major-domo-database/MIGRATION_METHODOLOGY.md

PostgreSQL Migration Testing Methodology

Overview

This document outlines the systematic approach for testing and refining the SQLite to PostgreSQL migration process before production deployment.

🔄 Iterative Testing Cycle

Phase 1: Discovery Testing

Goal: Identify all migration issues without fixing them

# Run discovery cycle
./test_migration_workflow.sh > migration_test_$(date +%Y%m%d_%H%M%S).log 2>&1

Process:

1. Reset PostgreSQL database
2. Run migration attempt
3. Document ALL errors (don't fix immediately)
4. Categorize issues by type
5. Assess impact and priority

Phase 2: Systematic Issue Resolution

Goal: Fix issues one category at a time

Priority Order:

1. Schema Issues (data types, constraints)
2. Data Integrity (NULL values, foreign keys)
3. Data Quality (string lengths, integer ranges)
4. Missing Dependencies (table existence)
5. Performance Issues (batch sizes, indexing)

Phase 3: Validation Testing

Goal: Verify fixes and ensure no regressions

# Run validation cycle
./test_migration_workflow.sh
python validate_migration.py

Phase 4: Production Readiness

Goal: Final verification before production

# Final comprehensive test
./production_readiness_check.sh

📊 Issue Tracking System

Issue Categories

1. Schema Compatibility

• NULL Constraints: Fields that require values in PostgreSQL
• Data Types: Type mismatches (BigInt, Varchar limits)
• Constraints: Unique, foreign key, check constraints

2. Data Integrity

• Foreign Key Violations: Missing parent records
• Orphaned Records: Child records without parents
• Referential Integrity: Cross-table consistency

3. Data Quality

• String Length: Values exceeding column limits
• Number Range: Values outside PostgreSQL type ranges
• Date/Time Format: Incompatible date representations

4. Migration Logic

• Table Dependencies: Incorrect migration order
• Batch Processing: Memory/performance issues
• Transaction Handling: Rollback scenarios

🎯 Testing Success Criteria

✅ Complete Success

• All tables migrated: 100%
• All record counts match: 100%
• Data validation passes: 100%
• Performance acceptable: < 2x SQLite query time

⚠️ Partial Success

• Core tables migrated: ≥ 90%
• Critical data intact: ≥ 95%
• Documented workarounds for remaining issues

❌ Failure

• Core tables failed: > 10%
• Data corruption detected
• Performance degradation: > 5x SQLite

📈 Progress Tracking

Test Run Template

Date: YYYY-MM-DD HH:MM
Test Run #: X
Previous Issues: X resolved, Y remaining
New Issues Found: Z

Results:
- Tables Migrated: X/Y (Z%)
- Records Migrated: X,XXX,XXX total
- Validation Status: PASS/FAIL
- Test Duration: X minutes

Issues Resolved This Run:
1. [CATEGORY] Description - Fix applied
2. [CATEGORY] Description - Fix applied

New Issues Found:
1. [CATEGORY] Description - Priority: HIGH/MED/LOW
2. [CATEGORY] Description - Priority: HIGH/MED/LOW

Next Actions:
- [ ] Fix issue #1
- [ ] Test scenario X
- [ ] Validate table Y

🔧 Development Workflow

Before Each Test Run

# 1. Document current state
git status
git add -A
git commit -m "Pre-test state: $(date)"

# 2. Reset environment  
python reset_postgres.py

# 3. Run test with logging
./test_migration_workflow.sh | tee "logs/test_run_$(date +%Y%m%d_%H%M%S).log"

After Each Test Run

# 1. Document results
cp migration_issues.md migration_issues_$(date +%Y%m%d).md

# 2. Update issue tracker
echo "## Test Run $(date)" >> migration_progress.md

# 3. Commit progress
git add -A  
git commit -m "Test run $(date): X issues resolved, Y remaining"

🚨 Critical Guidelines

DO NOT Skip Steps

• Always reset database between tests
• Always run full validation after fixes
• Always document issues before fixing

DO NOT Batch Fix Issues

• Fix one category at a time
• Test after each category of fixes
• Verify no regressions introduced

DO NOT Ignore "Minor" Issues

• All data discrepancies must be documented
• Even successful migrations need validation
• Performance issues compound in production

📋 Pre-Production Checklist

Data Verification

• All table counts match exactly
• Sample data integrity verified
• Foreign key relationships intact
• No data truncation occurred
• Character encoding preserved

Performance Verification

• Migration completes within time window
• Query performance acceptable
• Index creation successful
• Connection pooling tested

Operational Verification

• Backup/restore procedures tested
• Rollback plan verified
• Monitoring alerts configured
• Documentation updated

Security Verification

• Access controls migrated
• Connection security verified
• Audit trail preserved
• Compliance requirements met

🎯 Success Metrics

Quantitative

• Data Accuracy: 100% record count match
• Data Quality: 0 corruption events
• Performance: ≤ 2x query time vs SQLite
• Availability: < 1 hour downtime

Qualitative

• Confidence Level: High team confidence
• Documentation: Complete and accurate
• Rollback Plan: Tested and verified
• Team Training: Staff ready for operations

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This methodology ensures systematic, repeatable testing that builds confidence for production migration.