AWS Database Blog

Amazon Aurora Serverless v2 now supports scaling capacity down to 0 ACUs, enabling you to optimize costs during periods of database inactivity. Aurora Serverless is an on-demand, auto scaling configuration of Aurora that automatically adjusts your database capacity based on your workload requirements. Aurora Serverless measures database capacity in Aurora Capacity Units (ACUs) billed per second. 1 […]

Migrating an Oracle application and its underlying database to the cloud can be inherently complex. Complexity is significantly amplified by various factors, including operating system compatibility, database and application version, software availability, database storage technologies such as Automatic Storage Management (ASM), and stringent business downtime requirements. AWS Application Migration Service accelerates the migration of applications to Amazon Web Services (AWS) by automatically replicating entire servers at the block level. In this post, we show you the process of migrating Oracle E-Business Suite to AWS using Application Migration Service.

Aurora Limitless is a database solution that grows and shrinks vertically and horizontally with the current workload requirements. In this post, we show you how to test performance with the common tool pgbench. This tool is used with single-node database management systems (DBMS) and is optimized for single-node use cases. As we shall see in this post, this doesn’t mean that the tool measure what we think when it comes to multi-node systems. We demonstrate how it works with Aurora Limitless. We also discuss the obstacles and opportunities you might encounter when using this tool with Aurora Limitless.

In this post, we discuss how Coinbase migrated their user clustering system to Amazon Neptune Database, enabling them to solve complex and interconnected data challenges at scale.

Amazon DynamoDB is a serverless, NoSQL, fully managed database service that delivers single-digit millisecond latency at any scale. AWS recently announced the general availability of attribute-based access control (ABAC) for Amazon DynamoDB. ABAC is an authorization strategy that defines permissions based on attributes. In AWS, these attributes are called tags. You can attach tags to […]

PostgreSQL’s ability to handle concurrent access while maintaining data consistency relies heavily on its locking mechanisms, particularly at the row level. When multiple transactions attempt to lock the same row simultaneously, PostgreSQL turns to a specialized structure called MultiXact IDs. While MultiXacts provide an efficient way to manage multiple locks on a single row, they […]

This is the second post of a two-part series. In Part 1, we explored how you can use Automatic Data Optimization (ADO) and Oracle Information Lifecycle Management (ILM) policies for data compression. In this post, we demonstrate how to use Heat Map statistics to monitor data usage and integrate this information with hybrid partitioning and ILM data movement policies to move data to more cost-effective storage solutions.

In this two-part series, we demonstrate how to optimize storage for Oracle database workloads on AWS by using Oracle’s built-in features, such as Heat Map, Automatic Data Optimization (ADO), and hybrid partitioning. These features help classify data by its lifecycle stage and automate data management tasks to significantly reduce storage costs, while enhancing database performance, especially for growing datasets. In this post, we explore how to use ADO and Oracle ILM policies to automatically compress data based on usage patterns.

In this post, we guide you through the process of benchmarking the performance of Amazon RDS for PostgreSQL using the Dedicated Log Volume (DLV) feature. To do this, we use pgbench – a tool for running benchmark tests on PostgreSQL databases, pgbench repeatedly executes a defined sequence of SQL commands across multiple concurrent database sessions. Through our benchmarking, you’ll learn how to quantify the performance improvements delivered by DLV.

Our continued engineering investments on how efficiently we can operate DynamoDB allow us to identify and pass on cost savings to you. Effective November 1, 2024, DynamoDB has reduced prices for on-demand throughput by 50% and global tables by up to 67%, making it more cost-effective than ever to build, scale, and optimize applications. In this post, we discuss the benefits of these price reductions, on-demand mode, and global tables.