Let’s talk about a simple yet powerful database design pattern: the Read Replica Pattern.
It’s widely used in real-world systems like Amazon, Netflix, and many SaaS platforms to scale reads and reduce load on the primary database.
🚀 How It Works:
In this setup:
✅ All writes (insert, update, delete) go to the primary database
✅ All reads go to the replica databases
📦 Let’s walk through a real-world example:
1️⃣ Alice places an order on Amazon → the request goes to the Order Service
2️⃣ The order is recorded in the primary DB (write), and the data is replicated to two read replicas
3️⃣ When Alice views order details → the data is served from a replica (read)
4️⃣ When Alice checks her recent order history → again, data comes from a replica
⚠️ The Hidden Challenge: Replication Lag
Sometimes, due to network delays or load, replicas might lag behind the primary.
This means when Alice checks her order immediately after placing it, the order might not show up, even though it was created.
This can cause confusion and poor user experience.
🔧 How Do We Fix This?
To maintain read-after-write consistency, here are a few strategies:
1️⃣ Send latency-sensitive reads to the primary database
2️⃣ Route reads that follow immediately after writes to the primary
3️⃣ Use replication status tools in relational databases to check if the replica is caught up. If not:
- ❌ Fail the read
- ✅ Or fallback to the primary
📌 Why It Matters:
The Read Replica Pattern helps you scale read traffic, reduce load, and ensure faster response times. But like every pattern, it comes with trade-offs — and understanding those trade-offs is key to designing reliable systems.