chapter eleven

11 Operating Kafka

 

This chapter covers

  • The evolution of Kafka clusters
  • Monitoring for maintaining cluster health
  • Performance tuning strategies
  • Disaster recovery and backup considerations

When Kafka is used as a managed service in the cloud, many operational concerns are abstracted away. Tasks like upgrades, monitoring, and broker management are handled by the provider, allowing teams to focus on building applications. But running Kafka on-premises is a different story. It demands deep operational expertise, ranging from performing safe software upgrades to carefully tuning configurations and continuously monitoring cluster health.

In this chapter, we’ll explore what it takes to maintain a robust, self-managed Kafka cluster. You’ll learn how to

  • Safely perform hardware and software updates.
  • Add brokers to or remove them from the cluster.
  • Modify configurations at the broker, topic, and partition level.

Understanding these maintenance tasks is essential for keeping your Kafka deployment resilient, efficient, and ready to scale.

This chapter is not a complete operational guide. Operational practices and tooling evolve rapidly and are often tightly coupled with specific environments and monitoring stacks. Instead, we’ll focus on foundational principles and best practices that apply across most Kafka deployments.

11.1 Cluster evolution and upgrades

Several important reasons exist for upgrading the software of a Kafka cluster:

11.1.1 Adding brokers and distributing the load

11.1.2 Removing a broker from the cluster

11.1.3 Upgrading clients

11.1.4 Data mobility

11.2 Monitoring a Kafka cluster

11.2.1 Types of metrics in monitoring

11.2.2 Kafka monitoring objects

11.2.3 Ownership of monitoring responsibilities

11.2.4 Monitoring stacks and tools

11.3 Performance tuning clinic

11.3.1 Balancing throughput and latency

11.3.2 Balancing data safety and uptime