Overview

Scaling of Deployments and StatefulSets

Deployments and StatefulSets are the most common way to scale workloads with KEDA.

It allows you to define the Kubernetes Deployment or StatefulSet that you want KEDA to scale based on a scale trigger. KEDA will monitor that service and based on the events that occur it will automatically scale your resource out/in accordingly.

Behind the scenes, KEDA acts to monitor the event source and feed that data to Kubernetes and the HPA (Horizontal Pod Autoscaler) to drive rapid scale of a resource. Each replica of a resource is actively pulling items from the event source. With KEDA and scaling Deployments/StatefulSet you can scale based on events while also preserving rich connection and processing semantics with the event source (e.g. in-order processing, retries, deadletter, checkpointing).

For example, if you wanted to use KEDA with an Apache Kafka topic as event source, the flow of information would be:

• When no messages are pending processing, KEDA can scale the deployment to zero.
• When a message arrives, KEDA detects this event and activates the deployment.
• When the deployment starts running, one of the containers connects to Kafka and starts pulling messages.
• As more messages arrive at the Kafka Topic, KEDA can feed this data to the HPA to drive scale out.
• Each replica of the deployment is actively processing messages. Very likely, each replica is processing a batch of messages in a distributed manner.

Scaling of Custom Resources

With KEDA you can scale any workload defined as any Custom Resource (for example ArgoRollout resource). The scaling behaves the same way as scaling for arbitrary Kubernetes Deployment or StatefulSet.

The only constraint is that the target Custom Resource must define /scale subresource.

ScaledObject spec

This specification describes the ScaledObject Custom Resource definition which is used to define how KEDA should scale your application and what the triggers are. The .spec.ScaleTargetRef section holds the reference to the target resource, ie. Deployment, StatefulSet or Custom Resource.

scaledobject_types.go

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: {scaled-object-name}
  annotations:
    scaledobject.keda.sh/transfer-hpa-ownership: "true"     # Optional. Use to transfer an existing HPA ownership to this ScaledObject
    validations.keda.sh/hpa-ownership: "true"               # Optional. Use to disable HPA ownership validation on this ScaledObject
    autoscaling.keda.sh/paused: "true"                      # Optional. Use to pause autoscaling of objects explicitly
spec:
  scaleTargetRef:
    apiVersion:    {api-version-of-target-resource}         # Optional. Default: apps/v1
    kind:          {kind-of-target-resource}                # Optional. Default: Deployment
    name:          {name-of-target-resource}                # Mandatory. Must be in the same namespace as the ScaledObject
    envSourceContainerName: {container-name}                # Optional. Default: .spec.template.spec.containers[0]
  pollingInterval:  30                                      # Optional. Default: 30 seconds
  cooldownPeriod:   300                                     # Optional. Default: 300 seconds
  idleReplicaCount: 0                                       # Optional. Default: ignored, must be less than minReplicaCount
  minReplicaCount:  1                                       # Optional. Default: 0
  maxReplicaCount:  100                                     # Optional. Default: 100
  fallback:                                                 # Optional. Section to specify fallback options
    failureThreshold: 3                                     # Mandatory if fallback section is included
    replicas: 6                                             # Mandatory if fallback section is included
  advanced:                                                 # Optional. Section to specify advanced options
    restoreToOriginalReplicaCount: true/false               # Optional. Default: false
    horizontalPodAutoscalerConfig:                          # Optional. Section to specify HPA related options
      name: {name-of-hpa-resource}                          # Optional. Default: keda-hpa-{scaled-object-name}
      behavior:                                             # Optional. Use to modify HPA's scaling behavior
        scaleDown:
          stabilizationWindowSeconds: 300
          policies:
          - type: Percent
            value: 100
            periodSeconds: 15
  triggers:
  # {list of triggers to activate scaling of the target resource}

Details

  scaleTargetRef:
    apiVersion:    {api-version-of-target-resource}  # Optional. Default: apps/v1
    kind:          {kind-of-target-resource}         # Optional. Default: Deployment
    name:          {name-of-target-resource}         # Mandatory. Must be in the same namespace as the ScaledObject
    envSourceContainerName: {container-name}         # Optional. Default: .spec.template.spec.containers[0]

The reference to the resource this ScaledObject is configured for. This is the resource KEDA will scale up/down and setup an HPA for, based on the triggers defined in triggers:.

