⚖️ 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 Without proper consistency, users may see outdated or conflicting data.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