aws-native.ecs.Service

• REPLICA-The replica scheduling strategy places and maintains the 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. This scheduler strategy is required if the service uses the CODE_DEPLOY or EXTERNAL deployment controller types.
• 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 and will stop tasks that don't meet the placement constraints. When you're using this strategy, you don't need to specify a desired number of tasks, a task placement strategy, or use Service Auto Scaling policies. Tasks using the Fargate launch type or the CODE_DEPLOY or EXTERNAL deployment controller types don't support the DAEMON scheduling strategy.

• Maximum number of tags per resource - 50
• For each resource, each tag key must be unique, and each tag key can have only one value.
• Maximum key length - 128 Unicode characters in UTF-8
• Maximum value length - 256 Unicode characters in UTF-8
• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
• Tag keys and values are case-sensitive.
• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for either keys or values as it is reserved for AWS use. You cannot edit or delete tag keys or values with this prefix. Tags with this prefix do not count against your tags per resource limit.

• REPLICA-The replica scheduling strategy places and maintains the 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. This scheduler strategy is required if the service uses the CODE_DEPLOY or EXTERNAL deployment controller types.
• 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 and will stop tasks that don't meet the placement constraints. When you're using this strategy, you don't need to specify a desired number of tasks, a task placement strategy, or use Service Auto Scaling policies. Tasks using the Fargate launch type or the CODE_DEPLOY or EXTERNAL deployment controller types don't support the DAEMON scheduling strategy.

• Maximum number of tags per resource - 50
• For each resource, each tag key must be unique, and each tag key can have only one value.
• Maximum key length - 128 Unicode characters in UTF-8
• Maximum value length - 256 Unicode characters in UTF-8
• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
• Tag keys and values are case-sensitive.
• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for either keys or values as it is reserved for AWS use. You cannot edit or delete tag keys or values with this prefix. Tags with this prefix do not count against your tags per resource limit.

• REPLICA-The replica scheduling strategy places and maintains the 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. This scheduler strategy is required if the service uses the CODE_DEPLOY or EXTERNAL deployment controller types.
• 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 and will stop tasks that don't meet the placement constraints. When you're using this strategy, you don't need to specify a desired number of tasks, a task placement strategy, or use Service Auto Scaling policies. Tasks using the Fargate launch type or the CODE_DEPLOY or EXTERNAL deployment controller types don't support the DAEMON scheduling strategy.

• Maximum number of tags per resource - 50
• For each resource, each tag key must be unique, and each tag key can have only one value.
• Maximum key length - 128 Unicode characters in UTF-8
• Maximum value length - 256 Unicode characters in UTF-8
• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
• Tag keys and values are case-sensitive.
• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for either keys or values as it is reserved for AWS use. You cannot edit or delete tag keys or values with this prefix. Tags with this prefix do not count against your tags per resource limit.

• REPLICA-The replica scheduling strategy places and maintains the 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. This scheduler strategy is required if the service uses the CODE_DEPLOY or EXTERNAL deployment controller types.
• 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 and will stop tasks that don't meet the placement constraints. When you're using this strategy, you don't need to specify a desired number of tasks, a task placement strategy, or use Service Auto Scaling policies. Tasks using the Fargate launch type or the CODE_DEPLOY or EXTERNAL deployment controller types don't support the DAEMON scheduling strategy.

• Maximum number of tags per resource - 50
• For each resource, each tag key must be unique, and each tag key can have only one value.
• Maximum key length - 128 Unicode characters in UTF-8
• Maximum value length - 256 Unicode characters in UTF-8
• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
• Tag keys and values are case-sensitive.
• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for either keys or values as it is reserved for AWS use. You cannot edit or delete tag keys or values with this prefix. Tags with this prefix do not count against your tags per resource limit.

• REPLICA-The replica scheduling strategy places and maintains the 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. This scheduler strategy is required if the service uses the CODE_DEPLOY or EXTERNAL deployment controller types.
• 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 and will stop tasks that don't meet the placement constraints. When you're using this strategy, you don't need to specify a desired number of tasks, a task placement strategy, or use Service Auto Scaling policies. Tasks using the Fargate launch type or the CODE_DEPLOY or EXTERNAL deployment controller types don't support the DAEMON scheduling strategy.

• Maximum number of tags per resource - 50
• For each resource, each tag key must be unique, and each tag key can have only one value.
• Maximum key length - 128 Unicode characters in UTF-8
• Maximum value length - 256 Unicode characters in UTF-8
• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
• Tag keys and values are case-sensitive.
• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for either keys or values as it is reserved for AWS use. You cannot edit or delete tag keys or values with this prefix. Tags with this prefix do not count against your tags per resource limit.

• REPLICA-The replica scheduling strategy places and maintains the 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. This scheduler strategy is required if the service uses the CODE_DEPLOY or EXTERNAL deployment controller types.
• 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 and will stop tasks that don't meet the placement constraints. When you're using this strategy, you don't need to specify a desired number of tasks, a task placement strategy, or use Service Auto Scaling policies. Tasks using the Fargate launch type or the CODE_DEPLOY or EXTERNAL deployment controller types don't support the DAEMON scheduling strategy.

• Maximum number of tags per resource - 50
• For each resource, each tag key must be unique, and each tag key can have only one value.
• Maximum key length - 128 Unicode characters in UTF-8
• Maximum value length - 256 Unicode characters in UTF-8
• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
• Tag keys and values are case-sensitive.
• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for either keys or values as it is reserved for AWS use. You cannot edit or delete tag keys or values with this prefix. Tags with this prefix do not count against your tags per resource limit.

• Amazon ECS currently supports a subset of the logging drivers available to the Docker daemon. Additional log drivers may be available in future releases of the Amazon ECS container agent. For tasks on FARGATElong, the supported log drivers are awslogs, splunk, and awsfirelens. For tasks hosted on Amazon EC2 instances, the supported log drivers are awslogs, fluentd, gelf, json-file, journald,syslog, splunk, and awsfirelens.
• This parameter requires version 1.18 of the Docker Remote API or greater on your container instance.
• For tasks that are hosted on Amazon EC2 instances, the Amazon ECS container agent must register the available logging drivers with the ECS_AVAILABLE_LOGGING_DRIVERS environment variable before containers placed on that instance can use these log configuration options. For more information, see Amazon ECS container agent configuration in the Amazon Elastic Container Service Developer Guide.
• For tasks that are on FARGATElong, because you don't have access to the underlying infrastructure your tasks are hosted on, any additional software needed must be installed outside of the task. For example, the Fluentd output aggregators or a remote host running Logstash to send Gelf logs to.

• Amazon ECS currently supports a subset of the logging drivers available to the Docker daemon. Additional log drivers may be available in future releases of the Amazon ECS container agent. For tasks on FARGATElong, the supported log drivers are awslogs, splunk, and awsfirelens. For tasks hosted on Amazon EC2 instances, the supported log drivers are awslogs, fluentd, gelf, json-file, journald,syslog, splunk, and awsfirelens.
• This parameter requires version 1.18 of the Docker Remote API or greater on your container instance.
• For tasks that are hosted on Amazon EC2 instances, the Amazon ECS container agent must register the available logging drivers with the ECS_AVAILABLE_LOGGING_DRIVERS environment variable before containers placed on that instance can use these log configuration options. For more information, see Amazon ECS container agent configuration in the Amazon Elastic Container Service Developer Guide.
• For tasks that are on FARGATElong, because you don't have access to the underlying infrastructure your tasks are hosted on, any additional software needed must be installed outside of the task. For example, the Fluentd output aggregators or a remote host running Logstash to send Gelf logs to.

