CreateService (smaws-clients.Smaws_Client

Module Smaws_Client_ECS.CreateService

Runs and maintains your desired number of tasks from a specified task definition. If the number of tasks running in a service drops below the desiredCount, Amazon ECS runs another copy of the task in the specified cluster. To update an existing service, use UpdateService.

On March 21, 2024, a change was made to resolve the task definition revision before authorization. When a task definition revision is not specified, authorization will occur using the latest revision of a task definition.

Amazon Elastic Inference (EI) is no longer available to customers.

In addition to maintaining the desired count of tasks in your service, you can optionally run your service behind one or more load balancers. The load balancers distribute traffic across the tasks that are associated with the service. For more information, see Service load balancing in the Amazon Elastic Container Service Developer Guide.

You can attach Amazon EBS volumes to Amazon ECS tasks by configuring the volume when creating or updating a service. volumeConfigurations is only supported for REPLICA service and not DAEMON service. For more information, see Amazon EBS volumes in the Amazon Elastic Container Service Developer Guide.

Tasks for services that don't use a load balancer are considered healthy if they're in the RUNNING state. Tasks for services that use a load balancer are considered healthy if they're in the RUNNING state and are reported as healthy by the load balancer.

There are two service scheduler strategies available:

REPLICA - The replica scheduling strategy places and maintains your desired number of tasks across your cluster. By default, the service scheduler spreads tasks across Availability Zones. You can use task placement strategies and constraints to customize task placement decisions. For more information, see Service scheduler concepts in the Amazon Elastic Container Service Developer Guide.

DAEMON - The daemon scheduling strategy deploys exactly one task on each active container instance that meets all of the task placement constraints that you specify in your cluster. The service scheduler also evaluates the task placement constraints for running tasks. It also stops tasks that don't meet the placement constraints. When using this strategy, you don't need to specify a desired number of tasks, a task placement strategy, or use Service Auto Scaling policies. For more information, see Amazon ECS services in the Amazon Elastic Container Service Developer Guide.

The deployment controller is the mechanism that determines how tasks are deployed for your service. The valid options are:

ECS

When you create a service which uses the ECS deployment controller, you can choose between the following deployment strategies (which you can set in the “strategy” field in “deploymentConfiguration”): :

ROLLING: When you create a service which uses the rolling update (ROLLING) deployment strategy, the Amazon ECS service scheduler replaces the currently running tasks with new tasks. The number of tasks that Amazon ECS adds or removes from the service during a rolling update is controlled by the service deployment configuration. For more information, see Deploy Amazon ECS services by replacing tasks in the Amazon Elastic Container Service Developer Guide.
Rolling update deployments are best suited for the following scenarios:
- Gradual service updates: You need to update your service incrementally without taking the entire service offline at once.
- Limited resource requirements: You want to avoid the additional resource costs of running two complete environments simultaneously (as required by blue/green deployments).
- Acceptable deployment time: Your application can tolerate a longer deployment process, as rolling updates replace tasks one by one.
- No need for instant roll back: Your service can tolerate a rollback process that takes minutes rather than seconds.
- Simple deployment process: You prefer a straightforward deployment approach without the complexity of managing multiple environments, target groups, and listeners.
- No load balancer requirement: Your service doesn't use or require a load balancer, Application Load Balancer, Network Load Balancer, or Service Connect (which are required for blue/green deployments).
- Stateful applications: Your application maintains state that makes it difficult to run two parallel environments.
- Cost sensitivity: You want to minimize deployment costs by not running duplicate environments during deployment.
Rolling updates are the default deployment strategy for services and provide a balance between deployment safety and resource efficiency for many common application scenarios.
BLUE_GREEN: A blue/green deployment strategy (BLUE_GREEN) is a release methodology that reduces downtime and risk by running two identical production environments called blue and green. With Amazon ECS blue/green deployments, you can validate new service revisions before directing production traffic to them. This approach provides a safer way to deploy changes with the ability to quickly roll back if needed. For more information, see Amazon ECS blue/green deployments in the Amazon Elastic Container Service Developer Guide.
Amazon ECS blue/green deployments are best suited for the following scenarios:
- Service validation: When you need to validate new service revisions before directing production traffic to them
- Zero downtime: When your service requires zero-downtime deployments
- Instant roll back: When you need the ability to quickly roll back if issues are detected
- Load balancer requirement: When your service uses Application Load Balancer, Network Load Balancer, or Service Connect
LINEAR: A linear deployment strategy (LINEAR) gradually shifts traffic from the current production environment to a new environment in equal percentage increments. With Amazon ECS linear deployments, you can control the pace of traffic shifting and validate new service revisions with increasing amounts of production traffic.
Linear deployments are best suited for the following scenarios:
- Gradual validation: When you want to gradually validate your new service version with increasing traffic
- Performance monitoring: When you need time to monitor metrics and performance during the deployment
- Risk minimization: When you want to minimize risk by exposing the new version to production traffic incrementally
- Load balancer requirement: When your service uses Application Load Balancer or Service Connect
CANARY: A canary deployment strategy (CANARY) shifts a small percentage of traffic to the new service revision first, then shifts the remaining traffic all at once after a specified time period. This allows you to test the new version with a subset of users before full deployment.
Canary deployments are best suited for the following scenarios:
- Feature testing: When you want to test new features with a small subset of users before full rollout
- Production validation: When you need to validate performance and functionality with real production traffic
- Blast radius control: When you want to minimize blast radius if issues are discovered in the new version
- Load balancer requirement: When your service uses Application Load Balancer or Service Connect

