Insight: Why Aurora’s Reader Endpoint Isn’t Enough—The Real Problem with Query Routing
The Mirage of Simplicity
Aurora PostgreSQL makes a beautiful promise: scale reads with a simple reader endpoint and keep your writer node clean. You spin up replicas, wire your app to the provided DNS name, and expect the load to distribute itself. It feels seamless — until you realize your writer instance is still carrying the weight of most SELECT queries. Something’s not right. The infra looks healthy. The DNS is resolving. But the query traffic is misbehaving.
That’s because Aurora’s built-in reader endpoint is not a traffic controller — it’s a DNS trick. It doesn’t understand query intent. It doesn’t monitor replica lag. It doesn’t know your business logic or your application’s needs. It just points your app in a general direction and hopes for the best. And that illusion of simplicity is where many teams run aground.
The Problem Beneath the Problem
This isn’t just about DNS. It’s about visibility, intent, and trust. You don’t get to see which queries hit which replicas. You don’t get to enforce that reporting dashboards stay off the writer. And when a replica falls behind or fails over, your app might hang or silently re-route — all without telling you. Even worse, if your app opens too many database connections (hello, Lambda or Node.js), you’ll overwhelm Aurora’s limits before you even get to routing logic.
What users really want is something Aurora doesn’t offer: a way to shape query traffic based on intent, balance it across healthy replicas, and make all of that observable and tunable. The cloud provides the raw infrastructure — but the routing logic is up to you.
What Aurora Leaves to You
To be clear, Amazon does provide infrastructure for high availability. The reader endpoint is useful, and RDS Proxy helps manage idle connections. But neither of them makes decisions about how queries should be routed, when to promote a replica, or why a session should be sticky. That layer — the interpretation of workload and response — doesn’t scale by itself.
AWS scales infrastructure, not logic. You get durable storage, fast failover, and global reach. But when it comes to deciding who gets what, when, and how, you’re on your own. That’s where tools like PgPool, PgCat, PgBouncer, and others come into play — not to replace Aurora, but to complement it with the control it doesn’t provide.
Six Problems, Seven Tools
This series is about the real problems Aurora developers face — not just the tools. We’ll explore six areas where built-in features fall short:
- Query routing
- Reader/writer separation
- Connection pooling
- Failover management
- Latency control
- Observability and configuration
In the coming days, we’ll walk through seven tools that help you solve these — each one starting with the pain it addresses, and ending with real-world configs, tips, and even a few CLI helpers if they make sense. Tomorrow, we begin with PgPool, the seasoned veteran of query routing — beloved by some, avoided by others, but always worth understanding.
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Written by Aaron Rose, software engineer and technology writer at Tech-Reader.blog.
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