Grafana is an open-source observability and data visualization platform that allows users to query, visualize, and alert on time-series metrics, logs, and traces from multiple sources including Prometheus, Loki, Elasticsearch, InfluxDB, PostgreSQL, and MySQL. It is designed for SREs, DevOps engineers, and developers who need to monitor infrastructure, applications, and services in real time without vendor lock-in.
Built with Go and TypeScript, Grafana supports a plugin ecosystem for custom data sources and visualizations, and can be self-hosted or deployed via Grafana Cloud. It integrates with OpenTelemetry, supports dynamic dashboards with template variables, and offers ad-hoc data exploration with split-view comparisons across data sources and time ranges.
What You Get
- Visualizations - Fast, client-side graphs with 50+ panel plugins including graphs, gauges, heatmaps, and logs panels, supporting custom visualizations via plugin system.
- Dynamic Dashboards - Reusable dashboards with template variables that render as dropdowns, enabling parameterized views across environments or services.
- Explore Metrics - Ad-hoc querying with live data exploration, split-view comparison of time ranges, queries, and data sources side by side.
- Explore Logs - Unified log exploration with preserved label filters, live streaming, and seamless transition from metrics to logs using shared context.
- Alerting - Visual rule builder for metrics-based alerts with notifications to Slack, PagerDuty, VictorOps, and OpsGenie, with support for multiple data sources per alert.
- Mixed Data Sources - Combine queries from Prometheus, Loki, Elasticsearch, InfluxDB, PostgreSQL, MySQL, and custom plugins in a single panel or dashboard.
Common Use Cases
- Monitoring Kubernetes clusters - SREs use Grafana to visualize CPU, memory, and pod metrics from Prometheus and logs from Loki, with alerts for node failures or resource exhaustion.
- Debugging microservices - Developers correlate traces from Jaeger with metrics from Prometheus and logs from Loki to identify latency bottlenecks across services.
- Running production API dashboards - API teams build real-time dashboards showing request rates, error rates, and latency from InfluxDB and Elasticsearch to track SLAs.
- Cost-optimized telemetry - Engineering teams use Grafana Cloud’s Adaptive Telemetry to reduce cloud monitoring costs by 80% by automatically aggregating low-value data.
Under The Hood
Architecture
- Modular monorepo structure using Nx with clear boundaries between core, plugins, and shared libraries, enforcing encapsulation through project tags and dependency constraints
- Plugin system with standardized extension points for datasources, panels, and themes, enabling safe third-party contributions without core modifications
- Backend employs strict layering with pkg/ directories and unidirectional dependencies, preventing circular imports via automated dependency enforcement
- Frontend uses React with component-based UI and state management decoupled via Redux-like patterns, with dynamic theming generated at build time from Sass variables
- Dependency injection through service registries and Go interfaces ensures testability and plugin substitution without tight coupling
- Build and test pipelines enforce code ownership and architectural boundaries with automated linting and coverage collection
Tech Stack
- Go backend with modular architecture using Go modules, built via Makefile with custom build metadata injection
- TypeScript/React frontend powered by Nx monorepo and Webpack for scalable plugin management and theme generation
- Jest and Playwright for comprehensive unit and end-to-end testing, with custom reporters and codeowner-aware test execution
- Docker multi-stage builds using lightweight base images to produce minimal production containers with embedded artifacts
- i18next and Crowdin integrated for seamless localization across core and plugins
- Golangci-lint with strict dependency rules to maintain module boundaries across Go codebases
Code Quality
- Extensive test coverage across UI and data layers using React Testing Library with realistic interactions and snapshot validation
- Strong type safety through comprehensive TypeScript usage, with explicit type definitions for critical data structures
- Consistent naming and modular separation between data processing, UI components, and domain logic
- Robust error handling with defensive programming, input validation, and type guards to prevent malformed data propagation
- Integrated linting and testing tooling ensures quality standards are maintained across frontend and backend
- Scene-based dashboard architecture enables composable, state-driven components that improve maintainability and testability
What Makes It Unique
- Extensible plugin system allows third-party developers to seamlessly inject custom UI and data sources into the core interface, fostering a rich ecosystem
- Unified dashboard experience that integrates alerting, logging, and tracing with shared context and time ranges, breaking down telemetry silos
- Dynamic theming powered by runtime CSS injection and theme context enables real-time UI customization for dark/light modes and enterprise branding
- Sophisticated form state management with persistent field registration prevents data loss in complex, dynamically changing configurations
- High-performance trace visualization with optimized time-range primitives enabling smooth navigation across millions of spans with sub-millisecond latency