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Tech preview of KEDA in 4.11
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‎_topic_maps/_topic_map.yml

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Distros: openshift-enterprise,openshift-origin
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- Name: Automatically scaling pods with the horizontal pod autoscaler
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File: nodes-pods-autoscaling
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- Name: Automatically scaling pods with the custom metrics autoscaler
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File: nodes-pods-autoscaling-custom
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- Name: Automatically adjust pod resource levels with the vertical pod autoscaler
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File: nodes-pods-vertical-autoscaler
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- Name: Providing sensitive data to pods

‎_unused_topics/nodes-pods-autoscaling-custom-metrics.adoc

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== Supported metrics
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KEDA emits the following Kubernetes events:
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.Metrics
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[cols="3a,5a,5a",options="header"]
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|===
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|Metric |Description |API version
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|ScaledObjectReady
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|Normal
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|On the first time a ScaledObject is ready, or if the previous ready condition status of the object was Unknown or False
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|ScaledJobReady
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|Normal
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|On the first time a ScaledJob is ready, or if the previous ready condition status of the object was Unknown or False
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|ScaledObjectCheckFailed
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|Warning
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|If the check validation for a ScaledObject fails
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|ScaledJobCheckFailed
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|Warning
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|If the check validation for a ScaledJob fails
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|ScaledObjectDeleted
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|Normal
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|When a ScaledObject is deleted and removed from KEDA watch
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|ScaledJobDeleted
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|Normal
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|When a ScaledJob is deleted and removed from KEDA watch
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|KEDAScalersStarted
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|Normal
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|When Scalers watch loop have started for a ScaledObject or ScaledJob
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|KEDAScalersStopped
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|Normal
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|When Scalers watch loop have stopped for a ScaledObject or a ScaledJob
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|KEDAScalerFailed
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|Warning
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|When a Scaler fails to create or check its event source
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|KEDAScaleTargetActivated
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|Normal
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|When the scale target (Deployment, StatefulSet, etc) of a ScaledObject is scaled to 1
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|KEDAScaleTargetDeactivated
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|Normal
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|When the scale target (Deployment, StatefulSet, etc) of a ScaledObject is scaled to 0
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|KEDAScaleTargetActivationFailed
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|Warning
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|When KEDA fails to scale the scale target of a ScaledObject to 1
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|KEDAScaleTargetDeactivationFailed
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|Warning
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|When KEDA fails to scale the scale target of a ScaledObject to 0
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|KEDAJobsCreated
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|Normal
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|When KEDA creates jobs for a ScaledJob
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|TriggerAuthenticationAdded
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|Normal
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|When a new TriggerAuthentication is added
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|TriggerAuthenticationDeleted
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|Normal
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|When a TriggerAuthentication is deleted
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|ClusterTriggerAuthenticationAdded
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|Normal
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|When a new ClusterTriggerAuthentication is added
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|ClusterTriggerAuthenticationDeleted
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|Normal
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|When a ClusterTriggerAuthentication is deleted
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|===

‎modules/nodes-pods-autoscaling-custom-about.adoc

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// Module included in the following assemblies:
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//
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// * nodes/nodes-pods-autoscaling-custom.adoc
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:_content-type: CONCEPT
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[id="nodes-pods-autoscaling-custom-about_{context}"]
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= Understanding the custom metrics autoscaler
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The custom metrics autoscaler uses the Kubernetes-based Event Driven Autoscaler (KEDA) and is built on top of the {product-title} horizontal pod autoscaler (HPA).
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The Custom Metrics Autoscaler Operator scales your pods up and down based on custom, external metrics from specific applications. Your other applications continue to use other scaling methods. You configure _triggers_, also known as scalers, which are the source of events and metrics that the custom metrics autoscaler uses to determine how to scale. The custom metrics autoscaler uses a metrics API to convert the external metrics to a form that {product-title} can use. The custom metrics autoscaler creates a horizontal pod autoscaler (HPA) that performs the actual scaling. The custom metrics autoscaler currently supports only the Prometheus trigger, which can use the installed {product-title} monitoring or an external Prometheus server as the metrics source.
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To use the custom metrics autoscaler, you create a `ScaledObject` or `ScaledJob` object, which defines the scaling metadata. You specify the deployment or job to scale, the source of the metrics to scale on (trigger), and other parameters such as the minimum and maximum replica counts allowed.
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[NOTE]
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====
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You can create only one scaled object or scaled job for each workload that you want to scale. Also, you cannot use a scaled object or scaled job and the horizontal pod autoscaler (HPA) on the same workload.
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====
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////
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[NOTE]
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====
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You can create only one scaled object or scaled job for each workload that you want to scale. Also, you cannot use a scaled object or scaled job and the horizontal pod autoscaler (HPA) on the same workload. If you want to scale based on a custom trigger and CPU/Memory, you can create multiple triggers in the scaled object or scaled job.
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====
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////
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You can verify that the autoscaling has taken place by reviewing the number of pods in your custom resource or by reviewing the Custom Metrics Autoscaler Operator logs for messages similar to the following:
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[source,terminal]
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----
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Successfully set ScaleTarget replica count
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----
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[source,terminal]
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----
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Successfully updated ScaleTarget
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----

