VictoriaMetrics – Time Series Database That’s Built to Keep Up
What is VictoriaMetrics
VictoriaMetrics is a high-performance time series database built for modern telemetry workloads. If Prometheus starts to choke under load or your long-term retention plan becomes a storage nightmare — VictoriaMetrics is what usually comes next.
It’s fast, lightweight, and designed to ingest millions of metrics per second without falling apart. It stores time series data in an append-only, compressed format that’s optimized for both disk space and query speed.
Whether you’re running a single-node install or scaling out in a distributed cluster, VictoriaMetrics delivers consistent performance without needing a PhD in tuning.
Key Features
| Attribute | Description |
| Architecture | Single-node or cluster (vminsert, vmstorage, vmselect) |
| Ingestion Protocols | Prometheus remote_write, Influx, Graphite, OpenTSDB, OpenTelemetry |
| Query Language | MetricsQL (PromQL-compatible) |
| Compression | Up to 70x compared to uncompressed Prometheus TSDB |
| Resource Efficiency | Lower RAM and CPU usage at scale |
| Integrations | Native Grafana support, Alertmanager-compatible |
| Storage | Append-only, optimized for long-term retention |
| UI | Built-in web interface with real-time queries |
| License | Apache 2.0 |
| Website | https://victoriametrics.com |
Deployment Notes
For small setups, a single binary is enough:
./victoria-metrics-prod -retentionPeriod=12
For larger environments, switch to cluster mode with:
– vminsert – handles incoming write requests
– vmstorage – stores the actual time series data
– vmselect – serves query responses
Cluster components can be deployed manually or via Kubernetes with Helm charts. TLS, authentication, and long-term storage options are available out of the box.
When It Makes Sense
– Replacing Prometheus TSDB in environments with high ingestion rate
– Centralizing telemetry from multiple Prometheus instances
– Long-term storage of metrics (months or years) without performance loss
– Using MetricsQL for faster queries on large datasets
– Scaling horizontally without switching to a new monitoring stack
