How AWS Builds Cloud-Native Distributed SQL Databases

Author

Govardhana M K
December 18, 2025

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🧠 DEEP DIVE USE CASE

How AWS Builds Cloud-Native Distributed SQL Databases

Aurora is designed for high throughput workloads that demand consistent low latency access with built-in resilience.

It’s used by enterprises for mission critical OLTP systems, real-time analytics on transactional data, microservices architectures needing burstable reads, and even multi-tenant platforms that require fine-grained isolation across regions.

Aurora separates compute from storage. The storage layer is a distributed, shared volume that spans three Availability Zones, with six copies of your data automatically replicated across these zones.

In short, the storage layer is designed for high durability and elasticity:

  • 6 copies of data across 3 AZs

  • 4 of 6 required for writes, 3 of 6 for reads

  • Peer-to-peer replication for self-healing

  • Striped across hundreds of volumes

  • Auto expands up to 128 TB

Aurora has a single writer node (the master) and supports up to 15 Aurora read replicas to scale reads. All nodes share the same backend storage, which eliminates the need for data copying during replica creation or failover.

Failover is automatic and completes typically in less than 30 seconds. Additional capabilities include:

  • Cross region replication for global read scalability

  • Continuous backups to Amazon S3 without impacting performance

  • Low replica lag, typically under 10 milliseconds

This architecture delivers performance improvements of up to 5x over standard MySQL and 3x over PostgreSQL, while significantly reducing the operational complexity of traditional databases.

Next, we look at how Aurora handles:

  1. Cluster Endpoints and Traffic Routing

  2. Read Scaling with Auto Scaling Replicas

  3. Custom Endpoints for Query Routing

  4. Multi Master for Write Scale Workloads

1. Cluster Endpoints and Traffic Routing

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