Aurora PostgreSQL Archives

Tech Optimizer

April 23, 2026

Aurora Serverless: Faster performance, enhanced scaling, and still scales down to zero

Amazon Aurora Serverless offers an on-demand, auto-scaling database management solution that adjusts to workload demands, scaling up during high usage and down to zero during inactivity. The latest version, platform version 4, has a 30% performance increase over version 3 and allows users to monitor performance metrics effectively. Benchmark tests show that both Aurora MySQL and Aurora PostgreSQL on platform version 4 achieve a 27% to 34% increase in New Orders per Minute (NOPM) compared to version 3. The autoscaling mechanism in Aurora Serverless has been improved, doubling the default scaling rate and achieving maximum capacity in 22 minutes, a 45% improvement over the previous 40 minutes. A Sysbench workload test indicates that version 4 completes tasks 27% faster and at a 28% lower cost compared to version 3. Additionally, platform version 4 incorporates more metrics for better scaling decisions, enhancing performance in resource competition scenarios. To check the current platform version of an Aurora Serverless cluster, users can navigate to the Amazon RDS console or use the AWS CLI command provided.

Tech Optimizer

April 15, 2026

Connecting .NET Lambda to Amazon Aurora PostgreSQL via RDS Proxy

Many organizations are modernizing legacy .NET applications while reducing database costs and enhancing scalability, particularly during migrations to AWS Lambda and transitions from SQL Server to Amazon Aurora PostgreSQL-Compatible Edition. The process involves connecting Lambda functions to Aurora PostgreSQL using Amazon RDS Proxy, with AWS Secrets Manager managing secure credential storage. The architecture includes a .NET Lambda function interfacing with Aurora PostgreSQL via RDS Proxy, which provides connection pooling to minimize database connection overhead. The solution requires a CloudFormation template to provision resources such as a VPC with private subnets, an Aurora PostgreSQL cluster, RDS Proxy, and Secrets Manager. The RDS Proxy is configured with parameters including PostgreSQL engine family, idle client connection timeout, and Secrets Manager secret. A Windows EC2 instance is provisioned, and access is facilitated through AWS Systems Manager Fleet Manager. The solution includes creating a sample table named "employee" in the database and a new .NET Lambda project that utilizes Npgsql for database connections. The Lambda function is deployed using the AWS CLI, and testing involves invoking the function and retrieving logs from Amazon CloudWatch. Finally, resources created during the process should be deleted to prevent ongoing charges.

Tech Optimizer

April 8, 2026

PostgreSQL logical replication: How to replicate only the data that you need

Many organizations using PostgreSQL face challenges in replicating data across different logical databases while controlling shared information. PostgreSQL 15 introduces native logical replication with column-level and row-level filtering within publications, allowing precise control over data transfer between PostgreSQL databases. Logical replication operates on a publish-subscribe model, where the source database logs changes to the Write-Ahead Log (WAL) and applies publication filters to transmit only the relevant data to the target database. Two filtering modes are available: column-level filtering allows users to specify which columns to replicate, while row-level filtering uses WHERE clauses to determine which rows to include. Both filtering modes can be combined in a single publication. The SQL syntax for creating publications and subscriptions is consistent across various PostgreSQL environments, with specific configurations required for enabling logical replication. Use cases for logical replication include multi-tenant SaaS data isolation, regional data distribution for ecommerce, PCI DSS compliance in financial services, development and testing environments, and retail inventory management. Prerequisites for implementing logical replication include using PostgreSQL 15 or later, ensuring network connectivity, having a user with replication privileges, enabling logical replication on the source instance, and monitoring WAL retention. Logical replication has limitations, such as the need for a replica identity for UPDATE and DELETE operations, the non-replication of DDL changes and sequences, and potential performance overhead during initial synchronization and ongoing replication. After testing, it is advisable to clean up by removing replication resources to prevent unnecessary costs.

Tech Optimizer

March 26, 2026

Announcing Amazon Aurora PostgreSQL serverless database creation in seconds

Colin Lazier, Vice President of Databases at AWS, announced at re:Invent 2025 that developers can now create production-ready databases using Amazon DynamoDB and Amazon Aurora DSQL in seconds. AWS introduced an express configuration for Amazon Aurora PostgreSQL, allowing users to set up a serverless database in two clicks without needing an Amazon VPC network. This configuration includes preconfigured defaults, IAM authentication for passwordless access, and an internet access gateway for secure connections. Users can create an Aurora PostgreSQL serverless database via the AWS console or command-line interface, and they can customize settings during and after creation. The new setup supports additional features like read replicas and automated failover. AWS also enhanced the Free Tier program, providing up to 0 in credits for new users, and integrated Aurora with platforms like Vercel for rapid application development. Users are billed based on Aurora Capacity Units (ACUs), which scale automatically.