‎modules/nodes-pods-autoscaling-custom-adding.adoc

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// Module included in the following assemblies:
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//
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// * nodes/nodes-pods-autoscaling-custom.adoc
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:_content-type: CONCEPT
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[id="nodes-pods-autoscaling-custom-adding_{context}"]
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= Understanding how to add custom metrics autoscalers
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To add a custom metrics autoscaler, create a `ScaledObject` custom resource for a deployment, stateful set, or custom resource. Create a `ScaledJob` custom resource for a job.
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You can create only one scaled object or scaled job for each workload that you want to scale. Also, you cannot use a scaled object or scaled job and the horizontal pod autoscaler (HPA) on the same workload.
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// If you want to scale based on a custom trigger and CPU/Memory, you can create multiple triggers in the scaled object or scaled job.
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‎modules/nodes-pods-autoscaling-custom-creating-job.adoc

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// Module included in the following assemblies:
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//
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// * nodes/nodes-pods-autoscaling-custom.adoc
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:_content-type: PROCEDURE
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[id="nodes-pods-autoscaling-custom-creating-job_{context}"]
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= Adding a custom metrics autoscaler to a job
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You can create a custom metrics autoscaler for any `Job` object.
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.Prerequisites
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* The Custom Metrics Autoscaler Operator must be installed.
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.Procedure
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. Create a YAML file similar to the following:
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[source,yaml,options="nowrap"]
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----
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kind: ScaledJob
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apiVersion: keda.sh/v1alpha1
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metadata:
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name: scaledjob
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namespace: my-namespace
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spec:
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failedJobsHistoryLimit: 5
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jobTargetRef:
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activeDeadlineSeconds: 600 <1>
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backoffLimit: 6 <2>
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parallelism: 1 <3>
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completions: 1 <4>
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template: <5>
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metadata:
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name: pi
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spec:
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containers:
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- name: pi
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image: perl
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command: ["perl", "-Mbignum=bpi", "-wle", "print bpi(2000)"]
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maxReplicaCount: 100 <6>
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pollingInterval: 30 <7>
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successfulJobsHistoryLimit: 5 <8>
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failedJobsHistoryLimit: 5 <9>
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envSourceContainerName <10>
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rolloutStrategy: gradual <11>
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scalingStrategy: <12>
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strategy: "custom"
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customScalingQueueLengthDeduction: 1
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customScalingRunningJobPercentage: "0.5"
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pendingPodConditions:
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- "Ready"
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- "PodScheduled"
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- "AnyOtherCustomPodCondition"
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multipleScalersCalculation : "max"
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triggers:
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- type: prometheus <13>
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metadata:
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serverAddress: https://thanos-querier.openshift-monitoring.svc.cluster.local:9092
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namespace: kedatest
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metricName: http_requests_total
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threshold: '5'
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query: sum(rate(http_requests_total{job="test-app"}[1m]))
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authModes: "bearer"
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- authenticationRef: <14>
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name: prom-triggerauthentication
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metadata:
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name: prom-triggerauthentication
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type: object
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- authenticationRef: <15>
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name: prom-cluster-triggerauthentication
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metadata:
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name: prom-cluster-triggerauthentication
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type: object
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----
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<1> Specifies the maximum duration the job can run.
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<2> Specifies the number of retries for a job. The default is `6`.
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<3> Optional: Specifies how many pod replicas a job should run in parallel; defaults to `1`.
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* For non-parallel jobs, leave unset. When unset, the default is `1`.
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<4> Optional: Specifies how many successful pod completions are needed to mark a job completed.
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* For non-parallel jobs, leave unset. When unset, the default is `1`.
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* For parallel jobs with a fixed completion count, specify the number of completions.
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* For parallel jobs with a work queue, leave unset. When unset the default is the value of the `parallelism` parameter.
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<5> Specifies the template for the pod the controller creates.
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<6> Optional: Specifies the maximum number of replicas when scaling up. The default is `100`.
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<7> Optional: Specifies the interval in seconds to check each trigger on. The default is `30`.
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<8> Optional: Specifies the number of successful finished jobs should be kept. The default is `100`.
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<9> Optional: Specifies how many failed jobs should be kept. The default is `100`.
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<10> Optional: Specifies the name of the container in the target resource, from which the custom autoscaler gets environment variables holding secrets and so forth. The default is `.spec.template.spec.containers[0]`.
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<11> Optional: Specifies whether existing jobs are terminated whenever a scaled job is being updated:
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+
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--
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* `default`: The autoscaler terminates an existing job if its associated scaled job is updated. The autoscaler recreates the job with the latest specs.
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* `gradual`: The autoscaler does not terminate an existing job if its associated scaled job is updated. The autoscaler creates new jobs with the latest specs.
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--
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<12> Optional: Specifies a scaling strategy: `default`, `custom`, or `accurate`. The default is `default`. For more information, see the link in the "Additional resources" section that follows.
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<13> Specifies the trigger to use as the basis for scaling, as described in the "Understanding the custom metrics autoscaler triggers" section.
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<14> Optional: Specifies a trigger authentication, as described in the "Creating a custom metrics autoscaler trigger authentication" section.
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<15> Optional: Specifies a cluster trigger authentication, as described in the "Creating a custom metrics autoscaler trigger authentication" section.
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[NOTE]
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====
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It is not necessary to specify both a namespace trigger authentication and a cluster trigger authentication.
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====
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. Create the custom metrics autoscaler:
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[source,terminal]
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----
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$ oc create -f <file-name>.yaml
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----
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.Verification
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* View the command output to verify that the custom metrics autoscaler was created:
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[source,terminal]
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----
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$ oc get scaledjob <scaled_job_name>
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----
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.Example output
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[source,terminal]
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----
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NAME MAX TRIGGERS AUTHENTICATION READY ACTIVE AGE
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scaledjob 100 prometheus prom-triggerauthentication True True 8s
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----
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Note the following fields in the output:
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* `TRIGGERS`: Indicates the trigger, or scaler, that is being used.
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* `AUTHENTICATION`: Indicates the name of any trigger authentication being used.
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* `READY`: Indicates whether the scaled object is ready to start scaling:
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** If `True`, the scaled object is ready.
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** If `False`, the scaled object is not ready because of a problem in one or more of the objects you created.
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* `ACTIVE`: Indicates whether scaling is taking place:
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** If `True`, scaling is taking place.
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** If `False`, scaling is not taking place because there are no metrics or there is a problem in one or more of the objects you created.
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‎modules/nodes-pods-autoscaling-custom-creating-trigger-auth.adoc

