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About VictoriaMetrics
VictoriaMetrics is a high-performance, cost-effective time-series database designed as a long-term storage solution for Prometheus and other monitoring systems. Built from the ground up in Go, it offers exceptional compression rates, low resource consumption, and fast query performance. VictoriaMetrics supports the Prometheus query language (PromQL) along with its own enhanced MetricsQL, making it an ideal drop-in replacement for Prometheus with better resource efficiency. It's particularly popular for large-scale monitoring deployments where cost optimization and operational simplicity are priorities.
GreptimeDB vs. VictoriaMetrics
| Feature/Aspect | GreptimeDB | VictoriaMetrics |
|---|---|---|
| Data Model | Unified Observability Database | Prometheus-compatible Time-Series Database |
| Value Model | Multi-Value (supports complex data structures) | Single-Value (metrics-focused) |
| Multi-model Support | Metrics, Logs & Traces in one database | Metrics only (requires separate systems for logs/traces) |
| Ingestion Protocols | SQL gRPC InfluxDB Line Protocol Prometheus Remote Storage OpenTelemetry Loki Push API Elasticsearch Bulk API HTTP API | Prometheus Remote Write Pull-based scraping InfluxDB Line Protocol CSV imports |
| Query Languages | SQL & PromQL (dual interface) | MetricsQL (enhanced PromQL) PromQL compatibility |
| Data Retention | Flexible TTL policies with automatic tiering | Configurable retention with automatic downsampling |
| Continuous Aggregation | Built-in SQL aggregation, Pipeline ETL engine & Flow streaming computation | Recording rules and streaming aggregation |
| Use Cases | Unified observability, real-time analytics, IoT monitoring, edge computing | Prometheus long-term storage, cost-effective monitoring, large-scale metrics |
| Architecture | Cloud-native distributed with compute-storage separation | Single binary deployment with optional clustering |
| Storage Format | Apache Parquet (columnar, compressed) | Custom binary format with excellent compression |
| Resource Efficiency | Optimized for observability workloads | Extremely low memory and CPU usage |
| Compression Ratio | High compression with Parquet format | Industry-leading compression (up to 10x better than Prometheus) |
| Query Performance | Sub-second queries with advanced indexing | Fast PromQL queries with query caching |
| License | Apache 2.0 | Apache 2.0 |
| Prometheus Compatibility | Remote write support with PromQL interface | 100% Prometheus drop-in replacement |
| Operational Simplicity | Comprehensive observability platform | Simple deployment and maintenance |
| Cost Effectiveness | Unified platform reducing infrastructure complexity | Significant cost savings on storage and compute |
| Written Language | Rust (memory safety, performance) | Go (simplicity, fast development) |
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