This repository contains the research and experimental results for a project that explores SQL query optimization and the impact of different indexing strategies and PostgreSQL configurations on query execution times. The project was presented at the TCCD Research Day, where we proudly secured second place. 🎉
The primary focus of this research is to analyze and evaluate how different indexing strategies (such as single-column indexes, composite indexes, partial indexes, etc.) and PostgreSQL configuration parameters influence query performance in large relational databases. By answering key research questions, this project aims to provide data-driven insights into optimizing database queries, which can significantly improve performance, resource utilization, and user experience. 🚀
In today's data-driven world, relational databases are the backbone of many organizations. The performance of SQL queries is crucial for the speed and responsiveness of applications, as poorly optimized queries can lead to slow response times and inefficient use of system resources. This project focuses on optimizing query performance through indexing and database configuration adjustments.
The study investigates:
- The impact of different indexing strategies on query execution time. ⏱️
- How PostgreSQL configuration parameters affect performance. ⚙️
- The correlation between dataset size, query type, and execution times. 📈
The research seeks to answer the following questions:
- Indexing Strategies Impact:
- Is there a statistically significant difference in execution times between queries using different indexing strategies vs. no index?
- Effective Indexing Strategies:
- What are the most effective indexing strategies for reducing query execution time under various conditions?
- Query Type Performance:
- Are there significant differences in performance between different types of queries (e.g., SELECT, JOIN, complex queries)?
- Dataset Size and Query Type Correlation:
- How do query execution times correlate with the size of the dataset and the type of queries?
- Impact of PostgreSQL Configurations:
- How do configuration parameters (e.g., cache size, buffer pool settings) affect query execution times under various conditions?
The objectives of this research are:
- Analyze the impact of different indexing strategies on query execution time. 🔍
- Evaluate the impact of key PostgreSQL configuration parameters on query performance. ⚡
- Provide data-driven recommendations for optimal database performance tuning. 🛠️
This project demonstrates the importance of query optimization in relational databases, particularly through indexing strategies and PostgreSQL configuration tuning. By following the findings and recommendations in this study, developers and database administrators can significantly improve the performance of their database systems. ⚡
- The research was presented at TCCD Research Day, where we earned second place. 🥈
- Special thanks to our professors and mentors for their guidance and support throughout this project. 👩🏫👨🏫
This repository is licensed under the MIT License. See LICENSE for more details.
Ahmed Amr |
Ahmed Sobhy |
Anas Magdy |
Habiba Ayman |
Helana Nady |
Omar Gamal |