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AWS CodePipeline
User Guide (API Version 2015-07-09)

Invoke an AWS Lambda Function in a Pipeline in AWS CodePipeline

AWS Lambda is a compute service that lets you run code without provisioning or managing servers. You can create Lambda functions and add them as actions in your pipelines. Because Lambda allows you to write functions to perform almost any task, you can customize the way your pipeline works.

Note

Creating and running Lambda functions might result in charges to your AWS account. For more information, see Pricing.

The following are some ways Lambda functions can be used in pipelines:

  • To roll out changes to your environment by applying or updating an AWS CloudFormation template.

  • To create resources on demand in one stage of a pipeline using AWS CloudFormation and delete them in another stage.

  • To deploy application versions with zero downtime in AWS Elastic Beanstalk with a Lambda function that swaps CNAME values.

  • To deploy to Amazon ECS Docker instances.

  • To back up resources before building or deploying by creating an AMI snapshot.

  • To add integration with third-party products to your pipeline, such as posting messages to an IRC client.

This topic assumes you are familiar with AWS CodePipeline and AWS Lambda and know how to create pipelines, functions, and the IAM polices and roles on which they depend. In this topic, we will walk through the steps for:

  • Creating a Lambda function that tests whether a web page was deployed successfully.

  • Configuring the AWS CodePipeline and Lambda execution roles and the permissions required to run the function as part of the pipeline.

  • Editing a pipeline to add the Lambda function as an action.

  • Testing the action by manually releasing a change.

This topic includes sample functions to demonstrate the flexibility of working with Lambda functions in AWS CodePipeline:

  • Basic Lambda function

    • Creating a basic Lambda function to use with AWS CodePipeline.

    • Returning success or failure results to AWS CodePipeline in the Details link for the action.

  • A Sample Python Function That Uses an AWS CloudFormation Template

    • Using JSON-encoded user parameters to pass multiple configuration values to the function (get_user_params).

    • Interacting with .zip artifacts in an artifact bucket (get_template).

    • Using a continuation token to monitor a long-running asynchronous process (continue_job_later). This will allow the action to continue and the function to succeed even if it exceeds a five-minute runtime (a limitation in Lambda).

Each sample function includes information about the permissions you must add to the role. For information about limits in AWS Lambda, see Limits in the AWS Lambda Developer Guide.

Important

The sample code, roles, and polices included in this topic are meant as examples only, and are provided as-is.

Step 1: Create a Pipeline

In this step, you will create a pipeline to which you will later add the Lambda function. This is the same pipeline you created in AWS CodePipeline Tutorials. If that pipeline is still configured for your account and is in the same region where you will create the Lambda function, you can skip this step.

Important

You must create the pipeline and all of its resources in the same region where you will create the Lambda function.

To create the pipeline

  1. Follow the first three steps in Tutorial: Create a Simple Pipeline (Amazon S3 Bucket) to create an Amazon S3 bucket, AWS CodeDeploy resources, and a two-stage pipeline. Choose the Amazon Linux option for your instance types. You can use any name you want for the pipeline, but the steps in this topic use MyLambdaTestPipeline.

  2. On the status page for your pipeline, in the AWS CodeDeploy action, choose Details. On the deployment details page for the deployment group, choose an instance ID from the list.

  3. In the Amazon EC2 console, on the Description tab for the instance, copy the IP address in Public IP (for example, 192.0.2.4). You will use this address as the target of the function in AWS Lambda.

Note

The default service role for AWS CodePipeline, AWS-CodePipeline-Service, includes the Lambda permissions required to invoke the function, so you do not have to create an additional invocation policy or role. However, if you have modified the default service role or selected a different one, make sure the policy for the role allows the lambda:InvokeFunction and lambda:ListFunctions permissions. Otherwise, pipelines that include Lambda actions will fail.

Step 2: Create the Lambda Function

In this step, you will create a Lambda function that makes an HTTP request and checks for a line of text on a web page. As part of this step, you must also create an IAM policy and Lambda execution role. For more information about Lambda, execution roles and why this is required, see Permissions Model in the AWS Lambda Developer Guide.