To scale Kubernetes Deployments only name is needed to be specified, if one wants to scale a different resource such as StatefulSet or Custom Resource (that defines /scale subresource), appropriate apiVersion (following standard Kubernetes convention, ie. {api}/{version}) and kind need to be specified.

envSourceContainerName is an optional property that specifies the name of container in the target resource, from which KEDA should try to get environment properties holding secrets etc. If it is not defined, KEDA will try to get environment properties from the first Container, ie. from .spec.template.spec.containers[0].

Assumptions: Resource referenced by name (and apiVersion, kind) is in the same namespace as the ScaledObject

pollingInterval

  pollingInterval: 30  # Optional. Default: 30 seconds

This is the interval to check each trigger on. By default, KEDA will check each trigger source on every ScaledObject every 30 seconds.

Example: in a queue scenario, KEDA will check the queueLength every pollingInterval, and scale the resource up or down accordingly.

cooldownPeriod

  cooldownPeriod:  300 # Optional. Default: 300 seconds

The period to wait after the last trigger reported active before scaling the resource back to 0. By default, it’s 5 minutes (300 seconds).

The cooldownPeriod only applies after a trigger occurs; when you first create your Deployment (or StatefulSet/CustomResource), KEDA will immediately scale it to minReplicaCount. Additionally, the KEDA cooldownPeriod only applies when scaling to 0; scaling from 1 to N replicas is handled by the Kubernetes Horizontal Pod Autoscaler.

Example: wait 5 minutes after the last time KEDA checked the queue and it was empty. (this is obviously dependent on pollingInterval)

initialCooldownPeriod

   initialCooldownPeriod:  120 # Optional. Default: 0 seconds

The delay before the cooldownPeriod starts after the initial creation of the ScaledObject. By default, this is 0 seconds, meaning the cooldownPeriod begins immediately upon creation. If set to a value such as 120 seconds, the cooldownPeriod will only start after the ScaledObject has been active for that duration.

This parameter is particularly useful for managing the scale-down behavior during the initial phase of a ScaledObject. For instance, if initialCooldownPeriod is set to 120 seconds, KEDA will not scale the resource back to 0 until 120 seconds have passed since the ScaledObject creation, regardless of the activity triggers. This allows for a grace period in situations where immediate scaling down after creation is not desirable.

Example: Wait 120 seconds after the ScaledObject is created before starting the cooldownPeriod. For instance, if the initialCooldownPeriod is set to 120 seconds, KEDA will not initiate the cooldown process until 120 seconds have passed since the ScaledObject was first created, regardless of the triggers’ activity. This ensures a buffer period where the resource won’t be scaled down immediately after creation. (Note: This setting is independent of the pollingInterval.)

idleReplicaCount

  idleReplicaCount: 0   # Optional. Default: ignored, must be less than minReplicaCount

💡 NOTE: Due to limitations in HPA controller the only supported value for this property is 0, it will not work correctly otherwise. See this issue for more details.

In some cases, you always need at least n pod running. Thus, you can omit this property and set minReplicaCount to n.

Example You set minReplicaCount to 1 and maxReplicaCount to 10. If there’s no activity on triggers, the target resource is scaled down to minReplicaCount (1). Once there are activities, the target resource will scale base on the HPA rule. If there’s no activity on triggers, the resource is again scaled down to minReplicaCount (1).

If this property is set, KEDA will scale the resource down to this number of replicas. If there’s some activity on target triggers KEDA will scale the target resource immediately to minReplicaCount and then will be scaling handled by HPA. When there is no activity, the target resource is again scaled down to idleReplicaCount. This setting must be less than minReplicaCount.

Example: If there’s no activity on triggers the target resource is scaled down to idleReplicaCount (0), once there is an activity the target resource is immediately scaled to minReplicaCount (10) and then up to maxReplicaCount (100) as needed. If there’s no activity on triggers the resource is again scaled down to idleReplicaCount (0).

minReplicaCount

  minReplicaCount: 1   # Optional. Default: 0

Minimum number of replicas KEDA will scale the resource down to. By default, it’s scale to zero, but you can use it with some other value as well.

maxReplicaCount

  maxReplicaCount: 100 # Optional. Default: 100

This setting is passed to the HPA definition that KEDA will create for a given resource and holds the maximum number of replicas of the target resource.

fallback

  fallback:                                          # Optional. Section to specify fallback options
    failureThreshold: 3                              # Mandatory if fallback section is included
    replicas: 6                                      # Mandatory if fallback section is included

The fallback section is optional. It defines a number of replicas to fall back to if a scaler is in an error state.

