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Amazon Aurora: Insights and Benchmarks for Contemporary Application Scaling

by Rahul Goel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 70
Year of Publication: 2025
Authors: Rahul Goel
10.5120/ijca2025924554

Rahul Goel . Amazon Aurora: Insights and Benchmarks for Contemporary Application Scaling. International Journal of Computer Applications. 186, 70 ( Mar 2025), 29-31. DOI=10.5120/ijca2025924554

@article{ 10.5120/ijca2025924554,
author = { Rahul Goel },
title = { Amazon Aurora: Insights and Benchmarks for Contemporary Application Scaling },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2025 },
volume = { 186 },
number = { 70 },
month = { Mar },
year = { 2025 },
issn = { 0975-8887 },
pages = { 29-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number70/amazon-aurora-insights-and-benchmarks-for-contemporary-application-scaling/ },
doi = { 10.5120/ijca2025924554 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-01T12:38:59.260870+05:30
%A Rahul Goel
%T Amazon Aurora: Insights and Benchmarks for Contemporary Application Scaling
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 70
%P 29-31
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Amazon Aurora is a high-performance, fully managed relational database solution designed to combine the simplicity and cost-effectiveness of open-source databases with the performance of high-end commercial databases. This paper explores Aurora’s unique architectural components, including its distributed storage layer, adaptive scaling, and replication mechanisms. It also delves into optimization techniques for maximizing throughput and minimizing latency, providing insights for engineers to design efficient and scalable systems. By analyzing benchmarks and use cases, this paper highlights best practices and trade-offs to guide application architects in achieving optimal performance.

References
  1. Amazon Web Services, Aurora Documentation. Retrieved from https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Welcome.html
  2. Vogels, W. (2009). Eventually Consistent. Communications of the ACM, 52(1), 40-44. DeCandia, G., Hastorun, D., Jampani, M., et al. (2007)
  3. Dynamo: Amazon's Highly Available Key-Value Store
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  6. Cassandra: A Decentralized Structured Storage System. ACM SIGOPS Operating Systems Review, 44(2), 35-40. George, L. (2011)
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  8. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107-113.
  9. DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., et al. (2007). Dynamo: Amazon's Highly Available Key-value Store. ACM SIGOPS Operating Systems Review, 41(6), 205-220.
  10. George, L. (2011). HBase: The Definitive Guide. O'Reilly Media.
  11. Wang, H., Liu, G., & Meng, X. (2014). Predicting Key-Value Workload Characteristics to Improve NoSQL Performance. IEEE Transactions on Knowledge and Data Engineering, 26(8), 2052-2064.
  12. Neumann, T., & Leis, V. (2014). Compiling Database Queries into Machine Code. IEEE Data Engineering Bulletin, 37(1), 1-12.
Index Terms

Computer Science
Information Sciences
Replication
Scalability
Cloud Computing
Database management

Keywords

Aurora Relational Database Distributed Storage Scalability Replication Performance

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