Setting up a basic execution runner using AWS Lambda

In this guide, you will deploy a new AWS Lambda function that will subscribe to the runs topic and perform the correct action, based on the invocations set in Port.

Prerequisites

note

To follow this example, please contact us via Intercom to receive a dedicated Kafka topic.

AWS CLI installed and configured to your desired AWS account;
A Port API CLIENT_ID and CLIENT_SECRET;
Connection credentials to the Kafka topic, provided to you by Port. they will look like to this:

KAFKA_BROKERS=b-1-public.publicclusterprod.t9rw6w.c1.kafka.eu-west-1.amazonaws.com:9196,b-2-public.publicclusterprod.t9rw6w.c1.kafka.eu-west-1.amazonaws.com:9196,b-3-public.publicclusterprod.t9rw6w.c1.kafka.eu-west-1.amazonaws.com:9196
KAFKA_RUNS_TOPIC=YOUR_ORG_ID.runs
KAFKA_AUTHENTICATION_MECHANISM=scram-sha-512
KAFKA_ENABLE_SSL=true
KAFKA_USERNAME=YOUR_KAFKA_USERNAME
KAFKA_PASSWORD=YOUR_KAFKA_PASSWORD
KAFKA_CONSUMER_GROUP_NAME=YOUR_KAFKA_CONSUMER_GROUP

To get yourself started quickly, you can always take a look at the code repository for examples.

Interaction with Port will be primarily conducted using the API in this example, but everything can also be performed using the web UI as well.

Scenario

Let’s learn about Port’s execution capabilities. In order to do that, start by creating a new Lambda function that deploys a new VM in your AWS cloud environment

Creating the VM blueprint

Let’s configure a VM Blueprint. Its base structure is:

{
  "identifier": "vm",
  "title": "VM",
  "icon": "Server",
  "schema": {
    "properties": {
      "region": {
        "title": "Region",
        "type": "string",
        "description": "Region of the VM"
      },
      "cpu_cores": {
        "title": "CPU Cores",
        "type": "number",
        "description": "Number of allocated CPU cores"
      },
      "memory_size": {
        "title": "Memory Size ",
        "type": "number",
        "description": "Amount of allocated memory (GB)"
      },
      "storage_size": {
        "title": "Storage Size",
        "type": "number",
        "description": "Amount of allocated storage (GB)"
      },
      "deployed": {
        "title": "Deploy Status",
        "type": "string",
        "description": "The deployment status of this VM"
      }
    },
    "required": []
  },
  "calculationProperties": {}
}

Below you can see the python code to create this Blueprint (remember to insert your CLIENT_ID and CLIENT_SECRET in order to get an access token).

Click here to see the code

import requests

CLIENT_ID = 'YOUR_CLIENT_ID'
CLIENT_SECRET = 'YOUR_CLIENT_SECRET'

API_URL = 'https://api.getport.io/v1'

credentials = {'clientId': CLIENT_ID, 'clientSecret': CLIENT_SECRET}

token_response = requests.post(f'{API_URL}/auth/access_token', json=credentials)

access_token = token_response.json()['accessToken']

headers = {
    'Authorization': f'Bearer {access_token}'
}

blueprint = {
    "identifier": "vm",
    "title": "VM",
    "icon": "Server",
    "schema": {
        "properties": {
            "region": {
                "title": "Region",
                "type": "string",
                "description": "Region of the VM"
            },
            "cpu_cores": {
                "title": "CPU Cores",
                "type": "number",
                "description": "Number of allocated CPU cores"
            },
            "memory_size": {
                "title": "Memory Size ",
                "type": "number",
                "description": "Amount of allocated memory (GB)"
            },
            "storage_size": {
                "title": "Storage Size",
                "type": "number",
                "description": "Amount of allocated storage (GB)"
            },
            "deployed": {
                "title": "Deploy Status",
                "type": "string",
                "description": "What is the deployment status of this VM"
            }
        },
        "required": []
    },
    "calculationProperties": {},

}

response = requests.post(f'{API_URL}/blueprints', json=blueprint, headers=headers)

print(response.json())

Creating the VM CREATE action

Now let’s configure a Self-Service Action. You will add a CREATE action that will be triggered every time a developer creates a new VM entity, the Self-Service Action will trigger your Lambda.

