simplekafkacluster.yaml

apiVersion: kafka.banzaicloud.io/v1beta1
kind: KafkaCluster
metadata:
  labels:
    controller-tools.k8s.io: "1.0"
  name: kafka
spec:
  monitoringConfig:
    jmxImage: "ghcr.io/banzaicloud/jmx-javaagent:0.16.1"
  headlessServiceEnabled: true
  zkAddresses:
    - "zookeeper-client.zookeeper:2181"
  propagateLabels: false
  oneBrokerPerNode: false
  clusterImage: "ghcr.io/banzaicloud/kafka:2.13-3.1.0"
  readOnlyConfig: |
    auto.create.topics.enable=false
    cruise.control.metrics.topic.auto.create=true
    cruise.control.metrics.topic.num.partitions=1
    cruise.control.metrics.topic.replication.factor=2
  brokerConfigGroups:
    default:
      # podSecurityContext:
      #  runAsNonRoot: false
      # securityContext:
      #  privileged: true
      storageConfigs:
        - mountPath: "/kafka-logs"
          pvcSpec:
            accessModes:
              - ReadWriteOnce
            resources:
              requests:
                storage: 10Gi
      brokerAnnotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "9020"
      # brokerLabels:
      #   kafka_broker_group: "default_group"
  brokers:
    - id: 0
      brokerConfigGroup: "default"
      # brokerConfig:
      #   envs:
      #     - name: +CLASSPATH
      #       value: "/opt/kafka/libs/dev/*:"
      #     - name: CLASSPATH+
      #       value: ":/opt/kafka/libs/extra-jars/*"
    - id: 1
      brokerConfigGroup: "default"
    - id: 2
      brokerConfigGroup: "default"
  rollingUpgradeConfig:
    failureThreshold: 1
  listenersConfig:
    internalListeners:
      - type: "plaintext"
        name: "internal"
        containerPort: 29092
        usedForInnerBrokerCommunication: true
      - type: "plaintext"
        name: "controller"
        containerPort: 29093
        usedForInnerBrokerCommunication: false
        usedForControllerCommunication: true
  cruiseControlConfig:
    # podSecurityContext:
    #  runAsNonRoot: false
    # securityContext:
    #  privileged: true
    cruiseControlTaskSpec:
      RetryDurationMinutes: 5
    topicConfig:
      partitions: 12
      replicationFactor: 3
#    resourceRequirements:
#      requests:
#        cpu: 500m
#        memory: 1Gi
#      limits:
#        cpu: 500m
#        memory: 1Gi
#    image: "ghcr.io/banzaicloud/cruise-control:2.5.86"
    config: |
      # Copyright 2017 LinkedIn Corp. Licensed under the BSD 2-Clause License (the "License"). See License in the project root for license information.