• Amazon ECS currently supports a subset of the logging drivers available to the Docker daemon. Additional log drivers may be available in future releases of the Amazon ECS container agent. For tasks on FARGATElong, the supported log drivers are awslogs, splunk, and awsfirelens. For tasks hosted on Amazon EC2 instances, the supported log drivers are awslogs, fluentd, gelf, json-file, journald,syslog, splunk, and awsfirelens.
• This parameter requires version 1.18 of the Docker Remote API or greater on your container instance.
• For tasks that are hosted on Amazon EC2 instances, the Amazon ECS container agent must register the available logging drivers with the ECS_AVAILABLE_LOGGING_DRIVERS environment variable before containers placed on that instance can use these log configuration options. For more information, see Amazon ECS container agent configuration in the Amazon Elastic Container Service Developer Guide.
• For tasks that are on FARGATElong, because you don't have access to the underlying infrastructure your tasks are hosted on, any additional software needed must be installed outside of the task. For example, the Fluentd output aggregators or a remote host running Logstash to send Gelf logs to.

• Amazon ECS currently supports a subset of the logging drivers available to the Docker daemon. Additional log drivers may be available in future releases of the Amazon ECS container agent. For tasks on FARGATElong, the supported log drivers are awslogs, splunk, and awsfirelens. For tasks hosted on Amazon EC2 instances, the supported log drivers are awslogs, fluentd, gelf, json-file, journald,syslog, splunk, and awsfirelens.
• This parameter requires version 1.18 of the Docker Remote API or greater on your container instance.
• For tasks that are hosted on Amazon EC2 instances, the Amazon ECS container agent must register the available logging drivers with the ECS_AVAILABLE_LOGGING_DRIVERS environment variable before containers placed on that instance can use these log configuration options. For more information, see Amazon ECS container agent configuration in the Amazon Elastic Container Service Developer Guide.
• For tasks that are on FARGATElong, because you don't have access to the underlying infrastructure your tasks are hosted on, any additional software needed must be installed outside of the task. For example, the Fluentd output aggregators or a remote host running Logstash to send Gelf logs to.

• Amazon ECS currently supports a subset of the logging drivers available to the Docker daemon. Additional log drivers may be available in future releases of the Amazon ECS container agent. For tasks on FARGATElong, the supported log drivers are awslogs, splunk, and awsfirelens. For tasks hosted on Amazon EC2 instances, the supported log drivers are awslogs, fluentd, gelf, json-file, journald,syslog, splunk, and awsfirelens.
• This parameter requires version 1.18 of the Docker Remote API or greater on your container instance.
• For tasks that are hosted on Amazon EC2 instances, the Amazon ECS container agent must register the available logging drivers with the ECS_AVAILABLE_LOGGING_DRIVERS environment variable before containers placed on that instance can use these log configuration options. For more information, see Amazon ECS container agent configuration in the Amazon Elastic Container Service Developer Guide.
• For tasks that are on FARGATElong, because you don't have access to the underlying infrastructure your tasks are hosted on, any additional software needed must be installed outside of the task. For example, the Fluentd output aggregators or a remote host running Logstash to send Gelf logs to.

• Amazon ECS currently supports a subset of the logging drivers available to the Docker daemon. Additional log drivers may be available in future releases of the Amazon ECS container agent. For tasks on FARGATElong, the supported log drivers are awslogs, splunk, and awsfirelens. For tasks hosted on Amazon EC2 instances, the supported log drivers are awslogs, fluentd, gelf, json-file, journald,syslog, splunk, and awsfirelens.
• This parameter requires version 1.18 of the Docker Remote API or greater on your container instance.
• For tasks that are hosted on Amazon EC2 instances, the Amazon ECS container agent must register the available logging drivers with the ECS_AVAILABLE_LOGGING_DRIVERS environment variable before containers placed on that instance can use these log configuration options. For more information, see Amazon ECS container agent configuration in the Amazon Elastic Container Service Developer Guide.
• For tasks that are on FARGATElong, because you don't have access to the underlying infrastructure your tasks are hosted on, any additional software needed must be installed outside of the task. For example, the Fluentd output aggregators or a remote host running Logstash to send Gelf logs to.

If a service is using the rolling update (ECS) deployment type, the minimumHealthyPercent represents a lower limit on the number of your service's tasks that must remain in the RUNNING state during a deployment, as a percentage of the desiredCount (rounded up to the nearest integer). This parameter enables you to deploy without using additional cluster capacity. For example, if your service has a desiredCount of four tasks and a minimumHealthyPercent of 50%, the service scheduler may stop two existing tasks to free up cluster capacity before starting two new tasks. For services that do not use a load balancer, the following should be noted:

• A service is considered healthy if all essential containers within the tasks in the service pass their health checks.
• If a task has no essential containers with a health check defined, the service scheduler will wait for 40 seconds after a task reaches a RUNNING state before the task is counted towards the minimum healthy percent total.
• If a task has one or more essential containers with a health check defined, the service scheduler will wait for the task to reach a healthy status before counting it towards the minimum healthy percent total. A task is considered healthy when all essential containers within the task have passed their health checks. The amount of time the service scheduler can wait for is determined by the container health check settings.

For services that do use a load balancer, the following should be noted:

• If a task has no essential containers with a health check defined, the service scheduler will wait for the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.
• If a task has an essential container with a health check defined, the service scheduler will wait for both the task to reach a healthy status and the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.

The default value for a replica service for minimumHealthyPercent is 100%. The default minimumHealthyPercent value for a service using the DAEMON service schedule is 0% for the CLI, the AWS SDKs, and the APIs and 50% for the AWS Management Console. The minimum number of healthy tasks during a deployment is the desiredCount multiplied by the minimumHealthyPercent/100, rounded up to the nearest integer value. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the EC2 launch type, the minimum healthy percent value is set to the default value. The minimum healthy percent value is used to define the lower limit on the number of the tasks in the service that remain in the RUNNING state while the container instances are in the DRAINING state. You can't specify a custom minimumHealthyPercent value for a service that uses either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and has tasks that use the EC2 launch type. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the Fargate launch type, the minimum healthy percent value is not used, although it is returned when describing your service.

If a service is using the rolling update (ECS) deployment type, the minimumHealthyPercent represents a lower limit on the number of your service's tasks that must remain in the RUNNING state during a deployment, as a percentage of the desiredCount (rounded up to the nearest integer). This parameter enables you to deploy without using additional cluster capacity. For example, if your service has a desiredCount of four tasks and a minimumHealthyPercent of 50%, the service scheduler may stop two existing tasks to free up cluster capacity before starting two new tasks. For services that do not use a load balancer, the following should be noted:

• A service is considered healthy if all essential containers within the tasks in the service pass their health checks.
• If a task has no essential containers with a health check defined, the service scheduler will wait for 40 seconds after a task reaches a RUNNING state before the task is counted towards the minimum healthy percent total.
• If a task has one or more essential containers with a health check defined, the service scheduler will wait for the task to reach a healthy status before counting it towards the minimum healthy percent total. A task is considered healthy when all essential containers within the task have passed their health checks. The amount of time the service scheduler can wait for is determined by the container health check settings.

For services that do use a load balancer, the following should be noted:

• If a task has no essential containers with a health check defined, the service scheduler will wait for the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.
• If a task has an essential container with a health check defined, the service scheduler will wait for both the task to reach a healthy status and the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.

