Category: MySQL compatible

This is the second post in a series where we dive deep into performance testing MySQL environments being migrated from on premises. In Part 1, we compared the query playback and traffic mirroring approaches at a high level. In this post, we dive into the setup and configuration of query playback.

In this series of posts, we dive deep into performance testing of MySQL environments being migrated from on-premises to AWS. In this post, we review two different approaches to testing migrated environments with traffic that is representative of real production traffic: capturing and replaying traffic using a playback application, and mirroring traffic as it comes in using a proxy. This means you’re validating your environment using realistic data access patterns.

This post outlines a set of design and tuning strategies for a high-speed purge in an Amazon Relational Database Service (Amazon RDS) for MySQL DB instance and Amazon Aurora MySQL-Compatible Edition DB cluster. Purge is a housekeeping operation in a MySQL database. The InnoDB storage engine relies on it to clean up undo logs and delete-marked table records that are no longer needed for multiversion concurrency control (MVCC) or rollback operations.

Presence is a leading provider of live therapy and evaluation services for PreK-12 schools throughout the United States. Amazon RDS for MySQL has been a core part of Presence’s data architecture for many years. Presence used RDS read replicas, with replication filtering, to migrate applications from their centralized RDS for MySQL DB instance to dedicated DB instances. This approach allowed them to migrate each service, on its own schedule, with little downtime. In this post, we provide a practical example for migrating using the same method.

In this post, we discuss the steps to perform a blue/green deployment switchover and how to set up and perform a rollback strategy post switchover for Amazon Aurora MySQL-Compatible Edition.

Homogeneous data migrations in AWS DMS simplify the migration of on-premises databases to their Amazon RDS equivalents. In this post, we guide you through the steps of performing a homogeneous migration from an on-premises MySQL database to Amazon Aurora MySQL using AWS DMS homogeneous data migrations over a private network using network load balancer.

Ola Money is a financial service provided by Ola Financial Services (OFS), which is part of the Ola group of companies. In this post, we share the modernization journey of Ola Money’s MySQL workloads using Amazon Aurora, a relational database management system built for the cloud with MySQL and PostgreSQL compatibility that gives the performance and availability of commercial-grade databases at one-tenth the cost.

As customers are migrating to the AWS Cloud to take advantage of managed database services such as Amazon RDS for MySQL, Amazon RDS for MariaDB, and Amazon Aurora MySQL-Compatible Edition, they also look to automate these administrative tasks. This post shows how a DBA or other user with access to a custom API can make MySQL and MariaDB backup requests. It uses Infrastructure as Code (IaC) with the AWS CDK to simplify the deployment.

Storage in Amazon Aurora MySQL is managed differently from traditional MySQL databases. In this post, we explore the different types of storage available in Amazon Aurora MySQL, how Aurora uses those storage types, and how to monitor storage consumption. We also explore some of the database queries and Amazon CloudWatch metrics for Aurora that you can use to estimate Aurora storage billing.

In this post, we show you how to set up bi-directional replication between an on-premises MySQL instance and an Aurora MySQL instance. We cover how to configure and set up bi-directional replication and address important operational concepts such as monitoring, troubleshooting, and high availability. In certain use cases, native bi-directional binary log replication can either provide a simpler fallback plan for your migration or provide a way to migrate applications or schemas individually, rather than all at the same time.