Chapter 8. Cruise Control for cluster rebalancing You can deploy Cruise Control to your AMQ Streams cluster and use it to rebalance the Kafka cluster. Cruise Control is an open source system for automating Kafka operations, such as monitoring cluster workload, rebalancing a cluster based on predefined constraints, and detecting and fixing anomalies. It consists of four main components- the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor- and a REST API for client interactions. AMQ Streams utilizes the REST API to support the following Cruise Control features: Generating optimization proposals from multiple optimization goals . Rebalancing a Kafka cluster based on an optimization proposal. Other Cruise Control features are not currently supported, including anomaly detection, notifications, write-your-own goals, and changing the topic replication factor. Example YAML files for Cruise Control are provided in examples/cruise-control/ . 8.1. Why use Cruise Control? Cruise Control reduces the time and effort involved in running an efficient and balanced Kafka cluster. A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might be unevenly 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. When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster. When the cluster rebalancing operation is complete, the broker pods are used more effectively and the Kafka cluster is more evenly balanced. Additional resources Cruise Control Wiki 8.2. Optimization goals overview To rebalance a Kafka cluster, Cruise Control uses optimization goals to generate optimization proposals , which you can approve or reject. Optimization goals are constraints on workload redistribution and resource utilization across a Kafka cluster. AMQ Streams supports most of the optimization goals developed in the Cruise Control project. The supported goals, in the default descending order of priority, are as follows: Rack-awareness Minimum number of leader replicas per broker for a set of topics Replica capacity Capacity : Disk capacity, Network inbound capacity, Network outbound capacity, CPU capacity Replica distribution Potential network output Resource distribution : Disk utilization distribution, Network inbound utilization distribution, Network outbound utilization distribution, CPU utilization distribution Note The resource distribution goals are controlled using capacity limits on broker resources. Leader bytes-in rate distribution Topic replica distribution Leader replica distribution Preferred leader election For more information on each optimization goal, see Goals in the Cruise Control Wiki. Note Intra-broker disk goals, "Write your own" goals, and Kafka assigner goals are not yet supported. Goals configuration in AMQ Streams custom resources You configure optimization goals in Kafka and KafkaRebalance custom resources. Cruise Control has configurations for hard optimization goals that must be satisfied, as well as main , default , and user-provided optimization goals. Optimization goals for resource distribution (disk, network inbound, network outbound, and CPU) are subject to capacity limits on broker resources. The following sections describe each goal configuration in more detail. Hard goals and soft goals Hard goals are goals that must be satisfied in optimization proposals. Goals that are not configured as hard goals are known as soft goals . You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations. An optimization proposal that violates one or more soft goals, but satisfies all hard goals, is valid. Cruise Control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by Cruise Control and not sent to the user for approval. Note For example, you might have a soft goal to distribute a topic's replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met. In Cruise Control, the following main optimization goals are preset as hard goals: You configure hard goals in the Cruise Control deployment configuration, by editing the hard.goals property in Kafka.spec.cruiseControl.config . To inherit the preset hard goals from Cruise Control, do not specify the hard.goals property in Kafka.spec.cruiseControl.config To change the preset hard goals, specify the desired goals in the hard.goals property, using their fully-qualified domain names. Example Kafka configuration for hard optimization goals apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: topicOperator: {} userOperator: {} cruiseControl: brokerCapacity: inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s config: hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal # ... Increasing the number of configured hard goals will reduce the likelihood of Cruise Control generating valid optimization proposals. If skipHardGoalCheck: true is specified in the KafkaRebalance custom resource, Cruise Control does not check that the list of user-provided optimization goals (in KafkaRebalance.spec.goals ) contains all the configured hard goals ( hard.goals ). Therefore, if some, but not all, of