The default value for a replica service for minimumHealthyPercent is 100%. The default minimumHealthyPercent value for a service using the DAEMON service schedule is 0% for the CLI, the AWS SDKs, and the APIs and 50% for the AWS Management Console. The minimum number of healthy tasks during a deployment is the desiredCount multiplied by the minimumHealthyPercent/100, rounded up to the nearest integer value. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the EC2 launch type, the minimum healthy percent value is set to the default value. The minimum healthy percent value is used to define the lower limit on the number of the tasks in the service that remain in the RUNNING state while the container instances are in the DRAINING state. You can't specify a custom minimumHealthyPercent value for a service that uses either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and has tasks that use the EC2 launch type. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the Fargate launch type, the minimum healthy percent value is not used, although it is returned when describing your service.

If a service is using the rolling update (ECS) deployment type, the minimumHealthyPercent represents a lower limit on the number of your service's tasks that must remain in the RUNNING state during a deployment, as a percentage of the desiredCount (rounded up to the nearest integer). This parameter enables you to deploy without using additional cluster capacity. For example, if your service has a desiredCount of four tasks and a minimumHealthyPercent of 50%, the service scheduler may stop two existing tasks to free up cluster capacity before starting two new tasks. For services that do not use a load balancer, the following should be noted:

• A service is considered healthy if all essential containers within the tasks in the service pass their health checks.
• If a task has no essential containers with a health check defined, the service scheduler will wait for 40 seconds after a task reaches a RUNNING state before the task is counted towards the minimum healthy percent total.
• If a task has one or more essential containers with a health check defined, the service scheduler will wait for the task to reach a healthy status before counting it towards the minimum healthy percent total. A task is considered healthy when all essential containers within the task have passed their health checks. The amount of time the service scheduler can wait for is determined by the container health check settings.

For services that do use a load balancer, the following should be noted:

• If a task has no essential containers with a health check defined, the service scheduler will wait for the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.
• If a task has an essential container with a health check defined, the service scheduler will wait for both the task to reach a healthy status and the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.

The default value for a replica service for minimumHealthyPercent is 100%. The default minimumHealthyPercent value for a service using the DAEMON service schedule is 0% for the CLI, the AWS SDKs, and the APIs and 50% for the AWS Management Console. The minimum number of healthy tasks during a deployment is the desiredCount multiplied by the minimumHealthyPercent/100, rounded up to the nearest integer value. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the EC2 launch type, the minimum healthy percent value is set to the default value. The minimum healthy percent value is used to define the lower limit on the number of the tasks in the service that remain in the RUNNING state while the container instances are in the DRAINING state. You can't specify a custom minimumHealthyPercent value for a service that uses either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and has tasks that use the EC2 launch type. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the Fargate launch type, the minimum healthy percent value is not used, although it is returned when describing your service.

If a service is using the rolling update (ECS) deployment type, the minimumHealthyPercent represents a lower limit on the number of your service's tasks that must remain in the RUNNING state during a deployment, as a percentage of the desiredCount (rounded up to the nearest integer). This parameter enables you to deploy without using additional cluster capacity. For example, if your service has a desiredCount of four tasks and a minimumHealthyPercent of 50%, the service scheduler may stop two existing tasks to free up cluster capacity before starting two new tasks. For services that do not use a load balancer, the following should be noted:

• A service is considered healthy if all essential containers within the tasks in the service pass their health checks.
• If a task has no essential containers with a health check defined, the service scheduler will wait for 40 seconds after a task reaches a RUNNING state before the task is counted towards the minimum healthy percent total.
• If a task has one or more essential containers with a health check defined, the service scheduler will wait for the task to reach a healthy status before counting it towards the minimum healthy percent total. A task is considered healthy when all essential containers within the task have passed their health checks. The amount of time the service scheduler can wait for is determined by the container health check settings.

For services that do use a load balancer, the following should be noted:

• If a task has no essential containers with a health check defined, the service scheduler will wait for the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.
• If a task has an essential container with a health check defined, the service scheduler will wait for both the task to reach a healthy status and the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.

The default value for a replica service for minimumHealthyPercent is 100%. The default minimumHealthyPercent value for a service using the DAEMON service schedule is 0% for the CLI, the AWS SDKs, and the APIs and 50% for the AWS Management Console. The minimum number of healthy tasks during a deployment is the desiredCount multiplied by the minimumHealthyPercent/100, rounded up to the nearest integer value. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the EC2 launch type, the minimum healthy percent value is set to the default value. The minimum healthy percent value is used to define the lower limit on the number of the tasks in the service that remain in the RUNNING state while the container instances are in the DRAINING state. You can't specify a custom minimumHealthyPercent value for a service that uses either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and has tasks that use the EC2 launch type. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the Fargate launch type, the minimum healthy percent value is not used, although it is returned when describing your service.

If a service is using the rolling update (ECS) deployment type, the minimumHealthyPercent represents a lower limit on the number of your service's tasks that must remain in the RUNNING state during a deployment, as a percentage of the desiredCount (rounded up to the nearest integer). This parameter enables you to deploy without using additional cluster capacity. For example, if your service has a desiredCount of four tasks and a minimumHealthyPercent of 50%, the service scheduler may stop two existing tasks to free up cluster capacity before starting two new tasks. For services that do not use a load balancer, the following should be noted:

• A service is considered healthy if all essential containers within the tasks in the service pass their health checks.
• If a task has no essential containers with a health check defined, the service scheduler will wait for 40 seconds after a task reaches a RUNNING state before the task is counted towards the minimum healthy percent total.
• If a task has one or more essential containers with a health check defined, the service scheduler will wait for the task to reach a healthy status before counting it towards the minimum healthy percent total. A task is considered healthy when all essential containers within the task have passed their health checks. The amount of time the service scheduler can wait for is determined by the container health check settings.

For services that do use a load balancer, the following should be noted:

• If a task has no essential containers with a health check defined, the service scheduler will wait for the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.
• If a task has an essential container with a health check defined, the service scheduler will wait for both the task to reach a healthy status and the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.

The default value for a replica service for minimumHealthyPercent is 100%. The default minimumHealthyPercent value for a service using the DAEMON service schedule is 0% for the CLI, the AWS SDKs, and the APIs and 50% for the AWS Management Console. The minimum number of healthy tasks during a deployment is the desiredCount multiplied by the minimumHealthyPercent/100, rounded up to the nearest integer value. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the EC2 launch type, the minimum healthy percent value is set to the default value. The minimum healthy percent value is used to define the lower limit on the number of the tasks in the service that remain in the RUNNING state while the container instances are in the DRAINING state. You can't specify a custom minimumHealthyPercent value for a service that uses either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and has tasks that use the EC2 launch type. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the Fargate launch type, the minimum healthy percent value is not used, although it is returned when describing your service.

If a service is using the rolling update (ECS) deployment type, the minimumHealthyPercent represents a lower limit on the number of your service's tasks that must remain in the RUNNING state during a deployment, as a percentage of the desiredCount (rounded up to the nearest integer). This parameter enables you to deploy without using additional cluster capacity. For example, if your service has a desiredCount of four tasks and a minimumHealthyPercent of 50%, the service scheduler may stop two existing tasks to free up cluster capacity before starting two new tasks. For services that do not use a load balancer, the following should be noted:

• A service is considered healthy if all essential containers within the tasks in the service pass their health checks.
• If a task has no essential containers with a health check defined, the service scheduler will wait for 40 seconds after a task reaches a RUNNING state before the task is counted towards the minimum healthy percent total.
• If a task has one or more essential containers with a health check defined, the service scheduler will wait for the task to reach a healthy status before counting it towards the minimum healthy percent total. A task is considered healthy when all essential containers within the task have passed their health checks. The amount of time the service scheduler can wait for is determined by the container health check settings.

