Adding an auto scaling policy to an Amazon Aurora DB cluster

You can add a scaling policy to an Aurora DB cluster by using the AWS Management Console.

The following dialog box creates an Auto Scaling policy based an average CPU utilization of 40 percent. The policy specifies a minimum of 5 Aurora Replicas and a maximum of 15 Aurora Replicas.

The following dialog box creates an auto scaling policy based an average number of connections of 100. The policy specifies a minimum of two Aurora Replicas and a maximum of eight Aurora Replicas.

You can apply a scaling policy based on either a predefined or custom metric. To do so, you can use the AWS CLI or the Application Auto Scaling API. The first step is to register your Aurora DB cluster with Application Auto Scaling.

Registering an Aurora DB cluster

Before you can use Aurora Auto Scaling with an Aurora DB cluster, you register your Aurora DB cluster with Application Auto Scaling. You do so to define the scaling dimension and limits to be applied to that cluster. Application Auto Scaling dynamically scales the Aurora DB cluster along the rds:cluster:ReadReplicaCount scalable dimension, which represents the number of Aurora Replicas.

To register your Aurora DB cluster, you can use either the AWS CLI or the Application Auto Scaling API.

AWS CLI

To register your Aurora DB cluster, use the register-scalable-target AWS CLI command with the following parameters:

aws application-autoscaling register-scalable-target \
    --service-namespace rds \
    --resource-id cluster:myscalablecluster \
    --scalable-dimension rds:cluster:ReadReplicaCount \
    --min-capacity 1 \
    --max-capacity 8 \
                    

aws application-autoscaling register-scalable-target ^
    --service-namespace rds ^
    --resource-id cluster:myscalablecluster ^
    --scalable-dimension rds:cluster:ReadReplicaCount ^
    --min-capacity 1 ^
    --max-capacity 8 ^
                   

Application Auto Scaling API

To register your Aurora DB cluster with Application Auto Scaling, use the RegisterScalableTarget Application Auto Scaling API operation with the following parameters:

POST / HTTP/1.1
Host: autoscaling.us-east-2.amazonaws.com
Accept-Encoding: identity
Content-Length: 219
X-Amz-Target: AnyScaleFrontendService.RegisterScalableTarget
X-Amz-Date: 20160506T182145Z
User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8
Content-Type: application/x-amz-json-1.1
Authorization: AUTHPARAMS

{
    "ServiceNamespace": "rds",
    "ResourceId": "cluster:myscalablecluster",
    "ScalableDimension": "rds:cluster:ReadReplicaCount",
    "MinCapacity": 1,
    "MaxCapacity": 8
}

Defining a scaling policy for an Aurora DB cluster

A target-tracking scaling policy configuration is represented by a JSON block that the metrics and target values are defined in. You can save a scaling policy configuration as a JSON block in a text file. You use that text file when invoking the AWS CLI or the Application Auto Scaling API. For more information about policy configuration syntax, see TargetTrackingScalingPolicyConfiguration in the Application Auto Scaling API Reference.

The following options are available for defining a target-tracking scaling policy configuration.

Using a predefined metric

By using predefined metrics, you can quickly define a target-tracking scaling policy for an Aurora DB cluster that works well with both target tracking and dynamic scaling in Aurora Auto Scaling.

Currently, Aurora supports the following predefined metrics in Aurora Auto Scaling:

For more information about the CPUUtilization and DatabaseConnections metrics, see Amazon CloudWatch metrics for Amazon Aurora.

To use a predefined metric in your scaling policy, you create a target tracking configuration for your scaling policy. This configuration must include a PredefinedMetricSpecification for the predefined metric and a TargetValue for the target value of that metric.

{
    "TargetValue": 40.0,
    "PredefinedMetricSpecification":
    {
        "PredefinedMetricType": "RDSReaderAverageCPUUtilization"
    }
}

Using a custom metric

By using custom metrics, you can define a target-tracking scaling policy that meets your custom requirements. You can define a custom metric based on any Aurora metric that changes in proportion to scaling.

