Metadata-Version: 2.1
Name: aws-cdk.aws-autoscaling
Version: 1.107.0
Summary: The CDK Construct Library for AWS::AutoScaling
Home-page: https://github.com/aws/aws-cdk
Author: Amazon Web Services
License: Apache-2.0
Project-URL: Source, https://github.com/aws/aws-cdk.git
Description: # Amazon EC2 Auto Scaling Construct Library
        
        <!--BEGIN STABILITY BANNER-->---
        
        
        ![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)
        
        ---
        <!--END STABILITY BANNER-->
        
        This module is part of the [AWS Cloud Development Kit](https://github.com/aws/aws-cdk) project.
        
        ## Auto Scaling Group
        
        An `AutoScalingGroup` represents a number of instances on which you run your code. You
        pick the size of the fleet, the instance type and the OS image:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        import aws_cdk.aws_autoscaling as autoscaling
        import aws_cdk.aws_ec2 as ec2
        
        autoscaling.AutoScalingGroup(self, "ASG",
            vpc=vpc,
            instance_type=ec2.InstanceType.of(ec2.InstanceClass.BURSTABLE2, ec2.InstanceSize.MICRO),
            machine_image=ec2.AmazonLinuxImage()
        )
        ```
        
        NOTE: AutoScalingGroup has an property called `allowAllOutbound` (allowing the instances to contact the
        internet) which is set to `true` by default. Be sure to set this to `false`  if you don't want
        your instances to be able to start arbitrary connections. Alternatively, you can specify an existing security
        group to attach to the instances that are launched, rather than have the group create a new one.
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        my_security_group = ec2.SecurityGroup(self, "SecurityGroup", ...)
        autoscaling.AutoScalingGroup(self, "ASG",
            vpc=vpc,
            instance_type=ec2.InstanceType.of(ec2.InstanceClass.BURSTABLE2, ec2.InstanceSize.MICRO),
            machine_image=ec2.AmazonLinuxImage(),
            security_group=my_security_group
        )
        ```
        
        ## Machine Images (AMIs)
        
        AMIs control the OS that gets launched when you start your EC2 instance. The EC2
        library contains constructs to select the AMI you want to use.
        
        Depending on the type of AMI, you select it a different way.
        
        The latest version of Amazon Linux and Microsoft Windows images are
        selectable by instantiating one of these classes:
        
        ```python
        # Example automatically generated. See https://github.com/aws/jsii/issues/826
        # Pick a Windows edition to use
        windows = ec2.WindowsImage(ec2.WindowsVersion.WINDOWS_SERVER_2019_ENGLISH_FULL_BASE)
        
        # Pick the right Amazon Linux edition. All arguments shown are optional
        # and will default to these values when omitted.
        amzn_linux = ec2.AmazonLinuxImage(
            generation=ec2.AmazonLinuxGeneration.AMAZON_LINUX,
            edition=ec2.AmazonLinuxEdition.STANDARD,
            virtualization=ec2.AmazonLinuxVirt.HVM,
            storage=ec2.AmazonLinuxStorage.GENERAL_PURPOSE
        )
        
        # For other custom (Linux) images, instantiate a `GenericLinuxImage` with
        # a map giving the AMI to in for each region:
        
        linux = ec2.GenericLinuxImage({
            "us-east-1": "ami-97785bed",
            "eu-west-1": "ami-12345678"
        })
        ```
        
        > NOTE: The Amazon Linux images selected will be cached in your `cdk.json`, so that your
        > AutoScalingGroups don't automatically change out from under you when you're making unrelated
        > changes. To update to the latest version of Amazon Linux, remove the cache entry from the `context`
        > section of your `cdk.json`.
        >
        > We will add command-line options to make this step easier in the future.
        
        ## AutoScaling Instance Counts
        
        AutoScalingGroups make it possible to raise and lower the number of instances in the group,
        in response to (or in advance of) changes in workload.
        
        When you create your AutoScalingGroup, you specify a `minCapacity` and a
        `maxCapacity`. 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 and 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:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        auto_scaling_group = autoscaling.AutoScalingGroup(self, "ASG",
            min_capacity=5,
            max_capacity=100
        )
        
        # Step scaling
        auto_scaling_group.scale_on_metric(...)
        
        # Target tracking scaling
        auto_scaling_group.scale_on_cpu_utilization(...)
        auto_scaling_group.scale_on_incoming_bytes(...)
        auto_scaling_group.scale_on_outgoing_bytes(...)
        auto_scaling_group.scale_on_request_count(...)
        auto_scaling_group.scale_to_track_metric(...)
        
