Insight: A Clear-Eyed Look at Aurora DSQL—What Amazon's New Database Engine Gives Us...and What It Leaves Behind
Insight: A Clear-Eyed Look at Aurora DSQL—What Amazon's New Database Engine Gives Us...and What It Leaves Behind
The call came in late on a Friday. A lead architect had just been told that her team would be migrating their backend to Amazon Aurora DSQL—effective immediately. No negotiation. No pilot phase. Just a quiet directive from above: "This is what our cloud partner recommends." She opened the documentation, scanned for jsonb support, and felt her stomach drop.
That story is becoming more common.
Amazon Aurora DSQL arrived with a quiet splash in June 2025. If you only read the press release, you’d think we just got a drop-in, auto-scaling, serverless version of PostgreSQL, purpose-built for modern distributed applications. But as anyone who has worked on the ground with new AWS offerings knows, the truth lives in the edge cases. The truth lives in the constraints. And Aurora DSQL, for all its architectural ambition, carries real and lasting limitations that developers, architects, and operations teams need to account for now.
This post takes a deep dive into the landscape around Aurora DSQL as it stands at launch. It’s not a takedown, but it is a reality check. And if your team is facing a top-down mandate to use this new engine—or simply curious about where it fits—this is the guide we wish we had at the start.
The Promise: Distributed, Serverless, and Postgres-Flavored
Amazon bills Aurora DSQL as a distributed SQL engine built to deliver strong consistency and high availability, all with the elegance of serverless provisioning. That’s a compelling offer. Developers are tired of managing clusters. Ops teams want less tuning and patching. Executives love the idea of billing tied to consumption, not provisioned capacity. And the cherry on top: PostgreSQL compatibility.
But that word—"compatibility"—deserves scrutiny. DSQL doesn’t extend PostgreSQL. It implements a subset of it. At launch, it omits many features that real-world workloads depend on: jsonb, foreign keys, triggers, user-defined functions, extensions, and complex indexing patterns. That means compatibility is skin-deep. It might run a SELECT * FROM users just fine, but once you try to port your production schema or reporting logic, the gaps reveal themselves.
The question then becomes: is Aurora DSQL truly a PostgreSQL-compatible database, or is it a new engine with a familiar accent? For now, it’s more the latter.
Feature Gaps That Will Bite You Early
Let’s start with jsonb. If your application stores structured metadata, search filters, user preferences, or audit trails using JSON fields, you’ll hit a wall. DSQL doesn’t support native JSON or JSONB. You can still jam that data into a text column, but you lose indexing, validation, and efficient querying. That’s not just a speed issue—it’s a maintainability problem. You’ll have to build workarounds or move JSON logic into the app layer, both of which increase complexity.
Foreign keys are another silent landmine. DSQL will let you define them in your DDL, but they aren’t enforced. That means data can drift over time without warning. Relationships between users and orders, posts and comments, inventory and suppliers—none of those are protected by constraints. If you want integrity, you’ll need to simulate it in your application or write regular audits. That’s a cost.
Triggers and user-defined functions (UDFs) are also missing, which forces all reactive behavior and logic transformations into the app layer or external services like Lambda. That might align with modern event-driven patterns—but for legacy systems or complex business rules, it’s a jarring limitation.
The Billing Model: What’s a DPU Worth?
Aurora DSQL introduces a new unit of measurement: the Distributed Processing Unit (DPU). Pricing is based on how many DPUs your workload consumes, but the relationship between a DPU and query complexity, memory consumption, or I/O is not well documented. That opacity makes it hard to forecast cost or perform post-mortems on billing spikes.
Unlike Aurora Serverless v2, where you can ballpark based on ACUs and activity windows, DSQL’s billing doesn’t yet map cleanly to workload characteristics. A batch job or a slow query might consume a disproportionate number of DPUs, and until the metrics surface improves, cost optimization will involve guesswork, trial, and throttling.
Teams who value cost predictability will need to build their own monitoring and reporting layers to understand DPU trends. That adds overhead in a system that markets itself as “zero infrastructure management.”
Operational Simplicity...With a Catch
It’s true that Aurora DSQL handles provisioning, patching, failover, and replication under the hood. You don’t manage instances or worry about write quorum. That’s a real benefit, especially for lean teams or dev-heavy shops that don’t want to babysit infrastructure.
But that simplicity masks a new set of responsibilities. When constraints are invisible, teams must either discover them the hard way—or do deep research up front. DSQL requires a shift in mindset. It’s not a transparent replacement for Postgres. It’s a new system with new rules. You must know what it cannot do before you plan what it should do.
And because the documentation is still young, that knowledge doesn’t come easily. Community experience is thin. Edge-case behavior is largely undocumented. And AWS support may not yet have deep runbooks for every DSQL quirk. In other words, early adopters are also part-time pioneers.
Should You Use It?
That depends on your workload. If you’re building a new application that prioritizes elasticity, global consistency, and horizontal reads—and you’re prepared to work within a constrained subset of Postgres—you might love Aurora DSQL. If your use case is write-heavy, highly relational, or depends on advanced Postgres features, you’re better off waiting.
Teams already invested in Aurora may be tempted to try DSQL, thinking it will give them autoscaling with minimal migration effort. But DSQL isn’t just a new version of Aurora. It’s a new engine. And like all new engines, it’s fast in some lanes, unpredictable in others, and under-documented when it matters most.
This isn’t a rejection. It’s a caution. Aurora DSQL may evolve into something powerful, flexible, and production-worthy across a wide range of use cases. But as of June 2025, it’s not the right fit for every team—and it’s definitely not a drop-in replacement for Postgres.
