Performance Evaluation of MongoDB and RavenDB in IIoT-Inspired Data-Intensive Mobile and Web Applications

The exponential growth of data generated by modern digital applications, including systems inspired by Industrial Internet of Things (IIoT) requirements, has accelerated the adoption of NoSQL databases due to their scalability, flexibility, and performance advantages over traditional relational systems. Among document-oriented solutions, MongoDB and RavenDB stand out due to their architectural features and their ability to manage dynamic, large-scale datasets. This paper presents a comparative analysis of MongoDB and RavenDB, focusing on the performance of fundamental CRUD (Create, Read, Update, Delete) operations. To ensure a controlled performance evaluation, a mobile and web application for managing product orders was implemented as a case study inspired by IIoT data characteristics, such as high data volume and frequent transactional operations, with experiments conducted on datasets ranging from 1000 to 1,000,000 records. Beyond the core CRUD evaluation, the study also investigates advanced operational scenarios, including joint processing strategies (lookup versus document inclusion), bulk data ingestion techniques, aggregation performance, and full-text search capabilities. These complementary tests provide deeper insight into the systems’ architectural strengths and their behavior under more complex and data-intensive workloads. The experimental results highlight MongoDB’s consistent performance advantage in terms of response time, particularly with large data volumes, while RavenDB demonstrates competitive behavior and offers additional benefits such as built-in ACID compliance, automatic indexing, and optimized mechanisms for relational retrieval and bulk ingestion. The analysis does not propose a new benchmarking methodology but provides practical insights for selecting an appropriate document-oriented database for data intensive mobile and web application contexts, including IIoT-inspired data characteristics, based on a controlled single-node experimental setting, while acknowledging the limitations of a single-host experimental environment.