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Overview

As an alternate to scaling event-driven code as deployments you can also run and scale your code as Kubernetes Jobs. The primary reason to consider this option is to handle processing long-running executions. Rather than processing multiple events within a deployment, for each detected event a single Kubernetes Job is scheduled. That job will initialize, pull a single event from the message source, and process to completion and terminate.

For example, if you wanted to use KEDA to run a job for each message that lands on a RabbitMQ queue, the flow may be:

When no messages are awaiting processing, no jobs are created.
When a message arrives on the queue, KEDA creates a job.
When the job starts running, it pulls a single message and processes it to completion.
As additional messages arrive, additional jobs are created. Each job processes a single message to completion.
Periodically remove completed/failed job by the SuccessfulJobsHistoryLimit and FailedJobsHistoryLimit.

ScaledJob spec

This specification describes the ScaledJob custom resource definition which is used to define how KEDA should scale your application and what the triggers are.

scaledjob_types.go

apiVersion: keda.sh/v1alpha1
kind: ScaledJob
metadata:
  name: {scaled-job-name}
spec:
  jobTargetRef:
    parallelism: 1                            # [max number of desired pods](https://kubernetes.io/docs/concepts/workloads/controllers/job/#controlling-parallelism)
    completions: 1                            # [desired number of successfully finished pods](https://kubernetes.io/docs/concepts/workloads/controllers/job/#controlling-parallelism)
    activeDeadlineSeconds: 600                #  Specifies the duration in seconds relative to the startTime that the job may be active before the system tries to terminate it; value must be positive integer
    backoffLimit: 6                           # Specifies the number of retries before marking this job failed. Defaults to 6
    template:
      # describes the [job template](https://kubernetes.io/docs/concepts/workloads/controllers/job)
  pollingInterval: 30                         # Optional. Default: 30 seconds
  successfulJobsHistoryLimit: 5               # Optional. Default: 100. How many completed jobs should be kept.
  failedJobsHistoryLimit: 5                   # Optional. Default: 100. How many failed jobs should be kept.
  envSourceContainerName: {container-name}    # Optional. Default: .spec.JobTargetRef.template.spec.containers[0]
  maxReplicaCount: 100                        # Optional. Default: 100
  rolloutStrategy: gradual                    # Optional. Default: default. Which Rollout Strategy KEDA will use.
  scalingStrategy:
    strategy: "custom"                        # Optional. Default: default. Which Scaling Strategy to use. 
    customScalingQueueLengthDeduction: 1      # Optional. A parameter to optimize custom ScalingStrategy.
    customScalingRunningJobPercentage: "0.5"  # Optional. A parameter to optimize custom ScalingStrategy.
    pendingPodConditions:                     # Optional. A parameter to calculate pending job count per the specified pod conditions
      - "Ready"
      - "PodScheduled"
      - "AnyOtherCustomPodCondition"
    multipleScalersCalculation : "max" # Optional. Default: max. Specifies how to calculate the target metrics when multiple scalers are defined.
  triggers:
  # {list of triggers to create jobs}

You can find all supported triggers here.

Details

  jobTargetRef:
    parallelism: 1              # Max number of desired instances ([docs](https://kubernetes.io/docs/concepts/workloads/controllers/job/#controlling-parallelism))
    completions: 1              # Desired number of successfully finished instances ([docs](https://kubernetes.io/docs/concepts/workloads/controllers/job/#controlling-parallelism))
    activeDeadlineSeconds: 600  # Specifies the duration in seconds relative to the startTime that the job may be active before the system tries to terminate it; value must be positive integer
    backoffLimit: 6             # Specifies the number of retries before marking this job failed. Defaults to 6

  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 ScaledJob every 30 seconds.

  successfulJobsHistoryLimit: 5  # Optional. Default: 100. How many completed jobs should be kept.
  failedJobsHistoryLimit: 5      # Optional. Default: 100. How many failed jobs should be kept.

The successfulJobsHistoryLimit and failedJobsHistoryLimit fields are optional. These fields specify how many completed and failed jobs should be kept. By default, they are set to 100.

This concept is similar to Jobs History Limits allowing you to learn what the outcomes of your jobs are.

The actual number of jobs could exceed the limit in a short time. However, it is going to resolve in the cleanup period. Currently, the cleanup period is the same as the Polling interval.

  envSourceContainerName: {container-name}  # Optional. Default: .spec.JobTargetRef.template.spec.containers[0]

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

  maxReplicaCount: 100 # Optional. Default: 100

The max number of pods that is created within a single polling period. If there are running jobs, the number of running jobs will be deducted. This table is an example of the scaling logic.

Queue Length	Max Replica Count	Target Average Value	Running Job Count	Number of the Scale
10	3	1	0	3
10	3	2	0	3
10	3	1	1	2
10	100	1	0	10
4	3	5	0	1

Queue Length: The number of items in the queue.
Target Average Value: The number of messages that will be consumed on a job. It is defined on the scaler side. e.g. queueLength on Azure Storage Queue scaler.
Running Job Count: How many jobs are running.
Number of the Scale: The number of the job that is created.

  rolloutStrategy: default # Optional. Default: default. Which Rollout Strategy KEDA will use.

