Aerospike vs Traditional Databases: Solving the Speed vs. Consistency Dilemma
DOI:
https://doi.org/10.22399/ijcesen.3780Keywords:
Tunable Consistency, Hybrid Memory Architecture, Sub-Millisecond Latency, Edge Computing, High-Frequency Trading (HFT)Abstract
The tradeoff between speed and consistency is a fundamental architectural challenge in modern and highly regulated data-intensive systems such as FinTech. This paper presents a thorough comparative study of Aerospike, a high-throughput NoSQL database, and traditional Relational Database Management Systems (RDBMS) Oracle and PostgreSQL. This report evaluates five critical dimensions—latency, throughput, consistency models, scalability, and indexing strategies—to understand these systems' tradeoffs and technological differentiators. With tunable consistency and in-memory indexing, Aerospike's hybrid memory and SSD architecture provide sub-millisecond response times without sweat. While traditional RDBMS are excellent sources of ACID-compliant consistency and support complex queries, they also suffer from latency and scalability when performed in a distributed environment. By benchmarking YCSB and Aerospike ACT tools with real FinTech use cases, including fraud detection, trading systems, and risk analytics, the report quantifies performance differences and operational efficiencies in the real world. Industry leaders' insights and case studies, like PayPal's use of Aerospike for real-time fraud detection, enumerate how achieving high throughput, low latency, and low cost is practically possible. The study also discusses best practices for modeling, deploying, and monitoring Aerospike-based architectures and future trends such as HTAP capabilities, edge computing, and integration of AI/ML at the database layer. Empirical results and expert perspectives offer actionable insights for decision-makers addressing the speed–consistency tradeoff within performance-critical systems.
References
[1] Alvarez, P. M., Ayala, M. L., & Cisneros, S. O. (2022). Main Memory Management on Relational Database Systems. Springer International Publishing.
[2] Beineke, K., Nothaas, S., & Schöttner, M. (2016, December). High throughput log-based replication for many small in-memory objects. In 2016 IEEE 22nd International DOI: https://doi.org/10.1109/ICPADS.2016.0077
[3] Conference on Parallel and Distributed Systems (ICPADS) (pp. 535-544). IEEE.
[4] Bhattacharyya, S. (2024). Cloud Innovation: Scaling with Vectors and LLMs. Libertatem Media Private Limited.
[5] Bhimani, J., Maruf, A., Mi, N., Pandurangan, R., & Balakrishnan, V. (2020). Auto-tuning parameters for emerging multi-stream flash-based storage drives through new I/O pattern generations. IEEE Transactions on Computers, 71(2), 309-322. DOI: https://doi.org/10.1109/TC.2020.3048303
[6] Carrara, G. R., Burle, L. M., Medeiros, D. S., de Albuquerque, C. V. N., & Mattos, D. M. (2020). Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peerto-peer networking. Annals of Telecommunications, 75, 163-174. DOI: https://doi.org/10.1007/s12243-020-00751-w
[7] Chaudhry, N., & Yousaf, M. M. (2020). Architectural assessment of NoSQL and NewSQL systems. Distributed and Parallel Databases, 38(4), 881-926. DOI: https://doi.org/10.1007/s10619-020-07310-1
[8] Chavan, A. (2021). Eventual consistency vs. strong consistency: Making the right choice in microservices. International Journal of Software and Applications, 14(3), 45-56. https://ijsra.net/content/eventual-consistency-vs-strong-consistency-making-right-choice-microservices
[9] Dhanagari, M. R. (2024). MongoDB and data consistency: Bridging the gap between performance and reliability. Journal of Computer Science and Technology Studies, 6(2), 183-198. https://doi.org/10.32996/jcsts.2024.6.2.21 DOI: https://doi.org/10.32996/jcsts.2024.6.2.21
[10] Do, T. X., & Kim, Y. (2019). Topology-aware resource-efficient placement for high availability clusters over geo-distributed cloud infrastructure. IEEE Access, 7, 107234-107246. DOI: https://doi.org/10.1109/ACCESS.2019.2932477
[11] Feil, N., Bögelsack, A., Schulz, R., & Abrantes, G. (2024). Stage 2—Keeping Costs and Cloud Usage Under Control. In Public Cloud Potential in an Enterprise Environment: Public Cloud as a New IT Platform to Increase Business Value (pp. 123-201). Wiesbaden: Springer Fachmedien Wiesbaden. DOI: https://doi.org/10.1007/978-3-658-44491-4_6
[12] Goel, G., & Bhramhabhatt, R. (2024). Dual sourcing strategies. International Journal of Science and Research Archive, 13(2), 2155. https://doi.org/10.30574/ijsra.2024.13.2.2155 DOI: https://doi.org/10.30574/ijsra.2024.13.2.2155
[13] Gulczynski, M. T., Vennitti, A., Scarlatella, G., Calabuig, G. J. D., Blondel-Canepari, L., Weber, F., ... & Pasini, A. (2021, October). RLV applications: challenges and benefits of novel technologies for sustainable main stages. In Proceedings of the International Astronautical Congress, IAC (No. 64293). International Astronautical Federation, IAF.
[14] Gupta, D. (2024). The Cloud Computing Journey: Design and deploy resilient and secure multi-cloud systems with practical guidance. Packt Publishing Ltd.
