@article{ART003283092},
author={Youngmi Baek and Park Jung Kyu},
title={A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2025},
volume={11},
number={6},
pages={2}
TY - JOUR
AU - Youngmi Baek
AU - Park Jung Kyu
TI - A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration
JO - Journal of Internet of Things and Convergence
PY - 2025
VL - 11
IS - 6
PB - The Korea Internet of Things Society
SP - 2
EP -
SN - 2466-0078
AB - This study compares the write throughput and latency characteristics of MongoDB and InfluxDB in large-scale time-series data environments. Modern IoT and log analytics systems increasingly require processing millions of events per second, which traditional relational databases struggle to support efficiently. To reproduce such high-load environments accurately, we implemented a load generator using the Go programming language, which provides lightweight concurrency and efficient parallel execution, enabling stable generation of hundreds of thousands to millions of write operations per second. Four scenarios were evaluated under identical conditions, including MongoDB’s low-durability Write Concern w:1 (acknowledgment only from the primary node) and the stronger durability setting w:majority (acknowledgment from a majority of replica-set members). Experimental results show that InfluxDB achieved 1.54M TPS with a p95 latency of 243 ms, approximately three times higher throughput than MongoDB. MongoDB exhibited increased latency as Write Concern levels strengthened due to replication acknowledgment overhead. These findings highlight the architectural advantages of InfluxDB for high-throughput time-series workloads and offer practical guidance for database selection and tuning in large-scale log and IoT systems.
KW - Time-Series Database;MongoDB;InfluxDB;Performance Evaluation;Data Migration
DO -
UR -
ER -
Youngmi Baek and Park Jung Kyu. (2025). A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration. Journal of Internet of Things and Convergence, 11(6), 2.
Youngmi Baek and Park Jung Kyu. 2025, "A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration", Journal of Internet of Things and Convergence, vol.11, no.6 2.
Youngmi Baek, Park Jung Kyu "A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration" Journal of Internet of Things and Convergence 11.6 2 (2025) : 2.
Youngmi Baek, Park Jung Kyu. A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration. 2025; 11(6), 2.
Youngmi Baek and Park Jung Kyu. "A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration" Journal of Internet of Things and Convergence 11, no.6 (2025) : 2.
Youngmi Baek; Park Jung Kyu. A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration. Journal of Internet of Things and Convergence, 11(6), 2.
Youngmi Baek; Park Jung Kyu. A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration. Journal of Internet of Things and Convergence. 2025; 11(6) 2.
Youngmi Baek, Park Jung Kyu. A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration. 2025; 11(6), 2.
Youngmi Baek and Park Jung Kyu. "A Comparative Performance Study of MongoDB and InfluxDB for Large-Scale Time-Series Data Migration" Journal of Internet of Things and Convergence 11, no.6 (2025) : 2.
