Speed up protected software program releases with new built-in blue/inexperienced deployments in Amazon ECS

Whereas containers have revolutionized how improvement groups package deal and deploy purposes, these groups have needed to rigorously monitor releases and construct customized tooling to mitigate deployment dangers, which slows down delivery velocity. At scale, improvement groups spend invaluable cycles constructing and sustaining undifferentiated deployment instruments as an alternative of innovating for his or her enterprise.

Beginning right this moment, you should use the built-in blue/inexperienced deployment functionality in Amazon Elastic Container Service (Amazon ECS) to make your utility deployments safer and extra constant. This new functionality eliminates the necessity to construct customized deployment tooling whereas supplying you with the boldness to ship software program updates extra steadily with rollback functionality.

Right here’s how one can allow the built-in blue/inexperienced deployment functionality within the Amazon ECS console.

You create a brand new “inexperienced” utility atmosphere whereas your current “blue” atmosphere continues to serve stay site visitors. After monitoring and testing the inexperienced atmosphere totally, you route the stay site visitors from blue to inexperienced. With this functionality, Amazon ECS now gives built-in performance that makes containerized utility deployments safer and extra dependable.

Beneath is a diagram illustrating how blue/inexperienced deployment works by shifting utility site visitors from the blue atmosphere to the inexperienced atmosphere. You may study extra on the Amazon ECS blue/inexperienced service deployments workflow web page.

Amazon ECS orchestrates this complete workflow whereas offering occasion hooks to validate new variations utilizing artificial site visitors earlier than routing manufacturing site visitors. You may validate new software program variations in manufacturing environments earlier than exposing them to finish customers and roll again near-instantaneously if points come up. As a result of this performance is constructed straight into Amazon ECS, you’ll be able to add these safeguards by merely updating your configuration with out constructing any customized tooling.

Getting began
Let me stroll you thru an indication that showcases how one can configure and use blue/inexperienced deployments for an ECS service. Earlier than that, there are a couple of setup steps that I would like to finish, together with configuring AWS Id and Entry Administration (IAM) roles, which you could find on the Required assets for Amazon ECS blue/inexperienced deployments Documentation web page.

For this demonstration, I need to deploy a brand new model of my utility utilizing the blue/inexperienced technique to attenuate danger. First, I have to configure my ECS service to make use of blue/inexperienced deployments. I can do that by means of the ECS console, AWS Command Line Interface (AWS CLI), or utilizing infrastructure as code.

Utilizing the Amazon ECS console, I create a brand new service and configure it as common:

Within the Deployment Choices part, I select ECS because the Deployment controller sort, then Blue/inexperienced because the Deployment technique. Bake time is the time after the manufacturing site visitors has shifted to inexperienced, when prompt rollback to blue is obtainable. When the bake time expires, blue duties are eliminated.

We’re additionally introducing deployment lifecycle hooks. These are event-driven mechanisms you should use to enhance the deployment workflow. I can choose which AWS Lambda operate I’d like to make use of as a deployment lifecycle hook. The Lambda operate can carry out the required enterprise logic, nevertheless it should return a hook standing.

Amazon ECS helps the next lifecycle hooks throughout blue/inexperienced deployments. You may study extra about every stage on the Deployment lifecycle levels web page.

• Pre scale up
• Submit scale up
• Manufacturing site visitors shift
• Check site visitors shift
• Submit manufacturing site visitors shift
• Submit check site visitors shift

For my utility, I need to check when the check site visitors shift is full and the inexperienced service handles all the check site visitors. Since there’s no end-user site visitors, a rollback at this stage could have no influence on customers. This makes Submit check site visitors shift appropriate for my use case as I can check it first with my Lambda operate.

Switching context for a second, let’s concentrate on the Lambda operate that I exploit to validate the deployment earlier than permitting it to proceed. In my Lambda operate as a deployment lifecycle hook, I can carry out any enterprise logic, equivalent to artificial testing, calling one other API, or querying metrics.

Inside the Lambda operate, I need to return a hookStatus. A hookStatus will be SUCCEEDED, which can transfer the method to the subsequent step. If the standing is FAILED, it rolls again to the blue deployment. If it’s IN_PROGRESS, then Amazon ECS retries the Lambda operate in 30 seconds.

Within the following instance, I arrange my validation with a Lambda operate that performs file add as a part of a check suite for my utility.

import json
import urllib3
import logging
import base64
import os

# Configure logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

# Initialize HTTP consumer
http = urllib3.PoolManager()

def lambda_handler(occasion, context):
    """
    Validation hook that exams the inexperienced atmosphere with file add
    """
    logger.information(f"Occasion: {json.dumps(occasion)}")
    logger.information(f"Context: {context}")
    
    attempt:
        # In an actual situation, you'll assemble the check endpoint URL
        test_endpoint = os.getenv("APP_URL")
        
        # Create a check file for add
        test_file_content = "It is a check file for deployment validation"
        test_file_data = test_file_content.encode('utf-8')
        
        # Put together multipart type information for file add
        fields = {
            'file': ('check.txt', test_file_data, 'textual content/plain'),
            'description': 'Deployment validation check file'
        }
        
