3.5 Kafka Cluster Scaling скачать в хорошем качестве

3.5 Kafka Cluster Scaling

https://centralmesh.io Chapter 3: Kafka Architecture and Concepts In this section, we explore how Kafka clusters scale to handle growing workloads, focusing on adding brokers, rebalancing partitions, and ongoing monitoring to maintain performance and reliability. You’ll learn: How scaling helps Kafka handle increased traffic, improve performance, and maintain high availability as data volumes grow. Why adding brokers allows Kafka to distribute load more effectively without requiring downtime. How Kafka updates cluster metadata and redistributes partition leadership and replicas when new brokers join. Why rebalancing partitions is essential to prevent uneven load across brokers. How Kafka tools like kafka-reassign-partitions.sh help evenly distribute data and workload. Why scaling is an ongoing process that requires continuous monitoring and tuning. Which key metrics—throughput, latency, and disk usage—indicate when further scaling or tuning is needed. By the end of this section, you’ll be able to: Explain why and when a Kafka cluster needs to scale. Describe the steps involved in scaling a Kafka cluster. Understand how brokers and partitions work together to distribute load. Identify when and why partition rebalancing is required. Monitor critical metrics to maintain cluster health and performance. Design a Kafka cluster that scales reliably with increasing demand.