The fast-growing surge in cloud-native and globally distributed applications has heightened the need to have database systems that can support high-volume transactional workloads with low latency, scalability according to high workload, and with high reliability.Azure Cosmos DB has become a leading NoSQL database system that is capable of fulfilling these needs by its globally distributed design, elastic provisioning of throughput, and scalable consistency models.The data partitioning used in the central concept of scalability and performance allows the movement of horizontal scaling and efficient utilization of resources.But the best transactional performance in Cosmos DB is very sensitive to the best partitioning strategies and the work load sensitive data models.The given review paper gives an in-depth analysis of optimized high-volume transactions in Azure Cosmos DB with special focus on partitioning techniques.It discusses the architecture of Cosmos DB, the developed methods of partitioning in distributed NoSQL systems, and the effects of partition key selection, workload fit, and transactional boundaries on throughput, latency, and cost efficiency.Practical optimization techniques, challenges that may occur, including cross-partition transactions and partition key rigidity, and trade-offs between consistency, availability, and performance are also discussed in the paper.Moreover, the review has also identified the new research directions such as AI-based adaptive partitioning, event-based architectures, and self-tuning database mechanisms.This paper seeks to offer practical knowledge to both researchers and practitioners in designing and optimizing transactional workloads in the Azure Cosmos DB scalable and cost-effective in both bulk and scale by synthesizing academic research and system-level studies.
Saatwik Gilakattula (Thu,) studied this question.