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Package software.amazon.awscdk.services.applicationautoscaling

AWS Auto Scaling Construct Library

See: Description

Package software.amazon.awscdk.services.applicationautoscaling Description

AWS Auto Scaling Construct Library

---

cfn-resources: Stable

cdk-constructs: Stable


Application AutoScaling is used to configure autoscaling for all services other than scaling EC2 instances. For example, you will use this to scale ECS tasks, DynamoDB capacity, Spot Fleet sizes, Comprehend document classification endpoints, Lambda function provisioned concurrency and more.

As a CDK user, you will probably not have to interact with this library directly; instead, it will be used by other construct libraries to offer AutoScaling features for their own constructs.

This document will describe the general autoscaling features and concepts; your particular service may offer only a subset of these.

AutoScaling basics

Resources can offer one or more attributes to autoscale, typically representing some capacity dimension of the underlying service. For example, a DynamoDB Table offers autoscaling of the read and write capacity of the table proper and its Global Secondary Indexes, an ECS Service offers autoscaling of its task count, an RDS Aurora cluster offers scaling of its replica count, and so on.

When you enable autoscaling for an attribute, you specify a minimum and a maximum value for the capacity. AutoScaling policies that respond to metrics will never go higher or lower than the indicated capacity (but scheduled scaling actions might, see below).

There are three ways to scale your capacity:

The general pattern of autoscaling will look like this:

 SomeScalableResource resource;
 
 
 ScalableAttribute capacity = resource.autoScaleCapacity(new Caps()
         .minCapacity(5)
         .maxCapacity(100)
         );
 

Step Scaling

This type of scaling scales in and out in deterministic steps that you configure, in response to metric values. For example, your scaling strategy to scale in response to CPU usage might look like this:

  Scaling        -1          (no change)          +1       +3
             │        │                       │        │        │
             ├────────┼───────────────────────┼────────┼────────┤
             │        │                       │        │        │
 CPU usage   0%      10%                     50%       70%     100%
 

(Note that this is not necessarily a recommended scaling strategy, but it's a possible one. You will have to determine what thresholds are right for you).

You would configure it like this:

 ScalableAttribute capacity;
 Metric cpuUtilization;
 
 
 capacity.scaleOnMetric("ScaleToCPU", BasicStepScalingPolicyProps.builder()
         .metric(cpuUtilization)
         .scalingSteps(List.of(ScalingInterval.builder().upper(10).change(-1).build(), ScalingInterval.builder().lower(50).change(+1).build(), ScalingInterval.builder().lower(70).change(+3).build()))
 
         // Change this to AdjustmentType.PercentChangeInCapacity to interpret the
         // 'change' numbers before as percentages instead of capacity counts.
         .adjustmentType(AdjustmentType.CHANGE_IN_CAPACITY)
         .build());
 

The AutoScaling construct library will create the required CloudWatch alarms and AutoScaling policies for you.

Scaling based on multiple datapoints

The Step Scaling configuration above will initiate a scaling event when a single datapoint of the scaling metric is breaching a scaling step breakpoint. In cases where you might want to initiate scaling actions on a larger number of datapoints (ie in order to smooth out randomness in the metric data), you can use the optional evaluationPeriods and datapointsToAlarm properties:

 ScalableAttribute capacity;
 Metric cpuUtilization;
 
 
 capacity.scaleOnMetric("ScaleToCPUWithMultipleDatapoints", BasicStepScalingPolicyProps.builder()
         .metric(cpuUtilization)
         .scalingSteps(List.of(ScalingInterval.builder().upper(10).change(-1).build(), ScalingInterval.builder().lower(50).change(+1).build(), ScalingInterval.builder().lower(70).change(+3).build()))
 
         // if the cpuUtilization metric has a period of 1 minute, then data points
         // in the last 10 minutes will be evaluated
         .evaluationPeriods(10)
 
         // Only trigger a scaling action when 6 datapoints out of the last 10 are
         // breaching. If this is left unspecified, then ALL datapoints in the
         // evaluation period must be breaching to trigger a scaling action
         .datapointsToAlarm(6)
         .build());
 

Target Tracking Scaling

This type of scaling scales in and out in order to keep a metric (typically representing utilization) around a value you prefer. This type of scaling is typically heavily service-dependent in what metric you can use, and so different services will have different methods here to set up target tracking scaling.

