Connectivity

Data flow

• Device/protocol session (e.g., Modbus/OPC UA) → point mapping layer → runtime inputs/outputs
• Runtime outputs → point mapping → protocol writes
• Connection + error state → health events and diagnostics

Design principles

• Stable point identifiers so control logic doesn’t depend on vendor-specific addressing
• Explicit scaling/units and validation to prevent subtle “works but wrong” behavior
• Backpressure and rate limits to protect devices from overload

Design constraints

• Stability comes from correct timeouts/backoff and realistic polling rates
• Data quality checks catch subtle protocol/encoding issues
• Single-writer ownership reduces unsafe write conflicts

Architecture at a glance

• Endpoints (protocol sessions) → points (signals) → mappings (typed bindings) → control app ports
• Adapters isolate variable-latency protocol work from deterministic control execution paths
• Validation and data-quality checks sit between “connected” and “correct”
• This is a UI + backend + edge concern: changes affect real-world actuation

Typical workflow

• Define endpoints and point templates (units, scaling, ownership)
• Bind points to app ports and validate types/limits
• Commission using a canary device and verify data quality (staleness/range)
• Roll out with rate limits and monitoring for flaps and errors

System boundary

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

Engineering outcomes

• Stability comes from correct timeouts/backoff and realistic polling rates
• Data quality checks catch subtle protocol/encoding issues
• Single-writer ownership reduces unsafe write conflicts

Quick acceptance checks

• Validate endpoint settings (timeouts, retries, backoff) per protocol
• Ensure scaling/units are explicit and tested for critical points

Common failure modes

• Session flapping from aggressive polling or device session limits
• Timeout/backoff misconfiguration creating retry storms
• Backpressure issues: buffers fill, telemetry drops, or adapters stall
• Partial outages that create inconsistent, stale, or delayed signals

Acceptance tests

• Simulate network loss and verify reconnect/backoff behavior
• Load test polling rates and confirm devices are not overloaded
• Confirm store-and-forward covers expected outage windows
• 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

Common failure modes

• Connection flapping due to network quality or device limits
• Protocol-level errors (timeouts, invalid responses, permissions)
• Data quality issues (scaling, endian/encoding mismatches, stale values)
• Write conflicts when multiple systems attempt to control the same register/tag

What to monitor

• Connection uptime, reconnect frequency, and backoff patterns
• Read/write error rates by protocol endpoint and device
• Latency distributions (p50/p95) for reads/writes
• Staleness and out-of-range checks on critical points

Implementation checklist

• Validate endpoint settings (timeouts, retries, backoff) per protocol
• Ensure scaling/units are explicit and tested for critical points
• Set rate limits/backpressure so devices aren’t overloaded
• Monitor connection flaps and error rates per endpoint/device

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

• Simulate network loss and verify reconnect/backoff behavior
• Load test polling rates and confirm devices are not overloaded
• Confirm store-and-forward covers expected outage windows
• 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