      #
      # This is an example property file for Kafka Cruise Control. See KafkaCruiseControlConfig for more details.
      # Configuration for the metadata client.
      # =======================================
      # The maximum interval in milliseconds between two metadata refreshes.
      #metadata.max.age.ms=300000
      # Client id for the Cruise Control. It is used for the metadata client.
      #client.id=kafka-cruise-control
      # The size of TCP send buffer bytes for the metadata client.
      #send.buffer.bytes=131072
      # The size of TCP receive buffer size for the metadata client.
      #receive.buffer.bytes=131072
      # The time to wait before disconnect an idle TCP connection.
      #connections.max.idle.ms=540000
      # The time to wait before reconnect to a given host.
      #reconnect.backoff.ms=50
      # The time to wait for a response from a host after sending a request.
      #request.timeout.ms=30000
      # Configurations for the load monitor
      # =======================================
      # The number of metric fetcher thread to fetch metrics for the Kafka cluster
      num.metric.fetchers=1
      # The metric sampler class
      metric.sampler.class=com.linkedin.kafka.cruisecontrol.monitor.sampling.CruiseControlMetricsReporterSampler
      # Configurations for CruiseControlMetricsReporterSampler
      metric.reporter.topic.pattern=__CruiseControlMetrics
      # The sample store class name
      sample.store.class=com.linkedin.kafka.cruisecontrol.monitor.sampling.KafkaSampleStore
      # The config for the Kafka sample store to save the partition metric samples
      partition.metric.sample.store.topic=__KafkaCruiseControlPartitionMetricSamples
      # The config for the Kafka sample store to save the model training samples
      broker.metric.sample.store.topic=__KafkaCruiseControlModelTrainingSamples
      # The replication factor of Kafka metric sample store topic
      sample.store.topic.replication.factor=2
      # The config for the number of Kafka sample store consumer threads
      num.sample.loading.threads=8
      # The partition assignor class for the metric samplers
      metric.sampler.partition.assignor.class=com.linkedin.kafka.cruisecontrol.monitor.sampling.DefaultMetricSamplerPartitionAssignor
      # The metric sampling interval in milliseconds
      metric.sampling.interval.ms=120000
      metric.anomaly.detection.interval.ms=180000
      # The partition metrics window size in milliseconds
      partition.metrics.window.ms=300000
      # The number of partition metric windows to keep in memory
      num.partition.metrics.windows=1
      # The minimum partition metric samples required for a partition in each window
      min.samples.per.partition.metrics.window=1
      # The broker metrics window size in milliseconds
      broker.metrics.window.ms=300000
      # The number of broker metric windows to keep in memory
      num.broker.metrics.windows=20
      # The minimum broker metric samples required for a partition in each window
      min.samples.per.broker.metrics.window=1
      # The configuration for the BrokerCapacityConfigFileResolver (supports JBOD and non-JBOD broker capacities)
      capacity.config.file=config/capacity.json
      #capacity.config.file=config/capacityJBOD.json
      # Configurations for the analyzer
      # =======================================
      # The list of goals to optimize the Kafka cluster for with pre-computed proposals
      default.goals=com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.CpuCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.PotentialNwOutGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.CpuUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.TopicReplicaDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.LeaderBytesInDistributionGoal
      # The list of supported goals
      goals=com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.CpuCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.PotentialNwOutGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.CpuUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.TopicReplicaDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.LeaderBytesInDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.kafkaassigner.KafkaAssignerDiskUsageDistributionGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.PreferredLeaderElectionGoal
      # The list of supported hard goals
      hard.goals=com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.CpuCapacityGoal
      # The minimum percentage of well monitored partitions out of all the partitions
      min.monitored.partition.percentage=0.95
      # The balance threshold for CPU
      cpu.balance.threshold=1.1
      # The balance threshold for disk
      disk.balance.threshold=1.1
      # The balance threshold for network inbound utilization
      network.inbound.balance.threshold=1.1
      # The balance threshold for network outbound utilization
      network.outbound.balance.threshold=1.1
      # The balance threshold for the replica count
      replica.count.balance.threshold=1.1
      # The capacity threshold for CPU in percentage
      cpu.capacity.threshold=0.8
      # The capacity threshold for disk in percentage
      disk.capacity.threshold=0.8
      # The capacity threshold for network inbound utilization in percentage
      network.inbound.capacity.threshold=0.8
      # The capacity threshold for network outbound utilization in percentage
      network.outbound.capacity.threshold=0.8
      # The threshold to define the cluster to be in a low CPU utilization state
      cpu.low.utilization.threshold=0.0
      # The threshold to define the cluster to be in a low disk utilization state
      disk.low.utilization.threshold=0.0
      # The threshold to define the cluster to be in a low network inbound utilization state
      network.inbound.low.utilization.threshold=0.0
      # The threshold to define the cluster to be in a low disk utilization state
      network.outbound.low.utilization.threshold=0.0
      # The metric anomaly percentile upper threshold
      metric.anomaly.percentile.upper.threshold=90.0
      # The metric anomaly percentile lower threshold
      metric.anomaly.percentile.lower.threshold=10.0
      # How often should the cached proposal be expired and recalculated if necessary
      proposal.expiration.ms=60000
      # The maximum number of replicas that can reside on a broker at any given time.
      max.replicas.per.broker=10000
      # The number of threads to use for proposal candidate precomputing.
      num.proposal.precompute.threads=1
      # the topics that should be excluded from the partition movement.
      #topics.excluded.from.partition.movement
      # Configurations for the executor
      # =======================================
      # The max number of partitions to move in/out on a given broker at a given time.
      num.concurrent.partition.movements.per.broker=10
      # The interval between two execution progress checks.
      execution.progress.check.interval.ms=10000
      # Configurations for anomaly detector
      # =======================================
      # The goal violation notifier class
      anomaly.notifier.class=com.linkedin.kafka.cruisecontrol.detector.notifier.SelfHealingNotifier
      # The metric anomaly finder class
      metric.anomaly.finder.class=com.linkedin.kafka.cruisecontrol.detector.KafkaMetricAnomalyFinder
