Kafka cluster load balancing using Cruise Control

You can use Cruise Control as a load balancing component in large Kafka installations to automatically balance the partitions based on specific conditions for your deployment. The elements in the Cruise Control architecture are responsible for different parts of the rebalancing process that uses Kafka metrics and optimization goals.

The following illustration shows the architecture of Cruise Control.

Load Monitor

Generates a cluster workload model based on standard Kafka metrics and resource metrics to utilize disk, CPU, bytes-in rate, and bytes-out rate. Feeds the cluster model into Anomaly Detector and Analyzer.


Generates optimization proposals based on optimization goals provided by the user, and cluster workload model from Load Monitor. Hard goals and soft goals can be set. Hard goals must be fulfilled, while soft goals can be left unfulfilled if hard goals are reached. The optimization fails if the hard goal is violated by optimization results.

Anomaly Detector

Responsible for detecting anomalies that can happen during the rebalancing process. The following anomaly detections are supported in Cruise Control:
  • Broker failure
  • Goal violations
  • Disk failure
  • Slow broker as Metric Anomaly
  • Topic replication factor
The detected anomalies can be resolved by the self-healing feature of Cruise Control. For more information, see the How Cruise Control self-healing works documentation.


Carries out the optimization proposals and it can be safely interrupted when executing proposals. The executions are always resource-aware processes.