the user-provided optimization goals are in the hard.goals list, Cruise Control will still treat them as hard goals even if skipHardGoalCheck: true is specified. Main optimization goals The main optimization goals are available to all users. Goals that are not listed in the main optimization goals are not available for use in Cruise Control operations. Unless you change the Cruise Control deployment configuration , AMQ Streams will inherit the following main optimization goals from Cruise Control, in descending priority order: Six of these goals are preset as hard goals . To reduce complexity, we recommend that you use the inherited main optimization goals, unless you need to completely exclude one or more goals from use in KafkaRebalance resources. The priority order of the main optimization goals can be modified, if desired, in the configuration for default optimization goals . You configure main optimization goals, if necessary, in the Cruise Control deployment configuration: Kafka.spec.cruiseControl.config.goals To accept the inherited main optimization goals, do not specify the goals property in Kafka.spec.cruiseControl.config . If you need to modify the inherited main optimization goals, specify a list of goals, in descending priority order, in the goals configuration option. Note If you change the inherited main optimization goals, you must ensure that the hard goals, if configured in the hard.goals property in Kafka.spec.cruiseControl.config , are a subset of the main optimization goals that you configured. Otherwise, errors will occur when generating optimization proposals. Default optimization goals Cruise Control uses the default optimization goals to generate the cached optimization proposal . For more information about the cached optimization proposal, see Section 8.3, "Optimization proposals overview" . You can override the default optimization goals by setting user-provided optimization goals in a KafkaRebalance custom resource. Unless you specify default.goals in the Cruise Control deployment configuration , the main optimization goals are used as the default optimization goals. In this case, the cached optimization proposal is generated using the main optimization goals. To use the main optimization goals as the default goals, do not specify the default.goals property in Kafka.spec.cruiseControl.config . To modify the default optimization goals, edit the default.goals property in Kafka.spec.cruiseControl.config . You must use a subset of the main optimization goals. Example Kafka configuration for default optimization goals apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: topicOperator: {} userOperator: {} cruiseControl: brokerCapacity: inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s config: default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal # ... If no default optimization goals are specified, the cached proposal is generated using the main optimization goals. User-provided optimization goals User-provided optimization goals narrow down the configured default goals for a particular optimization proposal. You can set them, as required, in spec.goals in a KafkaRebalance custom resource: User-provided optimization goals can generate optimization proposals for different scenarios. For example, you might want to optimize leader replica distribution across the Kafka cluster without considering disk capacity or disk utilization. So, you create a KafkaRebalance custom resource containing a single user-provided goal for leader replica distribution. User-provided optimization goals must: Include all configured hard goals , or an error occurs Be a subset of the main optimization goals To ignore the configured hard goals when generating an optimization proposal, add the skipHardGoalCheck: true property to the KafkaRebalance custom resource. See Section 8.7, "Generating optimization proposals" . Additional resources Section 8.5, "Cruise Control configuration" Configurations in the Cruise Control Wiki. 8.3. Optimization proposals overview An optimization proposal is a summary of proposed changes that would produce a more balanced Kafka cluster, with partition workloads distributed more evenly among the brokers. Each optimization proposal is based on the set of optimization goals that was used to generate it, subject to any configured capacity limits on broker resources . An optimization proposal is contained in the Status.Optimization Result property of a KafkaRebalance custom resource. The information provided is a summary of the full optimization proposal. Use the summary to decide whether to: Approve the optimization proposal. This instructs Cruise Control to apply the proposal to the Kafka cluster and start a cluster rebalance operation. Reject the optimization proposal. You can change the optimization goals and then generate another proposal. All optimization proposals are dry runs : you cannot approve a cluster rebalance without first generating an optimization proposal. There is no limit to the number of optimization proposals that can be generated. Cached optimization proposal Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster. If you generate an optimization proposal using the default optimization goals, Cruise Control returns the most recent cached proposal. To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms setting in the Cruise Control deployment configuration. Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server. Contents of optimization proposals An optimization proposal consists of two main sections: The summary is stored in the status of the KafkaRebalance resource. The broker load is stored in a ConfigMap that contains data as a JSON string. The summary provides an overview of the proposed cluster rebalance and indicates the scale of the changes involved. The broker load shows before and after values for the proposed rebalance, so you can see the impact on each of the brokers in the cluster. Summary The following table explains the properties contained in the optimization proposal's summary section: Table 8.1. Properties contained in an optimization proposal JSON property Description numIntraBrokerReplicaMovements The total number of partition replicas that will be transferred between the disks of the cluster's brokers. Performance impact during rebalance operation : Relatively high, but lower than numReplicaMovements . excludedBrokersForLeadership Not yet supported. An empty list is returned. numReplicaMovements The number of partition replicas that will be moved between separate brokers. Performance impact during rebalance operation : Relatively high. onDemandBalancednessScoreBefore, onDemandBalancednessScoreAfter A measurement of the overall balancedness of a Kafka Cluster, before and after the optimization proposal was generated. The score is calculated by subtracting the sum of the BalancednessScore of each violated soft goal from 100. Cruise Control assigns a BalancednessScore to every optimization goal based on several factors, including priority- the goal's position in the list of default.goals or user-provided goals. The Before score is based on the current configuration of the Kafka cluster. The After score is based on the generated optimization proposal. intraBrokerDataToMoveMB The sum of the size of each partition replica that will be moved between disks on the same broker (see also numIntraBrokerReplicaMovements ). Performance impact during rebalance operation : Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see dataToMoveMB ). recentWindows The number of metrics windows upon which the optimization proposal is based. dataToMoveMB The sum of the size of each partition replica that will be moved to a separate broker (see also numReplicaMovements ). Performance impact during rebalance operation : Variable. The larger the number, the longer the cluster rebalance will take to complete. monitoredPartitionsPercentage The percentage of partitions in the Kafka cluster covered by the optimization proposal. Affected by the number of excludedTopics . excludedTopics If you specified a regular expression in the spec.excludedTopicsRegex property in the KafkaRebalance resource, all topic names matching that expression are listed here. These topics are excluded from the calculation of partition replica/leader movements in the optimization proposal. numLeaderMovements The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration. Performance impact during rebalance operation : Relatively low. excludedBrokersForReplicaMove Not yet supported. An empty list is returned. Broker load The broker load is stored in a ConfigMap (with the same name as the KafkaRebalance custom resource) as a JSON formatted string. This JSON string consists of a JSON object with keys for each broker IDs linking to a number of metrics for each broker. Each metric consist of three values. The first is the metric value before the optimization proposal is applied, the second is the expected value of the metric after the proposal is applied, and the third is the difference between the first two values (after minus before). To extract the JSON string from the ConfigMap you could use the following command, which uses the jq command line JSON parser tool: oc get configmap MY-REBALANCE -o json | jq '.