Describe the differences among strong consistency, eventual consistency, and causal consistency with examples.

• A. Strong: all reads reflect the latest writes; eventual: updates propagate eventually; causal: respects cause-effect ordering.
• B. Strong: all reads reflect the latest writes; eventual: updates propagate eventually; causal: reading after a causally related write may not reflect the write immediately.
• C. Strong is equivalent to eventual; eventual is stronger than strong.
• D. Causal consistency guarantees global synchronization of clocks.

Answer: (B)

In distributed systems, these terms describe how reads see writes across multiple replicas and how much waiting or coordination is needed to achieve a consistent view.

Strong consistency means reads always reflect the most recent write, as if there’s a single, instantly visible value across all replicas. In practice this is like a bank balance update: after you transfer funds, a balance check shows the updated amount immediately, no stale data.

Eventual consistency allows for temporary divergence: a write may take time to propagate, so a read might return an older value on some replicas. But if no new writes occur, all replicas will eventually converge to the same value. A common example is DNS caching or a social app’s feed where updates become consistent after a short delay.

Causal consistency enforces order for related operations: if one write causally affects another, reads must reflect that order. However, there’s no requirement for a global clock or a single global order for unrelated operations, and there can be propagation delays. A helpful example is editing a document where a change that introduces a reference should be visible before the reference itself; unrelated edits to different parts of the document can appear in different orders on different replicas.

The best description among the choices emphasizes that strong consistency keeps reads in line with the latest writes, eventual consistency allows updates to propagate over time, and causal consistency ensures causal relationships are observed in order while permitting delays in visibility.