Here is the action JSON:

{
  "identifier": "create_vm",
  "title": "Create VM",
  "icon": "Server",
  "description": "Create a new VM in cloud provider infrastructure",
  "trigger": "CREATE",
  "invocationMethod": { "type": "KAFKA" },
  "userInputs": {
    "properties": {
      "title": {
        "type": "string",
        "title": "Title of the new VM"
      },
      "cpu": {
        "type": "number",
        "title": "Number of CPU cores"
      },
      "memory": {
        "type": "number",
        "title": "Size of memory"
      },
      "storage": {
        "type": "number",
        "title": "Size of storage"
      },
      "region": {
        "type": "string",
        "title": "Deployment region",
        "enum": ["eu-west-1", "eu-west-2", "us-west-1", "us-east-1"]
      }
    },
    "required": ["cpu", "memory", "storage", "region"]
  }
}

Below you can see the python code to create this action (remember to insert your CLIENT_ID and CLIENT_SECRET in order to get an access token).

Specifying the target blueprint

Note how the vm Blueprint identifier is used to add the action to the new Blueprint

Click here to see code

import requests

CLIENT_ID = 'YOUR_CLIENT_ID'
CLIENT_SECRET = 'YOUR_CLIENT_SECRET'

API_URL = 'https://api.getport.io/v1'

credentials = {'clientId': CLIENT_ID, 'clientSecret': CLIENT_SECRET}

token_response = requests.post(f'{API_URL}/auth/access_token', json=credentials)

access_token = token_response.json()['accessToken']

headers = {
    'Authorization': f'Bearer {access_token}'
}

blueprint_identifier = 'vm'

action = {
    'identifier': 'create_vm',
    'title': 'Create VM',
    'icon': 'Server',
    'description': 'Create a new VM in cloud provider infrastructure',
    'trigger': 'CREATE',
    'invocationMethod': { 'type': 'KAFKA' },
    'userInputs': {
        'properties': {
            'title': {
                'type': 'string',
                'title': 'Title of the new VM'
            },
            'cpu': {
                'type': 'number',
                'title': 'Number of CPU cores'
            },
            'memory': {
                'type': 'number',
                'title': 'Size of memory'
            },
            'storage': {
                'type': 'number',
                'title': 'Size of storage'
            },
            'region': {
                'type': 'string',
                'title': 'Deployment region',
                'enum': ['eu-west-1', 'eu-west-2', 'us-west-1', 'us-east-1']
            }
        },
        'required': [
            'cpu', 'memory', 'storage', 'region'
        ]
    }
}

response = requests.post(f'{API_URL}/blueprints/{blueprint_identifier}/actions', json=action, headers=headers)

print(response.json())

Now that the Self-Service Action configured, you can begin invoking it.

The Self-Service Actions need a runner that will receive the invocation message and perform some logic based on the data provided.

Setting up AWS resources

In this example, The deployed AWS Lambda function will be written in python.

The AWS setup will require the following resources:

A secret stored in Secrets Manager with the Kafka authentication credentials.
An AWS Lambda execution role with access to the new secret.
An AWS Lambda layer for our extra python libraries.
An AWS Lambda is configured with the example python code, the code layer and execution role you created. Configured with a Kafka Trigger

Creating a secret for the Lambda

The Lambda function will use a secret configured in AWS Secret Manager to authenticate with the personal Kafka topic provided by Port, let’s go ahead and create that secret in the AWS CLI:

# Remember to replace YOUR_KAFKA_USERNAME and YOUR_KAFKA_PASSWORD with the real username and password provided to you by Port
# You can change the secret name to any name that works for you
aws secretsmanager create-secret --name "PortKafkaAuthCredentials" --secret-string '{"username":"YOUR_KAFKA_USERNAME", "password":"YOUR_KAFKA_PASSWORD"}'

You should see output similar to the following:

{
  "ARN": "arn:aws:secretsmanager:eu-west-1:1111111111:secret:PortKafkaAuthCredentials-aaaaaa",
  "Name": "PortKafkaAuthCredentials",
  "VersionId": "aaaaa00a-00aa-0000-00a0-00000aa00a0a"
}

Saving the ARN

Make sure to save the ARN value, you will need it to create an execution role for the Lambda function, which can access the newly created secret.

