Amazon ECS Construct Library

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This package contains constructs for working with Amazon Elastic Container Service (Amazon ECS).

Amazon ECS is a highly scalable, fast, container management service that makes it easy to run, stop, and manage Docker containers on a cluster of Amazon EC2 instances.

For further information on Amazon ECS, see the Amazon ECS documentation

The following example creates an Amazon ECS cluster, adds capacity to it, and instantiates the Amazon ECS Service with an automatic load balancer.

# Example may have issues. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_ecs as ecs

# Create an ECS cluster
cluster = ecs.Cluster(self, "Cluster",
    vpc=vpc
)

# Add capacity to it
cluster.add_capacity("DefaultAutoScalingGroupCapacity",
    instance_type=ec2.InstanceType("t2.xlarge"),
    desired_capacity=3
)

task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")

task_definition.add_container("DefaultContainer",
    image=ecs.ContainerImage.from_registry("amazon/amazon-ecs-sample"),
    memory_limit_mi_b=512
)

# Instantiate an Amazon ECS Service
ecs_service = ecs.Ec2Service(self, "Service",
    cluster=cluster,
    task_definition=task_definition
)

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For a set of constructs defining common ECS architectural patterns, see the @aws-cdk/aws-ecs-patterns package.

AWS Fargate vs Amazon ECS

There are two sets of constructs in this library; one to run tasks on Amazon ECS and one to run tasks on AWS Fargate.

  • Use the Ec2TaskDefinition and Ec2Service constructs to run tasks on Amazon EC2 instances running in your account.

  • Use the FargateTaskDefinition and FargateService constructs to run tasks on instances that are managed for you by AWS.

Here are the main differences:

  • Amazon EC2: instances are under your control. Complete control of task to host allocation. Required to specify at least a memory reseration or limit for every container. Can use Host, Bridge and AwsVpc networking modes. Can attach Classic Load Balancer. Can share volumes between container and host.

  • AWS Fargate: tasks run on AWS-managed instances, AWS manages task to host allocation for you. Requires specification of memory and cpu sizes at the taskdefinition level. Only supports AwsVpc networking modes and Application/Network Load Balancers. Only the AWS log driver is supported. Many host features are not supported such as adding kernel capabilities and mounting host devices/volumes inside the container.

For more information on Amazon EC2 vs AWS Fargate and networking see the AWS Documentation: AWS Fargate and Task Networking.

Clusters

A Cluster defines the infrastructure to run your tasks on. You can run many tasks on a single cluster.

The following code creates a cluster that can run AWS Fargate tasks:

# Example may have issues. See https://github.com/aws/jsii/issues/826
cluster = ecs.Cluster(self, "Cluster",
    vpc=vpc
)

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To use tasks with Amazon EC2 launch-type, you have to add capacity to the cluster in order for tasks to be scheduled on your instances. Typically, you add an AutoScalingGroup with instances running the latest Amazon ECS-optimized AMI to the cluster. There is a method to build and add such an AutoScalingGroup automatically, or you can supply a customized AutoScalingGroup that you construct yourself. It’s possible to add multiple AutoScalingGroups with various instance types.

The following example creates an Amazon ECS cluster and adds capacity to it:

# Example may have issues. See https://github.com/aws/jsii/issues/826
cluster = ecs.Cluster(self, "Cluster",
    vpc=vpc
)

# Either add default capacity
cluster.add_capacity("DefaultAutoScalingGroupCapacity",
    instance_type=ec2.InstanceType("t2.xlarge"),
    desired_capacity=3
)

# Or add customized capacity. Be sure to start the Amazon ECS-optimized AMI.
auto_scaling_group = autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=ec2.InstanceType("t2.xlarge"),
    machine_image=EcsOptimizedImage.amazon_linux(),
    # Or use Amazon ECS-Optimized Amazon Linux 2 AMI
    # machineImage: EcsOptimizedImage.amazonLinux2(),
    desired_capacity=3
)

cluster.add_auto_scaling_group(auto_scaling_group)

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If you omit the property vpc, the construct will create a new VPC with two AZs.