For services that do use a load balancer, the following should be noted:

• If a task has no essential containers with a health check defined, the service scheduler will wait for the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.
• If a task has an essential container with a health check defined, the service scheduler will wait for both the task to reach a healthy status and the load balancer target group health check to return a healthy status before counting the task towards the minimum healthy percent total.

The default value for a replica service for minimumHealthyPercent is 100%. The default minimumHealthyPercent value for a service using the DAEMON service schedule is 0% for the CLI, the AWS SDKs, and the APIs and 50% for the AWS Management Console. The minimum number of healthy tasks during a deployment is the desiredCount multiplied by the minimumHealthyPercent/100, rounded up to the nearest integer value. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the EC2 launch type, the minimum healthy percent value is set to the default value. The minimum healthy percent value is used to define the lower limit on the number of the tasks in the service that remain in the RUNNING state while the container instances are in the DRAINING state. You can't specify a custom minimumHealthyPercent value for a service that uses either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and has tasks that use the EC2 launch type. If a service is using either the blue/green (CODE_DEPLOY) or EXTERNAL deployment types and is running tasks that use the Fargate launch type, the minimum healthy percent value is not used, although it is returned when describing your service.

• ECS The rolling update (ECS) deployment type involves replacing the current running version of the container with the latest version. The number of containers Amazon ECS adds or removes from the service during a rolling update is controlled by adjusting the minimum and maximum number of healthy tasks allowed during a service deployment, as specified in the DeploymentConfiguration. + CODE_DEPLOY The blue/green (CODE_DEPLOY) deployment type uses the blue/green deployment model powered by , which allows you to verify a new deployment of a service before sending production traffic to it. + EXTERNAL The external (EXTERNAL) deployment type enables you to use any third-party deployment controller for full control over the deployment process for an Amazon ECS service.

• ECS The rolling update (ECS) deployment type involves replacing the current running version of the container with the latest version. The number of containers Amazon ECS adds or removes from the service during a rolling update is controlled by adjusting the minimum and maximum number of healthy tasks allowed during a service deployment, as specified in the DeploymentConfiguration. + CODE_DEPLOY The blue/green (CODE_DEPLOY) deployment type uses the blue/green deployment model powered by , which allows you to verify a new deployment of a service before sending production traffic to it. + EXTERNAL The external (EXTERNAL) deployment type enables you to use any third-party deployment controller for full control over the deployment process for an Amazon ECS service.

• ECS The rolling update (ECS) deployment type involves replacing the current running version of the container with the latest version. The number of containers Amazon ECS adds or removes from the service during a rolling update is controlled by adjusting the minimum and maximum number of healthy tasks allowed during a service deployment, as specified in the DeploymentConfiguration. + CODE_DEPLOY The blue/green (CODE_DEPLOY) deployment type uses the blue/green deployment model powered by , which allows you to verify a new deployment of a service before sending production traffic to it. + EXTERNAL The external (EXTERNAL) deployment type enables you to use any third-party deployment controller for full control over the deployment process for an Amazon ECS service.

• ECS The rolling update (ECS) deployment type involves replacing the current running version of the container with the latest version. The number of containers Amazon ECS adds or removes from the service during a rolling update is controlled by adjusting the minimum and maximum number of healthy tasks allowed during a service deployment, as specified in the DeploymentConfiguration. + CODE_DEPLOY The blue/green (CODE_DEPLOY) deployment type uses the blue/green deployment model powered by , which allows you to verify a new deployment of a service before sending production traffic to it. + EXTERNAL The external (EXTERNAL) deployment type enables you to use any third-party deployment controller for full control over the deployment process for an Amazon ECS service.

• ECS The rolling update (ECS) deployment type involves replacing the current running version of the container with the latest version. The number of containers Amazon ECS adds or removes from the service during a rolling update is controlled by adjusting the minimum and maximum number of healthy tasks allowed during a service deployment, as specified in the DeploymentConfiguration. + CODE_DEPLOY The blue/green (CODE_DEPLOY) deployment type uses the blue/green deployment model powered by , which allows you to verify a new deployment of a service before sending production traffic to it. + EXTERNAL The external (EXTERNAL) deployment type enables you to use any third-party deployment controller for full control over the deployment process for an Amazon ECS service.

• ECS The rolling update (ECS) deployment type involves replacing the current running version of the container with the latest version. The number of containers Amazon ECS adds or removes from the service during a rolling update is controlled by adjusting the minimum and maximum number of healthy tasks allowed during a service deployment, as specified in the DeploymentConfiguration. + CODE_DEPLOY The blue/green (CODE_DEPLOY) deployment type uses the blue/green deployment model powered by , which allows you to verify a new deployment of a service before sending production traffic to it. + EXTERNAL The external (EXTERNAL) deployment type enables you to use any third-party deployment controller for full control over the deployment process for an Amazon ECS service.

• awslogs-create-group Required: No Specify whether you want the log group to be created automatically. If this option isn't specified, it defaults to false. Your IAM policy must include the logs:CreateLogGroup permission before you attempt to use awslogs-create-group. + awslogs-region Required: Yes Specify the Region that the awslogs log driver is to send your Docker logs to. You can choose to send all of your logs from clusters in different Regions to a single region in CloudWatch Logs. This is so that they're all visible in one location. Otherwise, you can separate them by Region for more granularity. Make sure that the specified log group exists in the Region that you specify with this option. + awslogs-group Required: Yes Make sure to specify a log group that the awslogs log driver sends its log streams to. + awslogs-stream-prefix Required: Yes, when using the Fargate launch type.Optional for the EC2 launch type, required for the Fargate launch type. Use the awslogs-stream-prefix option to associate a log stream with the specified prefix, the container name, and the ID of the Amazon ECS task that the container belongs to. If you specify a prefix with this option, then the log stream takes the format prefix-name/container-name/ecs-task-id. If you don't specify a prefix with this option, then the log stream is named after the container ID that's assigned by the Docker daemon on the container instance. Because it's difficult to trace logs back to the container that sent them with just the Docker container ID (which is only available on the container instance), we recommend that you specify a prefix with this option. For Amazon ECS services, you can use the service name as the prefix. Doing so, you can trace log streams to the service that the container belongs to, the name of the container that sent them, and the ID of the task that the container belongs to. You must specify a stream-prefix for your logs to have your logs appear in the Log pane when using the Amazon ECS console. + awslogs-datetime-format Required: No This option defines a multiline start pattern in Python strftime format. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. One example of a use case for using this format is for parsing output such as a stack dump, which might otherwise be logged in multiple entries. The correct pattern allows it to be captured in a single entry. For more information, see awslogs-datetime-format. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + awslogs-multiline-pattern Required: No This option defines a multiline start pattern that uses a regular expression. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. For more information, see awslogs-multiline-pattern. This option is ignored if awslogs-datetime-format is also configured. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + mode Required: No Valid values: non-blocking | blocking This option defines the delivery mode of log messages from the container to CloudWatch Logs. The delivery mode you choose affects application availability when the flow of logs from container to CloudWatch is interrupted. If you use the blocking mode and the flow of logs to CloudWatch is interrupted, calls from container code to write to the stdout and stderr streams will block. The logging thread of the application will block as a result. This may cause the application to become unresponsive and lead to container healthcheck failure. If you use the non-blocking mode, the container's logs are instead stored in an in-memory intermediate buffer configured with the max-buffer-size option. This prevents the application from becoming unresponsive when logs cannot be sent to CloudWatch. We recommend using this mode if you want to ensure service availability and are okay with some log loss. For more information, see Preventing log loss with non-blocking mode in the awslogs container log driver. + max-buffer-size Required: No Default value: 1m When non-blocking mode is used, the max-buffer-size log option controls the size of the buffer that's used for intermediate message storage. Make sure to specify an adequate buffer size based on your application. When the buffer fills up, further logs cannot be stored. Logs that cannot be stored are lost. To route logs using the splunk log router, you need to specify a splunk-token and a splunk-url. When you use the awsfirelens log router to route logs to an AWS Service or AWS Partner Network destination for log storage and analytics, you can set the log-driver-buffer-limit option to limit the number of events that are buffered in memory, before being sent to the log router container. It can help to resolve potential log loss issue because high throughput might result in memory running out for the buffer inside of Docker. Other options you can specify when using awsfirelens to route logs depend on the destination. When you export logs to Amazon Data Firehose, you can specify the AWS Region with region and a name for the log stream with delivery_stream. When you export logs to Amazon Kinesis Data Streams, you can specify an AWS Region with region and a data stream name with stream. When you export logs to Amazon OpenSearch Service, you can specify options like Name, Host (OpenSearch Service endpoint without protocol), Port, Index, Type, Aws_auth, Aws_region, Suppress_Type_Name, and tls. When you export logs to Amazon S3, you can specify the bucket using the bucket option. You can also specify region, total_file_size, upload_timeout, and use_put_object as options. This parameter requires version 1.19 of the Docker Remote API or greater on your container instance. To check the Docker Remote API version on your container instance, log in to your container instance and run the following command: sudo docker version --format '{{.Server.APIVersion}}'

