VictoriaMetrics Time Series Database

VictoriaMetrics is a fast, cost-effective, and scalable time-series database and monitoring solution designed as a drop-in replacement for Prometheus and InfluxDB. Created by Aliaksandr Valialkin and open-sourced in 2018, VictoriaMetrics has become one of the fastest-growing time-series databases. Key features: high-performance storage engine optimized for time-series data with 7x better compression than Prometheus, reducing storage costs and improving query performance. Drop-in Prometheus replacement via vmagent (Prometheus-compatible scraping), vmalert (Prometheus-compatible alerting), and vmsingle/vmcluster (Prometheus-compatible remote read/write). Single-node (vmsingle) and cluster (vmcluster) deployment modes. vmcluster with vmstorage (data nodes), vminsert (write load balancer), and vmselect (read load balancer) for horizontal scaling. vmagent for scraping Prometheus metrics, remote write forwarding, metric relabeling, and multi-tenant data collection. vmalert for evaluating recording and alerting rules compatible with Prometheus AlertManager. MetricsQL query language as a superset of PromQL with additional functions for time-series analysis. vmui web interface for exploring metrics, building dashboards, and debugging queries. vmgateway for rate limiting, tenant isolation, and authentication. Multi-tenant support with tenant isolation via TenantID header for SaaS and shared monitoring. Downsampling and replication for long-term storage and high availability. vmrestore and vmbackup for data backup and recovery to S3-compatible storage. VictoriaLogs for log storage and querying with LogsQL query language. Open-source (Apache 2.0) and enterprise editions with additional features including downsampled data, RBAC, and audit logging. Used by Adidas, Roblox, Wix, and thousands of organizations for cost-effective monitoring at scale.