Category: Amazon Aurora

Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with practical guidance on index sizing, recall validation, and the scenarios where the approach works best.

Amazon Aurora DSQL offers time-based observability through Amazon CloudWatch Database Insights. Learn how the DSQL observability model, DASH, Database Insights, PromQL, and the system diagnostics AI skill help you find performance bottlenecks and connect session time directly to cost.

Stream Amazon Aurora DSQL change data capture (CDC) events into Apache Iceberg tables on Amazon S3 with Amazon Data Firehose, then query them using Amazon Athena. This post walks through a two-table design that keeps a full audit trail and a current-state view, plus deployment and a dashboard for exploring the results.

Aurora serverless now automatically adds 12 Aurora Capacity Units to its current capacity within a second, and continues scaling to 256 ACUs as your workload grows. In this post, we show how an Aurora serverless cluster responds to a sudden workload spike, and compare its throughput against a provisioned db.r8g.xlarge instance using benchmark data.

Standard upgrade paths break active Debezium CDC replication slots on Amazon Aurora PostgreSQL, forcing hours-long re-snapshots. This post shows how to use native PostgreSQL logical replication to bridge a source and target cluster and cut your Debezium connectors over to the new major version with a brief, measured write pause and no re-snapshot.

When an Amazon RDS or Amazon Aurora PostgreSQL certificate expires and client trust stores aren’t updated, connections fail without warning. This post shows how to enforce TLS for all PostgreSQL connections, configure client-side certificate verification, and deploy automated monitoring that alerts you before certificate rotation events.

Learn how to configure connection pooling for the AWS Advanced JDBC Wrapper on Amazon Aurora and Amazon RDS. This post explains how the wrapper’s external and internal pooling differ, how to choose between them, and how the JDBC-WRAPPER-CONFIGURATION-ASSISTANT helps you build the right configuration.

Learn how to set up the Amazon Aurora DSQL MCP server and use it from your AI coding assistant to run queries, evolve schemas, and check Aurora DSQL compatibility without leaving your IDE. This post walks through installation, the available MCP tools, practical integration patterns, and the security model.

Learn how to use the calling services feature in Amazon CloudWatch Database Insights to identify which applications are calling your databases and view their performance metrics, so you can pinpoint root causes and contact the right team in minutes rather than hours.

In this post, we provide practical guidance for designing applications that scale effectively with the Amazon Aurora DSQL distributed architecture. You will learn how to identify common patterns that limit scalability, apply proven design patterns that distribute workload efficiently, and implement transaction strategies optimized for Aurora DSQL. We cover primary key selection, schema design principles, indexing strategies, and multi-Region optimization, while maintaining full ACID (atomicity, consistency, isolation, and durability) compliance across AWS Regions.