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.
AWS Auto Scaling Construct Library
![cfn-resources: Stable](https://img.shields.io/badge/cfn--resources-stable-success.svg?style=for-the-badge)
![cdk-constructs: Stable](https://img.shields.io/badge/cdk--constructs-stable-success.svg?style=for-the-badge)
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:
- 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:
declare const resource: SomeScalableResource;
const capacity = resource.autoScaleCapacity({
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:
declare const capacity: ScalableAttribute;
declare const cpuUtilization: cloudwatch.Metric;
capacity.scaleOnMetric('ScaleToCPU', {
metric: cpuUtilization,
scalingSteps: [
{ upper: 10, change: -1 },
{ lower: 50, change: +1 },
{ lower: 70, change: +3 },
],
adjustmentType: appscaling.AdjustmentType.CHANGE_IN_CAPACITY,
});
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:
declare const capacity: ScalableAttribute;
declare const cpuUtilization: cloudwatch.Metric;
capacity.scaleOnMetric('ScaleToCPUWithMultipleDatapoints', {
metric: cpuUtilization,
scalingSteps: [
{ upper: 10, change: -1 },
{ lower: 50, change: +1 },
{ lower: 70, change: +3 },
],
evaluationPeriods: 10,
datapointsToAlarm: 6
});
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 * as dynamodb from '@aws-cdk/aws-dynamodb';
declare const table: dynamodb.Table;
const readCapacity = table.autoScaleReadCapacity({
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 timerate(value unit)
-- scale every minute/hour/daycron(mm hh dd mm dow)
-- scale on arbitrary schedules
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:
declare const resource: SomeScalableResource;
const capacity = resource.autoScaleCapacity({
minCapacity: 1,
maxCapacity: 50,
});
capacity.scaleOnSchedule('PrescaleInTheMorning', {
schedule: appscaling.Schedule.cron({ hour: '8', minute: '0' }),
minCapacity: 20,
});
capacity.scaleOnSchedule('AllowDownscalingAtNight', {
schedule: appscaling.Schedule.cron({ hour: '20', minute: '0' }),
minCapacity: 1
});
Examples
Lambda Provisioned Concurrency Auto Scaling
import * as lambda from '@aws-cdk/aws-lambda';
declare const code: lambda.Code;
const handler = new lambda.Function(this, 'MyFunction', {
runtime: lambda.Runtime.PYTHON_3_7,
handler: 'index.handler',
code,
reservedConcurrentExecutions: 2,
});
const fnVer = handler.currentVersion;
const target = new appscaling.ScalableTarget(this, 'ScalableTarget', {
serviceNamespace: appscaling.ServiceNamespace.LAMBDA,
maxCapacity: 100,
minCapacity: 10,
resourceId: `function:${handler.functionName}:${fnVer.version}`,
scalableDimension: 'lambda:function:ProvisionedConcurrency',
})
target.scaleToTrackMetric('PceTracking', {
targetValue: 0.9,
predefinedMetric: appscaling.PredefinedMetric.LAMBDA_PROVISIONED_CONCURRENCY_UTILIZATION,
})
ElastiCache Redis shards scaling with target value
const shardsScalableTarget = new appscaling.ScalableTarget(this, 'ElastiCacheRedisShardsScalableTarget', {
serviceNamespace: appscaling.ServiceNamespace.ELASTICACHE,
scalableDimension: 'elasticache:replication-group:NodeGroups',
minCapacity: 2,
maxCapacity: 10,
resourceId: 'replication-group/main-cluster',
});
shardsScalableTarget.scaleToTrackMetric('ElastiCacheRedisShardsCPUUtilization', {
targetValue: 20,
predefinedMetric: appscaling.PredefinedMetric.ELASTICACHE_PRIMARY_ENGINE_CPU_UTILIZATION,
});