Explain Eventual vs Strong vs Causal Consistency.

Eventual consistency, strong consistency, and causal consistency are three consistency models in distributed systems: strong consistency ensures immediate global updates, eventual consistency allows temporary delays but guarantees convergence, and causal consistency enforces cause-and-effect ordering without total synchronization.

When to Use

• Strong consistency: banking, stock trading, or critical data.
• Eventual consistency: social media feeds, DNS, shopping carts.
• Causal consistency: chat apps, collaborative tools where order matters.

Example

If Alice posts and Bob likes it:

• Strong: Everyone instantly sees Bob’s like.
• Eventual: Some may see it later.
• Causal: Bob’s like shows only after Alice’s post.

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Why Is It Important

Choosing the right model impacts availability, latency, and user trust in real-world systems.

Interview Tips

Be ready to define each model, give real-world examples, and tie it to CAP theorem. Highlight trade-offs with context (banking vs. social media).

Trade-offs

• Strong: Consistency, but lower availability and higher latency.
• Eventual: High availability and performance, but stale reads possible.
• Causal: Guarantees logical order, but no total order.

Pitfalls

• Misusing strong consistency for non-critical data.
• Assuming eventual consistency is acceptable everywhere.
• Confusing causal consistency with full global consistency.