That doesn’t mean you shouldn’t use it. It just means you should walk in with both eyes open, your schema in one hand and a red pen in the other.
That story is becoming more common.
Amazon Aurora DSQL arrived with a quiet splash in June 2025. If you only read the press release, you’d think we just got a drop-in, auto-scaling, serverless version of PostgreSQL, purpose-built for modern distributed applications. But as anyone who has worked on the ground with new AWS offerings knows, the truth lives in the edge cases. The truth lives in the constraints. And Aurora DSQL, for all its architectural ambition, carries real and lasting limitations that developers, architects, and operations teams need to account for now.
This post takes a deep dive into the landscape around Aurora DSQL as it stands at launch. It’s not a takedown, but it is a reality check. And if your team is facing a top-down mandate to use this new engine—or simply curious about where it fits—this is the guide we wish we had at the start.
The Promise: Distributed, Serverless, and Postgres-Flavored
Amazon bills Aurora DSQL as a distributed SQL engine built to deliver strong consistency and high availability, all with the elegance of serverless provisioning. That’s a compelling offer. Developers are tired of managing clusters. Ops teams want less tuning and patching. Executives love the idea of billing tied to consumption, not provisioned capacity. And the cherry on top: PostgreSQL compatibility.
But that word—"compatibility"—deserves scrutiny. DSQL doesn’t extend PostgreSQL. It implements a subset of it. At launch, it omits many features that real-world workloads depend on: jsonb, foreign keys, triggers, user-defined functions, extensions, and complex indexing patterns. That means compatibility is skin-deep. It might run a SELECT * FROM users just fine, but once you try to port your production schema or reporting logic, the gaps reveal themselves.
The question then becomes: is Aurora DSQL truly a PostgreSQL-compatible database, or is it a new engine with a familiar accent? For now, it’s more the latter.
Feature Gaps That Will Bite You Early
Let’s start with jsonb. If your application stores structured metadata, search filters, user preferences, or audit trails using JSON fields, you’ll hit a wall. DSQL doesn’t support native JSON or JSONB. You can still jam that data into a text column, but you lose indexing, validation, and efficient querying. That’s not just a speed issue—it’s a maintainability problem. You’ll have to build workarounds or move JSON logic into the app layer, both of which increase complexity.
Foreign keys are another silent landmine. DSQL will let you define them in your DDL, but they aren’t enforced. That means data can drift over time without warning. Relationships between users and orders, posts and comments, inventory and suppliers—none of those are protected by constraints. If you want integrity, you’ll need to simulate it in your application or write regular audits. That’s a cost.
Triggers and user-defined functions (UDFs) are also missing, which forces all reactive behavior and logic transformations into the app layer or external services like Lambda. That might align with modern event-driven patterns—but for legacy systems or complex business rules, it’s a jarring limitation.
The Billing Model: What’s a DPU Worth?
Aurora DSQL introduces a new unit of measurement: the Distributed Processing Unit (DPU). Pricing is based on how many DPUs your workload consumes, but the relationship between a DPU and query complexity, memory consumption, or I/O is not well documented. That opacity makes it hard to forecast cost or perform post-mortems on billing spikes.
Unlike Aurora Serverless v2, where you can ballpark based on ACUs and activity windows, DSQL’s billing doesn’t yet map cleanly to workload characteristics. A batch job or a slow query might consume a disproportionate number of DPUs, and until the metrics surface improves, cost optimization will involve guesswork, trial, and throttling.
Teams who value cost predictability will need to build their own monitoring and reporting layers to understand DPU trends. That adds overhead in a system that markets itself as “zero infrastructure management.”
Operational Simplicity...With a Catch
It’s true that Aurora DSQL handles provisioning, patching, failover, and replication under the hood. You don’t manage instances or worry about write quorum. That’s a real benefit, especially for lean teams or dev-heavy shops that don’t want to babysit infrastructure.
But that simplicity masks a new set of responsibilities. When constraints are invisible, teams must either discover them the hard way—or do deep research up front. DSQL requires a shift in mindset. It’s not a transparent replacement for Postgres. It’s a new system with new rules. You must know what it cannot do before you plan what it should do.
And because the documentation is still young, that knowledge doesn’t come easily. Community experience is thin. Edge-case behavior is largely undocumented. And AWS support may not yet have deep runbooks for every DSQL quirk. In other words, early adopters are also part-time pioneers.
Should You Use It?
That depends on your workload. If you’re building a new application that prioritizes elasticity, global consistency, and horizontal reads—and you’re prepared to work within a constrained subset of Postgres—you might love Aurora DSQL. If your use case is write-heavy, highly relational, or depends on advanced Postgres features, you’re better off waiting.
Teams already invested in Aurora may be tempted to try DSQL, thinking it will give them autoscaling with minimal migration effort. But DSQL isn’t just a new version of Aurora. It’s a new engine. And like all new engines, it’s fast in some lanes, unpredictable in others, and under-documented when it matters most.
This isn’t a rejection. It’s a caution. Aurora DSQL may evolve into something powerful, flexible, and production-worthy across a wide range of use cases. But as of June 2025, it’s not the right fit for every team—and it’s definitely not a drop-in replacement for Postgres.
That doesn’t mean you shouldn’t use it. It just means you should walk in with both eyes open, your schema in one hand and a red pen in the other.
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Written by Aaron Rose, software engineer and technology writer at Tech-Reader.blog.
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