Telemetry

Ingestion (conceptual)

• Authenticate the edge source and validate payload shape
• Normalize metadata (device/resource/app/deployment IDs)
• Store telemetry and events in query-friendly form for dashboards

Design constraints

• Normalization at ingest is what makes queries reliable
• Cardinality control keeps dashboards fast and affordable
• Out-of-order handling is required (clock skew/batching)

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 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.

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

• Normalization at ingest is what makes queries reliable
• Cardinality control keeps dashboards fast and affordable
• Out-of-order handling is required (clock skew/batching)

Quick acceptance checks

• Normalize identifiers (device/resource/app/deployment) at ingest
• Define query patterns: per-site timelines and rollout comparisons

What to monitor

High-cardinality keys, inconsistent identifiers, and out-of-order timestamps. Normalize early and keep joining keys stable.

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

Query patterns

• Per-site timelines: “what happened on this device over the last hour?”
• Cross-fleet comparisons: “did the rollout change error rates?”
• Version correlation: “which snapshot introduced this symptom?”

Failure modes

• High-cardinality telemetry that becomes slow/expensive to query
• Inconsistent identifiers that prevent reliable joins/correlation
• Out-of-order events due to clock skew or batching

Implementation checklist

• Normalize identifiers (device/resource/app/deployment) at ingest
• Define query patterns: per-site timelines and rollout comparisons
• Control cardinality and retention so queries stay fast
• Ensure out-of-order events are handled (clock skew/batching)

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