Spot Instances

To add spot instances into the cluster, you must specify the spotPrice in the ecs.AddCapacityOptions and optionally enable the spotInstanceDraining property.

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Add an AutoScalingGroup with spot instances to the existing cluster
cluster.add_capacity("AsgSpot",
    max_capacity=2,
    min_capacity=2,
    desired_capacity=2,
    instance_type=ec2.InstanceType("c5.xlarge"),
    spot_price="0.0735",
    # Enable the Automated Spot Draining support for Amazon ECS
    spot_instance_draining=True
)

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Task definitions

A task Definition describes what a single copy of a task should look like. A task definition has one or more containers; typically, it has one main container (the default container is the first one that’s added to the task definition, and it is marked essential) and optionally some supporting containers which are used to support the main container, doings things like upload logs or metrics to monitoring services.

To run a task or service with Amazon EC2 launch type, use the Ec2TaskDefinition. For AWS Fargate tasks/services, use the FargateTaskDefinition. These classes provide a simplified API that only contain properties relevant for that specific launch type.

For a FargateTaskDefinition, specify the task size (memoryLimitMiB and cpu):

# Example may have issues. See https://github.com/aws/jsii/issues/826
fargate_task_definition = ecs.FargateTaskDefinition(self, "TaskDef",
    memory_limit_mi_b=512,
    cpu=256
)

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To add containers to a task definition, call addContainer():

# Example may have issues. See https://github.com/aws/jsii/issues/826
container = fargate_task_definition.add_container("WebContainer",
    # Use an image from DockerHub
    image=ecs.ContainerImage.from_registry("amazon/amazon-ecs-sample")
)

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For a Ec2TaskDefinition:

# Example may have issues. See https://github.com/aws/jsii/issues/826
ec2_task_definition = ecs.Ec2TaskDefinition(self, "TaskDef",
    network_mode=NetworkMode.BRIDGE
)

container = ec2_task_definition.add_container("WebContainer",
    # Use an image from DockerHub
    image=ecs.ContainerImage.from_registry("amazon/amazon-ecs-sample"),
    memory_limit_mi_b=1024
)

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You can specify container properties when you add them to the task definition, or with various methods, e.g.:

# Example may have issues. See https://github.com/aws/jsii/issues/826
container.add_port_mappings(
    container_port=3000
)

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To use a TaskDefinition that can be used with either Amazon EC2 or AWS Fargate launch types, use the TaskDefinition construct.

When creating a task definition you have to specify what kind of tasks you intend to run: Amazon EC2, AWS Fargate, or both. The following example uses both:

# Example may have issues. See https://github.com/aws/jsii/issues/826
task_definition = ecs.TaskDefinition(self, "TaskDef",
    memory_mi_b="512",
    cpu="256",
    network_mode=NetworkMode.AWS_VPC,
    compatibility=ecs.Compatibility.EC2_AND_FARGATE
)

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Images

Images supply the software that runs inside the container. Images can be obtained from either DockerHub or from ECR repositories, or built directly from a local Dockerfile.

  • ecs.ContainerImage.fromRegistry(imageName): use a public image.

  • ecs.ContainerImage.fromRegistry(imageName, { credentials: mySecret }): use a private image that requires credentials.

  • ecs.ContainerImage.fromEcrRepository(repo, tag): use the given ECR repository as the image to start. If no tag is provided, “latest” is assumed.

  • ecs.ContainerImage.fromAsset('./image'): build and upload an image directly from a Dockerfile in your source directory.

Environment variables

To pass environment variables to the container, use the environment and secrets props.

# Example may have issues. See https://github.com/aws/jsii/issues/826
task_definition.add_container("container",
    image=ecs.ContainerImage.from_registry("amazon/amazon-ecs-sample"),
    memory_limit_mi_b=1024,
    environment={# clear text, not for sensitive data
        "STAGE": "prod"},
    secrets={# Retrieved from AWS Secrets Manager or AWS Systems Manager Parameter Store at container start-up.
        "SECRET": ecs.Secret.from_secrets_manager(secret),
        "PARAMETER": ecs.Secret.from_ssm_parameter(parameter)}
)

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The task execution role is automatically granted read permissions on the secrets/parameters.