To create the execution role

  1. Sign in to the IAM console at https://console.aws.amazon.com/iam/.

  2. Choose Policies, and then choose Create Policy.

  3. On the Create Policy page, choose the Select button next to Create Your Own Policy.

  4. On the Review Policy page, in Policy Name, type a name for the policy (for example, CodePipelineLambdaExecPolicy). In Description, type Enables Lambda to execute code. In Policy Document, copy and paste the following policy into the policy box, and then choose Validate Policy.

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    { "Version": "2012-10-17", "Statement": [ { "Action": [ "logs:*" ], "Effect": "Allow", "Resource": "arn:aws:logs:*:*:*" }, { "Action": [ "codepipeline:PutJobSuccessResult", "codepipeline:PutJobFailureResult" ], "Effect": "Allow", "Resource": "*" } ] }

    After the policy is validated, choose Create Policy.

    Note

    These are the minimum permissions required for a Lambda function to interoperate with AWS CodePipeline and Amazon CloudWatch. If you want to expand this policy to allow functions that interact with other AWS resources, you should modify this policy to allow the actions required by those Lambda functions.

  5. On the policy dashboard page, choose Roles, and then choose Create New Role.

  6. On the Set Role Name page, type a name for the role (for example, CodePipelineLambdaExecRole), and then choose Next Step.

  7. On the Select Role Type page, in the list of roles under AWS Service Roles, choose the Select button next to AWS Lambda.

  8. On the Attach Policy page, select the check box next to CodePipelineLambdaExecPolicy, and then choose Next Step.

  9. On the Review page, choose Create Role.

To create the sample Lambda function to use with AWS CodePipeline

  1. Sign in to the AWS Management Console and open the AWS Lambda console at https://console.aws.amazon.com/lambda/.

  2. On the Lambda: Function list page, choose Create a Lambda function.

    Note

    If you see a Welcome page instead of the Lambda: Function list page, choose Get Started Now.

  3. On the Select blueprint page, choose Skip.

  4. On the Configure function page, in Name, type a name for your Lambda function (for example, MyLambdaFunctionForAWSCodePipeline). Optionally, in Description, type a description for the function (for example, A sample test to check whether the website responds with a 200 (OK) and contains a specific word on the page). In the Runtime list, choose Node.js, and then copy the following code into the Lambda function code box:

    Note

    The event object, under the CodePipeline.job key, contains the job details. For a full example of the JSON event AWS CodePipeline returns to Lambda, see Example JSON Event.

    Copy
    var assert = require('assert'); var AWS = require('aws-sdk'); var http = require('http'); exports.handler = function(event, context) { var codepipeline = new AWS.CodePipeline(); // Retrieve the Job ID from the Lambda action var jobId = event["CodePipeline.job"].id; // Retrieve the value of UserParameters from the Lambda action configuration in AWS CodePipeline, in this case a URL which will be // health checked by this function. var url = event["CodePipeline.job"].data.actionConfiguration.configuration.UserParameters; // Notify AWS CodePipeline of a successful job var putJobSuccess = function(message) { var params = { jobId: jobId }; codepipeline.putJobSuccessResult(params, function(err, data) { if(err) { context.fail(err); } else { context.succeed(message); } }); }; // Notify AWS CodePipeline of a failed job var putJobFailure = function(message) { var params = { jobId: jobId, failureDetails: { message: JSON.stringify(message), type: 'JobFailed', externalExecutionId: context.invokeid } }; codepipeline.putJobFailureResult(params, function(err, data) { context.fail(message); }); }; // Validate the URL passed in UserParameters if(!url || url.indexOf('http://') === -1) { putJobFailure('The UserParameters field must contain a valid URL address to test, including http:// or https://'); return; } // Helper function to make a HTTP GET request to the page. // The helper will test the response and succeed or fail the job accordingly var getPage = function(url, callback) { var pageObject = { body: '', statusCode: 0, contains: function(search) { return this.body.indexOf(search) > -1; } }; http.get(url, function(response) { pageObject.body = ''; pageObject.statusCode = response.statusCode; response.on('data', function (chunk) { pageObject.body += chunk; }); response.on('end', function () { callback(pageObject); }); response.resume(); }).on('error', function(error) { // Fail the job if our request failed putJobFailure(error); }); }; getPage(url, function(returnedPage) { try { // Check if the HTTP response has a 200 status assert(returnedPage.statusCode === 200); // Check if the page contains the text "Congratulations" // You can change this to check for different text, or add other tests as required assert(returnedPage.contains('Congratulations')); // Succeed the job putJobSuccess("Tests passed."); } catch (ex) { // If any of the assertions failed then fail the job putJobFailure(ex); } }); };
  5. Leave the value of Handler name at the default value, but change Role to CodePipelineLambdaExecRole.