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// Module included in the following assemblies:
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//
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// * nodes/nodes-pods-autoscaling-custom.adoc
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:_content-type: PROCEDURE
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[id="nodes-pods-autoscaling-custom-creating-trigger-auth_{context}"]
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= Using trigger authentications
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You use trigger authentications and cluster trigger authentications by using a custom resource to create the authentication, then add a reference to a scaled object or scaled job.
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.Prerequisites
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* The Custom Metrics Autoscaler Operator must be installed.
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* If you are using a secret, the `Secret` object must exist, for example:
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.Example secret
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[source,yaml]
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----
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apiVersion: v1
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kind: Secret
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metadata:
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name: my-secret
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data:
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user-name: <base64_username>
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password: <base64_password>
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----
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.Procedure
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. Create the `TriggerAuthentication` or `ClusterTriggerAuthentication` object.
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.. Create a YAML file that defines the object:
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.Example trigger authentication with a secret
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[source,yaml]
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----
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kind: TriggerAuthentication
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apiVersion: keda.sh/v1alpha1
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metadata:
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name: prom-triggerauthentication
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namespace: my-namespace
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spec:
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secretTargetRef:
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- parameter: user-name
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name: my-secret
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key: USER_NAME
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- parameter: password
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name: my-secret
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key: PASSWORD
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----
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.. Create the `TriggerAuthentication` object:
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[source,terminal]
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----
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$ oc create -f <file-name>.yaml
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----
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. Create or edit a `ScaledObject` YAML file:
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.Example scaled object
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[source,yaml,options="nowrap"]
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----
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apiVersion: keda.sh/v1alpha1
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kind: ScaledObject
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metadata:
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name: scaledobject
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namespace: my-namespace
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spec:
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scaleTargetRef:
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name: example-deployment
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maxReplicaCount: 100
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minReplicaCount: 0
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pollingInterval: 30
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triggers:
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- authenticationRef:
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type: prometheus
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metadata:
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serverAddress: https://thanos-querier.openshift-monitoring.svc.cluster.local:9092
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namespace: kedatest # replace <NAMESPACE>
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metricName: http_requests_total
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threshold: '5'
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query: sum(rate(http_requests_total{job="test-app"}[1m]))
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authModes: "basic"
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- authenticationRef: <1>
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name: prom-triggerauthentication
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metadata:
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name: prom-triggerauthentication
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type: object
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- authenticationRef: <2>
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name: prom-cluster-triggerauthentication
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kind: ClusterTriggerAuthentication
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metadata:
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name: prom-cluster-triggerauthentication
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type: object
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----
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<1> Optional: Specify a trigger authentication.
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<2> Optional: Specify a cluster trigger authentication. You must include the `kind: ClusterTriggerAuthentication` parameter.
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[NOTE]
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====
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It is not necessary to specify both a namespace trigger authentication and a cluster trigger authentication.
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====
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. Create the object. For example:
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[source,terminal]
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----
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$ oc apply -f <file-name>
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----
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