Telemetry transport

Where it helps (examples)

• Deployments: queue rollout steps and capture state transitions reliably
• Telemetry: buffer ingestion, processing, enrichment, and indexing
• Notifications: emit events for alerts and operational workflows

Design constraints

• Idempotency is the foundation for safe retries
• Dead-letter handling prevents silent workflow loss
• Ordering must be explicit; assume out-of-order delivery

Architecture at a glance

• Define a stable artifact boundary (what you deploy) and a stable signal boundary (what you observe)
• Treat changes as versioned, testable, rollbackable units
• Use health + telemetry gates to scale safely

Typical workflow

• Define scope and success criteria (what should change, what must stay stable)
• Create or update a snapshot, then validate against a canary environment/site
• Deploy progressively with health/telemetry gates and explicit rollback criteria
• Confirm acceptance tests and operational dashboards before expanding

System boundary

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

Engineering outcomes

• Idempotency is the foundation for safe retries
• Dead-letter handling prevents silent workflow loss
• Ordering must be explicit; assume out-of-order delivery

Quick acceptance checks

• Make workflows idempotent so retries are safe
• Define dead-letter/poison message handling and alerting

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

Failure modes

• Poison messages that repeatedly fail processing
• Out-of-order handling when ordering isn’t guaranteed
• Backlogs that grow without backpressure or scaling policies

Implementation checklist

• Make workflows idempotent so retries are safe
• Define dead-letter/poison message handling and alerting
• Use backpressure and scaling policies to avoid runaway backlogs
• Encode ordering assumptions explicitly (don’t rely on luck)

Operational notes

Use correlation IDs, record state transitions, and surface per-step outcomes so a single failed step is visible.

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