Comparative Evaluation of Database Systems for High-Volume Seismic Prediction Data Management in Real-Time Applications

Abstract

The Earthquake Early Warning System (EEWS) plays a pivotal role in mitigating structural damage and minimizing casualties by issuing alerts prior to the arrival of destructive seismic waves (S-waves), through the detection of the earlier and faster P-waves. The operational effectiveness of EEWS depends not only on the accuracy of its predictive algorithms but also on the efficiency of the underlying data storage and management infrastructure. This study presents a comparative evaluation of three data storage approaches i.e. MongoDB, MongoDB with sharding, and InfluxDB, as well as the MiniSEED (mseed) binary format, with a focus on their performance in managing real-time seismic prediction data. Benchmarking was conducted based on two key metrics: Input/Output Operations Per Second (IOPS) and data throughput. The results indicate that both MongoDB and InfluxDB offer strong performance in high-ingestion scenarios, with MongoDB demonstrating higher IOPS, while InfluxDB exhibits better scalability and consistency as data volume increases. Conversely, the mseed format achieves exceptionally high throughput due to its flat-file structure but lacks the responsiveness and query capabilities required for real-time analytics. These findings suggest that MongoDB and InfluxDB are well-suited for integration into scalable EEWS infrastructures, offering a balance between performance and flexibility. Future work will extend this evaluation to larger-scale datasets and alternative architectures such as data lake systems to improve disaster response readiness.