• awslogs-create-group Required: No Specify whether you want the log group to be created automatically. If this option isn't specified, it defaults to false. Your IAM policy must include the logs:CreateLogGroup permission before you attempt to use awslogs-create-group. + awslogs-region Required: Yes Specify the Region that the awslogs log driver is to send your Docker logs to. You can choose to send all of your logs from clusters in different Regions to a single region in CloudWatch Logs. This is so that they're all visible in one location. Otherwise, you can separate them by Region for more granularity. Make sure that the specified log group exists in the Region that you specify with this option. + awslogs-group Required: Yes Make sure to specify a log group that the awslogs log driver sends its log streams to. + awslogs-stream-prefix Required: Yes, when using the Fargate launch type.Optional for the EC2 launch type, required for the Fargate launch type. Use the awslogs-stream-prefix option to associate a log stream with the specified prefix, the container name, and the ID of the Amazon ECS task that the container belongs to. If you specify a prefix with this option, then the log stream takes the format prefix-name/container-name/ecs-task-id. If you don't specify a prefix with this option, then the log stream is named after the container ID that's assigned by the Docker daemon on the container instance. Because it's difficult to trace logs back to the container that sent them with just the Docker container ID (which is only available on the container instance), we recommend that you specify a prefix with this option. For Amazon ECS services, you can use the service name as the prefix. Doing so, you can trace log streams to the service that the container belongs to, the name of the container that sent them, and the ID of the task that the container belongs to. You must specify a stream-prefix for your logs to have your logs appear in the Log pane when using the Amazon ECS console. + awslogs-datetime-format Required: No This option defines a multiline start pattern in Python strftime format. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. One example of a use case for using this format is for parsing output such as a stack dump, which might otherwise be logged in multiple entries. The correct pattern allows it to be captured in a single entry. For more information, see awslogs-datetime-format. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + awslogs-multiline-pattern Required: No This option defines a multiline start pattern that uses a regular expression. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. For more information, see awslogs-multiline-pattern. This option is ignored if awslogs-datetime-format is also configured. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + mode Required: No Valid values: non-blocking | blocking This option defines the delivery mode of log messages from the container to CloudWatch Logs. The delivery mode you choose affects application availability when the flow of logs from container to CloudWatch is interrupted. If you use the blocking mode and the flow of logs to CloudWatch is interrupted, calls from container code to write to the stdout and stderr streams will block. The logging thread of the application will block as a result. This may cause the application to become unresponsive and lead to container healthcheck failure. If you use the non-blocking mode, the container's logs are instead stored in an in-memory intermediate buffer configured with the max-buffer-size option. This prevents the application from becoming unresponsive when logs cannot be sent to CloudWatch. We recommend using this mode if you want to ensure service availability and are okay with some log loss. For more information, see Preventing log loss with non-blocking mode in the awslogs container log driver. + max-buffer-size Required: No Default value: 1m When non-blocking mode is used, the max-buffer-size log option controls the size of the buffer that's used for intermediate message storage. Make sure to specify an adequate buffer size based on your application. When the buffer fills up, further logs cannot be stored. Logs that cannot be stored are lost. To route logs using the splunk log router, you need to specify a splunk-token and a splunk-url. When you use the awsfirelens log router to route logs to an AWS Service or AWS Partner Network destination for log storage and analytics, you can set the log-driver-buffer-limit option to limit the number of events that are buffered in memory, before being sent to the log router container. It can help to resolve potential log loss issue because high throughput might result in memory running out for the buffer inside of Docker. Other options you can specify when using awsfirelens to route logs depend on the destination. When you export logs to Amazon Data Firehose, you can specify the AWS Region with region and a name for the log stream with delivery_stream. When you export logs to Amazon Kinesis Data Streams, you can specify an AWS Region with region and a data stream name with stream. When you export logs to Amazon OpenSearch Service, you can specify options like Name, Host (OpenSearch Service endpoint without protocol), Port, Index, Type, Aws_auth, Aws_region, Suppress_Type_Name, and tls. When you export logs to Amazon S3, you can specify the bucket using the bucket option. You can also specify region, total_file_size, upload_timeout, and use_put_object as options. This parameter requires version 1.19 of the Docker Remote API or greater on your container instance. To check the Docker Remote API version on your container instance, log in to your container instance and run the following command: sudo docker version --format '{{.Server.APIVersion}}'