["data"]["brokerLoad.json"]|fromjson|.' The following table explains the properties contained in the optimization proposal's broker load ConfigMap: JSON property Description leaders The number of replicas on this broker that are partition leaders. replicas The number of replicas on this broker. cpuPercentage The CPU utilization as a percentage of the defined capacity. diskUsedPercentage The disk utilization as a percentage of the defined capacity. diskUsedMB The absolute disk usage in MB. networkOutRate The total network output rate for the broker. leaderNetworkInRate The network input rate for all partition leader replicas on this broker. followerNetworkInRate The network input rate for all follower replicas on this broker. potentialMaxNetworkOutRate The hypothetical maximum network output rate that would be realized if this broker became the leader of all the replicas it currently hosts. Additional resources Section 8.2, "Optimization goals overview" Section 8.7, "Generating optimization proposals" Section 8.8, "Approving an optimization proposal" 8.4. Rebalance performance tuning overview You can adjust several performance tuning options for cluster rebalances. These options control how partition replica and leadership movements in a rebalance are executed, as well as the bandwidth that is allocated to a rebalance operation. Partition reassignment commands Optimization proposals are comprised of separate partition reassignment commands. When you approve a proposal, the Cruise Control server applies these commands to the Kafka cluster. A partition reassignment command consists of either of the following types of operations: Partition movement: Involves transferring the partition replica and its data to a new location. Partition movements can take one of two forms: Inter-broker movement: The partition replica is moved to a log directory on a different broker. Intra-broker movement: The partition replica is moved to a different log directory on the same broker. Leadership movement: This involves switching the leader of the partition's replicas. Cruise Control issues partition reassignment commands to the Kafka cluster in batches. The performance of the cluster during the rebalance is affected by the number of each type of movement contained in each batch. Replica movement strategies Cluster rebalance performance is also influenced by the replica movement strategy that is applied to the batches of partition reassignment commands. By default, Cruise Control uses the BaseReplicaMovementStrategy , which simply applies the commands in the order they were generated. However, if there are some very large partition reassignments early in the proposal, this strategy can slow down the application of the other reassignments. Cruise Control provides four alternative replica movement strategies that can be applied to optimization proposals: PrioritizeSmallReplicaMovementStrategy : Order reassignments in order of ascending size. PrioritizeLargeReplicaMovementStrategy : Order reassignments in order of descending size. PostponeUrpReplicaMovementStrategy : Prioritize reassignments for replicas of partitions which have no out-of-sync replicas. PrioritizeMinIsrWithOfflineReplicasStrategy : Prioritize reassignments with (At/Under)MinISR partitions with offline replicas. This strategy will only work if cruiseControl.config.concurrency.adjuster.min.isr.check.enabled is set to true in the Kafka custom resource's spec. These strategies can be configured as a sequence. The first strategy attempts to compare two partition reassignments using its internal logic. If the reassignments are equivalent, then it passes them to the strategy in the sequence to decide the order, and so on. Rebalance tuning options Cruise Control provides several configuration options for tuning the rebalance parameters discussed above. You can set these tuning options at either the Cruise Control server or optimization proposal levels: The Cruise Control server setting can be set in the Kafka custom resource under Kafka.spec.cruiseControl.config . The individual rebalance performance configurations can be set under KafkaRebalance.spec . The relevant configurations are summarized below: Server and KafkaRebalance Configuration Description Default Value num.concurrent.partition.movements.per.broker The maximum number of inter-broker partition movements in each partition reassignment batch 5 concurrentPartitionMovementsPerBroker num.concurrent.intra.broker.partition.movements The maximum number of intra-broker partition movements in each partition reassignment batch 2 concurrentIntraBrokerPartitionMovements num.concurrent.leader.movements The maximum number of partition leadership changes in each partition reassignment batch 1000 concurrentLeaderMovements default.replication.throttle The bandwidth (in bytes per second) to be assigned to the reassigning of partitions No Limit replicationThrottle default.replica.movement.strategies The list of strategies (in priority order) used to determine the order in which partition reassignment commands are executed for generated proposals. For the server setting, use a comma separated string with the fully qualified names of the strategy class (add com.linkedin.kafka.cruisecontrol.executor.strategy. to the start of each class name). For the KafkaRebalance resource setting use a YAML array of strategy class names. BaseReplicaMovementStrategy replicaMovementStrategies Changing the default settings affects the length of time that the rebalance takes to complete, as well as the load placed on the Kafka cluster during the rebalance. Using lower values reduces the load but increases the amount of time taken, and vice versa. Additional resources Section 13.2.50, " CruiseControlSpec schema reference" . Section 13.2.131, " KafkaRebalanceSpec schema reference" . 