Aurora PostgreSQL Archives

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.

Tech Optimizer

December 18, 2025

Improve Aurora PostgreSQL throughput by up to 165% and price-performance ratio by up to 120% using Optimized Reads on AWS Graviton4-based R8gd instances

AWS has introduced Graviton4-based R8gd instances for Amazon Aurora, which offer significant performance enhancements over Graviton2-based db.r6g instances. Users can expect up to 165% higher throughput, a 120% improvement in price-performance ratio, and an 80% enhancement in application response time. The integration of an Optimized Reads-enabled tiered cache allows for expanded database caching capacity, minimizing network storage access and improving query response times. Organizations like Mindbody and Claroty have reported substantial performance improvements and cost savings after implementing these features. Aurora PostgreSQL 17 has introduced smarter storage batching algorithms and optimized writes, further enhancing performance for I/O-intensive applications. The db.r8gd instances allow for scaling up to 48xl, providing 192 vCPUs, 50 Gigabits of network bandwidth, and 10.4 TiB of local NVMe capacity. The benchmark testing using HammerDB demonstrated significant improvements in throughput, price-performance ratio, and response times when upgrading from db.r6g to db.r8gd instances. The Aurora Optimized Reads-enabled tiered cache is available for Aurora PostgreSQL versions 17.4 and higher, among others.

Tech Optimizer

December 12, 2025

Optimize and troubleshoot database performance in Amazon Aurora PostgreSQL by analyzing execution plans using CloudWatch Database Insights

Amazon Web Services (AWS) launched CloudWatch Database Insights in December 2024, a monitoring solution for Aurora (PostgreSQL and MySQL) and Amazon RDS engines, aimed at identifying and resolving database performance issues. The tool includes an Advanced mode for SQL execution plan analysis, which helps users troubleshoot query performance within Aurora PostgreSQL clusters. The PostgreSQL query optimizer generates execution plans to determine the most efficient way to execute SQL queries, which can be analyzed using the EXPLAIN command. Users can compare execution plans to identify performance degradation, such as changes in execution behavior due to dropped indexes or inefficient join strategies. For example, a comparison of execution plans for a SQL statement retrieving order details showed significant differences: one plan used an efficient index scan while another resorted to sequential scans due to missing indexes, leading to increased query costs and execution times. Additionally, analysis of execution plans can reveal performance bottlenecks, such as insufficient memory allocation for sorting operations. Adjusting the work_mem parameter allowed for in-memory sorting, improving performance significantly. CloudWatch Database Insights facilitates the identification of these issues, enabling users to optimize query performance effectively.

Tech Optimizer

December 2, 2025

AWS Transform announces full-stack Windows modernization capabilities

AWS launched AWS Transform for .NET in May, aimed at modernizing .NET applications. In response to user feedback, AWS introduced AWS Transform for full-stack Windows modernization, which streamlines modernization tasks across the Windows application stack. This service enhances modernization efficiency by up to five times and supports migration from .NET Framework to cross-platform .NET and SQL Server to Amazon Aurora PostgreSQL-Compatible Edition. It includes features for validating and testing applications, customizable deployment configurations, and modernizing ASP.NET Web Forms UI to Blazor. AWS Transform connects to source code repositories and database servers to analyze dependencies and create modernization waves. Users can configure database connectors for SQL Server and source code repositories, leading to a wave-based transformation plan. The service generates a SQL Modernization Assessment Report detailing the proposed modernization strategy. The transformation process involves converting SQL Server schema to PostgreSQL, migrating data, transforming .NET application code, and deploying applications for testing. AWS Transform exclusively supports codebases in .NET 6 or later. It also facilitates UI modernization from ASP.NET Web Forms to Blazor, managing the conversion of various project files. Additional features include support for porting to .NET 10 and .NET Standard, an editable transformation report, real-time tracking of transformation progress, and a markdown file outlining remaining tasks post-transformation. AWS Transform for full-stack Windows modernization is available in the US East (N. Virginia) Region, with no additional charges for the features beyond standard resource usage. It supports SQL Server versions from 2008 R2 to 2022 and Entity Framework versions 6.3 to 8.0.

Tech Optimizer

November 30, 2025

Netflix consolidates relational database infrastructure on Amazon Aurora, achieving up to 75% improved performance

Netflix operates a global streaming service using a distributed microservices architecture, with the Online Data Stores (ODS) team managing persistent data store solutions. The ODS team aims to enhance developer productivity and operational efficiency but faced challenges due to a fragmented relational database strategy involving multiple PostgreSQL-compatible engines, leading to operational inefficiencies and increased overhead for both infrastructure and application teams. To address these challenges, Netflix evaluated database solutions based on criteria focused on developer productivity, operational efficiency, performance reliability, and scalability. After thorough evaluation, Netflix selected Aurora PostgreSQL as the preferred database solution for relational workloads. Key reasons for choosing Aurora include its ability to handle single-Region and multi-Region workloads effectively, employing asynchronous storage-based replication for low-latency operations. Aurora's log-based write operations and shared storage architecture provide higher write throughput and lower latency. As a fully managed service, Aurora eliminates the manual operational overhead associated with self-managed databases, allowing developers to leverage existing PostgreSQL expertise with minimal code changes. Post-migration results for applications like Front50 and the Policy Engine showed significant improvements in latency and stability. Front50 experienced a 50% reduction in average latency and a 70% reduction in maximum latency. The Policy Engine noted overall latency improvements across all endpoints, with specific reductions in key functions, enhancing responsiveness and operational efficiency.

Tech Optimizer

November 27, 2025

How Letta builds production-ready AI agents with Amazon Aurora PostgreSQL

AI agents benefit from persistent memory, enabling them to recall past interactions and maintain context, which enhances customer experience in scenarios like customer service. The Letta Developer Platform allows developers to create stateful agents with advanced context management features. It supports self-hosting within a virtual private cloud and uses Amazon Aurora PostgreSQL for long-term memory storage. Aurora PostgreSQL offers capabilities such as efficient similarity searches with the pgvector extension, sub-second query latency, and dynamic storage scaling. Letta operates as a persistent service, allowing agents to function independently and scale horizontally using Kubernetes. The platform supports multi-tenancy and includes enterprise features for security and monitoring. To set up an Aurora Serverless cluster for Letta, users must create a database on Amazon RDS, configure security group access, and install the pgvector extension. Letta connects to Aurora using a PostgreSQL connection string. After establishing the connection, users can create agents, send messages, and view agent states and conversation histories stored in Aurora. Letta organizes data in 42 tables, including those for agents, messages, and memory blocks. Users can explore the database schema and examine the structure of stored messages and embeddings. Finally, it is recommended to delete the Aurora cluster and associated resources after completing the integration to avoid charges.

Tech Optimizer

November 25, 2025

Protect sensitive data with dynamic data masking for Amazon Aurora PostgreSQL

Amazon Aurora PostgreSQL-Compatible Edition has introduced a dynamic data masking feature that provides column-level protection, enhancing security alongside PostgreSQL’s row-level security. This feature is available for Aurora PostgreSQL versions 16.10 and higher, as well as 17.6. Dynamic data masking allows organizations to maintain a single copy of their data while applying role-based access controls, ensuring sensitive information is protected in real-time without data duplication. The pg_columnmask extension enables policy-based masking and runtime query rewriting, allowing different users to see varying levels of data based on their roles. For example, customer service representatives may see only the last four digits of account numbers, while executives see full details. The feature supports compliance with regulations such as GDPR, HIPAA, and PCI DSS. Users can create masking policies using standard SQL commands, and the policies can be defined to utilize built-in or custom functions for data masking.