• awslogs-create-group Required: No Specify whether you want the log group to be created automatically. If this option isn't specified, it defaults to false. Your IAM policy must include the logs:CreateLogGroup permission before you attempt to use awslogs-create-group. + awslogs-region Required: Yes Specify the Region that the awslogs log driver is to send your Docker logs to. You can choose to send all of your logs from clusters in different Regions to a single region in CloudWatch Logs. This is so that they're all visible in one location. Otherwise, you can separate them by Region for more granularity. Make sure that the specified log group exists in the Region that you specify with this option. + awslogs-group Required: Yes Make sure to specify a log group that the awslogs log driver sends its log streams to. + awslogs-stream-prefix Required: Yes, when using the Fargate launch type.Optional for the EC2 launch type, required for the Fargate launch type. Use the awslogs-stream-prefix option to associate a log stream with the specified prefix, the container name, and the ID of the Amazon ECS task that the container belongs to. If you specify a prefix with this option, then the log stream takes the format prefix-name/container-name/ecs-task-id. If you don't specify a prefix with this option, then the log stream is named after the container ID that's assigned by the Docker daemon on the container instance. Because it's difficult to trace logs back to the container that sent them with just the Docker container ID (which is only available on the container instance), we recommend that you specify a prefix with this option. For Amazon ECS services, you can use the service name as the prefix. Doing so, you can trace log streams to the service that the container belongs to, the name of the container that sent them, and the ID of the task that the container belongs to. You must specify a stream-prefix for your logs to have your logs appear in the Log pane when using the Amazon ECS console. + awslogs-datetime-format Required: No This option defines a multiline start pattern in Python strftime format. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. One example of a use case for using this format is for parsing output such as a stack dump, which might otherwise be logged in multiple entries. The correct pattern allows it to be captured in a single entry. For more information, see awslogs-datetime-format. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + awslogs-multiline-pattern Required: No This option defines a multiline start pattern that uses a regular expression. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. For more information, see awslogs-multiline-pattern. This option is ignored if awslogs-datetime-format is also configured. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + mode Required: No Valid values: non-blocking | blocking This option defines the delivery mode of log messages from the container to CloudWatch Logs. The delivery mode you choose affects application availability when the flow of logs from container to CloudWatch is interrupted. If you use the blocking mode and the flow of logs to CloudWatch is interrupted, calls from container code to write to the stdout and stderr streams will block. The logging thread of the application will block as a result. This may cause the application to become unresponsive and lead to container healthcheck failure. If you use the non-blocking mode, the container's logs are instead stored in an in-memory intermediate buffer configured with the max-buffer-size option. This prevents the application from becoming unresponsive when logs cannot be sent to CloudWatch. We recommend using this mode if you want to ensure service availability and are okay with some log loss. For more information, see Preventing log loss with non-blocking mode in the awslogs container log driver. + max-buffer-size Required: No Default value: 1m When non-blocking mode is used, the max-buffer-size log option controls the size of the buffer that's used for intermediate message storage. Make sure to specify an adequate buffer size based on your application. When the buffer fills up, further logs cannot be stored. Logs that cannot be stored are lost. To route logs using the splunk log router, you need to specify a splunk-token and a splunk-url. When you use the awsfirelens log router to route logs to an AWS Service or AWS Partner Network destination for log storage and analytics, you can set the log-driver-buffer-limit option to limit the number of events that are buffered in memory, before being sent to the log router container. It can help to resolve potential log loss issue because high throughput might result in memory running out for the buffer inside of Docker. Other options you can specify when using awsfirelens to route logs depend on the destination. When you export logs to Amazon Data Firehose, you can specify the AWS Region with region and a name for the log stream with delivery_stream. When you export logs to Amazon Kinesis Data Streams, you can specify an AWS Region with region and a data stream name with stream. When you export logs to Amazon OpenSearch Service, you can specify options like Name, Host (OpenSearch Service endpoint without protocol), Port, Index, Type, Aws_auth, Aws_region, Suppress_Type_Name, and tls. When you export logs to Amazon S3, you can specify the bucket using the bucket option. You can also specify region, total_file_size, upload_timeout, and use_put_object as options. This parameter requires version 1.19 of the Docker Remote API or greater on your container instance. To check the Docker Remote API version on your container instance, log in to your container instance and run the following command: sudo docker version --format '{{.Server.APIVersion}}'

• awslogs-create-group Required: No Specify whether you want the log group to be created automatically. If this option isn't specified, it defaults to false. Your IAM policy must include the logs:CreateLogGroup permission before you attempt to use awslogs-create-group. + awslogs-region Required: Yes Specify the Region that the awslogs log driver is to send your Docker logs to. You can choose to send all of your logs from clusters in different Regions to a single region in CloudWatch Logs. This is so that they're all visible in one location. Otherwise, you can separate them by Region for more granularity. Make sure that the specified log group exists in the Region that you specify with this option. + awslogs-group Required: Yes Make sure to specify a log group that the awslogs log driver sends its log streams to. + awslogs-stream-prefix Required: Yes, when using the Fargate launch type.Optional for the EC2 launch type, required for the Fargate launch type. Use the awslogs-stream-prefix option to associate a log stream with the specified prefix, the container name, and the ID of the Amazon ECS task that the container belongs to. If you specify a prefix with this option, then the log stream takes the format prefix-name/container-name/ecs-task-id. If you don't specify a prefix with this option, then the log stream is named after the container ID that's assigned by the Docker daemon on the container instance. Because it's difficult to trace logs back to the container that sent them with just the Docker container ID (which is only available on the container instance), we recommend that you specify a prefix with this option. For Amazon ECS services, you can use the service name as the prefix. Doing so, you can trace log streams to the service that the container belongs to, the name of the container that sent them, and the ID of the task that the container belongs to. You must specify a stream-prefix for your logs to have your logs appear in the Log pane when using the Amazon ECS console. + awslogs-datetime-format Required: No This option defines a multiline start pattern in Python strftime format. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. One example of a use case for using this format is for parsing output such as a stack dump, which might otherwise be logged in multiple entries. The correct pattern allows it to be captured in a single entry. For more information, see awslogs-datetime-format. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + awslogs-multiline-pattern Required: No This option defines a multiline start pattern that uses a regular expression. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. For more information, see awslogs-multiline-pattern. This option is ignored if awslogs-datetime-format is also configured. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + mode Required: No Valid values: non-blocking | blocking This option defines the delivery mode of log messages from the container to CloudWatch Logs. The delivery mode you choose affects application availability when the flow of logs from container to CloudWatch is interrupted. If you use the blocking mode and the flow of logs to CloudWatch is interrupted, calls from container code to write to the stdout and stderr streams will block. The logging thread of the application will block as a result. This may cause the application to become unresponsive and lead to container healthcheck failure. If you use the non-blocking mode, the container's logs are instead stored in an in-memory intermediate buffer configured with the max-buffer-size option. This prevents the application from becoming unresponsive when logs cannot be sent to CloudWatch. We recommend using this mode if you want to ensure service availability and are okay with some log loss. For more information, see Preventing log loss with non-blocking mode in the awslogs container log driver. + max-buffer-size Required: No Default value: 1m When non-blocking mode is used, the max-buffer-size log option controls the size of the buffer that's used for intermediate message storage. Make sure to specify an adequate buffer size based on your application. When the buffer fills up, further logs cannot be stored. Logs that cannot be stored are lost. To route logs using the splunk log router, you need to specify a splunk-token and a splunk-url. When you use the awsfirelens log router to route logs to an AWS Service or AWS Partner Network destination for log storage and analytics, you can set the log-driver-buffer-limit option to limit the number of events that are buffered in memory, before being sent to the log router container. It can help to resolve potential log loss issue because high throughput might result in memory running out for the buffer inside of Docker. Other options you can specify when using awsfirelens to route logs depend on the destination. When you export logs to Amazon Data Firehose, you can specify the AWS Region with region and a name for the log stream with delivery_stream. When you export logs to Amazon Kinesis Data Streams, you can specify an AWS Region with region and a data stream name with stream. When you export logs to Amazon OpenSearch Service, you can specify options like Name, Host (OpenSearch Service endpoint without protocol), Port, Index, Type, Aws_auth, Aws_region, Suppress_Type_Name, and tls. When you export logs to Amazon S3, you can specify the bucket using the bucket option. You can also specify region, total_file_size, upload_timeout, and use_put_object as options. This parameter requires version 1.19 of the Docker Remote API or greater on your container instance. To check the Docker Remote API version on your container instance, log in to your container instance and run the following command: sudo docker version --format '{{.Server.APIVersion}}'

