Category: DSQL

Connecting to Amazon Aurora DSQL requires a different approach than traditional PostgreSQL databases. Instead of long-lived passwords, you use short-lived IAM authentication tokens. Instead of static endpoints, you work with distributed cluster endpoints that route connections across Availability Zones. In this post, you learn how to configure connection strings, set up drivers in Python, Java, and Node.js, and implement best practices for authentication, connection pooling, and lifecycle management with Amazon Aurora DSQL.

In this post, we demonstrate how to use AWS Glue to migrate data from an Amazon Aurora database snapshot into an Aurora DSQL cluster.

In this post, we first examine why traditional approaches to distributed consistency fall short for financial workloads. We then walk through how the Amazon Aurora DSQL architecture addresses these challenges, and apply it to three production use cases: core banking, global spend management, and digital currency infrastructure. We close with implementation considerations and how to get started with the Amazon Aurora DSQL Free Tier

This post is for database architects, developers, and DBAs who must evaluate Amazon Aurora DSQL or work with PostgreSQL workloads on a distributed database. Knowing exactly where Amazon Aurora DSQL aligns with standard PostgreSQL and where it diverges helps you to reduce risk and design schemas that perform well from day one. You might find that most existing PostgreSQL applications work with minimal changes.

In this post, we walk through the steps to set up the custom migration assistant agent and migrate a PostgreSQL database to Aurora DSQL. We demonstrate how to use natural language prompts to analyze database schemas, generate compatibility reports, apply converted schemas, and manage data replication through AWS DMS. As of this writing, AWS DMS does not support Aurora DSQL as target endpoint. To address this, our solution uses Amazon Simple Storage Service (Amazon S3) and AWS Lambda functions as a bridge to load data into Aurora DSQL.

Amazon Aurora DSQL now supports PostgreSQL-compatible identity columns and sequence objects, so developers can generate unique integer identifiers with configurable performance characteristics optimized for distributed workloads. In distributed database environments, generating unique, sequential identifiers is a fundamental challenge: coordinating across multiple nodes creates performance bottlenecks, especially under high concurrency workloads. In this post, we show you how to create and manage identity columns for auto-incrementing IDs, selecting between identity columns and standalone sequence objects, and improving cache settings while choosing between UUIDs and integer sequences for your workload requirements.

In this post, we demonstrate how to efficiently migrate relational-style data from NoSQL to Aurora DSQL, using Kiro CLI as our generative AI tool to optimize schema design and streamline the migration process.

In this post, we give insights into Aurora DSQL Auto Analyze, a probabilistic and de-facto stateless method to automatically compute DSQL optimizer statistics. Users who are familiar with PostgreSQL will appreciate the similarity to autovacuum analyze.

Applications using Aurora DSQL multi-Region clusters should implement a DNS-based routing solution (such as Amazon Route 53) to automatically redirect traffic between AWS Regions. In this post, we show you automated solution for redirecting database traffic to alternate regional endpoints without requiring manual configuration changes, particularly in mixed data store environments.

In this post, I explore how Amazon Aurora DSQL uses Amazon Time Sync Service to build a hybrid logical clock solution.