Zero-Downtime Database Migration Pipeline: PostgreSQL to Aurora

Project Overview

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The Challenge

A production PostgreSQL database serving 50,000 daily active users required migration to Amazon Aurora PostgreSQL. The constraints were significant:

• No maintenance window available. The business could not accept any downtime
• 500GB of data with continuous write operations
• Strict data integrity requirements. Financial transaction data
• Reproducibility needed. The solution had to work across dev, staging, and production environments

The existing self-managed PostgreSQL infrastructure was consuming significant engineering resources for maintenance, backup management, and replication troubleshooting. Aurora would provide managed operations, improved performance, and native AWS integration.

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The Approach

We implemented a blue-green database migration strategy using AWS Database Migration Service (DMS). The solution comprises four major components:

Architecture

The migration follows a continuous replication pattern:

1. Full Load Phase: DMS performs bulk data transfer from source to target
2. CDC Phase: Change Data Capture streams ongoing modifications in near-real-time
3. Validation Phase: Automated checks verify data integrity across both databases
4. Cutover Phase: Orchestrated switchover with health checks and rollback capability

Core Components

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Technical Implementation

Phase 1: Infrastructure Foundation

We developed modular Terraform configurations for all AWS resources. The DMS module handles replication instance provisioning, endpoint configuration, and task creation:

module "dms" {
  source = "./modules/dms"

  replication_instance_class = "dms.r5.4xlarge"
  multi_az                   = true

  source_db_host     = var.source_db_host
  target_db_host     = module.aurora.cluster_endpoint

  # Table mapping and task settings
  migration_type     = "full-load-and-cdc"
  max_file_size      = 131072
  parallel_load_threads = 8
}

The Aurora module provisions the target cluster with appropriate sizing, read replicas, and parameter groups optimized for the migration workload.

Phase 2: Validation Framework

Before cutover, the system validates data integrity through multiple checks:

The validation script supports different modes for quick checks versus comprehensive verification:

# Quick validation using table statistics
python validation.py --quick

# Full validation with exact counts and checksums
python validation.py --full --output results.json

Phase 3: Cutover Orchestration

The cutover script implements a phased approach with state persistence:

State is saved to JSON after each phase, enabling resume capability if the process is interrupted.

Phase 4: CI/CD Integration

GitHub Actions workflows provide automated operations with appropriate controls:

• Terraform Plan: Runs on pull requests, posts plan output as PR comments
• Terraform Apply: Deploys infrastructure on merge to main
• Validation: On-demand data integrity checks
• Cutover: Requires manual approval for production environment

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Results and Impact

Quantitative Improvements

Operational Improvements

• Zero downtime achieved. Application availability maintained throughout migration
• Full audit trail. All validation results and cutover states preserved as artifacts
• Reproducible process. Same framework used for staging environment refresh
• Team confidence. Documented runbook and rehearsed procedures reduced stress

Migration Metrics

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Technologies Used

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Key Deliverables

1. Terraform Module Library

   • DMS replication infrastructure
   • Aurora PostgreSQL cluster configuration
   • Security group and network rules
   • CloudWatch monitoring stack

2. Python Automation Suite

   • Data validation framework
   • Cutover orchestration with state management
   • Rollback procedures
   • Database utility libraries

3. GitHub Actions Workflows

   • Infrastructure deployment pipelines
   • Validation and cutover automation
   • Approval gates for production operations

4. Operational Documentation

   • Migration runbook
   • Troubleshooting guide
   • Configuration reference

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Repository

The complete migration framework is available as open source:

GitHub: github.com/mateenali66/zero-downtime-db-migration

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About the Author

Mateen Anjum is a DevOps engineer specializing in database reliability, infrastructure automation, and cloud migrations. With experience across AWS, Kubernetes, and Terraform, he focuses on building systems that are both performant and maintainable.