AWS Aurora DSQL: General Overview

What is Aurora DSQL Anyway?

Aurora DSQL is Amazon’s innovative take on combining the familiarity of relational databases with the scalability and flexibility of serverless distributed systems. It’s designed to deliver the power of PostgreSQL while shedding the traditional infrastructure management overhead. In short, it’s a database built for modern, cloud-native applications that need to handle unpredictable workloads with ease.

Here’s the high-level pitch:

• PostgreSQL-Compatible (But Not Fully)Aurora DSQL supports PostgreSQL’s wire protocol, so you can use SQL queries and familiar tools. However, some advanced features like triggers, views, and nested transactions aren’t supported, so it’s not a one-to-one replacement for PostgreSQL.
• Truly ServerlessForget about provisioning, scaling, or patching servers. Aurora DSQL scales automatically to match your application’s needs, whether you're dealing with low traffic or a massive spike.
• Distributed and Fault-TolerantData is distributed across multiple nodes, providing high availability, fast read performance, and resilience to node failures.

Aurora DSQL is Amazon’s answer for teams who want relational database capabilities in a serverless package, making it an attractive option for elastic, high-concurrency workloads. But as with any innovation, it comes with its own set of trade-offs, which we’ll explore further.

The features we love to see:

• Serverless You don't manage, scale, and patch any servers, you only get an endpoint.
• Pay-as-you-go You only pay for what you use.
• It’s PostgreSQL-ishIf you’ve ever worked with PostgreSQL, the learning curve for Aurora DSQL is almost nonexistent. You can use familiar syntax and tools, making migrations relatively straightforward. That said, don’t expect a full PostgreSQL feature set—there are a few “missing pieces” we’ll cover in a bit.
• AWS Ecosystem Integration Being part of AWS means Aurora DSQL plays nicely with other AWS services. IAM authentication, CloudWatch monitoring, and KMS for encryption are all baked in, simplifying your DevOps workflows.

But... There Are Some Quirks

Let’s be real—Aurora DSQL isn’t perfect - like every product. There are some limitations and “gotchas” you need to know before jumping in.

Missing PostgreSQL Features:

Aurora DSQL might claim PostgreSQL compatibility, but let's be honest—it’s more like Postgre-Wire Protocol Compatible. Why? While it supports the syntax and APIs for basic SQL operations, many of PostgreSQL's powerful features are notably absent. Here's the rundown:

• No viewsIf you rely on views for simplifying complex queries, you’ll need to think a different way.

• No triggers Aurora DSQL skips one of PostgreSQL's most powerful automation tools. If you want to find an event-driven database logic, you will not find it here. 

• No sequencesAuto-incrementing primary keys is a thing of the past. UUIDs are your new best friend.

• No nested transactions
• No JSONB:  If you're storing semi-structured data, you’ll need to adapt or look elsewhere.
• No enforcement on foreign key constraints: It’s the part where you lose one of the core benefits of relational databases: referential integrity. While this helps with scalability, it shifts the responsibility of data integrity to the application layer.
• Almost No ExtensionsForget adding your favorite PostgreSQL extensions like pgcrypto, PostGIS, PGVector or hstore.
• No Geospatial or Vector Support:  Geospatial data types (geometry, geography) and vector types (useful for machine learning models or embeddings) are completely unsupported. This makes it a poor choice for applications like mapping, logistics, or AI-driven systems.
• No Support for postgre
  : If your application relies on real-time notifications or pub/sub messaging through PostgreSQL's LISTEN and NOTIFY commands, Aurora DSQL doesn’t offer any equivalent functionality.
• Indexing Trade-OffsWhile DSQL supports basic indexing, it lacks advanced indexing features like partial indexes and covering indices ( INCLUDE in PostgreSQL). This can lead to less optimized query performance for complex workloads.
• YES Vendor Lock-In While not a technical quirk, it’s worth noting that Aurora DSQL’s heavy reliance on AWS services like IAM and differences between PostgreSQL means you’re deeply tied to the AWS ecosystem. Migrating to another provider in the future could be a challenging task.

