AWS RDS to EDB Migration Guide: Boost PostgreSQL Scalability

There are several advantages to migrating from AWS RDS to EnterpriseDB (EDB), namely cost savings, flexibility, performance, and support:

Cost Savings

While AWS RDS operates on a usage-based pricing structure, EDB offers a subscription-based pricing model. This model eliminates the per-processor licensing fees of proprietary databases, leading to long-term savings, especially for large-scale operations. Moreover, EDB’s pricing structure can reduce total cost of ownership (TCO), allowing easier budget management. EDB also minimizes administrative overhead through automated maintenance and resource management.

Deployment and Customization Flexibility

EDB supports more database options and versions than AWS RDS. Its open source nature also allows more customization options, such as defining custom data types and integrating third-party tools. Furthermore, EDB's ability to deploy across hybrid and multi-cloud environments enhances organizational agility, leveraging the cloud without vendor lock-in.

Better Performance

EDB Postgres® AI Cloud Service uses a proprietary storage architecture optimized for resilience and scalability across availability zones. Furthermore, EDB can achieve higher availability than RDS, promising up to 99.995% uptime compared to RDS’s 99.95% SLA.

Ongoing Support

EDB users enjoy comprehensive support services, including technical guidance during migration, ongoing system maintenance, and a focus on optimizing database performance. Organizations can ensure that mission-critical workloads remain uninterrupted and efficient with support from EDB experts.

A comprehensive migration strategy from AWS RDS to EDB involves multiple steps to ensure that the transition is seamless and meets organizational objectives.

Step 1: Assessment and Planning

• Inventory analysis: Identify all databases, schemas, and functionalities used in AWS RDS. Assess the specific RDS instance types, sizes, and configurations currently used.
• Data volume assessment: Determine the volume of data stored in the current AWS RDS instance, analyzing growth rates and expected future needs.
• Dependency mapping: Evaluate application dependencies linked to RDS (e.g., app code, integrations, third-party tools).
• Migration goals definition: Clearly outline the migration objectives, including cost reduction, enhanced performance, and the flexibility to scale effectively.
• Risk identification: Identify potential risks and challenges involved in migration, including data loss, downtime, and compatibility issues.

Step 2: Schema and Data Migration

• Schema assessment: Analyze the schema in AWS RDS and prepare it for migration by ensuring compatibility with EDB standards. Tools such as EDB’s Migration Portal can assist in assessing the compatibility of database schemas with EDB Postgres Advanced Server.
• Data migration tools: Utilize tools such as EDB’s Migration Toolkit and Replication Server to migrate data from RDS to EDB Postgres Advanced Server. These tools facilitate snapshot-based or continuous data migration techniques.
• Data transformation: Modify data types and SQL syntax to ensure compatibility with EDB. Conduct pre-migration data-cleansing to identify and rectify discrepancies or redundant data.
• Data transfer: Perform the actual data migration using selected tools while ensuring minimal services interruption. Consider a phased migration approach to reduce risks associated with downtime.

Step 3: Application Migration

• Code adaptation: Review and modify application code to accommodate EDB’s specific functionalities and conventions. This may require changes in database connectivity and query syntax.
• Testing connectivity: Ensure that all application interfaces are properly connected to the new EDB database and function as expected. Use EDB-compatible drivers and connectors for smooth integration.
• Compatibility checks: Validate that the application can effectively manage data transactions, leveraging EDB-specific features where necessary.

Step 4: Testing and Validation

• Post-migration testing: Conduct functional and performance testing to ensure that all applications interact correctly with the EDB database. Include user acceptance testing (UAT) to get feedback from stakeholders and identify any issues.
• Cross-check data integrity: Utilize automated testing tools to validate that migrated data matches the original data sets and maintains integrity.

Step 5: EDB Deployment Options

• Kubernetes operators: For a containerized environment, use the EDB Postgres for Kubernetes operators to manage workloads effectively. This option provides high availability and scalability through native streaming replication.
• On-premises deployment: Consider on-prem deployments for organizations requiring greater control over their database environment. This option allows high customization and adherence to internal policies.
• Cloud deployments: Explore public cloud options, where EDB services such as EDB Postgres AI Cloud Service provide fully managed solutions.
• Hybrid and private cloud: Implement hybrid deployments that leverage on-prem and cloud capabilities to optimize performance while maintaining compliance requirements.