Creating an execution role

Before you deploy your Lambda function, it needs an execution role with access to the Kafka username and password secret you created. Let’s create a basic execution role with assumeRole permission and basic permissions for cloudWatch

aws iam create-role --role-name lambda-port-execution-role --assume-role-policy-document '{"Version": "2012-10-17","Statement": [{ "Effect": "Allow", "Principal": {"Service": "lambda.amazonaws.com"}, "Action": "sts:AssumeRole"}]}'

You should see output similar to the following:

{
  "Role": {
    "Path": "/",
    "RoleName": "lambda-port-execution-role",
    "RoleId": "AROAQFOXMPL6TZ6ITKWND",
    "Arn": "arn:aws:iam::123456789012:role/lambda-port-execution-role",
    "CreateDate": "2020-01-17T23:19:12Z",
    "AssumeRolePolicyDocument": {
      "Version": "2012-10-17",
      "Statement": [
        {
          "Effect": "Allow",
          "Action": [
            "secretsmanager:GetResourcePolicy",
            "secretsmanager:GetSecretValue",
            "secretsmanager:DescribeSecret",
            "secretsmanager:ListSecretVersionIds"
          ],
          "Resource": [
            "arn:aws:secretsmanager:eu-west-1:1111111111:secret:PortKafkaAuthCredentials-aaaaaa"
          ]
        },
        {
          "Effect": "Allow",
          "Action": "secretsmanager:ListSecrets",
          "Resource": "*"
        }
      ]
    }
  }
}

saving the ARN

Again, make sure to save the Arn value, you will use it when deploying your Lambda function

Let’s attach basic Lambda execution permissions to this role with the following command:

aws iam attach-role-policy --role-name lambda-port-execution-role --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole

Now let’s add the following policy (You can refer to this AWS document for more information), we’ll create a file called execution-policy.json and paste the following content:

note

Remember to replace ARN value listed under Resource with the ARN you received as output when creating the secret

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "secretsmanager:GetResourcePolicy",
        "secretsmanager:GetSecretValue",
        "secretsmanager:DescribeSecret",
        "secretsmanager:ListSecretVersionIds"
      ],
      "Resource": [
        "arn:aws:secretsmanager:eu-west-1:1111111111:secret:PortKafkaAuthCredentials-aaaaaa"
      ]
    },
    {
      "Effect": "Allow",
      "Action": "secretsmanager:ListSecrets",
      "Resource": "*"
    }
  ]
}

Now let’s update the execution role (we’re assuming the execution-policy.json file is in the same directory as the terminal you are running the command from):

aws iam put-role-policy --role-name lambda-port-execution-role --policy-name managed-kafka-secret-access-policy --policy-document file://execution-policy.json

Creating an AWS Lambda layer

Now let’s create a Lambda Layer that will include the extra libraries the Lambda function will use.

The Lambda only needs the requests library, but the example below also includes jsonpickle for some of the log output to make the Lambda logs more verbose and easier to understand when starting to modify the code.

Now, let’s just run all of the commands to both create the layer zip, and deploy it to AWS (be sure to specify the region that you want the layer and lambda to be available in):

# Create layer directory and specify requests as a required library
mkdir lambda_layer
cd lambda_layer
echo requests==2.28.1 > requirements.txt
echo jsonpickle==2.2.0 >> requirements.txt

# Create layer based on requirements.txt in python/ directory
pip install -r requirements.txt --platform manylinux2014_x86_64 --target ./python --only-binary=:all:
# Create a zip of the layer
zip -r layer.zip python
# Upload a new layer version to AWS
aws lambda publish-layer-version --layer-name lambda_port_execution_package_layer --description "Python pacakges layer for lambda Port execution example" --compatible-runtimes python3.6 python3.7 python3.8 python3.9 --zip-file fileb://layer.zip --region eu-west-1

You should see output similar to the following:

{
  "Content": {
    "Location": "https://awslambda-eu-west-1-layers.s3.eu-west-1.amazonaws.com/snapshots/123456789012/my-layer-4aaa2fbb-ff77-4b0a-ad92-5b78a716a96a?versionId=27iWyA73cCAYqyH...",
    "CodeSha256": "tv9jJO+rPbXUUXuRKi7CwHzKtLDkDRJLB3cC3Z/ouXo=",
    "CodeSize": 169
  },
  "LayerArn": "arn:aws:lambda:eu-west-1:123456789012:layer:lambda_port_execution_package_layer",
  "LayerVersionArn": "arn:aws:lambda:eu-west-1:123456789012:layer:lambda_port_execution_package_layer:1",
  "Description": "Python pacakges layer for lambda Port execution example",
  "CreatedDate": "2018-11-14T23:03:52.894+0000",
  "Version": 1,
  "CompatibleArchitectures": ["x86_64"],
  "LicenseInfo": "MIT",
  "CompatibleRuntimes": ["python3.6", "python3.7", "python3.8", "python3.9"]
}

info

Again, make sure to save the LayerVersionArn value, you will use it to deploy your Lambda function

Creating the Lambda function

You can now create the Lambda function, the initial function is very basic and has specific comments where your actual execution runner logic should go.

