Insight: Aurora DSQL vs. Aurora PostgreSQL—A Quick Explainer on What’s Changed and Why It Matters
Insight: Aurora DSQL vs. Aurora PostgreSQL—A Quick Explainer on What’s Changed and Why It Matters
If you’ve spent years building on Amazon Aurora PostgreSQL, the arrival of Aurora DSQL might look like a natural progression. Same AWS branding, same PostgreSQL heritage—so how different could it be?
As it turns out? It's very different.
Aurora DSQL is a new engine built to scale horizontally and simplify operations. But that simplicity comes at the cost of compatibility. It’s not a superset of PostgreSQL—it’s a smaller, faster-moving target. This post walks through the key differences so you can make clear, informed decisions.
Walking Through the Differences
PostgreSQL Compatibility
Aurora DSQL supports only a subset of the PostgreSQL feature set. That means tools, extensions, and SQL patterns that work in Aurora PostgreSQL may fail or behave differently in DSQL. This isn't just a compatibility issue—it’s an ecosystem shift.
JSON/JSONB Support
Many modern apps store flexible metadata using jsonb. Aurora DSQL doesn’t support it. That’s not a minor footnote—it breaks any part of your stack that relies on structured JSON querying. Workarounds require storing JSON as escaped text, which is harder to query, index, and validate.
Foreign Keys
You can define them, but DSQL won’t enforce them. That means relational integrity isn’t guaranteed at the database level. If your application expects foreign keys to act as a safety net, you'll need to simulate that enforcement elsewhere—or accept the risk of orphaned data.
User-Defined Functions (UDFs)
In classic Aurora PostgreSQL, UDFs let you encapsulate business logic inside the database. Aurora DSQL doesn’t support them. That pushes logic into the app layer or external services like AWS Lambda, increasing architectural sprawl.
Triggers
Triggers allow the database to react to inserts, updates, or deletes automatically. Without them, DSQL can't enforce reactive behaviors like audit logging or sync rules. These now have to be managed externally.
Scaling Model
Aurora DSQL is serverless-first. It auto-scales based on workload using DPUs. Classic Aurora PostgreSQL uses provisioned instances or Serverless v2, which auto-scales more conservatively. DSQL’s model is elegant but less predictable.
Billing Unit
DPUs (Distributed Processing Units) are new and poorly documented. This makes cost forecasting harder. In contrast, Aurora PostgreSQL’s ACUs or instance-based pricing are better understood, making them easier to manage.
Operational Management
DSQL is fully hands-off when it comes to provisioning and scaling. There are no instances to manage. That’s great for simplicity—but limits control. Aurora PostgreSQL gives you more tuning knobs if your workload needs them.
Consistency Model
Aurora DSQL is built for distributed consistency. That’s good for globally scaled apps but may introduce latency. Classic Aurora PostgreSQL operates within a single region, offering lower latency but less geographic flexibility.
Ideal Use Case
DSQL is best suited for stateless, horizontally scaled, event-driven systems. Classic Aurora PostgreSQL is better for apps with tight data relationships, in-database logic, or complex reporting.
What This Table Really Tells Us
Aurora DSQL isn’t just a tuned-up version of Postgres. It’s a different tool with a different design philosophy. That doesn’t make it worse—it just makes it something you have to plan for.
The more your app relies on traditional database features, the more friction you’ll feel in DSQL. But if you’re designing with scale, elasticity, and simplicity in mind—and you’re willing to re-architect parts of your logic—DSQL can be a strong option.
Use this table not just to compare, but to make decisions. It’s a cheat sheet for system design. Print it. Mark it. Argue with it. That’s how you avoid surprises later.
As it turns out? It's very different.
Aurora DSQL is a new engine built to scale horizontally and simplify operations. But that simplicity comes at the cost of compatibility. It’s not a superset of PostgreSQL—it’s a smaller, faster-moving target. This post walks through the key differences so you can make clear, informed decisions.
Walking Through the Differences
PostgreSQL Compatibility
Aurora DSQL supports only a subset of the PostgreSQL feature set. That means tools, extensions, and SQL patterns that work in Aurora PostgreSQL may fail or behave differently in DSQL. This isn't just a compatibility issue—it’s an ecosystem shift.
JSON/JSONB Support
Many modern apps store flexible metadata using jsonb. Aurora DSQL doesn’t support it. That’s not a minor footnote—it breaks any part of your stack that relies on structured JSON querying. Workarounds require storing JSON as escaped text, which is harder to query, index, and validate.
Foreign Keys
You can define them, but DSQL won’t enforce them. That means relational integrity isn’t guaranteed at the database level. If your application expects foreign keys to act as a safety net, you'll need to simulate that enforcement elsewhere—or accept the risk of orphaned data.
User-Defined Functions (UDFs)
In classic Aurora PostgreSQL, UDFs let you encapsulate business logic inside the database. Aurora DSQL doesn’t support them. That pushes logic into the app layer or external services like AWS Lambda, increasing architectural sprawl.
Triggers
Triggers allow the database to react to inserts, updates, or deletes automatically. Without them, DSQL can't enforce reactive behaviors like audit logging or sync rules. These now have to be managed externally.
Scaling Model
Aurora DSQL is serverless-first. It auto-scales based on workload using DPUs. Classic Aurora PostgreSQL uses provisioned instances or Serverless v2, which auto-scales more conservatively. DSQL’s model is elegant but less predictable.
Billing Unit
DPUs (Distributed Processing Units) are new and poorly documented. This makes cost forecasting harder. In contrast, Aurora PostgreSQL’s ACUs or instance-based pricing are better understood, making them easier to manage.
Operational Management
DSQL is fully hands-off when it comes to provisioning and scaling. There are no instances to manage. That’s great for simplicity—but limits control. Aurora PostgreSQL gives you more tuning knobs if your workload needs them.
Consistency Model
Aurora DSQL is built for distributed consistency. That’s good for globally scaled apps but may introduce latency. Classic Aurora PostgreSQL operates within a single region, offering lower latency but less geographic flexibility.
Ideal Use Case
DSQL is best suited for stateless, horizontally scaled, event-driven systems. Classic Aurora PostgreSQL is better for apps with tight data relationships, in-database logic, or complex reporting.
What This Table Really Tells Us
Aurora DSQL isn’t just a tuned-up version of Postgres. It’s a different tool with a different design philosophy. That doesn’t make it worse—it just makes it something you have to plan for.
The more your app relies on traditional database features, the more friction you’ll feel in DSQL. But if you’re designing with scale, elasticity, and simplicity in mind—and you’re willing to re-architect parts of your logic—DSQL can be a strong option.
Use this table not just to compare, but to make decisions. It’s a cheat sheet for system design. Print it. Mark it. Argue with it. That’s how you avoid surprises later.
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Written by Aaron Rose, software engineer and technology writer at Tech-Reader.blog.
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