Source
VictoriaMetrics docs
Official documentation for VictoriaMetrics architecture, components, and operating model.
VictoriaMetrics is a high-performance TSDB built for cost-efficient metric storage and a scalable observability path. In the practical TSDB map it is often considered alongside Prometheus as a backend for long retention and high-cardinality workloads.
History: key milestones
Public launch
VictoriaMetrics was released as an open-source TSDB focused on efficient metric storage.
Performance and storage-density focus
The project gained traction as a low-resource option for Prometheus-compatible workloads.
Cluster profile maturation
The vmselect/vminsert/vmstorage architecture stabilized for horizontal scalability.
Ecosystem expansion
vmagent, vmalert, and multi-tenant deployment patterns became more widely adopted.
Production-scale adoption
Migration patterns from Prometheus-based stacks to VictoriaMetrics for long retention became common.
Observability-stack evolution
Cluster deployment, cost optimization, and enterprise monitoring integration patterns continued to mature.
VictoriaMetrics specifics
Prometheus-compatible interface
Prometheus API and remote_write/read support simplify integration into existing monitoring stacks.
Efficient metric storage
Storage optimizations and background merges allow longer retention windows with fewer resources.
Cluster architecture
Splitting responsibilities across vmagent/vminsert/vmstorage/vmselect gives a clear write/read path.
Rule-driven monitoring
vmalert plus Alertmanager integration creates a controlled recording/alerting rule loop.
VictoriaMetrics architecture by layers
At a high level, the pipeline can be read as: ingest -> write routing -> storage parts/merge -> query fan-out -> rules/alerts -> external integrations.
Key features
VictoriaMetrics is optimized for cost-efficient metric storage, Prometheus-compatible APIs, and growth from single-node to cluster deployments.
Compression and storage
Prometheus compatibility
Scalability
DDL vs DML: VictoriaMetrics model
Like most TSDB engines, VictoriaMetrics has no literal SQL DDL/DML layer. For system-design reasoning it is useful to split DDL-like operations (topology/configuration updates) and DML-like operations (sample movement and query read execution).
How the DDL/DML model works in VictoriaMetrics
DDL-like: topology/config updates. DML-like: sample flow and query read path.
1. Ingest samples
Samples + queriesvmagent or remote_write sends fresh metrics to the write endpoint.
2. Parse and relabel
Samples + queriesSamples are parsed, labels are enriched, and data is prepared for routing.
3. vminsert shard routing
Samples + queriesvminsert distributes data to vmstorage nodes using hash/tenant routing.
4. vmstorage append + merge
Samples + queriesvmstorage appends samples to local parts and merges them in the background.
5. vmselect read path
Samples + queriesvmselect fans out across shards, applies dedup/aggregation, and returns results.
Active step
1. Ingest samples
vmagent or remote_write sends fresh metrics to the write endpoint.
Data and query path
- The DML-like path covers ingestion, storage, compaction, and query execution.
- In cluster mode, write/read paths scale horizontally across shards.
- Label cardinality and tenant skew are key drivers of latency and cost.
Source
Prometheus docs
Reference context for a Prometheus-compatible observability stack.
VictoriaMetrics vs Prometheus
Core profile
VictoriaMetrics: Strong focus on cost-efficient storage, scalable write/read paths, and Prometheus compatibility.
Prometheus: Canonical monitoring stack with pull-based collection, PromQL, and a built-in TSDB for operational use.
Query model
VictoriaMetrics: PromQL-compatible querying with MetricsQL extensions for production analytics.
Prometheus: PromQL as the baseline language for metric analysis and alert-driven workflows.
Scalability
VictoriaMetrics: Cluster mode (vminsert/vmstorage/vmselect) for large-scale data and long-term retention.
Prometheus: Often scaled via single-node + federation/remote storage patterns.
Operating model
VictoriaMetrics: Often used as a consolidated metrics backend in large observability platforms.
Prometheus: Often acts as the primary scrape/rule engine with external long-term storage integration.
Why VictoriaMetrics is often chosen in production
Practical interpretation for system design workloads:
- VictoriaMetrics is often chosen for high storage efficiency and predictable long-retention cost.
- Prometheus compatibility lowers migration effort and preserves existing dashboards and alert definitions.
- The vmagent/vminsert/vmstorage/vmselect write/read path scales cleanly using shard-based topology.
- Single-node and cluster deployment modes provide a practical growth path from small setups to large-scale production.