Data Storage and Speed: Why Some Businesses Use Both MongoDB and Aerospike

Abstract

This paper presents a research study that seeks to understand the performance optimization that can be established through the co-deployment of MongoDB and Aerospike databases in contemporary applications and their relative advantages concerning supporting different types of workloads. The document-based NoSQL database is MongoDB, which excels in schema flexibility, extensive document manipulation, and complex queries, making it ideal for dynamic setups like user profiles or product inventory examples. Aerospike is a low-latency, high-performance key-value store, tailored toward low-latency, high-throughput workloads, and specifically to use cases such as session management and real-time data feeds. Operating Aerospike and MongoDB concurrently enables an organization to leverage the support of both databases to query and make high-volume transactions whenever a workload comes along. The study investigates the realization of such high-performance architecture through the aggregation of the said systems and enlists the benefits and compromises of this dual-stacking technique. Some significant areas of future research are on understanding the performance of various configurations, making latency and storage costs controllable, and the predictive modelling of workload behavior using machine learning-based techniques such as correlation-based feature selection (CFS) and principal component analysis (PCA). Also, the paper points out a lack of recent empirical knowledge about dual-stack deployment. It presents future work opportunities, including the use of larger datasets, comparisons of MongoDB and Aerospike with additional NoSQL systems, and performance tuning automation via online learning techniques. Results indicate that co-deployment of MongoDB and Aerospike is optimal when there is a need to operate flexibly querying systems that must achieve reliable, low-latency behavior, resulting in a developer-friendly system that is operationally scalable.