  6. In Advanced settings, for Timeout (s), type 20.

  7. After you have finished configuring these details, choose Next.

  8. On the Review page, choose Create function.

Step 3: Add the Lambda Function to a Pipeline in the AWS CodePipeline Console

In this step, you will add a new stage to your pipeline, and then add an action—a Lambda action that calls your function— to that stage.

To add a stage

  1. Sign in to the AWS Management Console and open the AWS CodePipeline console at http://console.aws.amazon.com/codepipeline.

  2. On the Welcome page, choose the pipeline you created from the list of pipelines.

  3. On the pipeline view page, choose Edit.

  4. On the Edit page, choose the option to add a stage after the deployment stage with the AWS CodeDeploy action. Type a name for the stage (for example, LambdaStage), and then choose the option to add an action to the stage.

    Note

    You can also choose to add your Lambda action to an existing stage. For demonstration purposes, we are adding the Lambda function as the only action in a stage to allow you to easily view its progress as artifacts progress through a pipeline.

  5. In the Add action panel, in Action category, choose Invoke. In Invoke actions, in Action name, type a name for your Lambda action (for example, MyLambdaAction). In Provider, choose AWS Lambda. In Function name, choose or type the name of your Lambda function (for example, MyLambdaFunctionForAWSCodePipeline). In User parameters, specify the IP address for the Amazon EC2 instance you copied earlier (for example, http://192.0.2.4), and then choose Add action.

    
                        The configuration for a Lambda action in the Add
                                action form.

    Note

    This topic uses an IP address, but in a real-world scenario, you could provide your registered website name instead (for example, http://www.example.com). For more information about event data and handlers in AWS Lambda, see Programming Model in the AWS Lambda Developer Guide.

  6. On the Edit page, choose Save pipeline changes.

Step 4: Test the Pipeline with the Lambda function

To test the function, release the most recent change through the pipeline.

To use the console to run the most recent version of an artifact through a pipeline

  1. On the pipeline details page, choose Release change. This will run the most recent revision available in each source location specified in a source action through the pipeline.

  2. When the Lambda action is complete, choose the Details link to view the log stream for the function in Amazon CloudWatch, including the billed duration of the event. If the function failed, the CloudWatch log will provide information about the cause.

Step 5: Next Steps

Now that you've successfully created a Lambda function and added it as an action in a pipeline, you can try the following:

  • Add more Lambda actions to your stage to check other web pages.

  • Modify the Lambda function to check for a different text string.

  • Explore Lambda functions and create and add your own Lambda functions to pipelines.


                An AWS Lambda action running through a pipeline.

After you have finished experimenting with the Lambda function, consider removing it from your pipeline, deleting it from AWS Lambda, and deleting the role from IAM in order to avoid possible charges. For more information, see Edit a Pipeline in AWS CodePipeline, Delete a Pipeline in AWS CodePipeline, and Deleting Roles or Instance Profiles.

Example JSON Event

The following example shows a sample JSON event sent to Lambda by AWS CodePipeline. The structure of this event is similar to the response to the GetJobDetails API, but without the actionTypeId and pipelineContext data types. Two action configuration details, FunctionName and UserParameters, are included in both the JSON event and the response to the GetJobDetails API. The values in red italic text are examples or explanations, not real values.