Click here to see the function code

# file: lambda_function.py
# lambda entrypoint: lambda_handler

import base64
import os
import logging
import jsonpickle
import json
import requests
import traceback

logger = logging.getLogger()
logger.setLevel(logging.INFO)

CLIENT_ID = os.environ['PORT_CLIENT_ID']
CLIENT_SECRET = os.environ['PORT_CLIENT_SECRET']

CREATE_TRIGGER = 'CREATE'

API_URL = 'https://api.getport.io/v1'


def convert_status_code_to_run_status(status_code: int):
    if 200 <= status_code < 300:
        return "SUCCESS"
    if status_code >= 400:
        return "FAILURE"
    return "IN_PROGRESS"


def get_port_api_token():
    '''
    Get a Port API access token

    This function uses a global ``CLIENT_ID`` and ``CLIENT_SECRET``
    '''
    credentials = {'clientId': CLIENT_ID, 'clientSecret': CLIENT_SECRET}

    token_response = requests.post(f'{API_URL}/auth/access_token', json=credentials)
    access_token = token_response.json()['accessToken']

    return access_token


def report_to_port(run_id: str ,entity_props: dict):
    '''
    Reports to Port on a new entity based on provided ``entity_props``
    '''
    logger.info('Fetching token')
    token = get_port_api_token()

    blueprint_identifier = 'vm'

    headers = {
        'Authorization': f'Bearer {token}'
    }

    params = {
      'run_id': run_id
    }

    entity = {
        'identifier': entity_props['title'].replace(' ', '-').lower(),
        'title': entity_props['title'],
        'properties': {
            'cpu_cores': entity_props['cpu'],
            'memory_size': entity_props['memory'],
            'storage_size': entity_props['storage'],
            'region': entity_props['region'],
            'deployed': 'Deploying'
        }
    }

    logger.info('Creating entity:')
    logger.info(json.dumps(entity))
    response = requests.post(f'{API_URL}/blueprints/{blueprint_identifier}/entities', json=entity, headers=headers, params=params)
    logger.info(response.status_code)
    logger.info(json.dumps(response.json()))

    return response.status_code


def report_action_status(run_id: str, status: str):
    '''
    Reports to Port on the status of an action run ``entity_props``
    '''
    logger.info('Fetching token')
    token = get_port_api_token()

    headers = {
        'Authorization': f'Bearer {token}'
    }

    body = {
        "status": status,
        "message": {
            "message": f"The action status is {status}"
        }
    }

    logger.info(f'Reporting action {run_id} status:')
    logger.info(json.dumps(body))
    response = requests.patch(f'{API_URL}/actions/runs/{run_id}', json=body, headers=headers)
    logger.info(response.status_code)
    logger.info(json.dumps(response.json()))

    return response.status_code


def lambda_handler(event, context):
    '''
    Receives an event from AWS, if configured with a Kafka Trigger, the event includes an array of base64 encoded messages from the different topic partitions
    '''
    logger.info('## ENVIRONMENT VARIABLES\r' + jsonpickle.encode(dict(**os.environ)))
    logger.info('## EVENT\r' + jsonpickle.encode(event))
    logger.info('## CONTEXT\r' + jsonpickle.encode(context))
    for messages in event['records'].values():
        for encoded_message in messages:
            try:
                message_json_string = base64.b64decode(encoded_message['value']).decode('utf-8')
                logger.info('Received message:')
                logger.info(message_json_string)
                message = json.loads(message_json_string)

                run_id = message['context']['runId']

                # "message" includes one execution invocation object
                # You can use the message object as shown here to filter the handling of different actions you configured in Port
                action_type = message['payload']['action']['trigger']
                if action_type != CREATE_TRIGGER:
                    return {'message': 'Message not directed to our service'}

                # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
                # Your handler code for the action execution comes here #
                # # # # # # # # # # # # # # # # # # # # # # # # # # # # #

                # All of the input fields you specified in the action invocation are available under message['payload']['properties']
                # For this example, we are simply invoking a simple reporter function which will send data about the new entity to Port
                status_code = report_to_port(run_id, message['payload']['properties'])
                report_action_status(run_id, convert_status_code_to_run_status(status_code))
            except Exception as e:
                traceback.print_exc()
                logger.warn(f'Error: {e}')
    return {"message": "ok"}


if __name__ == "__main__":
    pass

Deploying the Lambda function

In order to deploy the Lambda function, run the following commands from the terminal (Note the comment where you need to paste in the Lambda code to the new file):

using our saved ARNs

Be sure to replace ROLE_ARN with the ARN you received as an output when you created an execution role for the Lambda

mkdir port_execution_lambda
cd port_execution_lambda
touch lambda_function.py
# Open lambda_function.py in your favorite editor or IDE and paste
# in the python code written above
# Once the code is in, we can package the lambda and deploy it
zip -FSr function.zip lambda_function.py
# Now let's deploy the Lambda to AWS
aws lambda create-function --function-name port-execution-lambda \
--zip-file fileb://function.zip --handler lambda_function.lambda_handler --runtime python3.9 \
--role ROLE_ARN --timeout 30