KEDA will keep track of the number of consecutive times each scaler has failed to get metrics from its source. Once that value passes the failureThreshold, instead of not propagating a metric to the HPA (the default error behaviour), the scaler will, instead, return a normalised metric using the formula:

target metric value * fallback replicas

Due to the HPA metric being of type AverageValue (see below), this will have the effect of the HPA scaling the deployment to the defined number of fallback replicas.

Example: When my instance of prometheus is unavailable 3 consecutive times, KEDA will change the HPA metric such that the deployment will scale to 6 replicas.

There are a few limitations to using a fallback:

• It only supports scalers whose target is an AverageValue metric. Thus, it is not supported by the CPU & memory scalers, or by scalers whose metric target type is Value. In these cases, it will assume that fallback is disabled.
• It is only supported by ScaledObjects not ScaledJobs.

advanced

advanced:
  restoreToOriginalReplicaCount: true/false        # Optional. Default: false

This property specifies whether the target resource (Deployment, StatefulSet,…) should be scaled back to original replicas count, after the ScaledObject is deleted. Default behavior is to keep the replica count at the same number as it is in the moment of ScaledObject's deletion.

For example a Deployment with 3 replicas is created, then ScaledObject is created and the Deployment is scaled by KEDA to 10 replicas. Then ScaledObject is deleted:

1. if restoreToOriginalReplicaCount = false (default behavior) then Deployment replicas count is 10
2. if restoreToOriginalReplicaCount = true then Deployment replicas count is set back to 3 (the original value)

advanced:
  horizontalPodAutoscalerConfig:                   # Optional. Section to specify HPA related options
    name: {name-of-hpa-resource}                   # Optional. Default: keda-hpa-{scaled-object-name}
    behavior:                                      # Optional. Use to modify HPA's scaling behavior
      scaleDown:
        stabilizationWindowSeconds: 300
        policies:
        - type: Percent
          value: 100
          periodSeconds: 15

horizontalPodAutoscalerConfig:

horizontalPodAutoscalerConfig.name

The name of the HPA resource KEDA will create. By default, it’s keda-hpa-{scaled-object-name}

horizontalPodAutoscalerConfig.behavior

Starting from Kubernetes v1.18 the autoscaling API allows scaling behavior to be configured through the HPA behavior field. This way one can directly affect scaling of 1<->N replicas, which is internally being handled by HPA. KEDA would feed values from this section directly to the HPA’s behavior field. Please follow Kubernetes documentation for details.

Assumptions: KEDA must be running on Kubernetes cluster v1.18+, in order to be able to benefit from this setting.

advanced:
  scalingModifiers:                                       # Optional. Section to specify scaling modifiers
    target: {target-value-to-scale-on}                        # Mandatory. New target if metrics are anyhow composed together
    activationTarget: {activation-target-value-to-scale-on}   # Optional. New activation target if metrics are anyhow composed together
    metricType:  {metric-tipe-for-the-modifier}               # Optional. Metric type to be used if metrics are anyhow composed together
    formula: {formula-for-fetched-metrics}                    # Mandatory. Formula for calculation

scalingModifiers

The scalingModifiers is optional and experimental. If defined, both target and formula are mandatory. Using this structure creates composite-metric for the HPA that will replace all requests for external metrics and handle them internally. With scalingModifiers each trigger used in the formula must have a name defined.

scalingModifiers.target

target defines new target value to scale on for the composed metric.

scalingModifiers.activationTarget

activationTarget defines new activation target value to scale on for the composed metric. (Default: 0, Optional)

scalingModifiers.metricType

metricType defines metric type used for this new composite-metric. (Values: AverageValue, Value, Default: AverageValue, Optional)

scalingModifiers.formula

formula composes metrics together and allows them to be modified/manipulated. It accepts mathematical/conditional statements using this external project. If the fallback scaling feature is in effect, the formula will NOT modify its metrics (therefore it modifies metrics only when all of their triggers are healthy). Complete language definition of expr package can be found here. Formula must return a single value (not boolean).

For examples of this feature see section Scaling Modifiers below.

triggers

  triggers:
  # {list of triggers to activate scaling of the target resource}

💡 NOTE: You can find all supported triggers here.

