Setting capacity estimations and goals

Cruise Control rebalancing works using capacity estimations and goals. You need to configure the capacity estimates based on your resources, and set the goals for Cruise Control to achieve the Kafka partition rebalancing that meets your requirements.

When configuring Cruise Control, you need to make sure that the Kafka topics and partitions, the capacity estimates, and the proper goals are provided so the rebalancing process works as expected.

You can find the capacity estimate and goal configurations at the following location in Cloudera Manager:
  1. Navigate to Management Console > Environments, and select the environment where you have created your cluster.
  2. Select Cloudera Manager from the services.
  3. Select Clusters > Cruise Control.
  4. Click Configuration.
  5. Select Main from the Filters.

Configuring capacity estimations

The values for capacity estimation needs to be provided based on your available resources for CPU and network. Beside the capacity estimation, you also need to provide information about the broker and partition metrics. You can set the capacity estimations and Kafka properties in Cloudera Manager.

For the rebalancing, you need to provide the capacity values of your resources. These values are used for specifying the rebalancing criteria for your deployment. The following capacity values must be set:
Capacity Description
capacity.default.cpu 100 by default Given by the internet provider

The optimizers in Cruise Control use the network incoming and outgoing capacities to define a boundary for optimization. The capacity estimates are generated and read by Cruise Control. A capacity.json file is generated when Cruise Control is started. When a new broker is added, Cruise Control uses the default broker capacity values. However, in case disk related goals are used, Cruise Control must be restarted to load the actual disk capacity metrics of the new broker.

The following table lists all the configurations that are needed to configure Cruise Control specifically to your environment:

Configuration Description
num.metric.fetchers Parallel threads for fetching metrics from the Cloudera Manager database Storing Cruise Control metrics Storing Cruise Control metircs Time window size for partition metrics Time window size for broker metrics Number of stored partition windows Number of stored broker windows

Configuring goals

After setting the capacity estimates, you can provide different goals that define the optimization proposals given by Cruise Control.

Default.goals are used to pre-compute optimization proposals that can be applied regardless of any anomalies. These default goal settings on a healthy cluster can optimize resource utilization. Supported goals are also available to assist the optimized rebalancing process. When these goals are fulfilled, the rebalancing is successful. When the goals are violated, self-healing can be used and rebalancing must be carried out.

Cruise Control has an anomaly detection feature where goal violations can also be set. The anomaly.detection.goals configuration defines when the goals are not met, thus causing a violation. These anomalies can be fixed by the proposal generated from the self.healing.goals configuration. In case there is no self-healing goal specified, Cruise Control uses the default.goals setting. Hard goals can also be set to guarantee the fulfilment of any optimization or self-healing process.

For more information about self-healing and goal violation, see the How Cruise Control self-healing works section.

Configuration Description
default.goals List of default goals
goals List of supported goals
hard.goals List of goals that any optimization proposal must fulfill
self.healing.goals List of goals to be used for self-healing relevant anomalies
anomaly.detection.goals List of goals that the anomaly detector should detect if they are violated

Multi-level rack-aware distribution goal

You can use the MultiLevelRackAwareDistributionGoal to ensure rack awareness on a higher level than for the standard rack aware goal for Kafka clusters using Cruise Control.

The MultiLevelRackAwareDistributionGoal behaves differently than the default RackAwareGoal or RackAwareDistributionGoal in Cruise Control. The standard goals have lighter requirements on rack awareness, and always optimize based on the current state of the cluster and with the priority on making all replicas come back online.

This means that in case a network partition failure occurs, and a data center goes offline, a Cruise Control rebalance operation using a standard rack-aware goal ignores the data center that is not working, and moves replicas around as if there were one fewer data center in the cluster. For example, if a Kafka cluster has three data centers and one goes offline, the standard goals are not aware of the existence of the third data center, and act as if only two data centers are used in the cluster.

The MultiLevelRackAwareDistributionGoal acts differently in the following aspects:
  • Handles rack IDs as multi-level rack IDs, respecting the hierarchy of racks when distributing replicas
  • Keeps track of the whole state of the cluster with caching previous states to make sure that all racks are visible
  • Prioritizes multi-level rack awareness guarantees over bringing all replicas back online

In the same failure situation, where one data center is offline out of three, the multi-level rack-aware goal is still aware of the existence of the third data center. This means that the offline replicas are not moved from the third data center if the migration violates the multi-level rack awareness guarantees. The goal allows optimizations to pass even in the presence of offline replicas, which can be configured with property. If the is set to true, the goal causes the rebalance operation to fail if the replicas would stay offline after the optimizations are implemented.