Tech Optimizer

March 11, 2026

Netflix Automates RDS PostgreSQL to Aurora PostgreSQL Migration Across 400 Production Clusters

Netflix has developed an internal automation platform to migrate Amazon RDS for PostgreSQL databases to Amazon Aurora PostgreSQL, reducing operational risks and downtime for nearly 400 production clusters. The platform allows service teams to perform migrations through a self-service workflow while ensuring processes like replication validation and rollback safeguards are maintained. Database access is managed through a platform-managed layer using Envoy, which standardizes mutual TLS and abstracts database endpoints, enhancing security and efficiency. The migration process starts with creating an Aurora PostgreSQL cluster as a read replica of the source RDS instance, initialized from a storage snapshot and continuously replaying write-ahead log (WAL) records. Validation checks are performed to ensure the replica can handle peak write throughput before cutover. For change data capture workloads, the system coordinates the state of replication slots and pauses CDC consumers to prevent excessive WAL retention. The Enablement Applications team at Netflix successfully migrated databases for device certification and partner billing workflows, addressing issues like elevated replication lag due to inactive logical replication slots. As replication lag decreases, the system enters a controlled quiescence phase, adjusts security rules, and reboots the source RDS instance. Once all transactions are processed and the Aurora replica is ready, it is promoted to a writable cluster, and traffic is rerouted. Rollback capabilities are prioritized, allowing redirection back to the original RDS instance if validation checks fail or anomalies are detected post-promotion. This setup enables seamless restoration without redeployment, and CDC consumers can resume from recorded slot positions if needed.

Tech Optimizer

March 4, 2026

Troubleshoot RDS PostgreSQL High CPU Utilization

Vetrivel presents a guide on troubleshooting high CPU utilization in Amazon RDS and Amazon Aurora PostgreSQL-Compatible databases, focusing on three primary techniques to identify CPU spikes: 1. **Amazon CloudWatch**: Provides real-time monitoring and operational data to visualize CPU utilization trends and identify anomalies. 2. **Enhanced Monitoring**: Offers deeper insights into performance metrics for a more granular analysis of CPU usage. 3. **Database Engine Insights**: Allows diagnosis of performance issues directly through the database engine for better workload management. The video is segmented into chapters for easy navigation, covering the methods in detail.

Tech Optimizer

February 27, 2026

Replicate spatial data using AWS DMS and Amazon RDS for PostgreSQL

The process of migrating spatial data from self-managed PostgreSQL, Amazon RDS for PostgreSQL, or Amazon Aurora PostgreSQL-Compatible Edition to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition using AWS Database Migration Service (AWS DMS) is outlined. Spatial data includes points, lines, polygons, and collections of these features, which are essential for applications like mapping and asset tracking. AWS DMS treats spatial columns as large objects (LOBs) and requires proper configuration for LOB handling and PostGIS support to avoid migration failures. PostGIS is an extension for PostgreSQL that manages geospatial data types and functions, enabling operations such as radius searches and distance calculations. Amazon RDS for PostgreSQL fully integrates with PostGIS, making it suitable for geospatial applications. The migration solution involves ensuring compatibility of PostGIS versions between source and target databases, configuring AWS DMS, and validating the migration. Prerequisites include having an AWS account, RDS instances for source and target, a DMS replication instance, and the PostGIS extension installed on both databases. Tables must have primary or unique key constraints, and geometry columns must be nullable for LOB handling. Best practices for configuring PostGIS include validating version compatibility, monitoring resource usage, and establishing backup strategies. Implementation steps involve creating a spatial table in the source database, preparing the target database, configuring the AWS DMS task with specific settings, and monitoring the migration process through CloudWatch logs. Common errors during replication may relate to LOB handling or PostGIS data types. Post-migration validation includes comparing row counts, verifying geometry data types, and ensuring the functionality of PostGIS spatial functions. Finally, it is recommended to clean up all resources post-migration to avoid ongoing charges.

