def lambda_handler()

in server/TenantPipeline/resources/lambda-deploy-tenant-stack.py [0:0]

• 33 lines of code
• 9 McCabe index (conditional complexity)

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']
        
        artifact = params['artifact']
        template_file = params['template_file']
        commit_id = params['commit_id']

        # Get all the stacks for each tenant to be updated/created from tenant stack mapping table
        mappings = table_tenant_stack_mapping.scan()
        print (mappings)
        #Update/Create stacks for all tenants
        for mapping in mappings['Items']:
            stack = mapping['stackName']
            tenantId = mapping['tenantId']
            applyLatestRelease = mapping['applyLatestRelease']

            if (applyLatestRelease):
                # Get the parameters to be passed to the Cloudformation from tenant table
                params = get_tenant_params(tenantId)
                # Passing parameter to enable canary deployment for lambda's
                add_parameter(params, 'LambdaCanaryDeploymentPreference', "True")
                
                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_url = get_template_url(s3, artifact_data, template_file)
                    
                    # Kick off a stack update or create
                    start_update_or_create(job_id, stack, template_url, params)  

                    # If we are applying the release, update tenant stack mapping with the pipe line id
                    update_tenantstackmapping(tenantId, commit_id)
    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))
    
    #put_job_success(job_id, "Changeset executed successfully")
    print('Function complete.')   
    return "Complete."