Date of Award
6-14-2024
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2024
Department
Computer Science and Engineering
Abstract
Today, the presence of smart devices is constantly on the rise, especially for Internet of Things (IoT) devices. However, their utility-based design means that these devices are limited in computing power. Due to this limited computing power, the devices are more prone to cyber-security threats, and it is vital to construct a method to aid in network traffic analysis, bolstering defense mechanisms to thwart any malicious attacks. To analyze their network data efficiently and externally, we constructed a test-bed infrastructure to measure and evaluate the performance of databases. In this thesis, we have chosen to compare two databases which are both used widely. InfluxDB is a time-series database while DuckDB is an in-process analytical database. The comparison metrics we gathered are CPU utilization and memory utilization. We found that DuckDB’s memory utilization increased linearly whenever a batch of packets was inserted. In contrast, DuckDB’s CPU utilization results indicate spikes during data insertion, but otherwise these results show a steady CPU usage. The results obtained for InfluxDB were more stable, with CPU utilization remaining within a certain range. Likewise, InfluxDB’s memory usage remained constant throughout our testing period.
The differences in the results allude to the structural variances between the two databases. DuckDB, being an in-process database, shares resources with the interacting application. In contrast, InfluxDB, being a time series database, utilizes it’s own set of resources, distinct from those used by the interacting application. As a result, we observe that the memory utilization of DuckDB is higher when we insert data. Future work for our project breaks down into two components: changing the design of our test-bed, and improving the accuracy of measuring performance. The former would involve incorporating additional databases (i.e., PostgreSQL) and also obtaining alternative metrics for comparing them.
Recommended Citation
Vong, James and Kareti, Daniel, "Performance Evaluation of Databases for Packet Capture and Analysis" (2024). Computer Science and Engineering Senior Theses. 292.
https://scholarcommons.scu.edu/cseng_senior/292