Step 6: Deployment and Optimization

• Go-live preparation: Establish a detailed cutover plan to transition from AWS RDS to EDB with minimal disruption. Include timelines outlining when systems should come online and when legacy systems will be retired.
• Monitoring and performance optimization: Post-migration, employ tools such as EDB Postgres Enterprise Manager for ongoing performance monitoring and optimization. Set up alerts for performance thresholds and implement metrics monitoring for resource consumption.
• Continuous improvement: Gather user feedback to enhance database configurations and application performances continuously. Regularly audit systems for compliance and integrity.

To ensure a successful transition from AWS RDS to PostgreSQL and maintain ongoing database health, it is essential to incorporate best practices across several key areas, including performance tuning, monitoring, backup and recovery, high availability, security, and updates.

Performance Tuning

• Hardware configuration: Ensure that adequate CPU, RAM, and disk resources are tailored to your workload.
• PostgreSQL configuration: Adjust key configuration parameters based on your specific workload.
• max_connections: Set according to the expected number of concurrent users.
• shared_buffers: Allocate up to 25% of total system RAM for caching data.
• maintenance_work_mem: Increase for maintenance operations, such as indexing, to enhance performance.
• WAL tuning: Optimize write-ahead logging (WAL) settings to reduce performance overhead during write operations. Consider parameters such as checkpoint_timeout and max_wal_size to balance data integrity and performance.
• Indexing: Create appropriate indexes on frequently queried columns to speed up data retrieval while being mindful of the overhead.
• Vacuuming: Regularly use the VACUUM command to reclaim storage by removing obsolete records. Configure autovacuum to run automatically, based on your database’s activity.

Regular Monitoring

• pgAdmin: This provides graphical monitoring and management of PostgreSQL databases, including server activity and performance metrics.
• pgBadger: This analyzes PostgreSQL log files to highlight slow queries and performance bottlenecks.
• Prometheus or Zabbix: This is used for comprehensive performance metrics and alerting capabilities.

Backup and Recovery

• Logical backups: Use pg_dump for logical backups of your databases. This allows easier migrations and robust recovery options.
• Physical backups: Utilize pg_basebackup for full data directory backups, which can be critical during disaster recovery scenarios.
• Incremental backups: Implement incremental backups to reduce backup times and storage requirements. This enables backups to include only changes from the last backup.
• Regular testing: Regularly test your backup restoration process to ensure that backups can be restored quickly and accurately during data loss. This should be part of your routine maintenance.
• WAL archiving: Implement continuous archiving of WAL files for point-in-time recovery (PITR), allowing the database to be restored to any specific point in time.

High Availability

• Read replicas: Create read replicas to offload read traffic from the primary instance, improving performance and availability. This setup can also allow failover during primary instance outages.
• Streaming replication: Use PostgreSQL’s built-in feature to continuously replicate data from primary to standby servers, ensuring that the standby is always up to date.
• Failover solutions: Implement automated failover processes using tools such as Pgpool or Patroni, which facilitate seamless transitions to standby servers in the event of a failure.

Security

• Access control: Utilize roles and permissions to fine-tune user access to sensitive data. Follow the principle of least privilege to minimize risks.
• Transport security: Ensure that all data transmissions between the client and server are encrypted using Transport Layer Security (TLS) to protect against eavesdropping.
• Database security measures: Enforce row-level security policies, monitor access logs, and utilize firewalls to control incoming and outgoing traffic.

Updates

• Scheduled updates: Plan routine major and minor version updates. Regular upgrades enhance security and provide new features and performance improvements.
• Auto Minor Version upgrades: Consider enabling the Auto Minor Version upgrade feature where supported in cloud environments, to ensure that your PostgreSQL instances benefit from immediate updates without manual intervention.
• Testing updates: Before applying updates, thoroughly test new versions in a staging environment to identify potential issues and ensure compatibility with existing applications and workloads.