staged-rollout

name	staged-rollout
description	Roll out a change in stages behind a feature flag with a monitoring window and a written rollback plan - use before shipping anything user-facing, instead of flipping it on for everyone at once

Staged Rollout

Overview

A change reaches all users gradually, behind a switch you can flip back, while someone watches the signals. Big-bang releases convert a small bug into a full outage. A staged rollout converts the same bug into a contained blip caught at 1% traffic.

Core principle: Every outward-facing change ships behind a flag, advances in stages with a monitoring window between them, and has a rollback plan written before launch — not improvised during the incident.

This depends on the change being observable first: see [[instrument-observability]] for the signals the monitoring window watches.

When to Use

• Any user-facing or externally-observable change (new endpoint, UI, behavior change, schema migration)
• Risky internal changes where a bad deploy degrades many users at once

Skip only for trivially reversible, low-blast-radius changes (a copy fix, an internal doc). When unsure, stage it — the cost is one flag.

Process

1. Pre-launch checklist. Confirm before any traffic shift:

   • Validation and review already passed (see [[validate]] / [[review-pr]])
   • Instrumentation is live and the dashboards read real data
   • The feature flag exists and defaults to off
   • The rollback plan is written (step 4)

2. Feature-flag gating. Put the new behavior behind a flag, default off. The flag must be flippable at runtime without a redeploy — that is what makes rollback fast.

3. Staged / canary rollout. Advance through stages, not in one jump. A typical ramp:

   Stage	Audience	Hold for
   Canary	1% (or internal/dogfood)	a monitoring window
   Early	10%	a monitoring window
   Majority	50%	a monitoring window
   Full	100%	—

   Monitoring window: between each stage, watch the RED metrics and alerts long enough to span real traffic before promoting. Do not promote on a clean dashboard you've watched for thirty seconds. If a symptom breaches, stop and roll back — do not push forward hoping it settles.

4. Written rollback plan. Before launch, document: the exact trigger conditions (which metric/alert at which threshold), the precise rollback action (flip flag X to off; revert migration Y), who can execute it, and the expected recovery time. A rollback that requires a redeploy is too slow — prefer the flag.

Rationalizations

Excuse	Reality
"It's a small change, ship to everyone"	Small changes cause large outages. The flag costs minutes; the outage costs hours.
"I'll write the rollback plan if something breaks"	Mid-incident is the worst time to design recovery. Write it cold, before launch.
"Dashboard looked clean, I promoted right away"	A clean dashboard watched for seconds proves nothing. Hold the window across real traffic.
"Rollback is just redeploy the old version"	A redeploy is minutes of continued damage. A flag flip is seconds. Gate behind a flag.
"Canary at 1% will take forever"	1% surfaces the catastrophic bugs cheaply. That's the point.

Red Flags

• Going straight to 100% with no canary stage
• A flag that needs a redeploy to toggle
• Promoting between stages without watching the metrics in between
• No documented trigger condition or named rollback action before launch
• "We'll roll it back by reverting the commit" as the entire plan

Verification

• Pre-launch checklist complete; flag exists and defaults to off
• The change is gated behind a runtime-flippable feature flag
• A staged/canary ramp is defined with a monitoring window between stages
• A written rollback plan exists naming trigger conditions, the exact rollback action, the owner, and expected recovery time
• The plan was written before launch, not after the first stage