MySQL to PostgreSQL Production Migration: AI-Optimized Technical Guide

Executive Summary

Reality Check: Plan for 3-6 months for complete production migration, not the "2-week estimate" from project management. Every DBA has horror stories about "simple" migrations turning into 6-month projects.

Critical Failure Points:

• Data quality issues cause 40% of initial migration failures
• Missing or corrupted data in 60% of migrations due to forgotten databases
• Performance degradation requiring emergency rollbacks
• Zero downtime is marketing bullshit - plan for 10-30 minutes minimum

Timeline and Resource Requirements

Actual vs Estimated Timeline

Phase	Estimated	Reality	Multiplier
Planning & Assessment	1 week	2-4 weeks	3x
Tool Setup & Testing	3 days	2-3 weeks	7x
Data Migration	1 week	1-4 weeks	3x
Application Testing	2 weeks	4-8 weeks	3x
Production Cutover	2 days	1-2 weeks	5x

Total Reality: 3-6 months vs initial 2-week estimates

Resource Requirements

• Dedicated DBA time: 80-100% for 2-3 months
• Application developer time: 40-60% for 4-6 weeks
• DevOps engineer time: 20-30% throughout project
• Budget multiplier: Plan for 3x initial cost estimates

Tool Comparison Matrix

Tool	Database Size Limit	Downtime	Setup Complexity	Monthly Cost	Failure Rate
pgloader	< 1TB	2-12 hours	Medium	Free	Medium
AWS DMS	< 5TB	30min-2hrs	High	$1,500-8,000	Low
Foreign Data Wrapper	< 500GB	< 30 minutes	High	Free	High
Custom Scripts	Unlimited	Variable	Very High	Dev time	Very High

Tool Selection Criteria

Use pgloader when:

• One-time migration with complex data types
• Linux/Docker expertise available
• Budget constraints (free)
• Database size < 1TB

Use AWS DMS when:

• Already on AWS infrastructure
• Need ongoing replication
• Can afford $1,500-8,000/month cost
• Require minimal downtime

Avoid custom scripts unless:

• Heavy data transformation required
• Existing tools can't handle specific edge cases
• Dedicated database engineering resources available

Critical Data Quality Issues

MySQL-Specific Problems That Break PostgreSQL

MySQL Issue	PostgreSQL Impact	Fix Required
0000-00-00 00:00:00 dates	Migration failure	Convert to 1970-01-01 00:00:00
TINYINT(1) as boolean	Data type mismatch	Convert 0/1 to true/false
ENUM types	Rigid implementation	Create CHECK constraints
Case-insensitive comparisons	Query result differences	Add case-insensitive indexes
AUTO_INCREMENT sequences	Out-of-sync sequences	Reset sequences to max values

Data Validation Queries

-- Find invalid dates (will crash PostgreSQL)
SELECT COUNT(*) FROM table WHERE date_column = '0000-00-00 00:00:00';

-- Find potential encoding issues
SELECT COUNT(*) FROM table WHERE column_name REGEXP '[^\\x00-\\x7F]';

-- Check constraint violations
SELECT * FROM child_table c
LEFT JOIN parent_table p ON c.parent_id = p.id
WHERE p.id IS NULL;

Migration Day Critical Procedures

Pre-Migration Configuration

PostgreSQL Memory Settings for Migration:

shared_buffers = '25% of total RAM'
work_mem = '256MB'                    # Higher during migration
maintenance_work_mem = '2GB'          # Critical for index creation
effective_cache_size = '75% of total RAM'
max_wal_size = '10GB'                # Prevent checkpoint storms

MySQL Preparation:

-- Fix zero dates before migration
UPDATE table SET date_column = '1970-01-01 00:00:00'
WHERE date_column = '0000-00-00 00:00:00';

-- Handle boolean conversions
UPDATE table SET boolean_column = CASE WHEN tinyint_column = 0 THEN FALSE ELSE TRUE END;

-- Fix foreign key issues
DELETE FROM child_table WHERE parent_id NOT IN (SELECT id FROM parent_table);

pgloader Configuration

LOAD DATABASE
    FROM mysql://user:password@mysql_host:3306/source_db
    INTO postgresql://user:password@pg_host:5432/production_pg

WITH include drop, create tables, create indexes, reset sequences,
     workers = 8, concurrency = 2,
     multiple readers per thread, rows per range = 50000

CAST type tinyint(1) to boolean drop typemod,
     type datetime to timestamp drop default drop not null using zero-dates-to-null,
     type enum when (= 'active') to boolean drop typemod

Common Failure Scenarios and Solutions

Sequence Synchronization Issues (90% of migrations)

Problem: Auto-increment sequences out of sync, causing primary key violations
Solution:

DO $$
DECLARE
    rec RECORD;
    max_val INTEGER;
BEGIN
    FOR rec IN
        SELECT sequence_name, table_name, column_name
        FROM information_schema.sequences
    LOOP
        EXECUTE 'SELECT COALESCE(MAX(' || rec.column_name || '), 1) FROM ' || rec.table_name INTO max_val;
        EXECUTE 'ALTER SEQUENCE ' || rec.sequence_name || ' RESTART WITH ' || (max_val + 1);
    END LOOP;
END
$$;

