@aws-cdk/aws-applicationautoscaling

What is @aws-cdk/aws-applicationautoscaling?

@aws-cdk/aws-applicationautoscaling is an AWS CDK library that allows you to configure application auto-scaling for various AWS resources. It provides constructs for defining scaling policies and targets, enabling you to automatically adjust capacity based on demand.

What are @aws-cdk/aws-applicationautoscaling's main functionalities?

Auto-scaling for DynamoDB

This feature allows you to set up auto-scaling for a DynamoDB table. The code sample demonstrates how to create a DynamoDB table and configure auto-scaling for its read capacity based on utilization.

const app = new cdk.App();
const stack = new cdk.Stack(app, 'AutoScalingStack');

const table = new dynamodb.Table(stack, 'MyTable', {
  partitionKey: { name: 'id', type: dynamodb.AttributeType.STRING },
  billingMode: dynamodb.BillingMode.PROVISIONED,
});

const readScaling = table.autoScaleReadCapacity({
  minCapacity: 1,
  maxCapacity: 10,
});

readScaling.scaleOnUtilization({
  targetUtilizationPercent: 75,
});

Auto-scaling for ECS Services

This feature allows you to set up auto-scaling for ECS services. The code sample demonstrates how to create an ECS Fargate service and configure auto-scaling based on CPU utilization.

const app = new cdk.App();
const stack = new cdk.Stack(app, 'AutoScalingStack');

const cluster = new ecs.Cluster(stack, 'Cluster', {
  vpc,
});

const taskDefinition = new ecs.FargateTaskDefinition(stack, 'TaskDef');

const container = taskDefinition.addContainer('WebContainer', {
  image: ecs.ContainerImage.fromRegistry('amazon/amazon-ecs-sample'),
  memoryLimitMiB: 512,
});

const service = new ecs.FargateService(stack, 'Service', {
  cluster,
  taskDefinition,
});

const scaling = service.autoScaleTaskCount({
  minCapacity: 1,
  maxCapacity: 10,
});

scaling.scaleOnCpuUtilization('CpuScaling', {
  targetUtilizationPercent: 50,
});

Auto-scaling for Lambda Functions

This feature allows you to set up auto-scaling for Lambda functions. The code sample demonstrates how to create a Lambda function and configure auto-scaling based on utilization.

const app = new cdk.App();
const stack = new cdk.Stack(app, 'AutoScalingStack');

const fn = new lambda.Function(stack, 'MyFunction', {
  runtime: lambda.Runtime.NODEJS_14_X,
  handler: 'index.handler',
  code: lambda.Code.fromAsset('lambda'),
});

const alias = new lambda.Alias(stack, 'Alias', {
  aliasName: 'prod',
  version: fn.currentVersion,
});

const scaling = alias.addAutoScaling({
  minCapacity: 1,
  maxCapacity: 10,
});

scaling.scaleOnUtilization({
  utilizationTarget: 0.75,
});

Other packages similar to @aws-cdk/aws-applicationautoscaling

aws-sdk

The aws-sdk package provides a comprehensive set of tools for interacting with AWS services, including auto-scaling. However, it requires more manual setup and configuration compared to the higher-level abstractions provided by @aws-cdk/aws-applicationautoscaling.

serverless

The serverless framework allows you to deploy and manage serverless applications on AWS and other cloud providers. It includes support for auto-scaling Lambda functions and other resources, but it is more focused on serverless architectures compared to the broader scope of @aws-cdk/aws-applicationautoscaling.

terraform

Terraform is an infrastructure as code tool that allows you to define and provision infrastructure using a declarative configuration language. It supports auto-scaling for various AWS resources, but it uses a different approach and syntax compared to the AWS CDK.

README

AWS Application AutoScaling Construct Library

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 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:

• In response to a metric (also known as step scaling); for example, you might want to scale out if the CPU usage across your cluster starts to rise, and scale in when it drops again.
• By trying to keep a certain metric around a given value (also known as target tracking scaling); you might want to automatically scale out an in to keep your CPU usage around 50%.
• On a schedule; you might want to organize your scaling around traffic flows you expect, by scaling out in the morning and scaling in in the evening.

The general pattern of autoscaling will look like this:

const capacity = resource.autoScaleCapacity({
  minCapacity: 5,
  maxCapacity: 100
});

// Enable a type of metric scaling and/or schedule scaling
capacity.scaleOnMetric(...);
capacity.scaleToTrackMetric(...);
capacity.scaleOnSchedule(...);

Step Scaling

This type of scaling scales in and out in deterministics 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:

capacity.scaleOnMetric('ScaleToCPU', {
  metric: service.metricCpuUtilization(),
  scalingSteps: [
    { upper: 10, change: -1 },
    { lower: 50, change: +1 },
    { lower: 70, change: +3 },
  ],

  // Change this to AdjustmentType.PercentChangeInCapacity to interpret the
  // 'change' numbers before as percentages instead of capacity counts.
  adjustmentType: autoscaling.AdjustmentType.ChangeInCapacity,
});

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

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:

const readCapacity = table.autosScaleReadCapacity({
  minCapacity: 10,
  maxCapacity: 1000
});
readCapacity.scaleOnUtilization({
  targetUtilizationPercent: 60
});

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:

• Scale in and out on a schedule by setting the minCapacity high or the maxCapacity low.
• Still allow the regular scaling actions to do their job, but restrict the range they can scale over (by setting both minCapacity and maxCapacity but changing their range over time).

The following schedule expressions can be used:

• at(yyyy-mm-ddThh:mm:ss) -- scale at a particular moment in time
• rate(value unit) -- scale every minute/hour/day
• cron(mm hh dd mm dow) -- scale on arbitrary schedules

Of these, the cron expression is the most useful but also the most complicated. There is a Cron helper class to help build cron expressions.

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

const capacity = resource.autoScaleCapacity({
  minCapacity: 1,
  maxCapacity: 50,
});

capacity.scaleOnSchedule('PrescaleInTheMorning', {
  schedule: autoscaling.Cron.dailyUtc(8),
  minCapacity: 20,
});

capacity.scaleOnSchedule('AllowDownscalingAtNight', {
  schedule: autoscaling.Cron.dailyUtc(20),
  minCapacity: 1
});