AWS Database Blog
Category: Advanced (300)
Implement a correctness-safe Bloom filter lookup with Amazon ElastiCache for Valkey and Amazon Aurora PostgreSQL
This post shows how to compose a Bloom filter with an exact-match cache and a relational source of truth into a three-tier, correctness-safe membership lookup using Amazon ElastiCache for Valkey and Amazon Aurora PostgreSQL, serving sub-millisecond decisions at peak throughput without false-positive risk.
How Intuit and AWS systematically improved resiliency on ElastiCache using AWS Fault Injection Service
Learn how Intuit and AWS validated Amazon ElastiCache resilience under a real Availability Zone impairment using AWS Fault Injection Service, cutting recovery from over 50 minutes to under 2 minutes with no manual intervention and reducing customer impact to effectively zero.
Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2
Part 1 showed how row lock contention degrades Amazon Aurora PostgreSQL throughput. In Part 2, use Amazon CloudWatch Database Insights and its Lock Tree to pinpoint blocking sessions, then resolve contention with query termination, timeout parameters, and architectural patterns such as SKIP LOCKED and row splitting that restore throughput.
Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 – Understanding row lock contention in PostgreSQL
Row lock contention can collapse database throughput during a flash sale even when CPU and I/O look healthy. In Part 1 of this series, learn how PostgreSQL row locking works and how to monitor lock contention in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL using system views, the pgrowlocks extension, and the log_lock_waits parameter.
Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL
Run ad hoc SQL analytics on your Amazon DynamoDB data with DuckDB. A zero-ETL integration replicates your table into Apache Iceberg tables on Amazon S3 Tables, and an AWS Lambda function running DuckDB serves SQL queries through an IAM-authorized function URL.
Building async Python applications with Tortoise ORM and Amazon Aurora DSQL
Build a high-concurrency async Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. This post walks through the key adaptations: UUID primary keys, IAM-authenticated asyncpg connections with a connection-pool patch, individual DDL execution, and optimistic concurrency control (OCC) retry logic.
Troubleshoot AWS Advanced JDBC Wrapper configuration for Aurora Global Database write forwarding
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.
Introducing strands-dynamodb-storage: Durable agent storage for the Strands Agents SDK
Announcing strands-dynamodb-storage, an open source Amazon DynamoDB storage backend for the Strands Agents SDK. Back a Strands agent’s session state, long-term memories, and transcripts with one DynamoDB table in your own account, and give the agent semantic recall with a vector index on that same table.
SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS
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.
Provision a secure Amazon DocumentDB cluster with Terraform
Learn how to provision a secure Amazon DocumentDB cluster using Terraform. This post applies infrastructure-as-code best practices with encryption, AWS Secrets Manager authentication, network isolation, and encrypted monitoring.









