Realtime Events (SignalR)

Live updates travel over one SignalR hub at /hub/realtime. Authentication uses the same JWT as the REST API, passed as ?access_token=<jwt> (or an Authorization header).

Note

The event stream is at-most-once and fire-and-forget — it is never the source of truth. Events missed during a disconnect are not replayed; authoritative state always comes from the REST API (/api/inference/status, /api/system/status) and the on-box database.

Subscription groups

High-frequency streams are opt-in per group, joined only while a consuming screen is mounted and re-joined automatically on reconnect:

Group	Carries	Consumed by
sensors	Live sensor reading batches	Realtime Monitor
predictions	New prediction batches	Dashboard charts
logs	Full log stream (all levels)	LogViewer page only
alerts	Warning/Error log entries only	Notification bell, app-wide

The alerts group exists so warnings surface everywhere without streaming the full logs firehose to every dashboard.

Events (server → client)

Event	Payload highlights
SensorReading	channelIndex, value, timestamp
NewPrediction	predictions, confidenceScores, modelId, inferenceTimeMs, window timestamps (below)
InferenceStateChanged	state ∈ idle · ready · running, active datasource, loaded model — emitted on every lifecycle transition
InferenceFaulted	reason, datasourceId — also raises the blocking fault banner
HealthMetricsUpdate	CPU/GPU/memory, uptime
ModelActivated	modelId, version, shapes, file size
ModelUploadProgress	Upload/validation phases, error message if failed
OutputWriteFailed	Coalesced per sink — immediate first alert, then one per 30 s window
LogEntryAdded	Log entry (level, message, logger, metadata)

Prediction timestamp semantics

A prediction describes a past window of input. All timestamps are Unix epoch milliseconds from one clock — the backend stamps each sample once at read time, and that value is echoed through the pipeline, never regenerated:

Field	Meaning
windowStartTimestamp	Oldest input sample in the window
windowEndTimestamp	Newest input sample — the “as-of” time the prediction is valid for
timestamp	Mirrors windowEndTimestamp (compatibility)
emittedAt	Backend wall-clock at emission — emittedAt − windowEndTimestamp ≈ end-to-end latency

On the dashboard the prediction trace visibly trails the sensor trace by the real pipeline latency. That gap is an intended operational signal, not a rendering defect.

Delivery guarantees

Slow consumers never throttle inference: dashboard broadcasts are fire-and-forget, and events to a client that can’t keep up are dropped and counted (/api/inference/backpressure). Notification-worthy events are deduplicated and rate-limited to keep the bell useful — see Notifications.

Next steps

API Overview REST surfaces and authentication.

Monitoring Where these events land in the UI.

Notifications Which events raise alerts, and what stays silent.