Category: Best Practices

In this post, we discuss methods to effectively monitor parallel replication performance and tune its related parameters for Amazon Aurora MySQL and Amazon Relational Database Service for MySQL and MariaDB.

In this first post, we dive into the world of MySQL replication, with a special focus on parallel replication techniques. We start with a quick overview of how MySQL replication works, then explore the intricacies of multithreaded replication. We discuss key configuration options and best practices for optimization.

In this post, we share infrastructure-level optimizations, RDS-specific performance features, and database design patterns to help improve MySQL performance on Amazon RDS. We focus on practical configurations and monitoring techniques that complement existing parameter tuning documentation, helping you make informed decisions for your specific workload requirements.

In this post, we explore the challenges of OID exhaustion in PostgreSQL, focusing on its impact on TOAST tables and how it leads to performance issues. We will cover how to identify the problem by reviewing wait events, session activity, and table usage. Additionally, we discuss practical solutions, from cleaning up data to more advanced strategies such as partitioning.

You can access an Amazon Aurora DSQL cluster by using a public endpoint and AWS PrivateLink endpoints. In this post, we demonstrate how to control access to your Aurora DSQL cluster by using public endpoints and private VPC endpoints through PrivateLink, both from inside and outside AWS.

Amazon ElastiCache now supports Bloom filters: a fast, memory-efficient, probabilistic data structure that lets you quickly insert items and check whether items exist. In this post, we discuss two real-world use cases demonstrating how Bloom filters work in ElastiCache, the best-practices to implement, and how you can save at least 90% in memory and cost compared to alternative implementations. Bloom filters are available in ElastiCache version 8.1 for Valkey in all AWS Regions and at no additional cost.

In this post, we show you how Amazon Aurora DSQL powers modern gaming use cases from real-time multiplayer interactions to globally consistent leaderboards by delivering seamless scalability, strong consistency and built-in multi-region availability.

For encrypting data at rest, Amazon RDS for Oracle offers two choices: AWS KMS and Oracle TDE. Although both AWS KMS and Oracle TDE provide encryption at rest capabilities, there are various factors to consider when choosing between them, such as licensing, edition dependency, encryption granularity, and feature restrictions. In this post, we provide guidance on choosing between the AWS KMS and Oracle TDE options for encrypting data at rest in RDS for Oracle, focusing on these key aspects.

Amazon DynamoDB is a fully managed, serverless NoSQL database that delivers single-digit millisecond performance for applications at any scale. DynamoDB global tables is a multi-active database feature that replicates data across AWS Regions, enabling local reads and writes. In this post, we explain why we strongly recommend all customers use the Current version for all global tables.

In this series of posts, we walk through the process of creating a DynamoDB data model using an example multi-tenant application, a customer issue tracking service. The goal of this series is to explore areas that are important for decision-making and provide insights into the influences to help you plan your data model for a multi-tenant application. In this last part of the series, we explore how to validate the chosen data model from both a performance and a security perspective. Additionally, we cover how to extend the data model as new access patterns and requirements arise.