How does replication.factor influence fault tolerance and load in a Kafka cluster?

How does replication.factor influence fault tolerance and load in a Kafka cluster?

• A. Lower replication factor increases fault tolerance.
• B. Replication factor determines the number of consumer groups.
• C. Higher replication factor increases fault tolerance and read resilience but uses more storage and network; it affects the number of in-sync replicas and required durabilities.
• D. Replication factor has no impact on read performance.

Answer: (C)

Replication factor controls how many copies of each partition exist across brokers. With more copies, the cluster can tolerate more broker failures because there are additional in-sync replicas that can take over leadership and continue serving data. This boosts fault tolerance and improves read resilience because the system can continue to respond even if some replicas or brokers go down, and leader elections can occur more smoothly when there are healthy in-sync replicas to promote. But this comes at a cost: more replicas mean more storage is used and more data must be replicated over the network, increasing network and I/O overhead. The number of in-sync replicas can grow with the replication factor, which also influences durability settings (for example, how many replicas must acknowledge a write). Higher replication factor lets you require more acknowledgments for stronger durability, but it can also add latency because a write must be propagated to more replicas before being considered complete. In short, increasing replication factor improves fault tolerance and read resilience, at the expense of storage, network usage, and replication overhead.

Replication factor controls how many copies of each partition exist across brokers. With more copies, the cluster can tolerate more broker failures because there are additional in-sync replicas that can take over leadership and continue serving data. This boosts fault tolerance and improves read resilience because the system can continue to respond even if some replicas or brokers go down, and leader elections can occur more smoothly when there are healthy in-sync replicas to promote.

But this comes at a cost: more replicas mean more storage is used and more data must be replicated over the network, increasing network and I/O overhead. The number of in-sync replicas can grow with the replication factor, which also influences durability settings (for example, how many replicas must acknowledge a write). Higher replication factor lets you require more acknowledgments for stronger durability, but it can also add latency because a write must be propagated to more replicas before being considered complete. In short, increasing replication factor improves fault tolerance and read resilience, at the expense of storage, network usage, and replication overhead.