Migration Planner

Execute safe, zero-downtime migrations with validation and rollback plans.

Migration Patterns

1. Feature Flag Migration (Safest)

Phase 1: Deploy new code (disabled)
Phase 2: Enable for 1% traffic
Phase 3: Ramp to 10%, 50%, 100%
Phase 4: Remove old code

2. Dual Write Migration

Phase 1: Write to both old and new
Phase 2: Backfill old → new
Phase 3: Read from new (write both)
Phase 4: Stop writing to old
Phase 5: Decommission old

3. Blue-Green Deployment

Blue (current) → Green (new)
Switch traffic: Blue → Green
Rollback available: Green → Blue

Complete Migration Plan Template

# Migration Plan: MySQL → PostgreSQL

## Overview

**What:** Migrate user database from MySQL to PostgreSQL
**Why:** Better JSON support, improved performance
**When:** Q1 2024
**Owner:** Database Team
**Risk Level:** HIGH

## Current State

- MySQL 8.0
- 500GB data
- 100K users
- 1000 writes/min
- 10,000 reads/min

## Target State

- PostgreSQL 15
- Same data model
- No downtime
- Data validation 100% match

## Phases

### Phase 1: Dual Write (Week 1-2)

**Goal:** Write to both databases

**Steps:**

1. Deploy PostgreSQL cluster
2. Create schema in PostgreSQL
3. Deploy dual-write code
4. Enable dual writes (MySQL primary, PostgreSQL secondary)

**Code:**

```typescript
async function createUser(data: CreateUserDto) {
  // Write to MySQL (primary)
  const mysqlUser = await mysql.users.create(data);

  // Write to PostgreSQL (secondary, fire and forget)
  postgres.users.create(data).catch((err) => {
    logger.error("PostgreSQL write failed", err);
  });

  return mysqlUser; // Still trust MySQL
}
```

Validation:

• Monitor PostgreSQL write success rate
• Compare row counts daily
• Alert if drift >0.1%

Rollback: Disable PostgreSQL writes

Phase 2: Backfill (Week 3-4)

Goal: Copy historical data

Steps:

1. Take MySQL snapshot
2. Run backfill script in batches
3. Validate data integrity
4. Resume from failure automatically

Script:

def backfill():
    last_id = get_last_migrated_id()
    batch_size = 1000

    while True:
        users = mysql.query(
            "SELECT * FROM users WHERE id > %s LIMIT %s",
            [last_id, batch_size]
        )

        if not users:
            break

        postgres.bulk_insert(users)
        last_id = users[-1]['id']
        save_checkpoint(last_id)

        time.sleep(0.1)  # Rate limit

Validation:

• Row count match
• Random sample comparison (1000 rows)
• Checksum comparison

Rollback: Delete PostgreSQL data

Phase 3: Dual Read (Week 5)

Goal: Validate PostgreSQL reads

Steps:

1. Deploy shadow read code
2. Read from both (MySQL primary)
3. Compare results
4. Log mismatches

Code:

async function getUser(id: string) {
  const mysqlUser = await mysql.users.findById(id);

  // Shadow read from PostgreSQL
  postgres.users.findById(id).then((pgUser) => {
    if (!deepEqual(mysqlUser, pgUser)) {
      logger.warn("Data mismatch", { id, mysqlUser, pgUser });
      metrics.increment("migration.mismatch");
    }
  });

  return mysqlUser; // Still trust MySQL
}

Validation:

• Mismatch rate <0.01%
• PostgreSQL query performance acceptable

Rollback: Remove shadow reads

Phase 4: Flip Read Traffic (Week 6)

Goal: Read from PostgreSQL

Steps:

1. Feature flag: read from PostgreSQL (1% traffic)
2. Monitor errors, latency
3. Ramp: 1% → 10% → 50% → 100%
4. Still writing to both

Code:

async function getUser(id: string) {
  if (featureFlags.readFromPostgres) {
    return postgres.users.findById(id);
  }
  return mysql.users.findById(id);
}

Validation:

• Error rate unchanged
• Latency p95 <500ms
• No user complaints

Rollback: Flip feature flag off

Phase 5: Stop MySQL Writes (Week 7)

Goal: PostgreSQL is now primary

Steps:

1. Stop writing to MySQL
2. Keep MySQL running (read-only)
3. Monitor for issues

Code:

async function createUser(data: CreateUserDto) {
  return postgres.users.create(data);
  // No longer writing to MySQL
}

Validation:

• All operations working
• MySQL not receiving writes

Rollback: Re-enable MySQL writes

Phase 6: Decommission (Week 8)

Goal: Remove MySQL

Steps:

1. Archive MySQL data
2. Shutdown MySQL cluster
3. Remove MySQL client code

Rollback: Not available (point of no return)

Validation Strategy

Data Integrity Checks

def validate_migration():
    # Row counts
    mysql_count = mysql.query("SELECT COUNT(*) FROM users")[0]
    pg_count = postgres.query("SELECT COUNT(*) FROM users")[0]
    assert mysql_count == pg_count

    # Random sampling
    sample = mysql.query("SELECT * FROM users ORDER BY RAND() LIMIT 1000")
    for row in sample:
        pg_row = postgres.query("SELECT * FROM users WHERE id = %s", [row['id']])
        assert row == pg_row

    # Checksums
    mysql_checksum = mysql.query("SELECT MD5(GROUP_CONCAT(id, email)) FROM users")
    pg_checksum = postgres.query("SELECT MD5(STRING_AGG(id::text || email, '')) FROM users")
    assert mysql_checksum == pg_checksum

Rollback Plans

Phase 1-3 Rollback (Easy)

• Disable PostgreSQL writes
• No impact to users
• Data in MySQL still valid

Phase 4 Rollback (Medium)

• Flip feature flag
• Route reads back to MySQL
• Minor user impact (seconds)

Phase 5+ Rollback (Hard)

• Must re-enable MySQL writes
• Potential data loss (writes since phase 5)
• Requires dual-write resumption

Risk Mitigation

Risk 1: Data Loss

Mitigation:

• Dual writes until validated
• Transaction logs captured
• Continuous backups

Risk 2: Performance Degradation

Mitigation:

• Load test PostgreSQL
• Query optimization
• Connection pooling

Risk 3: Schema Differences

Mitigation:

• Schema validation script
• Test migrations in staging
• Document data type differences

Communication Plan

Stakeholder Updates

**Week 0:** Migration announced
**Week 2:** Phase 1 complete (dual writes)
**Week 4:** Backfill complete
**Week 6:** Traffic shifted to PostgreSQL
**Week 8:** Migration complete

Status Dashboard

• Current phase
• Data sync status (%)
• Validation results
• Error rates

Testing Plan

Pre-Migration Testing

1. Test in development
2. Full migration in staging
3. Load test PostgreSQL
4. Validate rollback procedures

During Migration

1. Continuous monitoring
2. Automated validation
3. Manual spot checks
4. User acceptance testing

Best Practices

1. Small batches: Migrate incrementally
2. Dual write: Keep both systems synchronized
3. Feature flags: Control rollout
4. Validate continuously: Don't trust, verify
5. Rollback ready: Plan for worst case
6. Monitor closely: Track metrics
7. Communicate often: Keep stakeholders informed

Output Checklist

• Migration phases defined (5-7 phases)
• Dual write implementation
• Backfill script ready
• Validation strategy
• Feature flags configured
• Rollback plans per phase
• Risk mitigation strategies
• Communication plan
• Monitoring dashboard
• Testing checklist