        # Scheduled scaling
        auto_scaling_group.scale_on_schedule(...)
        ```
        
        ### 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 a metric that represents your average worker pool usage
        might look like this:
        
        ```plaintext
         Scaling        -1          (no change)          +1       +3
                    │        │                       │        │        │
                    ├────────┼───────────────────────┼────────┼────────┤
                    │        │                       │        │        │
        Worker use  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).
        
        Note that in order to set up this scaling strategy, you will have to emit a
        metric representing your worker utilization from your instances. After that,
        you would configure the scaling something like this:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        worker_utilization_metric = cloudwatch.Metric(
            namespace="MyService",
            metric_name="WorkerUtilization"
        )
        
        capacity.scale_on_metric("ScaleToCPU",
            metric=worker_utilization_metric,
            scaling_steps=[{"upper": 10, "change": -1}, {"lower": 50, "change": +1}, {"lower": 70, "change": +3}
            ],
        
            # Change this to AdjustmentType.PERCENT_CHANGE_IN_CAPACITY to interpret the
            # 'change' numbers before as percentages instead of capacity counts.
            adjustment_type=autoscaling.AdjustmentType.CHANGE_IN_CAPACITY
        )
        ```
        
        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 around a value
        you prefer. There are four types of predefined metrics you can track, or you can
        choose to track a custom metric. If you do choose to track a custom metric,
        be aware that the metric has to represent instance utilization in some way
        (AutoScaling will scale out if the metric is higher than the target, and scale
        in if the metric is lower than the target).
        
        If you configure multiple target tracking policies, AutoScaling will use the
        one that yields the highest capacity.
        
        The following example scales to keep the CPU usage of your instances around
        50% utilization:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        auto_scaling_group.scale_on_cpu_utilization("KeepSpareCPU",
            target_utilization_percent=50
        )
        ```
        
        To scale on average network traffic in and out of your instances:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        auto_scaling_group.scale_on_incoming_bytes("LimitIngressPerInstance",
            target_bytes_per_second=10 * 1024 * 1024
        )
        auto_scaling_group.scale_on_outcoming_bytes("LimitEgressPerInstance",
            target_bytes_per_second=10 * 1024 * 1024
        )
        ```
        
        To scale on the average request count per instance (only works for
        AutoScalingGroups that have been attached to Application Load
        Balancers):
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        auto_scaling_group.scale_on_request_count("LimitRPS",
            target_requests_per_second=1000
        )
        ```
        
        ### Scheduled Scaling
        
        This type of scaling is used to change capacities based on time. It works by
        changing `minCapacity`, `maxCapacity` and `desiredCapacity` of the
        AutoScalingGroup, 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).
        
        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, going back to natural
        scaling (all the way down to 1 instance if necessary) at night:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        auto_scaling_group.scale_on_schedule("PrescaleInTheMorning",
            schedule=autoscaling.Schedule.cron(hour="8", minute="0"),
            min_capacity=20
        )
        
        auto_scaling_group.scale_on_schedule("AllowDownscalingAtNight",
            schedule=autoscaling.Schedule.cron(hour="20", minute="0"),
            min_capacity=1
        )
        ```
        
        ## Configuring Instances using CloudFormation Init
        
        It is possible to use the CloudFormation Init mechanism to configure the
        instances in the AutoScalingGroup. You can write files to it, run commands,
        start services, etc. See the documentation of
        [AWS::CloudFormation::Init](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-init.html)
        and the documentation of CDK's `aws-ec2` library for more information.
        
        When you specify a CloudFormation Init configuration for an AutoScalingGroup:
        
        * you *must* also specify `signals` to configure how long CloudFormation
          should wait for the instances to successfully configure themselves.
        * you *should* also specify an `updatePolicy` to configure how instances
          should be updated when the AutoScalingGroup is updated (for example,
          when the AMI is updated). If you don't specify an update policy, a *rolling
          update* is chosen by default.
        
        Here's an example of using CloudFormation Init to write a file to the
        instance hosts on startup:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        autoscaling.AutoScalingGroup(self, "ASG",
            # ...
        
            init=ec2.CloudFormationInit.from_elements(
                ec2.InitFile.from_string("/etc/my_instance", "This got written during instance startup")),
            signals=autoscaling.Signals.wait_for_all(
                timeout=Duration.minutes(10)
            )
        )
        ```
        
        ## Signals
        
        In normal operation, CloudFormation will send a Create or Update command to
        an AutoScalingGroup and proceed with the rest of the deployment without waiting
        for the *instances in the AutoScalingGroup*.
        
        Configure `signals` to tell CloudFormation to wait for a specific number of
        instances in the AutoScalingGroup to have been started (or failed to start)
        before moving on. An instance is supposed to execute the
        [`cfn-signal`](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/cfn-signal.html)
        program as part of its startup to indicate whether it was started
        successfully or not.
        
