红帽全球峰会第 19 章 Rebalancing clusters using Cruise Control
Cruise Control is an open source system that supports the following Kafka operations:
- Monitoring cluster workload
- Rebalancing a cluster based on predefined constraints
The operations help with running a more balanced Kafka cluster that uses broker pods more efficiently.
A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might not be evenly distributed across the available brokers. To rebalance the cluster, administrators must monitor the load on brokers and manually reassign busy partitions to brokers with spare capacity.
Cruise Control automates the cluster rebalancing process. It constructs a workload model of resource utilization for the cluster—based on CPU, disk, and network load—and generates optimization proposals (that you can approve or reject) for more balanced partition assignments. A set of configurable optimization goals is used to calculate these proposals.
You can generate optimization proposals in specific modes. The default full mode rebalances partitions across all brokers. You can also use the add-brokers and remove-brokers modes to accommodate changes when scaling a cluster up or down.
When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster. You configure and generate optimization proposals using a KafkaRebalance resource. You can configure the resource using an annotation so that optimization proposals are approved automatically or manually.
Streams for Apache Kafka provides example configuration files for Cruise Control.
19.1. Cruise Control components and features
Cruise Control consists of four main components—the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor—and a REST API for client interactions. Streams for Apache Kafka utilizes the REST API to support the following Cruise Control features:
- Generating optimization proposals from optimization goals.
- Rebalancing a Kafka cluster based on an optimization proposal.
- Optimization goals
An optimization goal describes a specific objective to achieve from a rebalance. For example, a goal might be to distribute topic replicas across brokers more evenly. You can change what goals to include through configuration. A goal is defined as a hard goal or soft goal. You can add hard goals through Cruise Control deployment configuration. You also have main, default, and user-provided goals that fit into each of these categories.
- Hard goals are preset and must be satisfied for an optimization proposal to be successful.
- Soft goals do not need to be satisfied for an optimization proposal to be successful. They can be set aside if it means that all hard goals are met.
- Main goals are inherited from Cruise Control. Some are preset as hard goals. Main goals are used in optimization proposals by default.
- Default goals are the same as the main goals by default. You can specify your own set of default goals.
- User-provided goals are a subset of default goals that are configured for generating a specific optimization proposal.
- Optimization proposals
Optimization proposals comprise the goals you want to achieve from a rebalance. You generate an optimization proposal to create a summary of proposed changes and the results that are possible with the rebalance. The goals are assessed in a specific order of priority. You can then choose to approve or reject the proposal. You can reject the proposal to run it again with an adjusted set of goals.
You can generate an optimization proposal in one of three modes.
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fullis the default mode and runs a full rebalance. -
add-brokersis the mode you use after adding brokers when scaling up a Kafka cluster. -
remove-brokersis the mode you use before removing brokers when scaling down a Kafka cluster.
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Other Cruise Control features are not currently supported, including self healing, notifications, write-your-own goals, and changing the topic replication factor.
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