Amazon’s Relational Database Service (RDS) is Amazon's database hosting service that allows you to run relational databases like MySQL, PostgreSQL, Oracle, SQL Server, and Db2 in the cloud. RDS handles database management tasks so you can focus on creating your apps.
While RDS is a great service for hosting cloud databases, it has some limitations for high-volume or specialized use cases, which we’ll cover later. Understanding the pros and cons of RDS can help you decide when to use the service and when to seek a different solution.
In this article, we’ll:
Take a look at the services and limitations of Amazon RDS
Learn about the various RDS alternatives available on AWS
Evaluate which database solution is the best fit for your use case
What is Amazon RDS and what can it do?
RDS is a managed database service provided by AWS released in October 2009. It simplifies the setup, operation, and scaling of relational databases in the cloud. With Amazon RDS, you can deploy and manage some of the most popular database engines with just a few clicks.
It handles routine database management tasks such as provisioning hardware, setting up the database, patching, backups, and monitoring. Like many cloud services, it enables you to focus on your applications instead of worrying about the underlying database infrastructure.
The service offers several benefits, including automatic software patching, automatic backups, read replicas for improved read performance, and multi-availability zone deployments for high availability. It also integrates with other AWS services, such as AWS Secrets Manager for automated password rotation and CloudWatch for monitoring and alerting.
Amazon RDS has several use cases, including acting as a data layer for web applications and storing logged data. Many web apps use relational databases as the backend data store. Amazon RDS provides a scalable and highly available database solution that can handle the demands of web applications with ease.
It supports popular database engines like MySQL and PostgreSQL, making it easy to migrate existing applications to the cloud. Many RDS engines also support click-button read replicas to make scaling easy and built-in observability to reduce administrative requirements.
You can also use Amazon RDS to store and manage logged data from various sources, such as user audit information, record history, or sensor data. Its scalability and durability make it suitable for handling large volumes of logged data, while its querying capabilities allow for efficient data analysis and reporting.
Downsides of Amazon RDS
Amazon makes scaling your database in RDS quite easy. With a few clicks, you can scale your database vertically by increasing the instance size. However, this approach has limits, even if your wallet doesn’t. 🙃 Realistically, it’s common to be in a situation where you need to balance database performance with costs. As we’ve written about before, RDS costs can already be complicated to start with.
Although RDS handles routine tasks like provisioning, patching, and backups, you’re still responsible for optimizing the database's performance. As your application grows and workloads become more intensive, you may need to invest significant time and effort into tuning the database, indexing, and certain maintenance tasks like vacuums for PostgreSQL RDS databases.
Additionally, as the database and throughput grow, you may need to partition your database, implement read replicas, or even move to a different solution altogether. The need to stay on top of performance also pressures the application developers to ensure queries are as efficient as possible.
When working with specialized workloads like time series or geospatial applications, RDS may offer basic support, but advanced features such as advanced compression algorithms are needed for time-series data. Even if the required features are supported, RDS may not be as efficient as a specialized database. For example, we’ve seen up to 350x faster queries with Timescale Cloud over RDS for time-series data. Data ingestion is also 44 % speedier.
The supported versions of some extensions by RDS may trail the official release by weeks or months, which in today’s world can cause problems when trying to deliver your solution.
The need to perform complex analytical queries or leverage advanced features like column-store indexes or massively parallel processing is increasing faster and faster. In some of these cases, RDS may not be the best fit. These features are often crucial for data warehousing and business intelligence applications that require high-performance querying and analysis of large datasets. As the data set grows, the storage and compute requirements might outpace what AWS can provide at a reasonable price.
RDS Alternatives
While Amazon RDS is a popular choice for hosting relational databases in the cloud, several other managed database services provide additional features and capabilities tailored to specific workloads and use cases, both internal and external to the AWS cloud.
Amazon Aurora and Timescale are two notable alternatives to RDS that offer enhanced performance, scalability, and specialized optimizations for different types of workloads.
Aurora
Amazon Aurora is a cloud-native relational database service developed by AWS. It is compatible with MySQL and PostgreSQL databases and offers enhanced performance, reliability, and scalability compared to traditional RDS instances.