[15] Hammad, A., & Abu-Zaid, R. (2024). Applications of AI in Decentralized Computing Systems: Harnessing Artificial Intelligence for Enhanced Scalability, Efficiency, and Autonomous Decision-Making in Distributed Architectures. Applied Research in Artificial Intelligence and Cloud Computing, 7, 161-187.
[16] Harrison, G. (2015). Next Generation Databases: NoSQLand Big Data. Apress. DOI: https://doi.org/10.1007/978-1-4842-1329-2
[17] Hassan, M. (2024). Real-Time Risk Assessment in SaaS Payment Infrastructures: Examining Deep Learning Models and Deployment Strategies. Transactions on Artificial Intelligence, Machine Learning, and Cognitive Systems, 9(3), 1-10.
[18] Herker, S., An, X., Kiess, W., Beker, S., & Kirstaedter, A. (2015, December). Data-center architecture impacts on virtualized network functions service chain embedding with high availability requirements. In 2015 IEEE Globecom Workshops (GC Wkshps) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/GLOCOMW.2015.7414158
[19] Islam, A. (2024). DATA GOVERNANCE AND COMPLIANCE IN CLOUD-BASED BIG DATA ANALYTICS: A DATABASE-CENTRIC REVIEW.
[20] Karwa, K. (2024). Navigating the job market: Tailored career advice for design students. International Journal of Emerging Business, 23(2). https://www.ashwinanokha.com/ijeb-v23-2-2024.php
[21] Khurana, R. (2020). Fraud detection in ecommerce payment systems: The role of predictive ai in real-time transaction security and risk management. International Journal of Applied Machine Learning and Computational Intelligence, 10(6), 1-32.
[22] Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient
[23] Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf
[24] Lathar, P., Srinivasa, K. G., Kumar, A., & Siddiqui, N. (2018). Comparison study of different NoSQL and cloud paradigm for better data storage technology. Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science, 312-343. DOI: https://doi.org/10.4018/978-1-5225-5972-6.ch015
[25] Leppänen, T. (2021). Data visualization and monitoring with Grafana and Prometheus.
[26] Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203184230 DOI: https://doi.org/10.21275/SR24203184230
[27] Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf DOI: https://doi.org/10.21275/SR24926091431
[28] Rehrmann, R. (2023). Merging Queries in OLTP Workloads.
[29] Rui, R. (2020). Relational Joins on GPUs for In-memory Database Query Processing. University of South Florida.
[30] Sardana, J. (2022). The role of notification scheduling in improving patient outcomes. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient
[31] Sharma, S. (2016). Expanded cloud plumes hiding Big Data ecosystem. Future Generation Computer Systems, 59, 63-92. DOI: https://doi.org/10.1016/j.future.2016.01.003
[32] Singh, V. (2022). EDGE AI: Deploying deep learning models on microcontrollers for biomedical applications: Implementing efficient AI models on devices like Arduino for real-time health monitoring. International Journal of Computer Engineering & Management. https://ijcem.in/wp-content/uploads/EDGE-AI-DEPLOYING-DEEP-LEARNING-MODELS-ON-MICROCONTROLLERS-FOR-BIOMEDICAL-APPLICATIONS-IMPLEMENTING-EFFICIENT-AI-MODELS-ON-DEVICES-LIKE-ARDUINO-FOR-REAL-TIME-HEALTH.pdf
[33] Srinivasan, V., Bulkowski, B., Chu, W. L., Sayyaparaju, S., Gooding, A., Iyer, R., ... & Lopatic, T. (2016). Aerospike: Architecture of a real-time operational dbms. Proceedings of the VLDB Endowment, 9(13), 1389-1400. DOI: https://doi.org/10.14778/3007263.3007276
[34] Tehrany, N., & Trivedi, A. (2022). Understanding nvme zoned namespace (zns) flash ssd storage devices. arXiv preprint arXiv:2206.01547.
[35] Vassiliadis, P., Kolozoff, M. R., Zerva, M., & Zarras, A. V. (2019). Schema evolution and foreign keys: a study on usage, heartbeat of change and relationship of foreign keys to table activity. Computing, 101(10), 1431-1456. DOI: https://doi.org/10.1007/s00607-019-00702-x
[36] Verma, R. (2017). Understanding the technological trends and quantitative analysis of NewSQL databases. University of Maryland, Baltimore County.
[37] Yan, J., Chang, Z., Cheng, K., & Wang, S. (2023). A range query method for data access pattern protection based on uniform access frequency distribution. Journal of Networking and Network Applications, 3(1), 11-18. DOI: https://doi.org/10.33969/J-NaNA.2023.030102
[38] Yesin, V., Karpinski, M., Yesina, M., Vilihura, V., & Warwas, K. (2021). Ensuring data integrity in databases with the universal basis of relations. Applied Sciences, 11(18), 8781. DOI: https://doi.org/10.3390/app11188781
[39] Zhang, Y. (2020). Mitigating Insider Threats in Enterprise Storage Systems: A Security Framework for Data Integrity and Access Control. International Journal of Trend in Scientific Research and Development, 4(4), 1878-1890.
[40] Zhao, K., Gong, S., & Fonseca, P. (2021, April). On-demand-fork: A microsecond fork for memory-intensive and latency-sensitive applications. In Proceedings of the Sixteenth European Conference on Computer Systems (pp. 540-555. DOI: https://doi.org/10.1145/3447786.3456258
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