The following example configures the read capacity of a DynamoDB table to be around 60% utilization:

 import software.amazon.awscdk.services.dynamodb.*;
 
 Table table;
 
 
 IScalableTableAttribute readCapacity = table.autoScaleReadCapacity(EnableScalingProps.builder()
         .minCapacity(10)
         .maxCapacity(1000)
         .build());
 readCapacity.scaleOnUtilization(UtilizationScalingProps.builder()
         .targetUtilizationPercent(60)
         .build());
 

Scheduled Scaling

This type of scaling is used to change capacities based on time. It works by changing the minCapacity and maxCapacity of the attribute, and so can be used for two purposes:

The following schedule expressions can be used:

Of these, the cron expression is the most useful but also the most complicated. A schedule is expressed as a cron expression. The Schedule class has a cron method to help build cron expressions.

The following example scales the fleet out in the morning, and lets natural scaling take over at night:

 SomeScalableResource resource;
 
 
 ScalableAttribute capacity = resource.autoScaleCapacity(new Caps()
         .minCapacity(1)
         .maxCapacity(50)
         );
 
 capacity.scaleOnSchedule("PrescaleInTheMorning", ScalingSchedule.builder()
         .schedule(Schedule.cron(CronOptions.builder().hour("8").minute("0").build()))
         .minCapacity(20)
         .build());
 
 capacity.scaleOnSchedule("AllowDownscalingAtNight", ScalingSchedule.builder()
         .schedule(Schedule.cron(CronOptions.builder().hour("20").minute("0").build()))
         .minCapacity(1)
         .build());
 

Examples

Lambda Provisioned Concurrency Auto Scaling

 import software.amazon.awscdk.services.lambda.*;
 
 Code code;
 
 
 Function handler = Function.Builder.create(this, "MyFunction")
         .runtime(Runtime.PYTHON_3_7)
         .handler("index.handler")
         .code(code)
 
         .reservedConcurrentExecutions(2)
         .build();
 
 Version fnVer = handler.addVersion("CDKLambdaVersion", undefined, "demo alias", 10);
 
 ScalableTarget target = ScalableTarget.Builder.create(this, "ScalableTarget")
         .serviceNamespace(ServiceNamespace.LAMBDA)
         .maxCapacity(100)
         .minCapacity(10)
         .resourceId(String.format("function:%s:%s", handler.getFunctionName(), fnVer.getVersion()))
         .scalableDimension("lambda:function:ProvisionedConcurrency")
         .build();
 
 target.scaleToTrackMetric("PceTracking", BasicTargetTrackingScalingPolicyProps.builder()
         .targetValue(0.9)
         .predefinedMetric(PredefinedMetric.LAMBDA_PROVISIONED_CONCURRENCY_UTILIZATION)
         .build());
 

ElastiCache Redis shards scaling with target value

 ScalableTarget shardsScalableTarget = ScalableTarget.Builder.create(this, "ElastiCacheRedisShardsScalableTarget")
         .serviceNamespace(ServiceNamespace.ELASTICACHE)
         .scalableDimension("elasticache:replication-group:NodeGroups")
         .minCapacity(2)
         .maxCapacity(10)
         .resourceId("replication-group/main-cluster")
         .build();
 
 shardsScalableTarget.scaleToTrackMetric("ElastiCacheRedisShardsCPUUtilization", BasicTargetTrackingScalingPolicyProps.builder()
         .targetValue(20)
         .predefinedMetric(PredefinedMetric.ELASTICACHE_PRIMARY_ENGINE_CPU_UTILIZATION)
         .build());
 
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