Tech Optimizer

February 12, 2026

Build a custom solution to migrate SQL Server HierarchyID to PostgreSQL LTREE with AWS DMS

Data migration from SQL Server to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition often requires adjustments to the database schema or SQL commands. AWS provides DMS Schema Conversion to aid in converting existing database schemas and AWS Database Migration Service (AWS DMS) to assist in data migration, featuring enhanced security and minimized downtime. SQL Server uses the HierarchyID data type for managing hierarchical data, while PostgreSQL employs the LTREE extension for similar purposes. The migration process involves preparing both the source SQL Server and target PostgreSQL environments, creating tables, installing the LTREE extension, and converting schemas using AWS DMS Schema Conversion. The migration steps include creating sample tables in SQL Server with HierarchyID columns, enabling change data capture (CDC), creating the LTREE extension in PostgreSQL, and preparing the target table structure. AWS DMS endpoints are created for both source and target databases, followed by the creation and execution of an AWS DMS migration task. Post-migration, the original HierarchyID column is replaced with the LTREE column, and the IDENTITY column behavior is reverted to its original state. The migration process is verified by inserting rows in PostgreSQL and ensuring they are in the correct LTREE format. Common functions from SQL Server's HierarchyID are mapped to their PostgreSQL LTREE equivalents, facilitating the transition between the two systems.

Tech Optimizer

January 29, 2026

Aurora PostgreSQL 13 Deadline: Four Upgrade Paths to Beat February Cutoff

Standard support for Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL version 13 will end on February 28, 2026. PostgreSQL 13 will be deprecated by the community in November 2025, ceasing to receive bug fixes or security patches. AWS recommends upgrading to newer versions, such as 16 or 17, which offer significant performance enhancements and improved security. PostgreSQL 17 can achieve up to twice the write throughput and consumes 20 times less memory during vacuum operations. Version 16 introduces pg_stat_io for detailed I/O statistics, while version 14 includes a vacuum emergency mode. Aurora-specific enhancements in version 14.9 and later can lead to faster query latency and reduced costs. Version 14 introduces new roles for access control, and version 15 revokes certain permissions. Major upgrades in logical replication include automatic slot synchronization in version 17 and support for parallel apply in version 16. Transitioning between major versions requires careful examination of catalog changes, as some views and configuration parameters will evolve. Extensions must be verified, as most do not auto-upgrade. An in-place major version upgrade can be performed via the AWS Console or CLI, with downtime varying based on database size. AWS recommends snapshot-based testing beforehand. The CLI command can check valid upgrade targets, leading from version 13 to 14, 15, 16, or 17. Preparation involves validating instance classes and dropping replication slots. Amazon RDS Blue/Green deployments allow for near-zero downtime by synchronizing production with a staging environment, enabling application testing before traffic switching. This feature is supported from Aurora PostgreSQL version 13.12 onward. Logical replication through pglogical offers flexibility for minimal downtime, while AWS DMS supports homogeneous migration with Change Data Capture. Extended Support is available for a fee, providing up to three years of security patches. Best practices include replicating production environments in staging, conducting load tests, and validating queries against new catalogs. Recent minor releases, including Aurora PostgreSQL 17.6 and 16.10, showcase ongoing improvements. Engaging AWS Support is advisable for complex setups to ensure seamless transitions before the deadline.

Tech Optimizer

January 28, 2026

Strategies for upgrading Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL from version 13

Standard support for PostgreSQL version 13 will end on February 28, 2026. Upgrading to newer PostgreSQL versions can enhance database performance and introduce new features. Notable enhancements in recent versions include: - Vacuum emergency mode (v14+) to manage old row versions. - Improved I/O performance (v17) with better write throughput. - Enhanced query optimization (v17+) for IN clauses and parallel BRIN index builds. - Memory efficiency improvements (v17) reducing vacuum memory usage. - Advanced monitoring features like pg_stat_io (v16+) and pg_wait_events (v17+). - Logical replication improvements such as failover support and slot migration (v17+). - Developer experience enhancements including JSONB subscripting (v14+) and SQL/JSON JSON_TABLE (v17+). - Security enhancements with new roles for access control (v14+) and maintenance tasks (v17+). For Amazon Aurora PostgreSQL-Compatible, upgrading to versions v14.9+, v15.4+, v16.1+, and higher can yield performance optimizations, including faster query latency and cost savings. Changes in system catalog views and configuration parameters have occurred in PostgreSQL versions 14 to 17, impacting application compatibility. Upgrade strategies include in-place upgrades, blue/green deployments, logical replication, and using AWS Database Migration Service (AWS DMS). If an immediate upgrade is not possible, Amazon RDS Extended Support offers up to three years of continued security patches and bug fixes beyond the standard support end date.