Trigger fields:

• type: The type of trigger to use. (Mandatory)
• metadata: The configuration parameters that the trigger requires. (Mandatory)
• name: Name for this trigger. This value can be used to easily distinguish this specific trigger and its metrics when consuming Prometheus metrics. By default, the name is generated from the trigger type. (Optional)
• useCachedMetrics: Enables caching of metric values during polling interval (as specified in .spec.pollingInterval). For more information, see “Caching Metrics”. (Values: false, true, Default: false, Optional)
• authenticationRef: A reference to the TriggerAuthentication or ClusterTriggerAuthentication object that is used to authenticate the scaler with the environment.
  • More details can be found here. (Optional)
• metricType: The type of metric that should be used. (Values: AverageValue, Value, Utilization, Default: AverageValue, Optional)
  • Learn more about how the Horizontal Pod Autoscaler (HPA) calculates replicaCount based on metric type and value.
  • To show the differences between the metric types, let’s assume we want to scale a deployment with 3 running replicas based on a queue of messages:
    • With AverageValue metric type, we can control how many messages, on average, each replica will handle. If our metric is the queue size, the threshold is 5 messages, and the current message count in the queue is 20, HPA will scale the deployment to 20 / 5 = 4 replicas, regardless of the current replica count.
    • The Value metric type, on the other hand, can be used when we don’t want to take the average of the given metric across all replicas. For example, with the Value type, we can control the average time of messages in the queue. If our metric is average time in the queue, the threshold is 5 milliseconds, and the current average time is 20 milliseconds, HPA will scale the deployment to 3 * 20 / 5 = 12.

⚠️ NOTE: All scalers, except CPU and Memory, support metric types AverageValue and Value while CPU and Memory scalers both support AverageValue and Utilization.

Caching Metrics

This feature enables caching of metric values during polling interval (as specified in .spec.pollingInterval). Kubernetes (HPA controller) asks for a metric every few seconds (as defined by --horizontal-pod-autoscaler-sync-period, usually 15s), then this request is routed to KEDA Metrics Server, that by default queries the scaler and reads the metric values. Enabling this feature changes this behavior, KEDA Metrics Server tries to read metric from the cache first. This cache is being updated periodically during the polling interval.

Enabling this feature can significantly reduce the load on the scaler service.

This feature is not supported for cpu, memory or cron scaler.

Pause autoscaling

It can be useful to instruct KEDA to pause autoscaling of objects, if you want to do to cluster maintenance or you want to avoid resource starvation by removing non-mission-critical workloads. You can enable this by adding the below annotation to your ScaledObject definition:

metadata:
  annotations:
    autoscaling.keda.sh/paused-replicas: "0"
    autoscaling.keda.sh/paused: "true"

The presence of these annotations will pause autoscaling no matter what number of replicas is provided.

The annotation autoscaling.keda.sh/paused will pause scaling immediately and use the current instance count while the annotation autoscaling.keda.sh/paused-replicas: "<number>" will scale your current workload to specified amount of replicas and pause autoscaling. You can set the value of replicas for an object to be paused to any arbitrary number.

Typically, either one or the other is being used given they serve a different purpose/scenario. However, if both paused and paused-replicas are set, KEDA will scale your current workload to the number specified count in paused-replicas and then pause autoscaling.

To enable/unpause autoscaling again, simply remove all paused annotations from the ScaledObject definition. If you paused with autoscaling.keda.sh/paused, you can also set the annotation to false to unpause.

Scaling Modifiers (Experimental)

Example: compose average value

advanced:
  scalingModifiers:
    formula: "(trig_one + trig_two)/2"
    target: "2"
    activationTarget: "2"
    metricType: "AverageValue"
...
triggers:
  - type: kubernetes-workload
    name: trig_one
    metadata:
      podSelector: 'pod=workload-test'
  - type: metrics-api
    name: trig_two
    metadata:
      url: "https://mockbin.org/bin/336a8d99-9e09-4f1f-979d-851a6d1b1423"
      valueLocation: "tasks"

Formula composes 2 given metrics from 2 triggers kubernetes-workload named trig_one and metrics-api named trig_two together as an average value and returns one final metric which is used to make autoscaling decisions on.

Example: activationTarget

advanced:
  scalingModifiers:
    activationTarget: "2"

If the calculated value is <=2, the ScaledObject is not Active and it’ll scale to 0 if it’s allowed.

Example: ternary operator

advanced:
  scalingModifiers:
    formula: "trig_one > 2 ? trig_one + trig_two : 1"

If metric value of trigger trig_one is more than 2, then return trig_one + trig_two otherwise return 1.

Example: count function

advanced:
  scalingModifiers:
    formula: "count([trig_one,trig_two,trig_three],{#>1}) > 1 ? 5 : 0"

If at least 2 metrics (from the list trig_one,trig_two,trig_three) have value of more than 1, then return 5, otherwise return 0

Example: nested conditions and operators

advanced:
  scalingModifiers:
    formula: "trig_one < 2 ? trig_one+trig_two >= 2 ? 5 : 10 : 0"

Conditions can be used within another condition as well. If value of trig_one is less than 2 AND trig_one+trig_two is at least 2 then return 5, if only the first is true return 10, if the first condition is false then return 0.