Reality Check

Calling Aurora DSQL "PostgreSQL-compatible" feels a bit generous. There’s a more accurate description for that: “PostgreSQL wire-protocol compatible.” It can speak PostgreSQL, but it doesn't deliver the full experience of a traditional PostgreSQL database. If your app relies heavily on PostgreSQL’s advanced features, you’ll need to refactor—or rethink entirely.

Optimistic Concurrency Control:

Aurora DSQL uses optimistic concurrency control to manage simultaneous updates to the same data. Instead of locking rows, it detects conflicts when they happen. If two processes try to update the same data at the same time, one will succeed while the other gets an error and needs to retry. This design removes the risk of deadlocks entirely, but it does mean your application needs to handle these errors and implement retry logic.

Transaction Row Limit:

Aurora DSQL enforces a liste. If your app relies on bulk operations, like 10.000, you’ll need to rethink how you structure those tasks. Deleting 10,000 rows? You’ll have to break it into smaller chunks and commit them.

Where Aurora DSQL Fits (and Where It Doesn’t)

Aurora DSQL shines in scenarios where scalability, elasticity, and integration with AWS services are key. It’s a strong contender for:

• Gaming PlatformsHandle low-latency transactions for real-time multiplayer games with ease.
• E-CommerceManage unpredictable traffic spikes during seasonal events like Black Friday.
• Modern SaaS ApplicationsSupport multi-tenant environments with complex query requirements.
• Real-Time AnalyticsProcess high-concurrency OLTP workloads while supporting OLAP-style queries.
• Cost-Sensitive ProjectsThe pay-as-you-go model ensures you only pay for what you use, making it attractive for startups or variable workloads.

Where It Falls Short

There are also clear situations where Aurora DSQL isn’t the best fit:

• Feature-Rich PostgreSQL ApplicationsApps relying on views, triggers, sequences, or extensions will require heavy refactoring.
• High-Batch Processing WorkloadsThe 10,000-row transaction limit can disrupt workflows like bulk data imports or exports.
• Geospatial and AI-Driven ApplicationsWithout geospatial or vector support, it’s not suitable for mapping, logistics, or ML-based recommendation engines.

Aurora DSQL vs. The Competition

When evaluating Aurora DSQL, it’s important to see how it compares to other modern database solutions regarding features, scalability, and best-fit use cases. To help you make an informed decision, here’s a quick comparison of Aurora DSQL alongside Google Cloud AlloyDB, batch ,and AuroraDB Serverless.

Conclusion: Aurora DSQL – A Step Forward with Room to Grow

Aurora DSQL represents AWS’s vision for a distributed, serverless SQL database that blends scalability with simplicity. By combining PostgreSQL compatibility with serverless architecture, it offers an efficient solution for teams looking to focus on development rather than the operational complexities of managing infrastructure.

However, Aurora DSQL is not a drop-in replacement for PostgreSQL. While it supports the PostgreSQL wire protocol and basic SQL operations, it lacks advanced features like triggers, views, extensions, and certain data types such as JSONB. These omissions mean it’s better suited for new, cloud-native projects designed with its strengths and limitations in mind, rather than migrating feature-rich PostgreSQL applications.

Instead of thinking of Aurora DSQL as “PostgreSQL in the cloud,” consider it for its serverless, multipurpose capabilities. It’s an OLTP database that can also handle OLAP workloads, making it a strong alternative to DynamoDB for teams that need more powerful querying capabilities but don’t have predefined query patterns. Aurora DSQL feels like Postgres but drives like serverless—and we like that.

The serverless design remains its biggest advantage, offering dynamic scaling and cost efficiency. However, the quirks require careful consideration during the design phase.

We specialize in helping organizations navigate the complexities of modern databases. Whether you’re exploring Aurora DSQL for a new project or rethinking your database strategy, we’re here to provide practical insights and solutions tailored to your needs.

Get in touch with us to learn how we can help you unlock the potential of Aurora DSQL and AWS’s database offerings.