Chartbrew

Chartbrew is an open‑source, self‑hosted web application that turns raw database rows or API responses into interactive visualizations. From a developer’s perspective it functions as a data‑to‑chart pipeline: data connectors, query engines, chart rendering, and an embedding API are all bundled into a single codebase. The platform is designed to be plug‑in friendly; any data source that can be queried via SQL or REST can be surfaced as a chartable dataset, and developers can extend the visualizations by writing custom chart components in React.

Technical Stack

• Backend: Node.js (v20) running an Express‑style API built with TypeScript. The server layer is responsible for authentication, data source management, query execution, and chart metadata storage.
• Frontend: React (hooks & context) with a component‑based dashboard builder. The UI uses the Ant Design system for rapid development and consistent styling.
• Databases: Persists its own schema in either MySQL (5+) or PostgreSQL (12.5+). All user, chart, and data‑source metadata lives here.
• Cache & Queue: Redis (v6+) is used for session storage, rate‑limiting, and as a lightweight message broker for background job processing (e.g., scheduled query refreshes).
• Containerization: A Docker image (razvanilin/chartbrew) is available on Docker Hub. The repository contains a docker-compose.yml that wires together the API, frontend, database, and Redis for quick local or production deployment.

Core Capabilities

• Data Source Agnostic: Connect to any relational database (MySQL, PostgreSQL) or RESTful API. Custom connectors can be added by implementing a simple interface.
• Query & Request Editor: A built‑in SQL editor with syntax highlighting and live execution results. For APIs, developers can craft JSON payloads and view the parsed response.
• Chart Builder: Drag‑and‑drop chart types (bar, line, pie, map, etc.) with real‑time preview. Charts are stored as JSON definitions that can be exported or embedded.
• Dashboard API: Exposes REST endpoints for CRUD operations on charts, dashboards, and data sources. It also supports webhooks for event notifications (e.g., on data refresh).
• Embedding: Provides both iframe and programmatic API embedding. Developers can embed charts in external applications with authentication tokens or signed URLs.
• Multi‑Tenant & Permissions: Role‑based access control (admin, editor, viewer) and tenant isolation for SaaS deployments.

Deployment & Infrastructure

Chartbrew is intentionally lightweight, requiring only a Node.js runtime, a relational database, and Redis. For production:

• Scalability: Horizontal scaling is straightforward—multiple API instances can share the same database and Redis. The chart rendering is stateless, so a load balancer can distribute traffic evenly.
• Container Orchestration: The Docker image works out‑of‑the‑box with Kubernetes. Helm charts are available in the community repo, simplifying deployment to managed clusters.
• CI/CD: The project uses CircleCI for automated tests and Docker image publishing, ensuring that each release is battle‑tested before reaching users.

Integration & Extensibility

• Plugin System: Developers can create custom data connectors or chart types by implementing predefined interfaces. The plugin API is documented in the repo and can be loaded at runtime.
• Webhooks & Callbacks: On data refresh or chart deployment, external services can be notified via configurable webhooks.
• API SDKs: Although not bundled, the public REST API can be consumed by any HTTP client; community contributors have already produced lightweight SDKs in Python and Go.

Developer Experience

• Configuration: All runtime settings are exposed via environment variables (.env file). The documentation includes a comprehensive list of required and optional variables.
• Documentation: The official docs (docs.chartbrew.com) cover architecture, data source setup, API reference, and deployment guides. The GitHub repo’s issue tracker is active, and the Discord community provides rapid support.
• Open Source License: MIT‑licensed, allowing unrestricted use in commercial or internal projects without licensing fees.

Use Cases

1. Internal Analytics – Teams can pull metrics from PostgreSQL data warehouses and expose them via embedded dashboards in internal tools.
2. Client Reporting – A SaaS provider can host Chartbrew per client, giving each tenant isolated dashboards and secure API access for embedding.
3. Real‑Time Monitoring – Scheduled queries or webhook triggers can refresh charts on a minute‑by‑minute basis for operational dashboards.
4. Data Exploration – Data scientists can prototype visualizations quickly without leaving the browser, then export chart definitions for production use.

Advantages

• Performance: Server‑side query execution keeps heavy lifting out of the browser, while Redis caching reduces latency for frequent reads.
• Flexibility: The plugin architecture and raw query editor give developers full control over data shaping.
• Cost‑Effective: Self‑hosting eliminates subscription fees; the lightweight stack keeps infrastructure costs low.
• Community & Support: Active Discord channel, frequent releases, and an MIT license make it easy to contribute or extend the platform.

Chartbrew offers developers a robust, self‑hosted solution for turning data into interactive visual stories with minimal friction, making it an attractive choice for internal tooling, SaaS analytics, and data‑dr