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{ "CodePipeline.job": { "id": "11111111-abcd-1111-abcd-111111abcdef", "accountId": "111111111111", "data": { "actionConfiguration": { "configuration": { "FunctionName": "MyLambdaFunctionForAWSCodePipeline", "UserParameters": "some-input-such-as-a-URL" } }, "inputArtifacts": [ { "location": { "s3Location": { "bucketName": "the name of the bucket configured as the pipeline artifact store in Amazon S3, for example codepipeline-us-east-2-1234567890", "objectKey": "the name of the application, for example CodePipelineDemoApplication.zip" }, "type": "S3" }, "revision": null, "name": "ArtifactName" } ], "outputArtifacts": [], "artifactCredentials": { "secretAccessKey": "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY", "sessionToken": "MIICiTCCAfICCQD6m7oRw0uXOjANBgkqhkiG9w 0BAQUFADCBiDELMAkGA1UEBhMCVVMxCzAJBgNVBAgTAldBMRAwDgYDVQQHEwdTZ WF0dGxlMQ8wDQYDVQQKEwZBbWF6b24xFDASBgNVBAsTC0lBTSBDb25zb2xlMRIw EAYDVQQDEwlUZXN0Q2lsYWMxHzAdBgkqhkiG9w0BCQEWEG5vb25lQGFtYXpvbi5 jb20wHhcNMTEwNDI1MjA0NTIxWhcNMTIwNDI0MjA0NTIxWjCBiDELMAkGA1UEBh MCVVMxCzAJBgNVBAgTAldBMRAwDgYDVQQHEwdTZWF0dGxlMQ8wDQYDVQQKEwZBb WF6b24xFDASBgNVBAsTC0lBTSBDb25zb2xlMRIwEAYDVQQDEwlUZXN0Q2lsYWMx HzAdBgkqhkiG9w0BCQEWEG5vb25lQGFtYXpvbi5jb20wgZ8wDQYJKoZIhvcNAQE BBQADgY0AMIGJAoGBAMaK0dn+a4GmWIWJ21uUSfwfEvySWtC2XADZ4nB+BLYgVI k60CpiwsZ3G93vUEIO3IyNoH/f0wYK8m9TrDHudUZg3qX4waLG5M43q7Wgc/MbQ ITxOUSQv7c7ugFFDzQGBzZswY6786m86gpEIbb3OhjZnzcvQAaRHhdlQWIMm2nr AgMBAAEwDQYJKoZIhvcNAQEFBQADgYEAtCu4nUhVVxYUntneD9+h8Mg9q6q+auN KyExzyLwaxlAoo7TJHidbtS4J5iNmZgXL0FkbFFBjvSfpJIlJ00zbhNYS5f6Guo EDmFJl0ZxBHjJnyp378OD8uTs7fLvjx79LjSTbNYiytVbZPQUQ5Yaxu2jXnimvw 3rrszlaEXAMPLE=", "accessKeyId": "AKIAIOSFODNN7EXAMPLE" }, "continuationToken": "A continuation token if continuing job" } } }

Additional Sample Functions

The following sample Lambda functions demonstrate additional functionality you can leverage for your pipelines in AWS CodePipeline. To use these functions, you might have to make modifications to the policy for the Lambda execution role, as noted in the introduction for each sample.

A Sample Python Function That Uses an AWS CloudFormation Template

The following sample shows a function that creates or updates a stack based on a supplied AWS CloudFormation template. The template creates an Amazon S3 bucket. It is for demonstration purposes only, to minimize costs. Ideally, you should delete the stack before you upload anything to the bucket. If you upload files to the bucket, you will not be able to delete the bucket when you delete the stack. You will have to manually delete everything in the bucket before you can delete the bucket itself.

This Python sample assumes you have a pipeline that uses an Amazon S3 bucket as a source action, or that you have access to a versioned Amazon S3 bucket you can use with the pipeline. You will create the AWS CloudFormation template, compress it, and upload it to that bucket as a .zip file. You must then add a source action to your pipeline that retrieves this .zip file from the bucket.

This sample demonstrates:

  • The use of JSON-encoded user parameters to pass multiple configuration values to the function (get_user_params).

  • The interaction with .zip artifacts in an artifact bucket (get_template).

  • The use of a continuation token to monitor a long-running asynchronous process (continue_job_later). This will allow the action to continue and the function to succeed even if it exceeds a five-minute runtime (a limitation in Lambda).