External

Use a third-party deployment controller.

Blue/green deployment (powered by CodeDeploy)

CodeDeploy installs an updated version of the application as a new replacement task set and reroutes production traffic from the original application task set to the replacement task set. The original task set is terminated after a successful deployment. Use this deployment controller to verify a new deployment of a service before sending production traffic to it.

When creating a service that uses the EXTERNAL deployment controller, you can specify only parameters that aren't controlled at the task set level. The only required parameter is the service name. You control your services using the CreateTaskSet. For more information, see Amazon ECS deployment types in the Amazon Elastic Container Service Developer Guide.

When the service scheduler launches new tasks, it determines task placement. For information about task placement and task placement strategies, see Amazon ECS task placement in the Amazon Elastic Container Service Developer Guide

val error_to_string : 
  [ Smaws_Lib.Protocols.AwsJson.error
  | `AccessDeniedException of Types.access_denied_exception
  | `ClientException of Types.client_exception
  | `ClusterNotFoundException of Types.cluster_not_found_exception
  | `InvalidParameterException of Types.invalid_parameter_exception
  | `NamespaceNotFoundException of Types.namespace_not_found_exception
  | `PlatformTaskDefinitionIncompatibilityException of
    Types.platform_task_definition_incompatibility_exception
  | `PlatformUnknownException of Types.platform_unknown_exception
  | `ServerException of Types.server_exception
  | `UnsupportedFeatureException of Types.unsupported_feature_exception ] ->
  string

val request : 
  'http_type Smaws_Lib.Context.t ->
  Types.create_service_request ->
  (Types.create_service_response,
    [> Smaws_Lib.Protocols.AwsJson.error
    | `AccessDeniedException of Types.access_denied_exception
    | `ClientException of Types.client_exception
    | `ClusterNotFoundException of Types.cluster_not_found_exception
    | `InvalidParameterException of Types.invalid_parameter_exception
    | `NamespaceNotFoundException of Types.namespace_not_found_exception
    | `PlatformTaskDefinitionIncompatibilityException of
      Types.platform_task_definition_incompatibility_exception
    | `PlatformUnknownException of Types.platform_unknown_exception
    | `ServerException of Types.server_exception
    | `UnsupportedFeatureException of Types.unsupported_feature_exception ])
    Stdlib.result

val request_with_metadata : 
  'http_type Smaws_Lib.Context.t ->
  Types.create_service_request ->
  (Types.create_service_response Smaws_Lib.Response.t,
    [> Smaws_Lib.Protocols.AwsJson.error
    | `AccessDeniedException of Types.access_denied_exception
    | `ClientException of Types.client_exception
    | `ClusterNotFoundException of Types.cluster_not_found_exception
    | `InvalidParameterException of Types.invalid_parameter_exception
    | `NamespaceNotFoundException of Types.namespace_not_found_exception
    | `PlatformTaskDefinitionIncompatibilityException of
      Types.platform_task_definition_incompatibility_exception
    | `PlatformUnknownException of Types.platform_unknown_exception
    | `ServerException of Types.server_exception
    | `UnsupportedFeatureException of Types.unsupported_feature_exception ]
    * Smaws_Lib.Response.metadata)
    Stdlib.result