• awslogs-create-group Required: No Specify whether you want the log group to be created automatically. If this option isn't specified, it defaults to false. Your IAM policy must include the logs:CreateLogGroup permission before you attempt to use awslogs-create-group. + awslogs-region Required: Yes Specify the Region that the awslogs log driver is to send your Docker logs to. You can choose to send all of your logs from clusters in different Regions to a single region in CloudWatch Logs. This is so that they're all visible in one location. Otherwise, you can separate them by Region for more granularity. Make sure that the specified log group exists in the Region that you specify with this option. + awslogs-group Required: Yes Make sure to specify a log group that the awslogs log driver sends its log streams to. + awslogs-stream-prefix Required: Yes, when using the Fargate launch type.Optional for the EC2 launch type, required for the Fargate launch type. Use the awslogs-stream-prefix option to associate a log stream with the specified prefix, the container name, and the ID of the Amazon ECS task that the container belongs to. If you specify a prefix with this option, then the log stream takes the format prefix-name/container-name/ecs-task-id. If you don't specify a prefix with this option, then the log stream is named after the container ID that's assigned by the Docker daemon on the container instance. Because it's difficult to trace logs back to the container that sent them with just the Docker container ID (which is only available on the container instance), we recommend that you specify a prefix with this option. For Amazon ECS services, you can use the service name as the prefix. Doing so, you can trace log streams to the service that the container belongs to, the name of the container that sent them, and the ID of the task that the container belongs to. You must specify a stream-prefix for your logs to have your logs appear in the Log pane when using the Amazon ECS console. + awslogs-datetime-format Required: No This option defines a multiline start pattern in Python strftime format. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. One example of a use case for using this format is for parsing output such as a stack dump, which might otherwise be logged in multiple entries. The correct pattern allows it to be captured in a single entry. For more information, see awslogs-datetime-format. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + awslogs-multiline-pattern Required: No This option defines a multiline start pattern that uses a regular expression. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. For more information, see awslogs-multiline-pattern. This option is ignored if awslogs-datetime-format is also configured. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + mode Required: No Valid values: non-blocking | blocking This option defines the delivery mode of log messages from the container to CloudWatch Logs. The delivery mode you choose affects application availability when the flow of logs from container to CloudWatch is interrupted. If you use the blocking mode and the flow of logs to CloudWatch is interrupted, calls from container code to write to the stdout and stderr streams will block. The logging thread of the application will block as a result. This may cause the application to become unresponsive and lead to container healthcheck failure. If you use the non-blocking mode, the container's logs are instead stored in an in-memory intermediate buffer configured with the max-buffer-size option. This prevents the application from becoming unresponsive when logs cannot be sent to CloudWatch. We recommend using this mode if you want to ensure service availability and are okay with some log loss. For more information, see Preventing log loss with non-blocking mode in the awslogs container log driver. + max-buffer-size Required: No Default value: 1m When non-blocking mode is used, the max-buffer-size log option controls the size of the buffer that's used for intermediate message storage. Make sure to specify an adequate buffer size based on your application. When the buffer fills up, further logs cannot be stored. Logs that cannot be stored are lost. To route logs using the splunk log router, you need to specify a splunk-token and a splunk-url. When you use the awsfirelens log router to route logs to an AWS Service or AWS Partner Network destination for log storage and analytics, you can set the log-driver-buffer-limit option to limit the number of events that are buffered in memory, before being sent to the log router container. It can help to resolve potential log loss issue because high throughput might result in memory running out for the buffer inside of Docker. Other options you can specify when using awsfirelens to route logs depend on the destination. When you export logs to Amazon Data Firehose, you can specify the AWS Region with region and a name for the log stream with delivery_stream. When you export logs to Amazon Kinesis Data Streams, you can specify an AWS Region with region and a data stream name with stream. When you export logs to Amazon OpenSearch Service, you can specify options like Name, Host (OpenSearch Service endpoint without protocol), Port, Index, Type, Aws_auth, Aws_region, Suppress_Type_Name, and tls. When you export logs to Amazon S3, you can specify the bucket using the bucket option. You can also specify region, total_file_size, upload_timeout, and use_put_object as options. This parameter requires version 1.19 of the Docker Remote API or greater on your container instance. To check the Docker Remote API version on your container instance, log in to your container instance and run the following command: sudo docker version --format '{{.Server.APIVersion}}'

• awslogs-create-group Required: No Specify whether you want the log group to be created automatically. If this option isn't specified, it defaults to false. Your IAM policy must include the logs:CreateLogGroup permission before you attempt to use awslogs-create-group. + awslogs-region Required: Yes Specify the Region that the awslogs log driver is to send your Docker logs to. You can choose to send all of your logs from clusters in different Regions to a single region in CloudWatch Logs. This is so that they're all visible in one location. Otherwise, you can separate them by Region for more granularity. Make sure that the specified log group exists in the Region that you specify with this option. + awslogs-group Required: Yes Make sure to specify a log group that the awslogs log driver sends its log streams to. + awslogs-stream-prefix Required: Yes, when using the Fargate launch type.Optional for the EC2 launch type, required for the Fargate launch type. Use the awslogs-stream-prefix option to associate a log stream with the specified prefix, the container name, and the ID of the Amazon ECS task that the container belongs to. If you specify a prefix with this option, then the log stream takes the format prefix-name/container-name/ecs-task-id. If you don't specify a prefix with this option, then the log stream is named after the container ID that's assigned by the Docker daemon on the container instance. Because it's difficult to trace logs back to the container that sent them with just the Docker container ID (which is only available on the container instance), we recommend that you specify a prefix with this option. For Amazon ECS services, you can use the service name as the prefix. Doing so, you can trace log streams to the service that the container belongs to, the name of the container that sent them, and the ID of the task that the container belongs to. You must specify a stream-prefix for your logs to have your logs appear in the Log pane when using the Amazon ECS console. + awslogs-datetime-format Required: No This option defines a multiline start pattern in Python strftime format. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. One example of a use case for using this format is for parsing output such as a stack dump, which might otherwise be logged in multiple entries. The correct pattern allows it to be captured in a single entry. For more information, see awslogs-datetime-format. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + awslogs-multiline-pattern Required: No This option defines a multiline start pattern that uses a regular expression. A log message consists of a line that matches the pattern and any following lines that don’t match the pattern. The matched line is the delimiter between log messages. For more information, see awslogs-multiline-pattern. This option is ignored if awslogs-datetime-format is also configured. You cannot configure both the awslogs-datetime-format and awslogs-multiline-pattern options. Multiline logging performs regular expression parsing and matching of all log messages. This might have a negative impact on logging performance. + mode Required: No Valid values: non-blocking | blocking This option defines the delivery mode of log messages from the container to CloudWatch Logs. The delivery mode you choose affects application availability when the flow of logs from container to CloudWatch is interrupted. If you use the blocking mode and the flow of logs to CloudWatch is interrupted, calls from container code to write to the stdout and stderr streams will block. The logging thread of the application will block as a result. This may cause the application to become unresponsive and lead to container healthcheck failure. If you use the non-blocking mode, the container's logs are instead stored in an in-memory intermediate buffer configured with the max-buffer-size option. This prevents the application from becoming unresponsive when logs cannot be sent to CloudWatch. We recommend using this mode if you want to ensure service availability and are okay with some log loss. For more information, see Preventing log loss with non-blocking mode in the awslogs container log driver. + max-buffer-size Required: No Default value: 1m When non-blocking mode is used, the max-buffer-size log option controls the size of the buffer that's used for intermediate message storage. Make sure to specify an adequate buffer size based on your application. When the buffer fills up, further logs cannot be stored. Logs that cannot be stored are lost. To route logs using the splunk log router, you need to specify a splunk-token and a splunk-url. When you use the awsfirelens log router to route logs to an AWS Service or AWS Partner Network destination for log storage and analytics, you can set the log-driver-buffer-limit option to limit the number of events that are buffered in memory, before being sent to the log router container. It can help to resolve potential log loss issue because high throughput might result in memory running out for the buffer inside of Docker. Other options you can specify when using awsfirelens to route logs depend on the destination. When you export logs to Amazon Data Firehose, you can specify the AWS Region with region and a name for the log stream with delivery_stream. When you export logs to Amazon Kinesis Data Streams, you can specify an AWS Region with region and a data stream name with stream. When you export logs to Amazon OpenSearch Service, you can specify options like Name, Host (OpenSearch Service endpoint without protocol), Port, Index, Type, Aws_auth, Aws_region, Suppress_Type_Name, and tls. When you export logs to Amazon S3, you can specify the bucket using the bucket option. You can also specify region, total_file_size, upload_timeout, and use_put_object as options. This parameter requires version 1.19 of the Docker Remote API or greater on your container instance. To check the Docker Remote API version on your container instance, log in to your container instance and run the following command: sudo docker version --format '{{.Server.APIVersion}}'