To use this sample Lambda function, the policy for the Lambda execution role must have Allow permissions in AWS CloudFormation, Amazon S3, and AWS CodePipeline, as shown in this sample policy:

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{ "Version": "2012-10-17", "Statement": [ { "Action": [ "logs:*" ], "Effect": "Allow", "Resource": "arn:aws:logs:*:*:*" }, { "Action": [ "codepipeline:PutJobSuccessResult", "codepipeline:PutJobFailureResult" ], "Effect": "Allow", "Resource": "*" }, { "Action": [ "cloudformation:DescribeStacks", "cloudformation:CreateStack", "cloudformation:UpdateStack" ], "Effect": "Allow", "Resource": "*" }, { "Action": [ "s3:*" ], "Effect": "Allow", "Resource": "*" } ] }

To create the AWS CloudFormation template, open any plain-text editor and copy and paste the following code:

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{ "AWSTemplateFormatVersion" : "2010-09-09", "Description" : "AWS CloudFormation template which creates an S3 bucket", "Resources" : { "MySampleBucket" : { "Type" : "AWS::S3::Bucket", "Properties" : { } } }, "Outputs" : { "BucketName" : { "Value" : { "Ref" : "MySampleBucket" }, "Description" : "The name of the S3 bucket" } } }

Save this as a JSON file with the name template.json in a directory named template-package. Create a compressed (.zip) file of this directory and file named template-package.zip, and upload the compressed file to a versioned Amazon S3 bucket. If you already have a bucket configured for your pipeline, you can use it. Next, edit your pipeline to add a source action that retrieves the .zip file. Name the output for this action MyTemplate. For more information, see Edit a Pipeline in AWS CodePipeline.

Note

The sample Lambda function expects these file names and compressed structure. However, you can substitute your own AWS CloudFormation template for this sample. If you choose to use your own template, make sure you modify the policy for the Lambda execution role to allow any additional functionality required by your AWS CloudFormation template.

To add the following code as a function in Lambda

  1. Open the Lambda console and choose Create a Lambda function.

  2. On the Select blueprint page, choose Skip.

  3. On the Configure function page, in Name, type a name for your Lambda function. Optionally, in Description, type a description for the function.

  4. In the Runtime list, choose Python 2.7.

  5. Leave the value of Handler name at the default value, but change Role to your Lambda execution role (for example, CodePipelineLambdaExecRole).

