployments. This paper directs attention to some of these challenges in automated index tuning and explores ways in which machine learning (ML) techniques provide new opportunities in their mitigation. In particular, we reflect on our recent efforts in developing ML techniques for workload selection, candidate index filtering, speeding up index configuration search, reducing the amount of query optimizer calls, and lowering the chances of performance regressions. We highlight the key takeaways from these efforts and underline the gaps that need to be closed for their effective functioning within the traditional index tuning framework. Additionally, we present a preliminary cross-platform design aimed at democratizing index tuning across multiple SQL-like systems-an imperative in today's continuously expanding data system landscape. We believe our findings will help provide context and impetus to the research and development efforts in automated index tuning.
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Dissertations / Theses on the topic "SQL query performance tuning"

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Meng, Yabin. "SQL Query Disassembler: An Approach to Managing the Execution of Large SQL Queries." Thesis, 2007. http://hdl.handle.net/1974/701.

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In this thesis, we present an approach to managing the execution of large queries that involves the decomposition of large queries into an equivalent set of smaller queries and then scheduling the smaller queries so that the work is accomplished with less impact on other queries. We describe a prototype implementation of our approach for IBM DB2™ and present a set of experiments to evaluate the effectiveness of the approach.<br>Thesis (Master, Computing) -- Queen's University, 2007-09-17 22:05:05.304
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Books on the topic "SQL query performance tuning"

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Fritchey, Grant. SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3.

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Fritchey, Grant. SQL Server 2012 Query Performance Tuning. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8.

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Fritchey, Grant. SQL Server 2017 Query Performance Tuning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2.

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Fritchey, Grant. SQL Server 2022 Query Performance Tuning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8891-7.

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Fritchey, Grant. SQL Server 2012 Query Performance Tuning. Apress, 2012.

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Fritchey, Grant, and Sajal Dam. SQL Server 2008 Query Performance Tuning Distilled. Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-1903-3.

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Fritchey, Grant. SQL Server 2008 query performance tuning distilled. Apress, 2009.

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Sajal, Dam, ed. SQL Server 2008 query performance tuning distilled. Apress, 2009.

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Trudy, Pelzer, ed. SQL performance tuning. Addison-Wesley, 2003.

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Krogh, Jesper Wisborg. MySQL 8 Query Performance Tuning. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5584-1.

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Book chapters on the topic "SQL query performance tuning"

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Brimhall, Jason, David Dye, Jonathan Gennick, Andy Roberts, and Wayne Sheffield. "Query Performance Tuning." In SQL Server 2012 T-SQL Recipes. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4201-7_21.

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Fritchey, Grant. "SQL Query Performance Tuning." In SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_1.

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Fritchey, Grant. "SQL Query Performance Tuning." In SQL Server 2017 Query Performance Tuning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2_1.

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Fritchey, Grant. "SQL Query Performance Tuning." In SQL Server 2012 Query Performance Tuning. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8_1.

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Fritchey, Grant. "Query Performance Tuning." In SQL Server 2022 Query Performance Tuning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8891-7_1.

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Brimhall, Jason, Jonathan Gennick, and Wayne Sheffield. "Chapter 22: Query Performance Tuning." In SQL Server T-SQL Recipes. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0061-2_22.

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Fritchey, Grant. "Query Performance Metrics." In SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_6.

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Fritchey, Grant. "Analyzing Query Performance." In SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_7.

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Fritchey, Grant. "Query Recompilation." In SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_17.

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Fritchey, Grant. "SQL Query Performance Analysis." In SQL Server 2012 Query Performance Tuning. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8_3.

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Conference papers on the topic "SQL query performance tuning"

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Chai, Sheng, and Zhengrui Qin. "A Case Study of Cloud Query Performance Comparison Between SQL and NoSQL Database." In 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2024. https://doi.org/10.1109/wi-iat62293.2024.00117.

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Singhal, Rekha, and Chetan Phalak. "SQL Query Volume Performance Estimation Tool." In ICPE '17: ACM/SPEC International Conference on Performance Engineering. ACM, 2017. http://dx.doi.org/10.1145/3030207.3053663.

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Myalapalli, Vamsi Krishna, Thirumala Padmakumar Totakura, and Sunitha Geloth. "Augmenting database performance via SQL tuning." In 2015 International Conference on Energy Systems and Applications. IEEE, 2015. http://dx.doi.org/10.1109/icesa.2015.7503305.

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Lucas Filho, Edson Ramiro. "The Uniform Tuning Problem on SQL-On-Hadoop Query Processing." In the 2017 ACM International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3055167.3055172.

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Yanfei Lv, Huihong He, Hong Zhang, Zhe Liu, and Yasong Zheng. "Olap query performance tuning in Spark." In Third International Conference on Cyberspace Technology (CCT 2015). Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/cp.2015.0832.

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Wouw, Stefan van, José Viña, Alexandru Iosup, and Dick Epema. "An Empirical Performance Evaluation of Distributed SQL Query Engines." In ICPE'15: ACM/SPEC International Conference on Performance Engineering. ACM, 2015. http://dx.doi.org/10.1145/2668930.2688053.

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Belknap, Peter, Benoit Dageville, Karl Dias, and Khaled Yagoub. "Self-Tuning for SQL Performance in Oracle Database 11g." In 2009 IEEE 25th International Conference on Data Engineering (ICDE). IEEE, 2009. http://dx.doi.org/10.1109/icde.2009.165.

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Shen, Yijie, Jin Xiong, and Dejun Jiang. "Using Vectorized Execution to Improve SQL Query Performance on Spark." In ICPP 2021: 50th International Conference on Parallel Processing. ACM, 2021. http://dx.doi.org/10.1145/3472456.3472495.

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Shen, Yijie, Jin Xiong, and Dejun Jiang. "Using Vectorized Execution to Improve SQL Query Performance on Spark." In ICPP 2021: 50th International Conference on Parallel Processing. ACM, 2021. http://dx.doi.org/10.1145/3472456.3472495.

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Douglas, Graeme, and Ramon Lawrence. "Improving SQL query performance on embedded devices using pre-compilation." In SAC 2016: Symposium on Applied Computing. ACM, 2016. http://dx.doi.org/10.1145/2851613.2851657.

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Reports on the topic "SQL query performance tuning"

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Thost, Veronika, Jan Holste, and Özgür Özçep. On Implementing Temporal Query Answering in DL-Lite. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.218.

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Ontology-based data access augments classical query answering over fact bases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We implemented temporal query answering w.r.t. ontologies formulated in the Description Logic DL-Lite. Focusing on temporal conjunctive queries (TCQs), which combine conjunctive queries via the operators of propositional linear temporal logic, we regard three approaches for answering them: an iterative algorithm that considers all data available; a window-based algorithm; and a rewriting approach, which translates the TCQs to be answered into SQL queries. Since the relevant ontological knowledge is already encoded into the latter queries, they can be answered by a standard database system. Our evaluation especially shows that implementations of both the iterative and the window-based algorithm answer TCQs within a few milliseconds, and that the former achieves a constant performance, even if data is growing over time.
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