migration

name	migration
description	System migration and technology transition.

Activate When

• /godmode:migration, "migrate from X to Y"
• "convert to TypeScript", "move to microservices"
• "upgrade from React 17 to 19", "strangler fig"

Workflow

1. Migration Assessment

Source: <language, framework, architecture, data stores>
Target: <target stack and architecture>
Code size: <files, LOC, modules>
Test coverage: <percentage>
Team size: <N developers>

Type: Language | Framework | Architecture | Data | API
IF codebase > 50K LOC: use Strangler Fig (not Big Bang)
IF data-critical: use Parallel Run
IF internal component swap: use Branch by Abstraction

2. Strategy Selection

BIG BANG: rewrite all, switch over
  WHEN: <10K LOC, acceptable downtime
  Risk: HIGH — all-or-nothing
STRANGLER FIG: replace piece by piece via facade
  WHEN: large codebase, zero-downtime required
  Risk: LOW — each piece reversible
PARALLEL RUN: old+new simultaneously, compare
  WHEN: data integrity critical
  Risk: MEDIUM — double infra cost
BRANCH BY ABSTRACTION: abstraction layer, swap impl
  WHEN: internal component, same API contract
  Risk: LOW — abstraction isolates change

IF team < 3: avoid Big Bang (too risky with small team). IF match_rate < 99.0%: do NOT cutover. IF match_rate >= 99.9%: ramp 5% -> 25% -> 50% -> 100%.

3. Language/Framework Planning

JS -> TS: Phase 1: tsconfig with allowJs:true, strict:false Phase 2: Rename .js->.ts one file at a time (leaves first) Phase 3: Enable strict mode incrementally

REST -> GraphQL: Phase 1: GraphQL alongside REST (resolvers call services) Phase 2: Migrate clients one feature at a time Phase 3: Deprecate REST endpoints

Monolith -> Microservices: Phase 0: Identify bounded contexts, add module boundaries Phase 1: Extract easiest module as first service Phase 2: Feature flag, shadow traffic, ramp

4. Zero-Downtime Data Migration

Phase 1 — Dual-write: write BOTH stores, old=source
Phase 2 — Backfill: batch historical data, rate-limited
  Track: migrated/total, verify integrity per batch
Phase 3 — Cutover: read from new, stop old writes
Phase 4 — Cleanup: remove old after 2-week stability

# Verify data integrity
psql -c "SELECT count(*) FROM old_table"
psql -c "SELECT count(*) FROM new_table"
# Row counts must match within 0.01%

5. Parallel Run Verification

Route traffic to BOTH systems.
Compare outputs automatically.
Target: > 99.9% match rate before cutover.
IF mismatch > 1%: categorize, fix top 3, re-run.

6. Rollback Planning

Triggers: error rate > 1.1x baseline for 5 min,
  p99 latency > threshold for 5 min,
  data inconsistency detected.
Steps: switch traffic back, stop dual-writes,
  reconcile data, notify stakeholders, post-mortem.

7. Iterative Protocol

FOR each component (fewest dependencies first):
  1. EXTRACT + BUILD new implementation
  2. WRITE/migrate tests
  3. DEPLOY behind feature flag, shadow traffic
  4. PARALLEL RUN: target > 99.9% match
  5. IF match < 99.0%: fix and re-run
  6. IF match >= 99.9%: ramp 5%->25%->50%->100%
  7. REMOVE old after 2-week stability

# Run and verify migrations
npm run migrate:status
python manage.py showmigrations
npx prisma migrate status

Hard Rules

1. NEVER big-bang rewrite > 50K LOC.
2. EVERY step MUST have documented rollback plan.
3. NEVER migrate without tests (add characterization tests first if missing).
4. NEVER remove old system until new runs stable 2 weeks.
5. Feature flags control cutover (never deployment).

TSV Logging

Append .godmode/migration-results.tsv:

timestamp	source	target	strategy	status	match_rate

Keep/Discard

KEEP if: match > 99.9%, rollback tested,
  flags control cutover.
DISCARD if: match < 99%, no rollback,
  deployment-switched. Revert if error > 1.1x.

Stop Conditions

STOP when FIRST of:
  - Match rate > 99.9% + rollback tested
  - Data integrity verified + old system kept 2 weeks
  - User requests stop

Autonomous Operation

On failure: git reset --hard HEAD~1. Never pause.

Error Recovery

Failure	Action
Match < 99%	Categorize mismatches, fix top 3, re-run
Feature breaks	Flip flag back, add test before retry
Data integrity	Verify checksums, row counts, samples