Description

Modifies the desired count, deployment configuration, or task definition used in a service.

You can add to or subtract from the number of instantiations of a task definition in a service by specifying the cluster that the service is running in and a new desiredCount parameter.

You can use UpdateService to modify your task definition and deploy a new version of your service.

You can also update the deployment configuration of a service. When a deployment is triggered by updating the task definition of a service, the service scheduler uses the deployment configuration parameters, minimumHealthyPercent and maximumPercent, to determine the deployment strategy.

If the minimumHealthyPercent is below 100%, the scheduler can ignore the desiredCount temporarily during a deployment. For example, if your service has a desiredCount of four tasks, a minimumHealthyPercent of 50% allows the scheduler to stop two existing tasks before starting two new tasks. Tasks for services that do not use a load balancer are considered healthy if they are in the RUNNING state; tasks for services that do use a load balancer are considered healthy if they are in the RUNNING state and the container instance it is hosted on is reported as healthy by the load balancer.

The maximumPercent parameter represents an upper limit on the number of running tasks during a deployment, which enables you to define the deployment batch size. For example, if your service has a desiredCount of four tasks, a maximumPercent value of 200% starts four new tasks before stopping the four older tasks (provided that the cluster resources required to do this are available).

When UpdateService stops a task during a deployment, the equivalent of docker stop is issued to the containers running in the task. This results in a SIGTERM and a 30-second timeout, after which SIGKILL is sent and the containers are forcibly stopped. If the container handles the SIGTERM gracefully and exits within 30 seconds from receiving it, no SIGKILL is sent.

When the service scheduler launches new tasks, it attempts to balance them across the Availability Zones in your cluster with the following logic:

Determine which of the container instances in your cluster can support

your service's task definition (for example, they have the required CPU, memory, ports, and container instance attributes).

Sort the valid container instances by the fewest number of running tasks

for this service in the same Availability Zone as the instance. For example, if zone A has one running service task and zones B and C each have zero, valid container instances in either zone B or C are considered optimal for placement.

Place the new service task on a valid container instance in an optimal

Availability Zone (based on the previous steps), favoring container instances with the fewest number of running tasks for this service.