This optional property specifies the rollout strategy KEDA will use while updating an existing ScaledJob. Possible values are default or gradual.
When using the default rolloutStrategy, KEDA will terminate existing Jobs whenever a ScaledJob is being updated. Then, it will recreate those Jobs with the latest specs.
On the gradual rolloutStartegy, whenever a ScaledJob is being updated, KEDA will not delete existing Jobs. Only new Jobs will be created with the latest specs.

scalingStrategy:
  strategy: "default"                 # Optional. Default: default. Which Scaling Strategy to use.

Select a Scaling Strategy. Possible values are default, custom, or accurate. The default value is default.

💡 NOTE:
maxScale is not the running Job count. It is measured as follows:
maxScale = min(scaledJob.MaxReplicaCount(), divideWithCeil(queueLength, targetAverageValue))
That means it will use the value of queueLength divided by targetAvarageValue unless it is exceeding the MaxReplicaCount.
RunningJobCount represents the number of jobs that are currently running or have not finished yet.
It is measured as follows:
if !e.isJobFinished(&job) {
  	runningJobs++
}
PendingJobCount provides an indication of the amount of jobs that are in pending state. Pending jobs can be calculated in two ways:

Default behavior - Job that have not finished yet and the underlying pod is either not running or has not been completed yet
Setting pendingPodConditions - Job that has not finished yet and all specified pod conditions of the underlying pod mark as true by kubernetes.
It is measured as follows:
if !e.isJobFinished(&job) {
  if len(scaledJob.Spec.ScalingStrategy.PendingPodConditions) > 0 {
      if !e.areAllPendingPodConditionsFulfilled(&job, scaledJob.Spec.ScalingStrategy.PendingPodConditions) {
          pendingJobs++
      }
  } else {
      if !e.isAnyPodRunningOrCompleted(&job) {
          pendingJobs++
      }
  }
}

default This logic is the same as Job for V1. The number of the scale will be calculated as follows.

The number of the scale

maxScale - runningJobCount

custom You can customize the default scale logic. You need to configure the following parameters. If you don’t configure it, then the strategy will be default.

customScalingQueueLengthDeduction: 1      # Optional. A parameter to optimize custom ScalingStrategy.
customScalingRunningJobPercentage: "0.5"  # Optional. A parameter to optimize custom ScalingStrategy.

The number of the scale

min(maxScale-int64(*s.CustomScalingQueueLengthDeduction)-int64(float64(runningJobCount)*(*s.CustomScalingRunningJobPercentage)), maxReplicaCount)

accurate If the scaler returns queueLength (number of items in the queue) that does not include the number of locked messages, this strategy is recommended. Azure Storage Queue is one example. You can use this strategy if you delete a message once your app consumes it.

if (maxScale + runningJobCount) > maxReplicaCount {
		return maxReplicaCount - runningJobCount
	}
	return maxScale - pendingJobCount

For more details, you can refer to this PR.

scalingStrategy:
    multipleScalersCalculation : "max" # Optional. Default: max. Specifies how to calculate the target metrics (`queueLength` and `maxScale`) when multiple scalers are defined.

Select a behavior if you have multiple triggers. Possible values are max, min, avg, or sum. The default value is max.

max: - Use metrics from the scaler that has the max number of queueLength. (default)
min: - Use metrics from the scaler that has the min number of queueLength.
avg: - Sum up all the active scalers metrics and divide by the number of active scalers.
sum: - Sum up all the active scalers metrics.

Sample

apiVersion: v1
kind: Secret
metadata:
  name: rabbitmq-consumer
data:
  RabbitMqHost: <omitted>
---
apiVersion: keda.sh/v1alpha1
kind: ScaledJob
metadata:
  name: rabbitmq-consumer
  namespace: default
spec:
  jobTargetRef:
    template:
      spec:
        containers:
        - name: demo-rabbitmq-client
          image: demo-rabbitmq-client:1
          imagePullPolicy: Always
          command: ["receive",  "amqp://user:PASSWORD@rabbitmq.default.svc.cluster.local:5672"]
          envFrom:
            - secretRef:
                name: rabbitmq-consumer-secrets
        restartPolicy: Never
    backoffLimit: 4  
  pollingInterval: 10             # Optional. Default: 30 seconds
  maxReplicaCount: 30             # Optional. Default: 100
  successfulJobsHistoryLimit: 3   # Optional. Default: 100. How many completed jobs should be kept.
  failedJobsHistoryLimit: 2       # Optional. Default: 100. How many failed jobs should be kept.
  scalingStrategy:
    strategy: "custom"                        # Optional. Default: default. Which Scaling Strategy to use.
    customScalingQueueLengthDeduction: 1      # Optional. A parameter to optimize custom ScalingStrategy.
    customScalingRunningJobPercentage: "0.5"  # Optional. A parameter to optimize custom ScalingStrategy.
  triggers:
  - type: rabbitmq
    metadata:
      queueName: hello
      host: RabbitMqHost
      queueLength  : '5'