Telemetry + events

What gets sent

• Runtime health and lifecycle events (start/stop/restart/degraded)
• Operational metrics (buffer depth, upload latency, error counters)
• Selected point/value telemetry (sampled or report-by-exception)
• Deployment correlation (which snapshot/version is producing the data)

Delivery model

• Local buffering (store-and-forward) to survive link outages
• Batching and compression (conceptually) to reduce bandwidth overhead
• Backpressure-aware upload so edge execution remains stable under load

Design constraints

• Buffering + batching makes telemetry resilient to WAN instability
• Volume control (sampling/exceptions) keeps cost and UX predictable
• Version-correlation makes regressions attributable and reversible

Architecture at a glance

• Edge emits health/events/telemetry with stable IDs (device/resource/app/deploy)
• Store-and-forward buffers protect evidence during outages
• Backend correlates timelines to snapshots so debugging becomes “find the diff”
• This is a UI + backend + edge concern: visibility is required to scale safely

Typical workflow

• Define the minimal signal set: deployment state + runtime health + adapter health + telemetry
• Create dashboards that answer: what is running? is it healthy? is it correct?
• Use correlated timelines (events + telemetry + rollout state) to debug
• Codify alerts for restart bursts, buffer saturation, flapping, and drift

System boundary

Treat Telemetry + events as a repeatable interface between engineering intent (design) and runtime reality (deployments + signals). Keep site-specific details configurable so the same design scales across sites.

How it relates to snapshots

Because it lets you answer whether a symptom started with a specific release and rollback deterministically if needed.

Example artifact

t=00:00 deploy:start snapshot=snap_... device=edge-12
t=00:12 runtime:healthy resource=forte-1
t=00:20 adapter:connected protocol=modbus endpoint=pump-1
t=01:10 telemetry:ok points=128 gap=0s
t=03:40 alert:staleness point=pump_speed > 5s

Engineering outcomes

• Buffering + batching makes telemetry resilient to WAN instability
• Volume control (sampling/exceptions) keeps cost and UX predictable
• Version-correlation makes regressions attributable and reversible

Quick acceptance checks

• Confirm buffer sizing matches worst-case outage durations
• Set sampling/report-by-exception to control volume and cardinality

What to monitor

Buffer saturation, upstream auth/network failures, or mis-sized batching. Monitor buffer depth, drops, and upload retry counts.

Common failure modes

• Telemetry gaps from buffer saturation or upstream auth/network issues
• Clock skew and batching causing out-of-order timelines
• “Everything is green” while behavior is wrong (mapping/data-quality issue)
• Over-alerting: noisy signals that hide real regressions

Acceptance tests

• Verify the deployed snapshot/version matches intent (no drift)
• Run a canary validation: behavior, health, and telemetry align with expectations
• Verify rollback works and restores known-good behavior

Failure modes

• Buffer saturation when offline too long or telemetry volume is too high
• Clock skew that makes timelines look “out of order” across sites
• Authentication/authorization failures to upstream endpoints
• High-cardinality telemetry that becomes expensive to store/query

What to monitor

• Upload success rates and retry counts
• Edge buffer depth and “time behind” (how stale uploaded data is)
• Drop counters (if configured) and sampling/report-by-exception settings
• End-to-end latency from edge emit → cloud ingest → UI visible

Implementation checklist

• Confirm buffer sizing matches worst-case outage durations
• Set sampling/report-by-exception to control volume and cardinality
• Validate auth/permissions for uplink endpoints
• Track “time behind” and end-to-end latency edge → cloud → UI

Rollout guidance

• Start with a canary site that matches real conditions
• Use health + telemetry gates; stop expansion on regressions
• Keep rollback to a known-good snapshot fast and rehearsed

Acceptance tests

• Verify the deployed snapshot/version matches intent (no drift)
• Run a canary validation: behavior, health, and telemetry align with expectations
• Verify rollback works and restores known-good behavior