⚖️ Consistency Models in Distributed Systems

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore how distributed systems maintain data consistency across nodes and the trade-offs involved. 🔄💡

⚖️ Why Consistency Matters

In distributed systems, multiple nodes can store copies of the same data. Without proper consistency, users may see outdated or conflicting data.

1. 🔒 Strong Consistency

Strong consistency ensures that all nodes see the same data at the same time:

• ✅ Any read reflects the latest write
• 🧠 Easy for developers to reason about
• ⏱️ May increase latency due to synchronization across nodes

// Example: using a central database transaction
@Transactional
public void updateOrder(Order order) {
    orderRepository.save(order);
}

2. 🌐 Eventual Consistency

Eventual consistency ensures that all nodes will converge to the same state eventually:

• ⏳ Reads may return stale data temporarily
• ⚡ Higher availability and performance
• 📚 Common in distributed databases like Cassandra, DynamoDB

// Example: updating asynchronously via Kafka
producer.send(new ProducerRecord<>("orders", orderId, order));

🤔 Choosing the Right Model

Deciding between strong and eventual consistency depends on:

• 🔑 Criticality of real-time data
• ⏱️ Latency requirements
• ⚖️ Fault tolerance and availability needs

Next in the Series

In the next post, we’ll explore Caching Strategies 💾 in distributed systems, including local and distributed caches.

Label for this post: Distributed Systems