Complete language definition of expr package can be found here. Formula must return a single value (not boolean). All formulas are internally wrapped with float cast.

Activating and Scaling thresholds

To give a consistent solution to this problem, KEDA has 2 different phases during the autoscaling process.

• Activation phase: The activating (or deactivating) phase is the moment when KEDA (operator) has to decide if the workload should be scaled from/to zero. KEDA takes responsibility for this action based on the result of the scaler IsActive function and only applies to 0<->1 scaling. There are use-cases where the activating value (0-1 and 1-0) is totally different than 0, such as workloads scaled with the Prometheus scaler where the values go from -X to X.
• Scaling phase: The scaling phase is the moment when KEDA has decided to scale out to 1 instance and now it is the HPA controller who takes the scaling decisions based on the configuration defined in the generated HPA (from ScaledObject data) and the metrics exposed by KEDA (metrics server). This phase applies the to 1<->N scaling.

Managing Activation & Scaling Thresholds

KEDA allows you to specify different values for each scenario:

• Activation: Defines when the scaler is active or not and scales from/to 0 based on it.
• Scaling: Defines the target value to scale the workload from 1 to n instances and vice versa. To achieve this, KEDA passes the target value to the Horizontal Pod Autoscaler (HPA) and the built-in HPA controller will handle all the autoscaling.

⚠️ NOTE: If the minimum replicas is >= 1, the scaler is always active and the activation value will be ignored.

Each scaler defines parameters for their use-cases, but the activation will always be the same as the scaling value, appended by the prefix activation (ie: threshold for scaling and activationThreshold for activation).

There are some important topics to take into account:

• Opposite to scaling value, the activation value is always optional and the default value is 0.
• Activation only occurs when this value is greater than the set value; not greater than or equal to.
  • ie, in the default case: activationThreshold: 0 will only activate when the metric value is 1 or more
• The activation value has more priority than the scaling value in case of different decisions for each. ie: threshold: 10 and activationThreshold: 50, in case of 40 messages the scaler is not active and it’ll be scaled to zero even the HPA requires 4 instances.

⚠️ NOTE: If a scaler doesn’t define “activation” parameter (a property that starts with activation prefix), then this specific scaler doesn’t support configurable activation value and the activation value is always 0.

Transfer ownership of an existing HPA

If your environment already operates using kubernetes HPA, you can transfer the ownership of this resource to a new ScaledObject:

metadata:
  annotations:
    scaledobject.keda.sh/transfer-hpa-ownership: "true"
spec:
   advanced:
      horizontalPodAutoscalerConfig:
        name: {name-of-hpa-resource}

⚠️ NOTE: You need to specify a custom HPA name in your ScaledObject matching the existing HPA name you want it to manage.

Disable validations on an existing HPA

You are allowed to disable admission webhooks validations with the following snippet. It grants you better flexibility but also brings vulnerabilities. Do it at your own risk.

metadata:
  annotations:
    validations.keda.sh/hpa-ownership: "true"

Long-running executions

One important consideration to make is how this pattern can work with long-running executions. Imagine a deployment triggers on a RabbitMQ queue message. Each message takes 3 hours to process. It’s possible that if many queue messages arrive, KEDA will help drive scaling out to many replicas - let’s say 4. Now the HPA makes a decision to scale down from 4 replicas to 2. There is no way to control which of the 2 replicas get terminated to scale down. That means the HPA may attempt to terminate a replica that is 2.9 hours into processing a 3 hour queue message.

There are two main ways to handle this scenario.

Leverage the container lifecycle

Kubernetes provides a few lifecycle hooks that can be leveraged to delay termination. Imagine a replica is scheduled for termination and is 2.9 hours into processing a 3 hour message. Kubernetes will send a SIGTERM to signal the intent to terminate. Rather than immediately terminating, a deployment can delay termination until processing the current batch of messages has completed. Kubernetes will wait for a SIGTERM response or the terminationGracePeriodSeconds before killing the replica.

💡 NOTE: There are other ways to delay termination, including the preStop Hook.

Using this method can preserve a replica and enable long-running executions. However, one downside of this approach is while delaying termination, the pod phase will remain in the Terminating state. That means a pod that is delaying termination for a very long duration may show Terminating during that entire period of delay.

Run as jobs

The other alternative to handling long-running executions is by running the event driven code in Kubernetes Jobs instead of Deployments or Custom Resources. This approach is discussed in the next section.