        If you use CloudFormation Init support (described in the previous section),
        the appropriate call to `cfn-signal` is automatically added to the
        AutoScalingGroup's UserData. If you don't use the `signals` directly, you are
        responsible for adding such a call yourself.
        
        The following type of `Signals` are available:
        
        * `Signals.waitForAll([options])`: wait for all of `desiredCapacity` amount of instances
          to have started (recommended).
        * `Signals.waitForMinCapacity([options])`: wait for a `minCapacity` amount of instances
          to have started (use this if waiting for all instances takes too long and you are happy
          with a minimum count of healthy hosts).
        * `Signals.waitForCount(count, [options])`: wait for a specific amount of instances to have
          started.
        
        There are two `options` you can configure:
        
        * `timeout`: maximum time a host startup is allowed to take. If a host does not report
          success within this time, it is considered a failure. Default is 5 minutes.
        * `minSuccessPercentage`: percentage of hosts that needs to be healthy in order for the
          update to succeed. If you set this value lower than 100, some percentage of hosts may
          report failure, while still considering the deployment a success. Default is 100%.
        
        ## Update Policy
        
        The *update policy* describes what should happen to running instances when the definition
        of the AutoScalingGroup is changed. For example, if you add a command to the UserData
        of an AutoScalingGroup, do the existing instances get replaced with new instances that
        have executed the new UserData? Or do the "old" instances just keep on running?
        
        It is recommended to always use an update policy, otherwise the current state of your
        instances also depends the previous state of your instances, rather than just on your
        source code. This degrades the reproducibility of your deployments.
        
        The following update policies are available:
        
        * `UpdatePolicy.none()`: leave existing instances alone (not recommended).
        * `UpdatePolicy.rollingUpdate([options])`: progressively replace the existing
          instances with new instances, in small batches. At any point in time,
          roughly the same amount of total instances will be running. If the deployment
          needs to be rolled back, the fresh instances will be replaced with the "old"
          configuration again.
        * `UpdatePolicy.replacingUpdate([options])`: build a completely fresh copy
          of the new AutoScalingGroup next to the old one. Once the AutoScalingGroup
          has been successfully created (and the instances started, if `signals` is
          configured on the AutoScalingGroup), the old AutoScalingGroup is deleted.
          If the deployment needs to be rolled back, the new AutoScalingGroup is
          deleted and the old one is left unchanged.
        
        ## Allowing Connections
        
        See the documentation of the `@aws-cdk/aws-ec2` package for more information
        about allowing connections between resources backed by instances.
        
        ## Max Instance Lifetime
        
        To enable the max instance lifetime support, specify `maxInstanceLifetime` property
        for the `AutoscalingGroup` resource. The value must be between 7 and 365 days(inclusive).
        To clear a previously set value, leave this property undefined.
        
        ## Instance Monitoring
        
        To disable detailed instance monitoring, specify `instanceMonitoring` property
        for the `AutoscalingGroup` resource as `Monitoring.BASIC`. Otherwise detailed monitoring
        will be enabled.
        
        ## Monitoring Group Metrics
        
        Group metrics are used to monitor group level properties; they describe the group rather than any of its instances (e.g GroupMaxSize, the group maximum size). To enable group metrics monitoring, use the `groupMetrics` property.
        All group metrics are reported in a granularity of 1 minute at no additional charge.
        
        See [EC2 docs](https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-instance-monitoring.html#as-group-metrics) for a list of all available group metrics.
        
        To enable group metrics monitoring using the `groupMetrics` property:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        # Enable monitoring of all group metrics
        autoscaling.AutoScalingGroup(stack, "ASG",
            group_metrics=[GroupMetrics.all()]
        )
        
        # Enable monitoring for a subset of group metrics
        autoscaling.AutoScalingGroup(stack, "ASG",
            group_metrics=[autoscaling.GroupMetrics(GroupMetric.MIN_SIZE, GroupMetric.MAX_SIZE)]
        )
        ```
        
        ## Protecting new instances from being terminated on scale-in
        
        By default, Auto Scaling can terminate an instance at any time after launch when
        scaling in an Auto Scaling Group, subject to the group's [termination
        policy](https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-instance-termination.html).
        
        However, you may wish to protect newly-launched instances from being scaled in
        if they are going to run critical applications that should not be prematurely
        terminated. EC2 Capacity Providers for Amazon ECS requires this attribute be
        set to `true`.
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        autoscaling.AutoScalingGroup(stack, "ASG",
            new_instances_protected_from_scale_in=True
        )
        ```
        
        ## Future work
        
        * [ ] CloudWatch Events (impossible to add currently as the AutoScalingGroup ARN is
          necessary to make this rule and this cannot be accessed from CloudFormation).
        
Platform: UNKNOWN
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Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Typing :: Typed
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved
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