Live observability

How it works (high level)

• Every deployment is tied to a version/snapshot so you can answer “what changed?” precisely
• Edge agents and runtimes emit health + state signals with stable identifiers (device, resource, app, deployment)
• Cloud storage correlates time-series telemetry + events + deployment state so UI views can join them reliably

Typical workflow

• Start from a site/device view → confirm the deployed snapshot + current rollout status
• Check runtime health and recent events → look for state transitions (start/stop/restart/degraded)
• Inspect point-level telemetry to validate I/O mapping and expected control behavior
• If needed: compare against the previous known-good snapshot and roll back

Design constraints

• Version-aware timelines turn incidents into a solvable diff
• Standard health signals reduce time-to-diagnosis across the fleet
• Good IDs (device/resource/app/deploy) make data joinable and useful

Architecture at a glance

• UI captures engineering intent; backend persists models and versions; edge runs artifacts
• The UI must reflect operational truth: deployed snapshot, drift, and health
• Good UX encodes constraints so unsafe states are hard to create
• This is a UI + backend + edge concern: design decisions affect safety and speed

System boundary

Treat Live observability 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

topic: Live observability
plan: define -> snapshot -> canary -> expand
signals: health + telemetry + events tied to version
rollback: select known-good snapshot

Engineering outcomes

• Version-aware timelines turn incidents into a solvable diff
• Standard health signals reduce time-to-diagnosis across the fleet
• Good IDs (device/resource/app/deploy) make data joinable and useful

Quick acceptance checks

• Ensure device/resource/app identifiers are consistent across the fleet
• Confirm telemetry is tied to snapshot/deployment IDs for correlation

Common failure modes

• Drift between desired and actual running configuration
• Changes without clear rollback criteria
• Insufficient monitoring for acceptance after rollout

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

What to monitor

• Runtime: health, uptime, restart counts, CPU/memory budgets (coarse), scheduler timing signals
• Connectivity: adapter connection health, reconnect/backoff patterns, protocol error rates
• Telemetry: buffer depth, upload latency, drop counters, clock skew indicators
• Rollouts: canary vs fleet completion, failure reasons grouped by component

Common failure modes

• “Everything is green but behavior is wrong” → I/O mapping or scaling mismatch
• Intermittent faults → connectivity flapping, noisy signals, or timing pressure under load
• Telemetry gaps → store-and-forward buffer saturation or upstream auth/network issues
• Rollback doesn’t fix → environmental drift (firmware/device) vs configuration drift

Implementation checklist

• Ensure device/resource/app identifiers are consistent across the fleet
• Confirm telemetry is tied to snapshot/deployment IDs for correlation
• Define standard dashboards: rollout health, runtime health, adapter health
• Set alert thresholds for drop counters, buffer depth, and restart rates

Operational notes

Look for correlation: connectivity flaps, restart bursts, buffer saturation, and clock skew. Then compare against a known-good snapshot/site to isolate change vs environment.

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