8.5. Cruise Control configuration The config property in Kafka.spec.cruiseControl contains configuration options as keys with values as one of the following JSON types: String Number Boolean You can specify and configure all the options listed in the "Configurations" section of the Cruise Control documentation , apart from those managed directly by AMQ Streams. Specifically, you cannot modify configuration options with keys equal to or starting with one of the keys mentioned here . If restricted options are specified, they are ignored and a warning message is printed to the Cluster Operator log file. All the supported options are passed to Cruise Control. An example Cruise Control configuration apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # ... cruiseControl: # ... config: default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal cpu.balance.threshold: 1.1 metadata.max.age.ms: 300000 send.buffer.bytes: 131072 # ... Cross-Origin Resource Sharing configuration Cross-Origin Resource Sharing (CORS) allows you to specify allowed methods and originating URLs for accessing REST APIs. By default, CORS is disabled for the Cruise Control REST API. When enabled, only GET requests for read-only access to the Kafka cluster state are allowed. This means that external applications, which are running in different origins than the AMQ Streams components, cannot make POST requests to the Cruise Control API. However, those applications can make GET requests to access read-only information about the Kafka cluster, such as the current cluster load or the most recent optimization proposal. Enabling CORS for Cruise Control You enable and configure CORS in Kafka.spec.cruiseControl.config . apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # ... cruiseControl: # ... config: webserver.http.cors.enabled: true webserver.http.cors.origin: "*" webserver.http.cors.exposeheaders: "User-Task-ID,Content-Type" # ... For more information, see REST APIs in the Cruise Control Wiki . Capacity configuration Cruise Control uses capacity limits to determine if optimization goals for resource distribution are being broken. There are four goals of this type: DiskUsageDistributionGoal - Disk utilization distribution CpuUsageDistributionGoal - CPU utilization distribution NetworkInboundUsageDistributionGoal - Network inbound utilization distribution NetworkOutboundUsageDistributionGoal - Network outbound utilization distribution You specify capacity limits for Kafka broker resources in the brokerCapacity property in Kafka.spec.cruiseControl . They are enabled by default and you can change their default values. Capacity limits can be set for the following broker resources, using the standard OpenShift byte units (K, M, G and T) or their bibyte (power of two) equivalents (Ki, Mi, Gi and Ti): disk - Disk storage per broker (Default: 100000Mi) cpuUtilization - CPU utilization as a percentage (Default: 100) inboundNetwork - Inbound network throughput in byte units per second (Default: 10000KiB/s) outboundNetwork - Outbound network throughput in byte units per second (Default: 10000KiB/s) Because AMQ Streams Kafka brokers are homogeneous, Cruise Control applies the same capacity limits to every broker it is monitoring. An example Cruise Control brokerCapacity configuration using bibyte units apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # ... cruiseControl: # ... brokerCapacity: disk: 100Gi cpuUtilization: 100 inboundNetwork: 10000KiB/s outboundNetwork: 10000KiB/s # ... Additional resources For more information, refer to the Section 13.2.52, " BrokerCapacity schema reference" . Logging configuration Cruise Control has its own configurable logger: rootLogger.level Cruise Control uses the Apache log4j 2 logger implementation. Use the logging property to configure loggers and logger levels. You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.valueFrom.configMapKeyRef.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties . Both logging.valueFrom.configMapKeyRef.name and logging.valueFrom.configMapKeyRef.key properties are mandatory. A ConfigMap using the exact logging configuration specified is created with the custom resource when the Cluster Operator is running, then recreated after each reconciliation. If you do not specify a custom ConfigMap, default logging settings are used. If a specific logger value is not set, upper-level logger settings are inherited for that logger. Here we see examples of inline and external logging. Inline logging apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka # ... spec: cruiseControl: # ... logging: type: inline loggers: rootLogger.level: "INFO" # ... External logging apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka # ... spec: cruiseControl: # ... logging: type: external valueFrom: configMapKeyRef: name: customConfigMap key: cruise-control-log4j.properties # ... 