Implementing Victoriametrics as A Time Series Database For Kubernetes Cluster Monitoring

Abstract

Infrastructure monitoring is a critical component in managing Kubernetes clusters, particularly for ensuring service availability and analyzing system performance. As the complexity and scale of infrastructure increase, monitoring systems are required to efficiently handle large volumes of metric data. This study aims to analyze the performance of VictoriaMetrics as a time-series database within Kubernetes monitoring systems and compare it with Prometheus based on resource usage. The research employs a quantitative approach with benchmark experiments conducted under three load scenarios: 500, 750, and 1000 target hosts. The analyzed parameters include CPU usage, memory consumption, and storage capacity. The results indicate significant differences in resource efficiency, where VictoriaMetrics maintains CPU usage between 2–10% across all scenarios, substantially lower than Prometheus, which reaches 12–24%. In terms of memory consumption, VictoriaMetrics requires only 21–27%, whereas Prometheus increases to 41–67%. For storage usage, VictoriaMetrics consumes 5–13 GB, while Prometheus requires 13–45 GB. These findings are expected to serve as a reference for organizations in selecting an appropriate monitoring solution that aligns with their Kubernetes infrastructure scale and requirements.