Service

A Service instantiates a TaskDefinition on a Cluster a given number of times, optionally associating them with a load balancer. If a task fails, Amazon ECS automatically restarts the task.

# Example may have issues. See https://github.com/aws/jsii/issues/826
task_definition =

service = ecs.FargateService(self, "Service",
    cluster=cluster,
    task_definition=task_definition,
    desired_count=5
)

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Include an application/network load balancer

Services are load balancing targets and can be added to a target group, which will be attached to an application/network load balancers:

# Example may have issues. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_elasticloadbalancingv2 as elbv2

service = ecs.FargateService(self, "Service")

lb = elbv2.ApplicationLoadBalancer(self, "LB", vpc=vpc, internet_facing=True)
listener = lb.add_listener("Listener", port=80)
target_group1 = listener.add_targets("ECS1",
    port=80,
    targets=[service]
)
target_group2 = listener.add_targets("ECS2",
    port=80,
    targets=[service.load_balancer_target(
        container_name="MyContainer",
        container_port=8080
    )]
)

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Note that in the example above, the default service only allows you to register the first essential container or the first mapped port on the container as a target and add it to a new target group. To have more control over which container and port to register as targets, you can use service.loadBalancerTarget() to return a load balancing target for a specific container and port.

Alternatively, you can also create all load balancer targets to be registered in this service, add them to target groups, and attach target groups to listeners accordingly.

# Example may have issues. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_elasticloadbalancingv2 as elbv2

service = ecs.FargateService(self, "Service")

lb = elbv2.ApplicationLoadBalancer(self, "LB", vpc=vpc, internet_facing=True)
listener = lb.add_listener("Listener", port=80)
service.register_load_balancer_targets(
    container_target={
        "container_name": "web",
        "container_port": 80
    },
    target_group_id="ECS",
    listener=ecs.ListenerConfig.application_listener(listener,
        protocol=elbv2.ApplicationProtocol.HTTPS
    )
)

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Using a Load Balancer from a different Stack

If you want to put your Load Balancer and the Service it is load balancing to in different stacks, you may not be able to use the convenience methods loadBalancer.addListener() and listener.addTargets().

The reason is that these methods will create resources in the same Stack as the object they’re called on, which may lead to cyclic references between stacks. Instead, you will have to create an ApplicationListener in the service stack, or an empty TargetGroup in the load balancer stack that you attach your service to.

See the ecs/cross-stack-load-balancer example for the alternatives.

Include a classic load balancer

Services can also be directly attached to a classic load balancer as targets:

# Example may have issues. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_elasticloadbalancing as elb

service = ecs.Ec2Service(self, "Service")

lb = elb.LoadBalancer(stack, "LB", vpc=vpc)
lb.add_listener(external_port=80)
lb.add_target(service)

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Similarly, if you want to have more control over load balancer targeting:

# Example may have issues. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_elasticloadbalancing as elb

service = ecs.Ec2Service(self, "Service")

lb = elb.LoadBalancer(stack, "LB", vpc=vpc)
lb.add_listener(external_port=80)
lb.add_target(service.load_balancer_target,
    container_name="MyContainer",
    container_port=80
)

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There are two higher-level constructs available which include a load balancer for you that can be found in the aws-ecs-patterns module:

  • LoadBalancedFargateService

  • LoadBalancedEc2Service

Task Auto-Scaling

You can configure the task count of a service to match demand. Task auto-scaling is configured by calling autoScaleTaskCount():

# Example may have issues. See https://github.com/aws/jsii/issues/826
scaling = service.auto_scale_task_count(max_capacity=10)
scaling.scale_on_cpu_utilization("CpuScaling",
    target_utilization_percent=50
)

scaling.scale_on_request_count("RequestScaling",
    requests_per_target=10000,
    target_group=target
)

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Task auto-scaling is powered by Application Auto-Scaling. See that section for details.

