Managed Relational Databases with AWS RDS and Aurora

AWS has a couple of really interesting database services, which offer a managed installation of a relational database: Amazon Relational Database Service (RDS) and Amazon Aurora.

I know Aurora is technically listed as one of the available database engines for RDS, but it has some distinct characteristics and optimizations that set it apart, so I think it's worth considering separately.

In this article, we'll dive deep into the features and benefits of RDS and Aurora, explore their differences, and discuss best practices for leveraging these services effectively.

Understanding RDS and Aurora

Imagine you need to set up a relational database for your application. Traditionally, you would have to provision an EC2 instance, install your favorite database engine (such as MySQL or PostgreSQL), configure automated backups, set up monitoring tools, and deal with many other administrative tasks. This requires expertise in database administration (this is what a typical DBA does), and even for an expert it's time consuming.

Now let's consider RDS and Aurora. These managed services abstract away the complexity of database setup and management, allowing you to launch a fully configured and optimized database instance with just a few clicks. RDS supports several popular database engines, including MySQL, MariaDB, Microsoft SQL Server, Oracle, and PostgreSQL. Aurora, on the other hand, is a proprietary database engine built by AWS, compatible with MySQL and PostgreSQL, but optimized for better performance and scalability in the AWS cloud.

When you create an RDS or Aurora instance, you still have control over certain aspects of the database, such as the VPC and subnet it resides in, associated security groups, and other network-related configurations. However, AWS takes care of the heavy lifting, such as provisioning the underlying infrastructure, applying security patches, and handling backups and failover.

Amazon Aurora vs Amazon RDS: Advantages and Benefits

While RDS provides a convenient way to run popular database engines, Aurora takes it to the next level with its cloud-native optimizations. These are some of the key benefits that Aurora brings to the table, compared to RDS:

1. High Availability and Durability: Both Aurora and RDS support failover replicas, allowing you to replicate data across multiple Availability Zones (AZs) for durability, and automatically fail over in the event of an AZ failure (which makes your cluster highly available). The key difference is that in Amazon Aurora those replicas act both as read replicas and failover replicas, allowing you to serve read queries from them. With RDS, failover replicas just sit idly.

2. Improved Performance: Aurora leverages a distributed and shared storage architecture that separates compute from storage. This allows for faster and more efficient disk operations compared to RDS instances. Aurora's storage is automatically scaled based on your data growth, eliminating the need for manual storage provisioning. You can still fine-tune performance by configuring EBS volume types and IOPS.

3. Serverless Database with Aurora Serverless: Aurora Serverless automatically scales the database's compute capacity based on the workload, allowing you to pay only for the resources you consume. It works very similar to DynamoDB On Demand mode. This serverless mode eliminates the need for capacity planning and enables you to handle sudden spikes in traffic without overprovisioning and overpaying. Aurora Serverless is approximately 3 times more expensive than Aurora in serverful mode (without taking into account the reduction in unused capacity).

4. Global Databases with Aurora: With Aurora Global Database, you can create a primary database in one region and up to five replicas in different regions, allowing for multi-region failover. Aurora automatically replicates data from the primary to the secondary databases with a latency below 1 second. This allows you to build multi-region architectures that support your Disaster Recovery strategies.

Best Practices for RDS and Aurora

To make the most of RDS and Aurora, consider the following best practices:

1. Choose the Right Database Engine: If you're using MySQL or PostgreSQL, consider using Aurora for its performance optimizations and fantastic features. However, if you have specific requirements that are better suited for other database engines like Microsoft SQL Server or Oracle, RDS is still a great option.

2. Optimize Costs with Reserved Instances: If you have predictable database workloads, purchasing Reserved Instances can significantly reduce your RDS and Aurora costs. Reserved Instances offer discounts of up to 60% compared to on-demand pricing. In Aurora, Reserved Instances are flexible, meaning you can benefit from the reservation even if you move to a larger instance size.

3. Consider Aurora Serverless for Variable Workloads: For applications with variable or unpredictable workloads, Aurora Serverless can be a cost-effective solution. While it comes at a higher per-hour cost compared to regular Aurora instances, it can be cheaper if your average utilization is below 33%. Aurora Serverless also provides the flexibility to scale beyond your planned capacity without manual intervention or downtime, making it ideal for handling sudden traffic spikes.

4. Use Read Replicas for Read-Heavy Workloads: If your application has read-heavy workloads or requires separate reporting and analytics, consider adding read replicas to your RDS or Aurora instances. Read replicas allow you to offload read traffic from the primary instance, improving both read and write performance. In Aurora, read replicas also serve as failover replicas, automatically making your database cluster highly available.

5. Enable High Availability with Multi-AZ Deployments: For mission-critical applications that require high availability, configure your RDS and Aurora instances for Multi-AZ deployments. Multi-AZ creates a failover replica in a different Availability Zone, automatically failing over to the replica in case of a primary instance failure. In Aurora, replicas act as both read replicas and failover replicas, allowing you to offload read operations to the replicas.

6. Consider NoSQL Alternatives: While RDS and Aurora are excellent choices for relational databases, it's important to consider whether a relational database is the best fit for your use case. NoSQL databases like Amazon DynamoDB can handle many common scenarios with a simpler and more scalable data model, so long as you pay attention to the design. DynamoDB even supports transactions, making it a great option for applications that don't require complex joins or ad-hoc queries.

In conclusion, Amazon RDS and Aurora provide managed relational database solutions that simplify the process of running databases in AWS. RDS offers a wide range of popular database engines, while Aurora delivers a cloud-native, high-performance database compatible with MySQL and PostgreSQL.

When should I use Amazon Aurora over RDS?

The choice between RDS and Aurora is simple: Choose Aurora whenever you can, and if you can't use Aurora then use RDS. Aurora's features make it without a doubt the best choice for compatible Postgres and MySQL versions. If you need a different database engine, then RDS is the next best option.