Prometheus vs VictoriaMetrics

Prometheus and VictoriaMetrics are both powerful time-series databases suitable for monitoring and alerting tasks in complex IT environments. Although they pursue similar goals, there are some significant differences in their architecture, operation, and offered features.

Architecture and Data Collection

Prometheus uses a pull-based model, where it retrieves metrics from configured targets at specified intervals. It stores time-series data in its own efficient local storage format and supports multidimensional data collection, querying, and alerting. Prometheus can be deployed as a single binary on a server or on a container platform like Kubernetes.

VictoriaMetrics offers the ability to be deployed as a single-node instance for smaller applications or as a cluster setup for large-scale applications, providing horizontal scalability and replication for high availability and performance. VictoriaMetrics supports both pull and push models for data collection, making it versatile.

Performance and Scalability

Prometheus is designed for reliability and can be scaled vertically (single node with increased resources) or through federation (hierarchical setup where Prometheus servers retrieve metrics from other Prometheus servers). It is primarily optimized for real-time monitoring but not specifically designed for extensive long-term storage.

VictoriaMetrics, on the other hand, focuses strongly on high performance, storage efficiency, and scalability. It claims to collect data more efficiently than Prometheus, with less CPU usage, RAM, and storage space for the same data volume. Its architecture allows for both vertical and horizontal scaling.

Query Language and API

Prometheus uses PromQL (Prometheus Query Language), a flexible query language for data exploration and dashboarding, which differs from SQL. PromQL allows metrics to be filtered, aggregated, and trends predicted with high flexibility.

VictoriaMetrics is backward compatible with PromQL and additionally introduces MetricsQL, an extension of PromQL that enhances query capabilities and provides a more user-friendly handling of complex queries and aggregations.

High Availability and Reliability

Prometheus does not support clustering natively, meaning it does not offer built-in high availability. High availability can be achieved by running duplicate instances, but this requires manual effort and coordination.

VictoriaMetrics is designed with high availability in mind. It uses replication and clustering to ensure that data is not lost in the event of an instance failure, making it a more reliable option for critical applications.

Integration with Grafana

Visualizing data in Grafana is similar for both systems thanks to Grafana’s flexible data source support. This allows for easy integration and visualization of collected metrics.

Overall, Prometheus offers a robust solution for real-time monitoring and alerting with a strong community and comprehensive documentation. VictoriaMetrics, however, stands out for its performance, storage efficiency, and scalability, making it particularly suitable for applications with high data volumes and long-term storage requirements.