      # The anomaly detection interval
      anomaly.detection.interval.ms=10000
      # The goal violation to detect.
      anomaly.detection.goals=com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal,com.linkedin.kafka.cruisecontrol.analyzer.goals.CpuCapacityGoal
      # The interested metrics for metric anomaly analyzer.
      metric.anomaly.analyzer.metrics=BROKER_PRODUCE_LOCAL_TIME_MS_MAX,BROKER_PRODUCE_LOCAL_TIME_MS_MEAN,BROKER_CONSUMER_FETCH_LOCAL_TIME_MS_MAX,BROKER_CONSUMER_FETCH_LOCAL_TIME_MS_MEAN,BROKER_FOLLOWER_FETCH_LOCAL_TIME_MS_MAX,BROKER_FOLLOWER_FETCH_LOCAL_TIME_MS_MEAN,BROKER_LOG_FLUSH_TIME_MS_MAX,BROKER_LOG_FLUSH_TIME_MS_MEAN
      ## Adjust accordingly if your metrics reporter is an older version and does not produce these metrics.
      #metric.anomaly.analyzer.metrics=BROKER_PRODUCE_LOCAL_TIME_MS_50TH,BROKER_PRODUCE_LOCAL_TIME_MS_999TH,BROKER_CONSUMER_FETCH_LOCAL_TIME_MS_50TH,BROKER_CONSUMER_FETCH_LOCAL_TIME_MS_999TH,BROKER_FOLLOWER_FETCH_LOCAL_TIME_MS_50TH,BROKER_FOLLOWER_FETCH_LOCAL_TIME_MS_999TH,BROKER_LOG_FLUSH_TIME_MS_50TH,BROKER_LOG_FLUSH_TIME_MS_999TH
      # The zk path to store failed broker information.
      failed.brokers.zk.path=/CruiseControlBrokerList
      # Topic config provider class
      topic.config.provider.class=com.linkedin.kafka.cruisecontrol.config.KafkaTopicConfigProvider
      # The cluster configurations for the KafkaTopicConfigProvider
      cluster.configs.file=config/clusterConfigs.json
      # The maximum time in milliseconds to store the response and access details of a completed user task.
      completed.user.task.retention.time.ms=21600000
      # The maximum time in milliseconds to retain the demotion history of brokers.
      demotion.history.retention.time.ms=86400000
      # The maximum number of completed user tasks for which the response and access details will be cached.
      max.cached.completed.user.tasks=500
      # The maximum number of user tasks for concurrently running in async endpoints across all users.
      max.active.user.tasks=25
      # Enable self healing for all anomaly detectors, unless the particular anomaly detector is explicitly disabled
      self.healing.enabled=true
      # Enable self healing for broker failure detector
      #self.healing.broker.failure.enabled=true
      # Enable self healing for goal violation detector
      #self.healing.goal.violation.enabled=true
      # Enable self healing for metric anomaly detector
      #self.healing.metric.anomaly.enabled=true
      # configurations for the webserver
      # ================================
      # HTTP listen port
      webserver.http.port=9090
      # HTTP listen address
      webserver.http.address=0.0.0.0
      # Whether CORS support is enabled for API or not
      webserver.http.cors.enabled=false
      # Value for Access-Control-Allow-Origin
      webserver.http.cors.origin=http://localhost:8080/
      # Value for Access-Control-Request-Method
      webserver.http.cors.allowmethods=OPTIONS,GET,POST
      # Headers that should be exposed to the Browser (Webapp)
      # This is a special header that is used by the
      # User Tasks subsystem and should be explicitly
      # Enabled when CORS mode is used as part of the
      # Admin Interface
      webserver.http.cors.exposeheaders=User-Task-ID
      # REST API default prefix
      # (dont forget the ending *)
      webserver.api.urlprefix=/kafkacruisecontrol/*
      # Location where the Cruise Control frontend is deployed
      webserver.ui.diskpath=./cruise-control-ui/dist/
      # URL path prefix for UI
      # (dont forget the ending *)
      webserver.ui.urlprefix=/*
      # Time After which request is converted to Async
      webserver.request.maxBlockTimeMs=10000
      # Default Session Expiry Period
      webserver.session.maxExpiryTimeMs=60000
      # Session cookie path
      webserver.session.path=/
      # Server Access Logs
      webserver.accesslog.enabled=true
      # Location of HTTP Request Logs
      webserver.accesslog.path=access.log
      # HTTP Request Log retention days
      webserver.accesslog.retention.days=14
    clusterConfig: |
      {
        "min.insync.replicas": 3
      }