Choosing Partition Count and Replication Factor

The video tutorial discusses the critical parameters of partitions and replication factors in Kafka. It emphasizes the importance of setting these parameters correctly to ensure system performance and durability. Guidelines are provided for determining the number of partitions and replication factors based on cluster size and expected throughput. The tutorial also highlights the impact of these settings on system stability and scalability, offering practical advice for both small and large clusters.

Why is it crucial to set the partition counts and replication factors correctly from the beginning?

• To increase the number of brokers

• To simplify the configuration process

• To reduce the cost of Kafka deployment

• To ensure optimal performance and durability

What is one benefit of having more partitions in a Kafka topic?

• Better parallelism and throughput

• Increased latency

• Simplified consumer management

• Reduced storage requirements

What is a potential downside of having too many partitions in a Kafka cluster?

• Improved data replication

• Higher strain on the system during leader elections

• More efficient Zookeeper operations

• Increased broker utilization

For a small Kafka cluster with less than six brokers, what is the recommended number of partitions per topic?

• Three times the number of brokers

• Half the number of brokers

• Twice the number of brokers

• Equal to the number of brokers

What should be considered when determining the number of partitions for a topic?

• The number of brokers only

• The expected consumer load and throughput

• The replication factor

• The Kafka version

What is the recommended starting replication factor for a Kafka topic?

• Three

• Two

• Four

• One

Why should the replication factor never be set to one in production?

• It increases latency

• It simplifies the configuration

• It reduces storage costs

• It risks data loss if a broker fails