Configuring vertical autoscaling for Amazon EMR on EKS - Amazon EMR
Required parametersOptional parameters
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Configuring vertical autoscaling for Amazon EMR on EKS

You can configure vertical autoscaling when you submit Amazon EMR Spark jobs through the StartJobRun API. Set the autoscaling-related configuration parameters on the Spark driver pod as shown in the example in Submitting a Spark job with vertical autoscaling.

The Amazon EMR on EKS vertical autoscaling operator listens to driver pods that have autoscaling, then sets up integration with the Kubernetes Vertical Pod Autoscaler (VPA) with the settings on the driver pod. This facilitates resource tracking and autoscaling of Spark executor pods.

The following sections describe the parameters that you can use when you configure vertical autoscaling for your Amazon EKS cluster.

Note

Configure the feature toggle parameter as a label, and configure the remaining parameters as annotations on the Spark driver pod. The autoscaling parameters belong to the emr-containers.amazonaws.com/ domain and have the dynamic.sizing prefix.

Required parameters

You must include the following two parameters on the Spark job driver when you submit your job:

Key Description Accepted values Default value Type Spark parameter1

dynamic.sizing

Feature toggle

true, false

not set

label

spark.kubernetes.driver.label.emr-containers.amazonaws.com/dynamic.sizing

dynamic.sizing.signature

Job signature

string

not set

annotation

spark.kubernetes.driver.annotation.emr-containers.amazonaws.com/dynamic.sizing.signature

1 Use this parameter as a SparkSubmitParameter or ConfigurationOverride in the StartJobRun API.

  • dynamic.sizing – You can turn vertical autoscaling on and off with the dynamic.sizing label. To turn on vertical autoscaling, set dynamic.sizing to true on the Spark driver pod. If you omit this label or set it to any value other than true, vertical autoscaling is off.

  • dynamic.sizing.signature – Set the job signature with the dynamic.sizing.signature annotation on the driver pod. Vertical autoscaling aggregates your resource usage data across different runs of Amazon EMR Spark jobs to derive resource recommendations. You provide the unique identifier to tie the jobs together.

    Note

    If your job recurs at a fixed interval such as daily or weekly, then your job signature should remain the same for each new instance of the job. This ensures that vertical autoscaling can compute and aggregate recommendations across different runs of the job.

1 Use this parameter as a SparkSubmitParameter or ConfigurationOverride in the StartJobRun API.

Optional parameters

Vertical autoscaling also supports the following optional parameters. Set them as annotations on the driver pod.

Key Description Accepted values Default value Type Spark parameter1

dynamic.sizing.mode

Vertical autoscaling mode

Off, Initial, Auto

Off

annotation

spark.kubernetes.driver.label.emr-containers.amazonaws.com/dynamic.sizing.mode

dynamic.sizing.scale.memory

Enables memory scaling

true, false

true

annotation

spark.kubernetes.driver.label.emr-containers.amazonaws.com/dynamic.sizing.scale.memory

dynamic.sizing.scale.cpu

Turn CPU scaling on or off

true, false

false

annotation

spark.kubernetes.driver.label.emr-containers.amazonaws.com/dynamic.sizing.scale.cpu

dynamic.sizing.scale.memory.min

Minumum limit for memory scaling

 string, K8s resource quantity ex: 1G

not set

annotation

 spark.kubernetes.driver.label.emr-containers.amazonaws.com/dynamic.sizing.scale.memory.min

dynamic.sizing.scale.memory.max

Maximum limit for memory scaling

 string, K8s resource quantity ex: 4G

not set

annotation

 spark.kubernetes.driver.label.emr-containers.amazonaws.com/dynamic.sizing.scale.memory.max

dynamic.sizing.scale.cpu.min

Minimum limit for CPU scaling

 string, K8s resource quantity ex: 1

not set

annotation

 spark.kubernetes.driver.label.emr-containers.amazonaws.com/dynamic.sizing.scale.cpu.min

dynamic.sizing.scale.cpu.max

Maximum limit for CPU scaling

 string, K8s resource quantity ex: 2

not set

annotation

 spark.kubernetes.driver.label.emr-containers.amazonaws.com/dynamic.sizing.scale.cpu.max

Vertical autoscaling modes

The mode parameter maps to the different autoscaling modes that the VPA supports. Use the dynamic.sizing.mode annotation on the driver pod to set the mode. The following values are supported for this parameter:

  • Off – A dry-run mode where you can monitor recommendations, but autoscaling is not performed. This is the default mode for vertical autoscaling. In this mode, the associated vertical pod autoscaler resource computes recommendations, and you can monitor the recommendations through tools like kubectl, Prometheus, and Grafana.

  • Initial – In this mode, VPA autoscales resources when the job starts if recommendations are available based on historic runs of the job, such as in the case of a recurring job.

  • Auto – In this mode, VPA evicts Spark executor pods, and autoscales them with the recommended resource settings when the Spark driver pod restarts them. Sometimes, the VPA evicts running Spark executor pods, so it might result in additional latency when it retries the interrupted executor.

Resource scaling

When you set up vertical autoscaling, you can choose whether to scale CPU and memory resources. Set the dynamic.sizing.scale.cpu and dynamic.sizing.scale.memory annotations to true or false. By default, CPU scaling is set to false, and memory scaling is set to true.

Resource minimums and maximums (Bounds)

Optionally, you can also set boundaries on the CPU and memory resources. Choose a minimum and maximum value for these resources with the dynamic.sizing.[memory/cpu].[min/max] annotations when you enable autoscaling. By default, the resources have no limitations. Set the annotations as string values that represent a Kubernetes resource quantity. For example, set dynamic.sizing.memory.max to 4G to represent 4 GB.