        # Ship POST request with file add to /course of endpoint
        response = http.request(
            'POST', 
            test_endpoint,
            fields=fields,
            timeout=30
        )
        
        logger.information(f"POST /course of response standing: {response.standing}")
        
        # Verify if response has OK standing code (200-299 vary)
        if 200 <= response.standing < 300:
            logger.information("File add check handed - obtained OK standing code")
            return {
                "hookStatus": "SUCCEEDED"
            }
        else:
            logger.error(f"File add check failed - standing code: {response.standing}")
            return {
                "hookStatus": "FAILED"
            }
            
    besides Exception as error:
        logger.error(f"File add check failed: {str(error)}")
        return {
            "hookStatus": "FAILED"
        }

When the deployment reaches the lifecycle stage that’s related to the hook, Amazon ECS robotically invokes my Lambda operate with deployment context. My validation operate can run complete exams in opposition to the inexperienced revision—checking utility well being, operating integration exams, or validating efficiency metrics. The operate then indicators again to ECS whether or not to proceed or abort the deployment.

As I selected the blue/inexperienced deployment technique, I additionally have to configure the load balancers and/or Amazon ECS Service Join. Within the Load balancing part, I choose my Software Load Balancer.

Within the Listener part, I exploit an current listener on port 80 and choose two Goal teams.

Pleased with this configuration, I create the service and look forward to ECS to provision my new service.

Testing blue/inexperienced deployments
Now, it’s time to check my blue/inexperienced deployments. For this check, Amazon ECS will set off my Lambda operate after the check site visitors shift is accomplished. My Lambda operate will return FAILED on this case because it performs file add to my utility, however my utility doesn’t have this functionality.

I replace my service and test Drive new deployment, figuring out the blue/inexperienced deployment functionality will roll again if it detects a failure. I choose this feature as a result of I haven’t modified the duty definition however nonetheless have to set off a brand new deployment.

At this stage, I’ve each blue and inexperienced environments operating, with the inexperienced revision dealing with all of the check site visitors. In the meantime, based mostly on Amazon CloudWatch Logs of my Lambda operate, I additionally see that the deployment lifecycle hooks work as anticipated and emit the next payload:

[INFO]	2025-07-10T13:15:39.018Z	67d9b03e-12da-4fab-920d-9887d264308e	Occasion: 
{
    "executionDetails": {
        "testTrafficWeights": {},
        "productionTrafficWeights": {},
        "serviceArn": "arn:aws:ecs:us-west-2:123:service/EcsBlueGreenCluster/nginxBGservice",
        "targetServiceRevisionArn": "arn:aws:ecs:us-west-2:123:service-revision/EcsBlueGreenCluster/nginxBGservice/9386398427419951854"
    },
    "executionId": "a635edb5-a66b-4f44-bf3f-fcee4b3641a5",
    "lifecycleStage": "POST_TEST_TRAFFIC_SHIFT",
    "resourceArn": "arn:aws:ecs:us-west-2:123:service-deployment/EcsBlueGreenCluster/nginxBGservice/TFX5sH9q9XDboDTOv0rIt"
}

As anticipated, my AWS Lambda operate returns FAILED as hookStatus as a result of it didn’t carry out the check.

[ERROR]	2025-07-10T13:18:43.392Z	67d9b03e-12da-4fab-920d-9887d264308e	File add check failed: HTTPConnectionPool(host="xyz.us-west-2.elb.amazonaws.com", port=80): Max retries exceeded with url: / (Attributable to ConnectTimeoutError(, 'Connection to xyz.us-west-2.elb.amazonaws.com timed out. (join timeout=30)'))

As a result of the validation wasn’t accomplished efficiently, Amazon ECS tries to roll again to the blue model, which is the earlier working deployment model. I can monitor this course of by means of ECS occasions within the Occasions part, which gives detailed visibility into the deployment progress.

Amazon ECS efficiently rolls again the deployment to the earlier working model. The rollback occurs near-instantaneously as a result of the blue revision stays operating and able to obtain manufacturing site visitors. There isn’t any end-user influence throughout this course of, as manufacturing site visitors by no means shifted to the brand new utility model—ECS merely rolled again check site visitors to the unique steady model. This eliminates the everyday deployment downtime related to conventional rolling deployments.

I may see the rollback standing within the Final deployment part.

All through my testing, I noticed that the blue/inexperienced deployment technique gives constant and predictable conduct. Moreover, the deployment lifecycle hooks present extra flexibility to manage the conduct of the deployment. Every service revision maintains immutable configuration together with activity definition, load balancer settings, and Service Join configuration. Which means rollbacks restore precisely the identical atmosphere that was beforehand operating.

Further issues to know
Listed here are a few issues to notice:

• Pricing – The blue/inexperienced deployment functionality is included with Amazon ECS at no further cost. You pay just for the compute assets used in the course of the deployment course of.
• Availability – This functionality is obtainable in all industrial AWS Areas.

Get began with blue/inexperienced deployments by updating your Amazon ECS service configuration within the Amazon ECS console.

Completely satisfied deploying!
— Donnie