Not all Aurora metrics work for target tracking. The metric must be a valid utilization metric and describe how busy an instance is. The value of the metric must increase or decrease in proportion to the number of Aurora Replicas in the Aurora DB cluster. This proportional increase or decrease is necessary to use the metric data to proportionally scale out or in the number of Aurora Replicas.

{
    "TargetValue": 50,
    "CustomizedMetricSpecification":
    {
        "MetricName": "CPUUtilization",
        "Namespace": "AWS/RDS",
        "Dimensions": [
            {"Name": "DBClusterIdentifier","Value": "my-db-cluster"},
            {"Name": "Role","Value": "READER"}
        ],
        "Statistic": "Average",
        "Unit": "Percent"
    }
}

Using cooldown periods

You can specify a value, in seconds, for ScaleOutCooldown to add a cooldown period for scaling out your Aurora DB cluster. Similarly, you can add a value, in seconds, for ScaleInCooldown to add a cooldown period for scaling in your Aurora DB cluster. For more information about ScaleInCooldown and ScaleOutCooldown, see TargetTrackingScalingPolicyConfiguration in the Application Auto Scaling API Reference.

{
    "TargetValue": 40.0,
    "PredefinedMetricSpecification":
    {
        "PredefinedMetricType": "RDSReaderAverageCPUUtilization"
    },
    "ScaleInCooldown": 600,
    "ScaleOutCooldown": 300
}

Disabling scale-in activity

You can prevent the target-tracking scaling policy configuration from scaling in your Aurora DB cluster by disabling scale-in activity. Disabling scale-in activity prevents the scaling policy from deleting Aurora Replicas, while still allowing the scaling policy to create them as needed.

You can specify a Boolean value for DisableScaleIn to enable or disable scale in activity for your Aurora DB cluster. For more information about DisableScaleIn, see TargetTrackingScalingPolicyConfiguration in the Application Auto Scaling API Reference.

{
    "TargetValue": 40.0,
    "PredefinedMetricSpecification":
    {
        "PredefinedMetricType": "RDSReaderAverageCPUUtilization"
    },
    "DisableScaleIn": true
}

Applying a scaling policy to an Aurora DB cluster

After registering your Aurora DB cluster with Application Auto Scaling and defining a scaling policy, you apply the scaling policy to the registered Aurora DB cluster. To apply a scaling policy to an Aurora DB cluster, you can use the AWS CLI or the Application Auto Scaling API.

aws application-autoscaling put-scaling-policy \
    --policy-name myscalablepolicy \
    --policy-type TargetTrackingScaling \
    --resource-id cluster:myscalablecluster \
    --service-namespace rds \
    --scalable-dimension rds:cluster:ReadReplicaCount \
    --target-tracking-scaling-policy-configuration file://config.json

aws application-autoscaling put-scaling-policy ^
    --policy-name myscalablepolicy ^
    --policy-type TargetTrackingScaling ^
    --resource-id cluster:myscalablecluster ^
    --service-namespace rds ^
    --scalable-dimension rds:cluster:ReadReplicaCount ^
    --target-tracking-scaling-policy-configuration file://config.json

POST / HTTP/1.1
Host: autoscaling.us-east-2.amazonaws.com
Accept-Encoding: identity
Content-Length: 219
X-Amz-Target: AnyScaleFrontendService.PutScalingPolicy
X-Amz-Date: 20160506T182145Z
User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8
Content-Type: application/x-amz-json-1.1
Authorization: AUTHPARAMS

{
    "PolicyName": "myscalablepolicy",
    "ServiceNamespace": "rds",
    "ResourceId": "cluster:myscalablecluster",
    "ScalableDimension": "rds:cluster:ReadReplicaCount",
    "PolicyType": "TargetTrackingScaling",
    "TargetTrackingScalingPolicyConfiguration": {
        "TargetValue": 40.0,
        "PredefinedMetricSpecification":
        {
            "PredefinedMetricType": "RDSReaderAverageCPUUtilization"
        }
    }
}