Performance Degradation (Queries 10x slower)

Root Causes:

1. Missing indexes (not migrated properly)
2. Outdated table statistics
3. PostgreSQL configuration differences

Solutions:

-- Update statistics immediately after migration
ANALYZE VERBOSE;

-- Check if indexes migrated properly
SELECT schemaname, tablename, indexname, indexdef
FROM pg_indexes WHERE schemaname = 'public';

-- PostgreSQL-specific optimizations
SET random_page_cost = 1.1;  -- For SSD storage
SET effective_io_concurrency = 200;

Foreign Key Violations

Problem: MySQL allows broken referential integrity, PostgreSQL enforces it
Solution:

-- Temporarily disable constraints during migration
SET session_replication_role = replica;
-- Run migration
SET session_replication_role = DEFAULT;

Validation and Rollback Procedures

Data Validation Checklist

1. Row count validation across all tables
2. Business logic calculations must match exactly
3. Foreign key relationships integrity check
4. Performance benchmarking against MySQL baseline

Rollback Decision Triggers

Immediate rollback required when:

• Any missing or corrupted data discovered
• Average query response time > 2x MySQL baseline
• Error rate > 5% increase from baseline
• Database connections exhausted
• Critical business functions not working

Emergency Rollback Script

#!/bin/bash
# Stop applications immediately
sudo systemctl stop your-web-app

# Revert database configurations
cp /etc/app/database_config_mysql.yml /etc/app/database_config.yml

# Restart with MySQL connection
sudo systemctl start your-web-app

# Verify rollback success
curl -f http://your-app-domain.com/health/

Cost Analysis

Hidden Costs Not in Initial Estimates

• AWS DMS: $1,500-8,000/month during migration
• Development time: 3-4x initial estimates due to SQL syntax differences
• Performance tuning: 2-4 weeks of DBA time post-migration
• Application code changes: Every MySQL-specific query needs modification
• Training costs: Team needs PostgreSQL expertise
• Monitoring tools: New tools required for PostgreSQL-specific metrics

Cost Comparison

Migration Size	Tool Cost	Dev Time Cost	Total Reality
< 100GB	$0-500	$20,000-40,000	$25,000-50,000
100GB-1TB	$5,000-15,000	$40,000-80,000	$60,000-120,000
> 1TB	$15,000-50,000	$80,000-150,000	$120,000-250,000

Performance Optimization Post-Migration

PostgreSQL-Specific Tuning

-- Enable query performance monitoring
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- Identify performance bottlenecks
SELECT query, mean_time, calls, total_time
FROM pg_stat_statements
ORDER BY mean_time DESC LIMIT 10;

-- Index optimization for PostgreSQL
CREATE INDEX CONCURRENTLY idx_user_email_btree ON users (email);
CREATE INDEX CONCURRENTLY idx_order_date_brin ON orders USING BRIN (created_date);

Connection Pooling Changes

PostgreSQL requires different connection pooling strategies:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'CONN_MAX_AGE': 60,  # Connection reuse
        'OPTIONS': {
            'MAX_CONNS': 20,   # Per application instance
            'MIN_CONNS': 5,
        }
    }
}

Critical Success Factors

What Actually Prevents Disasters

1. Comprehensive discovery phase - reveals forgotten databases and dependencies
2. Data quality cleanup before migration starts
3. Staged application cutover - never migrate all applications simultaneously
4. Tested rollback procedures - practice the rollback before migration day
5. Performance baseline establishment - know what you're leaving behind
6. Clear rollback criteria - objective triggers for when to abort

Management Communication Template

"We're currently in the data validation phase of the MySQL to PostgreSQL migration. This is taking longer than originally estimated because:

1. Data Quality Issues (40% of time): We discovered [X] data integrity problems that would have caused application failures.
2. Performance Optimization (30% of time): PostgreSQL's query optimizer works differently than MySQL.
3. Application Compatibility (20% of time): Converting [Z] stored procedures and [A] SQL queries.

The extra time invested now prevents data loss incidents and performance degradation forcing emergency rollbacks."

Breaking Points and Absolute Limits

When Migrations Fail Catastrophically

• Database size > 5TB: Tools become unreliable, custom solutions required
• Complex stored procedures: PostgreSQL doesn't run MySQL procedures, 100% rewrite needed
• High-frequency writes during migration: Data consistency impossible to maintain
• Legacy applications without connection pooling: PostgreSQL's connection limits will break them

Tool-Specific Failure Points

• pgloader: Memory leaks with version 3.6.7, crashes on complex data types
• AWS DMS: Silent data loss with LOB columns > 32KB, JSON transformation failures
• Custom scripts: Development time 5-10x longer than estimates, high bug rate

Recovery Time Objectives

• Rollback execution time: 15-30 minutes if properly tested
• Data restoration time: 2-8 hours depending on backup size
• Application recovery time: 30 minutes to 2 hours for configuration changes
• Business impact duration: 4-24 hours total if rollback required