  6. In Advanced settings, for Timeout (s), replace the default of 3 seconds with 20.

  7. Copy the following code into the Lambda function code code box:

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    from __future__ import print_function from boto3.session import Session import json import urllib import boto3 import zipfile import tempfile import botocore import traceback print('Loading function') cf = boto3.client('cloudformation') code_pipeline = boto3.client('codepipeline') def find_artifact(artifacts, name): """Finds the artifact 'name' among the 'artifacts' Args: artifacts: The list of artifacts available to the function name: The artifact we wish to use Returns: The artifact dictionary found Raises: Exception: If no matching artifact is found """ for artifact in artifacts: if artifact['name'] == name: return artifact raise Exception('Input artifact named "{0}" not found in event'.format(name)) def get_template(s3, artifact, file_in_zip): """Gets the template artifact Downloads the artifact from the S3 artifact store to a temporary file then extracts the zip and returns the file containing the CloudFormation template. Args: artifact: The artifact to download file_in_zip: The path to the file within the zip containing the template Returns: The CloudFormation template as a string Raises: Exception: Any exception thrown while downloading the artifact or unzipping it """ tmp_file = tempfile.NamedTemporaryFile() bucket = artifact['location']['s3Location']['bucketName'] key = artifact['location']['s3Location']['objectKey'] with tempfile.NamedTemporaryFile() as tmp_file: s3.download_file(bucket, key, tmp_file.name) with zipfile.ZipFile(tmp_file.name, 'r') as zip: return zip.read(file_in_zip) def update_stack(stack, template): """Start a CloudFormation stack update Args: stack: The stack to update template: The template to apply Returns: True if an update was started, false if there were no changes to the template since the last update. Raises: Exception: Any exception besides "No updates are to be performed." """ try: cf.update_stack(StackName=stack, TemplateBody=template) return True except botocore.exceptions.ClientError as e: if e.response['Error']['Message'] == 'No updates are to be performed.': return False else: raise Exception('Error updating CloudFormation stack "{0}"'.format(stack), e) def stack_exists(stack): """Check if a stack exists or not Args: stack: The stack to check Returns: True or False depending on whether the stack exists Raises: Any exceptions raised .describe_stacks() besides that the stack doesn't exist. """ try: cf.describe_stacks(StackName=stack) return True except botocore.exceptions.ClientError as e: if "does not exist" in e.response['Error']['Message']: return False else: raise e def create_stack(stack, template): """Starts a new CloudFormation stack creation Args: stack: The stack to be created template: The template for the stack to be created with Throws: Exception: Any exception thrown by .create_stack() """ cf.create_stack(StackName=stack, TemplateBody=template) def get_stack_status(stack): """Get the status of an existing CloudFormation stack Args: stack: The name of the stack to check Returns: The CloudFormation status string of the stack such as CREATE_COMPLETE Raises: Exception: Any exception thrown by .describe_stacks() """ stack_description = cf.describe_stacks(StackName=stack) return stack_description['Stacks'][0]['StackStatus'] def put_job_success(job, message): """Notify CodePipeline of a successful job Args: job: The CodePipeline job ID message: A message to be logged relating to the job status Raises: Exception: Any exception thrown by .put_job_success_result() """ print('Putting job success') print(message) code_pipeline.put_job_success_result(jobId=job) def put_job_failure(job, message): """Notify CodePipeline of a failed job Args: job: The CodePipeline job ID message: A message to be logged relating to the job status Raises: Exception: Any exception thrown by .put_job_failure_result() """ print('Putting job failure') print(message) code_pipeline.put_job_failure_result(jobId=job, failureDetails={'message': message, 'type': 'JobFailed'}) def continue_job_later(job, message): """Notify CodePipeline of a continuing job This will cause CodePipeline to invoke the function again with the supplied continuation token. Args: job: The JobID message: A message to be logged relating to the job status continuation_token: The continuation token Raises: Exception: Any exception thrown by .put_job_success_result() """ # Use the continuation token to keep track of any job execution state # This data will be available when a new job is scheduled to continue the current execution continuation_token = json.dumps({'previous_job_id': job}) print('Putting job continuation') print(message) code_pipeline.put_job_success_result(jobId=job, continuationToken=continuation_token) def start_update_or_create(job_id, stack, template): """Starts the stack update or create process If the stack exists then update, otherwise create. Args: job_id: The ID of the CodePipeline job stack: The stack to create or update template: The template to create/update the stack with """ if stack_exists(stack): status = get_stack_status(stack) if status not in ['CREATE_COMPLETE', 'ROLLBACK_COMPLETE', 'UPDATE_COMPLETE']: # If the CloudFormation stack is not in a state where # it can be updated again then fail the job right away. put_job_failure(job_id, 'Stack cannot be updated when status is: ' + status) return were_updates = update_stack(stack, template) if were_updates: # If there were updates then continue the job so it can monitor # the progress of the update. continue_job_later(job_id, 'Stack update started') else: # If there were no updates then succeed the job immediately