The number of I/O operations per second (IOPS). For gp3, io1, and io2 volumes, this represents the number of IOPS that are provisioned for the volume. For gp2 volumes, this represents the baseline performance of the volume and the rate at which the volume accumulates I/O credits for bursting. The following are the supported values for each volume type.

• gp3: 3,000 - 16,000 IOPS
• io1: 100 - 64,000 IOPS
• io2: 100 - 256,000 IOPS

This parameter is required for io1 and io2 volume types. The default for gp3 volumes is 3,000 IOPS. This parameter is not supported for st1, sc1, or standard volume types. This parameter maps 1:1 with the Iops parameter of the CreateVolume API in the Amazon EC2 API Reference.

• gp2 and gp3: 1-16,384
• io1 and io2: 4-16,384
• st1 and sc1: 125-16,384
• standard: 1-1,024

• General Purpose SSD: gp2|gp3
• Provisioned IOPS SSD: io1|io2
• Throughput Optimized HDD: st1
• Cold HDD: sc1
• Magnetic: standard The magnetic volume type is not supported on Fargate.

The number of I/O operations per second (IOPS). For gp3, io1, and io2 volumes, this represents the number of IOPS that are provisioned for the volume. For gp2 volumes, this represents the baseline performance of the volume and the rate at which the volume accumulates I/O credits for bursting. The following are the supported values for each volume type.

• gp3: 3,000 - 16,000 IOPS
• io1: 100 - 64,000 IOPS
• io2: 100 - 256,000 IOPS

This parameter is required for io1 and io2 volume types. The default for gp3 volumes is 3,000 IOPS. This parameter is not supported for st1, sc1, or standard volume types. This parameter maps 1:1 with the Iops parameter of the CreateVolume API in the Amazon EC2 API Reference.

• gp2 and gp3: 1-16,384
• io1 and io2: 4-16,384
• st1 and sc1: 125-16,384
• standard: 1-1,024

• General Purpose SSD: gp2|gp3
• Provisioned IOPS SSD: io1|io2
• Throughput Optimized HDD: st1
• Cold HDD: sc1
• Magnetic: standard The magnetic volume type is not supported on Fargate.

The number of I/O operations per second (IOPS). For gp3, io1, and io2 volumes, this represents the number of IOPS that are provisioned for the volume. For gp2 volumes, this represents the baseline performance of the volume and the rate at which the volume accumulates I/O credits for bursting. The following are the supported values for each volume type.

• gp3: 3,000 - 16,000 IOPS
• io1: 100 - 64,000 IOPS
• io2: 100 - 256,000 IOPS

This parameter is required for io1 and io2 volume types. The default for gp3 volumes is 3,000 IOPS. This parameter is not supported for st1, sc1, or standard volume types. This parameter maps 1:1 with the Iops parameter of the CreateVolume API in the Amazon EC2 API Reference.

• gp2 and gp3: 1-16,384
• io1 and io2: 4-16,384
• st1 and sc1: 125-16,384
• standard: 1-1,024

• General Purpose SSD: gp2|gp3
• Provisioned IOPS SSD: io1|io2
• Throughput Optimized HDD: st1
• Cold HDD: sc1
• Magnetic: standard The magnetic volume type is not supported on Fargate.

The number of I/O operations per second (IOPS). For gp3, io1, and io2 volumes, this represents the number of IOPS that are provisioned for the volume. For gp2 volumes, this represents the baseline performance of the volume and the rate at which the volume accumulates I/O credits for bursting. The following are the supported values for each volume type.

• gp3: 3,000 - 16,000 IOPS
• io1: 100 - 64,000 IOPS
• io2: 100 - 256,000 IOPS

This parameter is required for io1 and io2 volume types. The default for gp3 volumes is 3,000 IOPS. This parameter is not supported for st1, sc1, or standard volume types. This parameter maps 1:1 with the Iops parameter of the CreateVolume API in the Amazon EC2 API Reference.

• gp2 and gp3: 1-16,384
• io1 and io2: 4-16,384
• st1 and sc1: 125-16,384
• standard: 1-1,024

• General Purpose SSD: gp2|gp3
• Provisioned IOPS SSD: io1|io2
• Throughput Optimized HDD: st1
• Cold HDD: sc1
• Magnetic: standard The magnetic volume type is not supported on Fargate.

The number of I/O operations per second (IOPS). For gp3, io1, and io2 volumes, this represents the number of IOPS that are provisioned for the volume. For gp2 volumes, this represents the baseline performance of the volume and the rate at which the volume accumulates I/O credits for bursting. The following are the supported values for each volume type.

• gp3: 3,000 - 16,000 IOPS
• io1: 100 - 64,000 IOPS
• io2: 100 - 256,000 IOPS

This parameter is required for io1 and io2 volume types. The default for gp3 volumes is 3,000 IOPS. This parameter is not supported for st1, sc1, or standard volume types. This parameter maps 1:1 with the Iops parameter of the CreateVolume API in the Amazon EC2 API Reference.

• gp2 and gp3: 1-16,384
• io1 and io2: 4-16,384
• st1 and sc1: 125-16,384
• standard: 1-1,024

• General Purpose SSD: gp2|gp3
• Provisioned IOPS SSD: io1|io2
• Throughput Optimized HDD: st1
• Cold HDD: sc1
• Magnetic: standard The magnetic volume type is not supported on Fargate.

The number of I/O operations per second (IOPS). For gp3, io1, and io2 volumes, this represents the number of IOPS that are provisioned for the volume. For gp2 volumes, this represents the baseline performance of the volume and the rate at which the volume accumulates I/O credits for bursting. The following are the supported values for each volume type.

• gp3: 3,000 - 16,000 IOPS
• io1: 100 - 64,000 IOPS
• io2: 100 - 256,000 IOPS

This parameter is required for io1 and io2 volume types. The default for gp3 volumes is 3,000 IOPS. This parameter is not supported for st1, sc1, or standard volume types. This parameter maps 1:1 with the Iops parameter of the CreateVolume API in the Amazon EC2 API Reference.

• gp2 and gp3: 1-16,384
• io1 and io2: 4-16,384
• st1 and sc1: 125-16,384
• standard: 1-1,024

• General Purpose SSD: gp2|gp3
• Provisioned IOPS SSD: io1|io2
• Throughput Optimized HDD: st1
• Cold HDD: sc1
• Magnetic: standard The magnetic volume type is not supported on Fargate.