Category: Amazon Aurora

Correlated subqueries can cause performance challenges in Amazon Aurora PostgreSQL which can cause applications to experience reduced performance as data volumes grow. In this post, we explore the advanced optimization configurations available in Aurora PostgreSQL that can transform these performance challenges into efficient operations without requiring you to modify a single line of SQL code.

In this post, we demonstrate how your workloads can benefit from upgrading Graviton2-based R6g and R6gd instances to Graviton4-based R8gd instances with Aurora PostgreSQL 17.5 on Aurora I/O-Optimized using an Optimized Reads-enabled tiered cache.

In this post, we show how you can turn your ideas into full-stack applications with Kiro powers for Aurora. We explore how a new innovation, Kiro powers, can help you use Amazon Aurora best practices built into your development workflow, automatically implementing configurations and optimizations that make sure your database layer is production-ready from day one.

Netflix operates a global streaming service that serves hundreds of millions of users through a distributed microservices architecture. In this post, we examine the technical and operational challenges encountered by their Online Data Stores (ODS) team with their current self-managed distributed PostgreSQL-compatible database, the evaluation criteria used to select a database solution, and why they chose to migrate to Amazon Aurora PostgreSQL to meet their current and future performance needs. The migration to Aurora PostgreSQL improved their database infrastructure, achieving up to 75% increase in performance and 28% cost savings across critical applications.

With the Letta Developer Platform, you can create stateful agents with built-in context management (compaction, context rewriting, and context offloading) and persistence. Using the Letta API, you can create agents that are long-lived or achieve complex tasks without worrying about context overflow or model lock-in. In this post, we guide you through setting up Amazon Aurora Serverless as a database repository for storing Letta long-term memory. We show how to create an Aurora cluster in the cloud, configure Letta to connect to it, and deploy agents that persist their memory to Aurora. We also explore how to query the database directly to view agent state.

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

Amazon Aurora DSQL employs an active-active distributed database design, wherein all database resources are peers and serve both write and read traffic within a Region and across Regions. This design facilitates synchronous data replication and automated zero data loss failover for single and multi-Region Aurora DSQL clusters. In this post, I discuss the individual components and the responsibilities of a multi-Region distributed database to provide an ACID-compliant, strongly consistent relational database.

In this third post of the series, I examine the end-to-end processing of the two transaction types in Aurora DSQL: read-only and read-write. Amazon Aurora DSQL doesn’t have write-only transactions, since it’s imperative to verify the table schema or ensure the uniqueness of primary keys on each change – which results them being read-write transactions as well.

In this second post, I examine Aurora DSQL’s architecture and explain how its design decisions impact functionality—such as optimistic locking and PostgreSQL feature support—so you can assess compatibility with your applications. I provide a comprehensive overview of the underlying architecture, which is fully abstracted from the user.

In this post, I dive deep into fundamental concepts that are important to comprehend the benefits of Aurora DSQL, its feature set, and its underlying components.