Instance Auto-Scaling

If you’re running on AWS Fargate, AWS manages the physical machines that your containers are running on for you. If you’re running an Amazon ECS cluster however, your Amazon EC2 instances might fill up as your number of Tasks goes up.

To avoid placement errors, configure auto-scaling for your Amazon EC2 instance group so that your instance count scales with demand. To keep your Amazon EC2 instances halfway loaded, scaling up to a maximum of 30 instances if required:

# Example may have issues. See https://github.com/aws/jsii/issues/826
auto_scaling_group = cluster.add_capacity("DefaultAutoScalingGroup",
    instance_type=ec2.InstanceType("t2.xlarge"),
    min_capacity=3,
    max_capacity=30,
    desired_capacity=3,

    # Give instances 5 minutes to drain running tasks when an instance is
    # terminated. This is the default, turn this off by specifying 0 or
    # change the timeout up to 900 seconds.
    task_drain_time=Duration.seconds(300)
)

auto_scaling_group.scale_on_cpu_utilization("KeepCpuHalfwayLoaded",
    target_utilization_percent=50
)

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See the @aws-cdk/aws-autoscaling library for more autoscaling options you can configure on your instances.

Integration with CloudWatch Events

To start an Amazon ECS task on an Amazon EC2-backed Cluster, instantiate an @aws-cdk/aws-events-targets.EcsTask instead of an Ec2Service:

# Example may have issues. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_events_targets as targets

# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_asset(path.resolve(__dirname, "..", "eventhandler-image")),
    memory_limit_mi_b=256,
    logging=ecs.AwsLogDriver(stream_prefix="EventDemo")
)

# An Rule that describes the event trigger (in this case a scheduled run)
rule = events.Rule(self, "Rule",
    schedule=events.Schedule.expression("rate(1 min)")
)

# Pass an environment variable to the container 'TheContainer' in the task
rule.add_target(targets.EcsTask(
    cluster=cluster,
    task_definition=task_definition,
    task_count=1,
    container_overrides=[ContainerOverride(
        container_name="TheContainer",
        environment=[TaskEnvironmentVariable(
            name="I_WAS_TRIGGERED",
            value="From CloudWatch Events"
        )]
    )]
))

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Log Drivers

Currently Supported Log Drivers:

  • awslogs

  • fluentd

  • gelf

  • journald

  • json-file

  • splunk

  • syslog

awslogs Log Driver

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_registry("example-image"),
    memory_limit_mi_b=256,
    logging=ecs.LogDrivers.awslogs(stream_prefix="EventDemo")
)

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fluentd Log Driver

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_registry("example-image"),
    memory_limit_mi_b=256,
    logging=ecs.LogDrivers.fluentd()
)

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gelf Log Driver

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_registry("example-image"),
    memory_limit_mi_b=256,
    logging=ecs.LogDrivers.gelf()
)

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journald Log Driver

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_registry("example-image"),
    memory_limit_mi_b=256,
    logging=ecs.LogDrivers.journald()
)

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json-file Log Driver

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_registry("example-image"),
    memory_limit_mi_b=256,
    logging=ecs.LogDrivers.json_file()
)

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splunk Log Driver

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_registry("example-image"),
    memory_limit_mi_b=256,
    logging=ecs.LogDrivers.splunk()
)

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syslog Log Driver

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_registry("example-image"),
    memory_limit_mi_b=256,
    logging=ecs.LogDrivers.syslog()
)

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Generic Log Driver

A generic log driver object exists to provide a lower level abstraction of the log driver configuration.

# Example may have issues. See https://github.com/aws/jsii/issues/826
# Create a Task Definition for the container to start
task_definition = ecs.Ec2TaskDefinition(self, "TaskDef")
task_definition.add_container("TheContainer",
    image=ecs.ContainerImage.from_registry("example-image"),
    memory_limit_mi_b=256,
    logging=ecs.GenericLogDriver(
        log_driver="fluentd",
        options={
            "tag": "example-tag"
        }
    )
)

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