Aurora PostgreSQL Archives

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.

Tech Optimizer

November 7, 2025

PostgreSQL 13’s Sunset: Why Database Pros Must Upgrade Before the Lights Go Out

Organizations using PostgreSQL 13 must upgrade before its end-of-life on November 13, 2025, as this will result in the cessation of security patches, bug fixes, and official support. Continuing to use an unsupported version exposes systems to vulnerabilities, which can lead to data breaches and compliance challenges. The last minor release for PostgreSQL 13 was 13.21 in May 2025. Upgrading to newer versions, such as PostgreSQL 16 or 17, offers performance improvements and enhanced features. Strategies for upgrading include using tools like pg_upgrade, pg_dump/pg_restore, and logical replication to minimize downtime. Compatibility issues may arise due to deprecated functions in PostgreSQL 13, necessitating code reviews. Managed services like those from Percona can provide support beyond EOL. The costs of not upgrading can be significant, with potential downtime from security breaches often exceeding migration expenses.

Tech Optimizer

November 4, 2025

PostgreSQL as a JSON database: Advanced patterns and best practices

Modern applications require adaptable data models, particularly in e-commerce, where product catalogs frequently change. JSON (JavaScript Object Notation) provides flexibility by allowing data models to evolve without rigid schemas. PostgreSQL supports JSON through two data types: JSON and JSONB. JSON stores text as-is but lacks indexing, while JSONB stores data in an optimized binary format, allowing for efficient processing and indexing. PostgreSQL is suitable for various applications, including web and mobile apps, content management systems (CMS), scientific research, geospatial applications, and those requiring advanced search features. In a CMS context, PostgreSQL's JSON capabilities can manage both structured and unstructured data, simplifying the retrieval of article information. The database structure can accommodate both structured and semi-structured data using JSONB for metadata. GIN indexing enhances performance for searching JSON fields. PostgreSQL allows for complex querying of JSON data, including full-text search and transforming JSON into tabular data using JSON_TABLE. Optimizing GIN indexes and employing strategies for efficient querying, such as using expression and partial indexes, can improve performance. PostgreSQL can integrate with Amazon OpenSearch Service for advanced search capabilities. AWS-hosted PostgreSQL services, like Amazon RDS and Aurora, provide additional benefits, such as distributed storage architecture and parallel query processing. Best practices for using PostgreSQL’s JSON capabilities include strategic data modeling, effective indexing strategies, query optimization, and considerations for schema evolution and storage management.

Tech Optimizer

November 1, 2025

Amazon Aurora makes PostgreSQL Serverless generally available

The PostgreSQL-compatible edition of Aurora Serverless is now generally available, allowing users to create database instances that operate only when necessary and automatically adjust capacity based on demand. Customers are billed on a per-second basis for active database capacity and standard Aurora storage costs. Users can specify minimum and maximum capacity when creating a database, and the architecture separates storage from computational resources, allowing automatic scaling from a minimum of 10GB to a maximum of 64 TB. Aurora Serverless PostgreSQL is accessible in regions including US East (N. Virginia and Ohio), US West (Oregon), EU (Ireland), and Asia Pacific (Tokyo).

Tech Optimizer

October 31, 2025

AI-powered tuning tools for Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL databases: PI Reporter

AWS offers services like Amazon CloudWatch and CloudWatch Database Insights for monitoring Amazon RDS performance metrics. Users can create custom dashboards for Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL-Compatible Edition. Essential metrics to monitor include Database Connections, CPU Utilization, Freeable Memory, and various I/O metrics. PI Reporter is an open-source tool designed to capture performance metrics and workload snapshots for Amazon Aurora PostgreSQL-Compatible Edition. It integrates with Amazon Bedrock to analyze data and provide actionable recommendations. Users can generate detailed HTML reports comparing performance metrics over time, assess workload changes, and identify performance issues. To use PI Reporter, prerequisites include enabling Amazon CloudWatch Database Insights and the pg_stat_statements extension. Installation involves cloning the repository, configuring IAM policies, and executing commands to create snapshots and reports. The tool is designed for ease of use and can be run on Amazon EC2 or on-premises. The report generated by PI Reporter includes a summary of findings, root causes, and recommendations for performance improvement. It also identifies resource utilization issues during specific operations, such as bulk data inserts or idle transactions. Users are advised to validate AI-generated recommendations with database experts and to monitor for idle transactions to optimize performance. Costs may be incurred from resources created by Amazon Bedrock, and users should clean up these resources when no longer needed.