put_job_success(job_id, 'There were no stack updates') else: # If the stack doesn't already exist then create it instead # of updating it. create_stack(stack, template) # Continue the job so the pipeline will wait for the CloudFormation # stack to be created. continue_job_later(job_id, 'Stack create started') def check_stack_update_status(job_id, stack): """Monitor an already-running CloudFormation update/create Succeeds, fails or continues the job depending on the stack status. Args: job_id: The CodePipeline job ID stack: The stack to monitor """ status = get_stack_status(stack) if status in ['UPDATE_COMPLETE', 'CREATE_COMPLETE']: # If the update/create finished successfully then # succeed the job and don't continue. put_job_success(job_id, 'Stack update complete') elif status in ['UPDATE_IN_PROGRESS', 'UPDATE_ROLLBACK_IN_PROGRESS', 'UPDATE_ROLLBACK_COMPLETE_CLEANUP_IN_PROGRESS', 'CREATE_IN_PROGRESS', 'ROLLBACK_IN_PROGRESS']: # If the job isn't finished yet then continue it continue_job_later(job_id, 'Stack update still in progress') else: # If the Stack is a state which isn't "in progress" or "complete" # then the stack update/create has failed so end the job with # a failed result. put_job_failure(job_id, 'Update failed: ' + status) def get_user_params(job_data): """Decodes the JSON user parameters and validates the required properties. Args: job_data: The job data structure containing the UserParameters string which should be a valid JSON structure Returns: The JSON parameters decoded as a dictionary. Raises: Exception: The JSON can't be decoded or a property is missing. """ try: # Get the user parameters which contain the stack, artifact and file settings user_parameters = job_data['actionConfiguration']['configuration']['UserParameters'] decoded_parameters = json.loads(user_parameters) except Exception as e: # We're expecting the user parameters to be encoded as JSON # so we can pass multiple values. If the JSON can't be decoded # then fail the job with a helpful message. raise Exception('UserParameters could not be decoded as JSON') if 'stack' not in decoded_parameters: # Validate that the stack is provided, otherwise fail the job # with a helpful message. raise Exception('Your UserParameters JSON must include the stack name') if 'artifact' not in decoded_parameters: # Validate that the artifact name is provided, otherwise fail the job # with a helpful message. raise Exception('Your UserParameters JSON must include the artifact name') if 'file' not in decoded_parameters: # Validate that the template file is provided, otherwise fail the job # with a helpful message. raise Exception('Your UserParameters JSON must include the template file name') return decoded_parameters def setup_s3_client(job_data): """Creates an S3 client Uses the credentials passed in the event by CodePipeline. These credentials can be used to access the artifact bucket. Args: job_data: The job data structure Returns: An S3 client with the appropriate credentials """ key_id = job_data['artifactCredentials']['accessKeyId'] key_secret = job_data['artifactCredentials']['secretAccessKey'] session_token = job_data['artifactCredentials']['sessionToken'] session = Session(aws_access_key_id=key_id, aws_secret_access_key=key_secret, aws_session_token=session_token) return session.client('s3', config=botocore.client.Config(signature_version='s3v4')) def lambda_handler(event, context): """The Lambda function handler If a continuing job then checks the CloudFormation stack status and updates the job accordingly. If a new job then kick of an update or creation of the target CloudFormation stack. Args: event: The event passed by Lambda context: The context passed by Lambda """ try: # Extract the Job ID job_id = event['CodePipeline.job']['id'] # Extract the Job Data job_data = event['CodePipeline.job']['data'] # Extract the params params = get_user_params(job_data) # Get the list of artifacts passed to the function artifacts = job_data['inputArtifacts'] stack = params['stack'] artifact = params['artifact'] template_file = params['file'] if 'continuationToken' in job_data: # If we're continuing then the create/update has already been triggered # we just need to check if it has finished. check_stack_update_status(job_id, stack) else: # Get the artifact details artifact_data = find_artifact(artifacts, artifact) # Get S3 client to access artifact with s3 = setup_s3_client(job_data) # Get the JSON template file out of the artifact template = get_template(s3, artifact_data, template_file) # Kick off a stack update or create start_update_or_create(job_id, stack, template) except Exception as e: # If any other exceptions which we didn't expect are raised # then fail the job and log the exception message. print('Function failed due to exception.') print(e) traceback.print_exc() put_job_failure(job_id, 'Function exception: ' + str(e)) print('Function complete.') return "Complete."
  8. Save the function.

  9. From the AWS CodePipeline console, edit the pipeline to add the function as an action in a stage in your pipeline. In UserParameters, you must provide a JSON string including curly braces with three parameters separated by commas: a stack name, the AWS CloudFormation template name and path to the file, and the application name.

    For example, to create a stack named MyTestStack, for a pipeline with the input artifact MyTemplate, in UserParameters, you would type: {"stack":"MyTestStack","file":"template-package/template.json", "artifact":"MyTemplate"}.

    Note

    Even though you have specified the input artifact in UserParameters, you must also specify this input artifact for the action in Input artifacts.

  10. Save your changes to the pipeline, and then manually release a change to test the action and Lambda function.