Category: Best Practices

In this post, we provide a comprehensive guide for addressing performance considerations when migrating Oracle databases from Exadata to Amazon RDS for Oracle. We explore methods to analyze Exadata workload characteristics, including determining Smart IO usage, examining database-level I/O patterns, and identifying SQLs that utilize Exadata-specific features. We also discuss various alternatives available on RDS for Oracle to mitigate potential performance impacts.

Recently, Amazon RDS launched the ability to shrink storage volumes using Amazon RDS Blue/Green Deployments – a nice addition to the list of new use cases that Blue/Green Deployments now supports. In this post, we cover how to use the new storage volume shrink feature in Amazon RDS Blue/Green Deployments to minimize the downtime required to perform the storage size reduction operation. We also review various mechanisms to monitor the progress of storage shrink and best practices on how to arrive at the optimal storage size for your shrink storage task.

You can create an Amazon RDS for Db2 instance by using the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS CloudFormation, Terraform by Hashicorp, AWS Lambda functions, or other methods. One of the prerequisites for creating an RDS for Db2 instance is to configure the virtual private cloud (VPC) appropriately. This post shows how to create a VPC with best practices for any Amazon RDS database in general and Amazon RDS for Db2 in particular through a one-click automated deployment.

With the recent addition of physical replication as an option for RDS Blue/Green Deployments, you can overcome most of the limitations of logical replication. This makes physical replication particularly well-suited for use cases like minor version upgrades, schema changes (DDL operations) in the blue environment, and storage adjustments. In this post, we delve into the advantages of using physical replication in RDS for PostgreSQL blue/green deployments to simplify database operations and scale with application demands. We explore the key benefits of physical replication and provide a step-by-step guide to help you get started with this new capability.

Flo is the largest app in the Health and Fitness category worldwide, with 70 million monthly active users. In this post, we explain best practices Flo implemented to scale to more than 70 million monthly active users while achieving 60% cost efficiency with Amazon DynamoDB.

The Amazon RDS for Oracle User Guide provides comprehensive coverage of the maintenance activities in Amazon RDS for Oracle. However, it could be cumbersome to quickly learn about the best practices around various maintenance activities in Amazon RDS for Oracle from the user guide. In this post, we describe the key maintenance activities and the best practices to be followed for each of them.

In Part 1 of this two-part blog post series, we focused on understanding how certain Amazon Aurora Serverless v2 database parameters influence the scaling of Aurora capacity units (ACUs) to its minimum and maximum amounts. This post is Part 2, and it focuses on understanding how the minimum and maximum configuration of ACUs impacts scaling behavior in Aurora Serverless v2 and how fast scaling occurs after it starts.

The unit of measure for Aurora Serverless v2 is the Aurora capacity unit (ACU). Each workload has unique minimum and maximum ACU requirements. Finding the right ACU configuration and understanding factors influencing Aurora Serverless v2 scaling is essential. This post is Part 1 of a two-part blog post series and focuses on understanding how certain database parameters impact Aurora Serverless v2 scaling behavior for PostgreSQL-compatible DB instances. This post considers minimum ACU to be 0.5 or higher and does not include the new automatic pause feature.

By combining DynamoDB streams with Lambda, you can build responsive, scalable, and cost-effective systems that automatically react to data changes in real time. In this post, we explore best practices for architecting event-driven systems using DynamoDB and Lambda. DynamoDB provides two options for capturing data changes (CDC): DynamoDB streams and Amazon Kinesis Data Streams (KDS). In this post, we focus exclusively on DynamoDB streams.

This is a guest post written by Jon Haddad an Apache Cassandra committer specializing in performance tuning, fixing broken clusters, and cost optimization. In this post, we discuss the basics of improving the performance of Amazon EBS with Cassandra to take advantage of the operational benefits. We explore some basic tools used by Cassandra operators to gain insight into key performance metrics. You can then apply these metrics to modify key operating system (OS) tuneables and Cassandra configuration. Finally, we review benchmarks on performance gains by implementing best practices for Amazon EBS.