8.6. Deploying Cruise Control To deploy Cruise Control to your AMQ Streams cluster, define the configuration using the cruiseControl property in the Kafka resource, and then create or update the resource. Deploy one instance of Cruise Control per Kafka cluster. Prerequisites An OpenShift cluster A running Cluster Operator Procedure Edit the Kafka resource and add the cruiseControl property. The properties you can configure are shown in this example configuration: apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # ... cruiseControl: brokerCapacity: 1 inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s # ... config: 2 default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal # ... cpu.balance.threshold: 1.1 metadata.max.age.ms: 300000 send.buffer.bytes: 131072 # ... resources: 3 requests: cpu: 1 memory: 512Mi limits: cpu: 2 memory: 2Gi logging: 4 type: inline loggers: rootLogger.level: "INFO" template: 5 pod: metadata: labels: label1: value1 securityContext: runAsUser: 1000001 fsGroup: 0 terminationGracePeriodSeconds: 120 readinessProbe: 6 initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: 7 initialDelaySeconds: 15 timeoutSeconds: 5 # ... 1 Specifies capacity limits for broker resources. For more information, see Capacity configuration . 2 Defines the Cruise Control configuration, including the default optimization goals (in default.goals ) and any customizations to the main optimization goals (in goals ) or the hard goals (in hard.goals ). You can provide any standard Cruise Control configuration option apart from those managed directly by AMQ Streams. For more information on configuring optimization goals, see Section 8.2, "Optimization goals overview" . 3 CPU and memory resources reserved for Cruise Control. For more information, see Section 13.1.5, " resources " . 4 Defined loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties key. Cruise Control has a single logger named rootLogger.level . You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information, see Logging configuration . 5 Customization of deployment templates and pods . 6 Healthcheck readiness probes . 7 Healthcheck liveness probes . Create or update the resource: oc apply -f kafka.yaml Verify that Cruise Control was successfully deployed: oc get deployments -l app.kubernetes.io/name=cruise-control Auto-created topics The following table shows the three topics that are automatically created when Cruise Control is deployed. These topics are required for Cruise Control to work properly and must not be deleted or changed. Table 8.2. Auto-created topics Auto-created topic Created by Function strimzi.cruisecontrol.metrics AMQ Streams Metrics Reporter Stores the raw metrics from the Metrics Reporter in each Kafka broker. strimzi.cruisecontrol.partitionmetricsamples Cruise Control Stores the derived metrics for each partition. These are created by the Metric Sample Aggregator . strimzi.cruisecontrol.modeltrainingsamples Cruise Control Stores the metrics samples used to create the Cluster Workload Model . To prevent the removal of records that are needed by Cruise Control, log compaction is disabled in the auto-created topics. What to do After configuring and deploying Cruise Control, you can generate optimization proposals . Additional resources Section 13.2.51, " CruiseControlTemplate schema reference" . 8.7. Generating optimization proposals When you create or update a KafkaRebalance resource, Cruise Control generates an optimization proposal for the Kafka cluster based on the configured optimization goals . Analyze the information in the optimization proposal and decide whether to approve it. Prerequisites You have deployed Cruise Control to your AMQ Streams cluster. You have configured optimization goals and, optionally, capacity limits on broker resources . Procedure Create a KafkaRebalance resource: To use the default optimization goals defined in the Kafka resource, leave the spec property empty: apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: {} To configure user-provided optimization goals instead of using the default goals, add the goals property and enter one or more goals. In the following example, rack awareness and replica capacity are configured as user-provided optimization goals: apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal To ignore the configured hard goals, add the skipHardGoalCheck: true property: apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal skipHardGoalCheck: true Create or update the resource: oc apply -f your-file The Cluster Operator requests the optimization proposal from Cruise Control. This might take a few minutes depending on the size of the Kafka cluster. Check the status of the KafkaRebalance resource: oc describe kafkarebalance rebalance-cr-name Cruise Control returns one of two statuses: PendingProposal : The rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready. ProposalReady : The optimization proposal is ready for review and, if desired, approval. The optimization