Aurora was designed and built from the ground up to take advantage of the cloud, providing a distributed, fault-tolerant architecture that replicates data across multiple availability zones for high availability and durability. It also contains a number of improvements to the database engine implemented by AWS themselves.
Amazon Aurora offers up to 5x better throughput than MySQL on RDS and up to 3x better performance than PostgreSQL on RDS. The service also automatically handles tasks like database replication, failover, and recovery. It also supports automatic storage scaling, allowing seamless scaling of the storage layer as the data requirements grow.
Aurora comes in two distinct flavors; the first is a standard provisioned database. Also available is Aurora Serverless, which automatically scales compute capacity based on your application’s needs, allowing cost savings when compute requirements are low.
Timescale Cloud is a cloud-native database service built on top of PostgreSQL, specifically optimized for time-series data and analytics workloads. It extends PostgreSQL's capabilities with specialized tools and services for efficient time-series data processing, storage, and querying. The service also includes several quality-of-life improvements, like automated backups, replicas, job scheduling, and one-click forking.
Timescale Cloud is designed to address the challenges of managing and analyzing large volumes of time-series data, such as IoT sensor data, application metrics, and financial data—the managed database offers numerous performance and cost-saving advantages over traditional databases like RDS while also providing the familiarity of a PostgreSQL database.
In a direct comparison using the Time Series Benchmark Suite, ingestion is 35 % faster with Timescale than with Amazon Aurora Serverless. Columnar compression makes data storage 95 % more efficient compared to AWS Aurora. Queries also perform between 1.15x to 16x faster for time-series data in all but two categories and can be up to 81x faster in all categories using an instance with 16 CPUs.
While Amazon RDS and Aurora provide managed database services that simplify operations and scaling, other alternatives are worth considering, especially for specific workloads or requirements.
Aurora and Timescale both offer managed database services, taking the burden of maintenance, scaling, and administration off your shoulders.
While Aurora offers improved performance over standard AWS RDS while still in the AWS ecosystem, Timescale provides faster ingest, more efficient storage, more cost-effective computing, and more efficient queries for time-series analyses.
When evaluating RDS alternatives, consider your specific requirements and workload characteristics. If you need a managed PostgreSQL database service with optimizations for time-series data, Timescale could be the ideal choice. Its faster ingest rates, efficient storage, and superior query performance for time-series analysis make it a compelling option.
However, if you require support for other database engines, such as MySQL, SQL Server, or Oracle, RDS or Aurora may be the better choice. Ultimately, your decision between Timescale and Aurora (or other RDS alternatives) will depend on your specific needs, performance requirements, and workload.
Conclusion
While RDS is a popular and convenient choice for hosting relational databases in the cloud, it may not always be the best fit for every requirement, especially when dealing with high-volume or specialized workloads. Being open to exploring alternatives like Amazon Aurora and Timescale Cloud can provide significant performance and cost benefits.
Amazon Aurora is a cloud-native relational database service that offers enhanced performance, scalability, and availability compared to traditional RDS instances. It is designed to take advantage of the cloud's capabilities, providing a distributed and fault-tolerant architecture. Aurora is a compelling option for general-purpose relational database workloads inside of the AWS ecosystem.
On the other hand, Timescale Cloud is a cloud-native database service built on top of PostgreSQL, specifically optimized for time-series data and analytics workloads but suitable for everyday PostgreSQL needs. It extends PostgreSQL's capabilities with specialized tools and services for efficient time-series data processing, storage, and querying—enabling you to use good old PostgreSQL for everything.
Timescale's benchmarks demonstrate superior performance in ingesting, querying, and storing time-series data compared to alternatives like Aurora, making it an ideal choice for time-series data use cases.
Timescale provides the best-in-class performance, efficient storage, and cost-effectiveness. Its optimizations for time-series workloads, including faster ingest rates, columnar compression, and specialized query processing, can result in significant performance improvements and cost savings compared to general-purpose databases like RDS or Aurora.
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