Web Dashboard Architecture
A dashboard's usefulness rests on the data path behind it: how live values arrive, update, and stay trustworthy in the browser.
The path from source to pixel
A dashboard is only as good as the data pipeline feeding it. Behind the charts sits a chain: a data source, a transport, a client-side store, and a rendering layer. Each link affects latency, freshness, and reliability, and each can fail in ways the visible chart must not hide.
Getting data to the client
- Polling: the client requests updates on an interval; simple but adds latency.
- Server-sent events or websockets: the server pushes updates, lowering latency for live data.
- Snapshot plus stream: load history once, then apply incremental updates.
State and rendering
The client keeps a store of current values and history, and the rendering layer redraws efficiently as data changes, updating only what moved rather than rebuilding every frame. For high-rate data, downsample for display while keeping the full series for query, so the chart stays smooth without lying about extremes.
Trust and failure
Show data age explicitly and degrade visibly when a source drops; never interpolate across a gap into a smooth line. Validate incoming values and clamp or flag impossible ones rather than plotting noise as signal. A frozen dashboard that looks live is a hazard.
Self-contained delivery
When a dashboard ships as a self-contained page, assets and logic are embedded so it renders without external dependencies, which matters for reliability and for controlled environments.
Kronos use
Operator and public dashboards are designed against simulated telemetry with explicit data-age indicators, ahead of the Q2 2027 construction start.