proposal is contained in the Status.Optimization Result property of the KafkaRebalance resource. Review the optimization proposal. oc describe kafkarebalance rebalance-cr-name Here is an example proposal: Status: Conditions: Last Transition Time: 2020-05-19T13:50:12.533Z Status: ProposalReady Type: State Observed Generation: 1 Optimization Result: Data To Move MB: 0 Excluded Brokers For Leadership: Excluded Brokers For Replica Move: Excluded Topics: Intra Broker Data To Move MB: 0 Monitored Partitions Percentage: 100 Num Intra Broker Replica Movements: 0 Num Leader Movements: 0 Num Replica Movements: 26 On Demand Balancedness Score After: 81.8666802863978 On Demand Balancedness Score Before: 78.01176356230222 Recent Windows: 1 Session Id: 05539377-ca7b-45ef-b359-e13564f1458c The properties in the Optimization Result section describe the pending cluster rebalance operation. For descriptions of each property, see Contents of optimization proposals . What to do Section 8.8, "Approving an optimization proposal" Additional resources Section 8.3, "Optimization proposals overview" 8.8. Approving an optimization proposal You can approve an optimization proposal generated by Cruise Control, if its status is ProposalReady . Cruise Control will then apply the optimization proposal to the Kafka cluster, reassigning partitions to brokers and changing partition leadership. Caution This is not a dry run. Before you approve an optimization proposal, you must: Refresh the proposal in case it has become out of date. Carefully review the contents of the proposal . Prerequisites You have generated an optimization proposal from Cruise Control. The KafkaRebalance custom resource status is ProposalReady . Procedure Perform these steps for the optimization proposal that you want to approve: Unless the optimization proposal is newly generated, check that it is based on current information about the state of the Kafka cluster. To do so, refresh the optimization proposal to make sure it uses the latest cluster metrics: Annotate the KafkaRebalance resource in OpenShift with refresh : oc annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh Check the status of the KafkaRebalance resource: oc describe kafkarebalance rebalance-cr-name Wait until the status changes to ProposalReady . Approve the optimization proposal that you want Cruise Control to apply. Annotate the KafkaRebalance resource in OpenShift: oc annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=approve The Cluster Operator detects the annotated resource and instructs Cruise Control to rebalance the Kafka cluster. Check the status of the KafkaRebalance resource: oc describe kafkarebalance rebalance-cr-name Cruise Control returns one of three statuses: Rebalancing: The cluster rebalance operation is in progress. Ready: The cluster rebalancing operation completed successfully. To use the same KafkaRebalance custom resource to generate another optimization proposal, apply the refresh annotation to the custom resource. This moves the custom resource to the PendingProposal or ProposalReady state. You can then review the optimization proposal and approve it, if desired. NotReady: An error occurred- see Section 8.10, "Fixing problems with a KafkaRebalance resource" . Additional resources Section 8.3, "Optimization proposals overview" Section 8.9, "Stopping a cluster rebalance" 8.9. Stopping a cluster rebalance Once started, a cluster rebalance operation might take some time to complete and affect the overall performance of the Kafka cluster. If you want to stop a cluster rebalance operation that is in progress, apply the stop annotation to the KafkaRebalance custom resource. This instructs Cruise Control to finish the current batch of partition reassignments and then stop the rebalance. When the rebalance has stopped, completed partition reassignments have already been applied; therefore, the state of the Kafka cluster is different when compared to prior to the start of the rebalance operation. If further rebalancing is required, you should generate a new optimization proposal. Note The performance of the Kafka cluster in the intermediate (stopped) state might be worse than in the initial state. Prerequisites You have approved the optimization proposal by annotating the KafkaRebalance custom resource with approve . The status of the KafkaRebalance custom resource is Rebalancing . Procedure Annotate the KafkaRebalance resource in OpenShift: oc annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=stop Check the status of the KafkaRebalance resource: oc describe kafkarebalance rebalance-cr-name Wait until the status changes to Stopped . Additional resources Section 8.3, "Optimization proposals overview" 8.10. Fixing problems with a KafkaRebalance resource If an issue occurs when creating a KafkaRebalance resource or interacting with Cruise Control, the error is reported in the resource status, along with details of how to fix it. The resource also moves to the NotReady state. To continue