Category: Amazon Aurora

Configuring the AWS Advanced JDBC Wrapper for Amazon Aurora Global Database with write forwarding requires Region-specific settings, and misconfiguration causes latency spikes and connection failures. This post walks through the correct dialect, plugins, host patterns, and write forwarding settings for the primary and secondary Regions.

Learn how to use AI agents with AWS DMS Schema Conversion to orchestrate SQL Server to Amazon Aurora PostgreSQL schema conversion through natural language. See how the conversion engine processes T-SQL, how the agent drives the workflow, and how to interpret and resolve CRITICAL action items using decision frameworks for common incompatibilities.

Circular role dependencies can stall or roll back a major version upgrade of Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL when you move from PostgreSQL 14 or earlier to 15 or later. Learn why this happens, how to detect it with a single pre-upgrade query, and how to clear it before you upgrade.

How CORTO scaled Amazon Aurora PostgreSQL with pgvector to 7.6 billion vectors and 2.5 billion documents in production, delivering sub-second legal search for 10,000+ law firms at 75% lower storage cost.

Migrating full-text search from SQL Server to PostgreSQL can silently change results because the engines handle text, linguistics, and accents differently. This post shows how to reproduce SQL Server full-text search on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL, covering collation, tokenization, accent-insensitive search, and synonyms.

PostgreSQL out-of-memory (OOM) events and excessive disk spilling are among the most common production incidents on Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL. Learn how PostgreSQL allocates and consumes memory, how to identify memory-intensive queries, and how to diagnose, prevent, and recover from OOM events on both engines.

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.