Optimizing database queries: Cost and performance analysis

Vasudevan Senathi Ramdoss *

Overland Park, Kansas, USA.
 
Review
International Journal of Science and Research Archive, 2021, 02(02), 293-297.
Article DOI: 10.30574/ijsra.2021.2.2.0025
Publication history: 
Received on 15 March 2021; revised on 20 May 2021; accepted on 23 May 2021
 
Abstract: 
Optimizing database query performance is critical for ensuring efficient data management, reducing costs, and improving user experience. Poorly optimized queries can cause slow response times, increased CPU and memory usage, and excessive disk I/O operations, affecting system performance. This paper explores various query optimization techniques, including cost-based analysis, indexing, query rewriting, execution plan analysis, parallel execution, and caching. Additionally, it compares optimization strategies across different database types, including SQL-based relational databases (MySQL, PostgreSQL, Oracle), NoSQL databases (MongoDB, Cassandra), and NewSQL databases (Google Spanner, CockroachDB). Real-world use cases from e-commerce, banking, healthcare, and financial sectors demonstrate the practical applications of these strategies.
 
Keywords: 
Database Queries; Query Optimization; SQL; NoSQL; NewSQL; Performance Tuning, Cost Analysis; Execution Plans; Indexing; Query Rewriting; Parallel Execution; Caching
 
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0