with the cluster rebalance operation, you must fix the problem in the KafkaRebalance resource itself or with the overall Cruise Control deployment. Problems might include the following: A misconfigured parameter in the KafkaRebalance resource. The strimzi.io/cluster label for specifying the Kafka cluster in the KafkaRebalance resource is missing. The Cruise Control server is not deployed as the cruiseControl property in the Kafka resource is missing. The Cruise Control server is not reachable. After fixing the issue, you need to add the refresh annotation to the KafkaRebalance resource. During a "refresh", a new optimization proposal is requested from the Cruise Control server. Prerequisites You have approved an optimization proposal . The status of the KafkaRebalance custom resource for the rebalance operation is NotReady . Procedure Get information about the error from the KafkaRebalance status: oc describe kafkarebalance rebalance-cr-name Attempt to resolve the issue in the KafkaRebalance resource. Annotate the KafkaRebalance resource in OpenShift: oc annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh Check the status of the KafkaRebalance resource: oc describe kafkarebalance rebalance-cr-name Wait until the status changes to PendingProposal , or directly to ProposalReady . Additional resources Section 8.3, "Optimization proposals overview"

Commands:
RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # zookeeper: # entityOperator: topicOperator: {} userOperator: {} cruiseControl: brokerCapacity: inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s config: hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal #
RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # zookeeper: # entityOperator: topicOperator: {} userOperator: {} cruiseControl: brokerCapacity: inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s config: default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal #
KafkaRebalance.spec.goals
get configmap MY-REBALANCE -o json | jq '.["data"]["brokerLoad.json"]|fromjson|.'
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # cruiseControl: # config: default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal cpu.balance.threshold: 1.1 metadata.max.age.ms: 300000 send.buffer.bytes: 131072 #
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # cruiseControl: # config: webserver.http.cors.enabled: true webserver.http.cors.origin: "*" webserver.http.cors.exposeheaders: "User-Task-ID,Content-Type" #
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # cruiseControl: # brokerCapacity: disk: 100Gi cpuUtilization: 100 inboundNetwork: 10000KiB/s outboundNetwork: 10000KiB/s #
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: cruiseControl: # logging: type: inline loggers: rootLogger.level: "INFO" #
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: cruiseControl: # logging: type: external valueFrom: configMapKeyRef: name: customConfigMap key: cruise-control-log4j.properties #
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # cruiseControl: brokerCapacity: 1 inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s # config: 2 default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal # cpu.balance.threshold: 1.1 metadata.max.age.ms: 300000 send.buffer.bytes: 131072 # resources: 3 requests: cpu: 1 memory: 512Mi limits: cpu: 2 memory: 2Gi logging: 4 type: inline loggers: rootLogger.level: "INFO" template: 5 pod: metadata: labels: label1: value1 securityContext: runAsUser: 1000001 fsGroup: 0 terminationGracePeriodSeconds: 120 readinessProbe: 6 initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: 7 initialDelaySeconds: 15 timeoutSeconds: 5
apply -f kafka.yaml
get deployments -l app.kubernetes.io/name=cruise-control
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: {}
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal skipHardGoalCheck: true
apply -f your-file
describe kafkarebalance rebalance-cr-name
describe kafkarebalance rebalance-cr-name
Status: Conditions: Last Transition Time: 2020-05-19T13:50:12.533Z Status: ProposalReady Type: State Observed Generation: 1 Optimization Result: Data To Move MB: 0 Excluded Brokers For Leadership: Excluded Brokers For Replica Move: Excluded Topics: Intra Broker Data To Move MB: 0 Monitored Partitions Percentage: 100 Num Intra Broker Replica Movements: 0 Num Leader Movements: 0 Num Replica Movements: 26 On Demand Balancedness Score After: 81.8666802863978 On Demand Balancedness Score Before: 78.01176356230222 Recent Windows: 1 Session Id: 05539377-ca7b-45ef-b359-e13564f1458c
annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh
describe kafkarebalance rebalance-cr-name
annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=approve
describe kafkarebalance rebalance-cr-name
annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=stop
describe kafkarebalance rebalance-cr-name
describe kafkarebalance rebalance-cr-name
annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh
describe kafkarebalance rebalance-cr-name