You should see output similar to the following:

{
    "FunctionName": "port-execution-lambda",
    "FunctionArn": "arn:aws:lambda:us-east-2:123456789012:function:port-execution-lambda",
    "Runtime": "python3.9",
    "Role": "arn:aws:iam::123456789012:role/lambda-port-execution-role",
    "Handler": "lambda_function.lambda_handler",
    "CodeSha256": "FpFMvUhayLkOoVBpNuNiIVML/tuGv2iJQ7t0yWVTU8c=",
    "Version": "$LATEST",
    "TracingConfig": {
        "Mode": "PassThrough"
    },
    "RevisionId": "88ebe1e1-bfdf-4dc3-84de-3017268fa1ff",
    ...
}

You are just a few steps away from a complete execution flow!

Putting everything together

There are just a few more steps left:

Add the code layer to the Lambda function
Add the Port CLIENT_ID and CLIENT_SECRET as environment variables to the Lambda
Add the Kafka trigger

In order to add the layer, you just need to run a simple CLI command:

# Be sure to replace the LAYER_VERSION_ARN with the value you saved
# from the layer creation output
aws lambda update-function-configuration --function-name port-execution-lambda \
--layers LAYER_VERSION_ARN

You should see an output showing that now the Layers array of the Lambda includes our layer

Now add the client_id and secret variables:

# Be sure to replace YOUR_CLIENT_ID and YOUR_CLIENT_SECRET with real values
aws lambda update-function-configuration --function-name port-execution-lambda --environment "Variables={PORT_CLIENT_ID=YOUR_CLIENT_ID,PORT_CLIENT_SECRET=YOUR_CLIENT_SECRET}" --query "Environment"

In the command output you should see all of the secrets you configured for your Lambda Function.

note

If your function needs multiple environment variables, it would be easier to put them all in a JSON file (for example environment.json) and run the following command:

aws lambda update-function-configuration --function-name port-execution-lambda --environment file://environment.json --query "Environment"

Time to add the Kafka trigger

# Remember to replace YOUR_ORG_ID, SECRET_ARN, and YOUR_KAFKA_CONSUMER_GROUP
aws lambda create-event-source-mapping --topics YOUR_ORG_ID.runs --source-access-configuration Type=SASL_SCRAM_512_AUTH,URI=SECRET_ARN \
          --function-name port-execution-lambda \
          --batch-size 1 --starting-position LATEST \
          --self-managed-kafka-event-source-config '{"ConsumerGroupId":"YOUR_KAFKA_CONSUMER_GROUP"}' \
          --self-managed-event-source '{"Endpoints":{"KAFKA_BOOTSTRAP_SERVERS":["b-1-public.publicclusterprod.t9rw6w.c1.kafka.eu-west-1.amazonaws.com:9196", "b-2-public.publicclusterprod.t9rw6w.c1.kafka.eu-west-1.amazonaws.com:9196", "b-3-public.publicclusterprod.t9rw6w.c1.kafka.eu-west-1.amazonaws.com:9196"]}}'

Triggering the action

Now that the Lambda is configured with a Kafka trigger, every action invocation will generate a new message in the dedicated Kafka topic you specified in the trigger. That message will be sent to the Lambda function you deployed, with all of the input data required to act on the action invocation.

For more information on the data format of managed Apache Kafka triggers, refer to the AWS docs. The code you wrote in the lambda_handler function already goes over all of the new messages, parses them, decodes and converts to a python dictionary for ease of use.

Now let’s invoke the Self-Service action using Port API

Click here to see the API call code

import requests

CLIENT_ID = 'YOUR_CLIENT_ID'
CLIENT_SECRET = 'YOUR_CLIENT_SECRET'

API_URL = 'https://api.getport.io/v1'

credentials = {'clientId': CLIENT_ID, 'clientSecret': CLIENT_SECRET}

token_response = requests.post(f'{API_URL}/auth/access_token', json=credentials)

access_token = token_response.json()['accessToken']

headers = {
    'Authorization': f'Bearer {access_token}'
}

blueprint_identifier = 'vm'

action_identifier = 'create_vm'

action_run = {
    'properties': {
        'title': 'Backend Prod VM',
        'cpu': 2,
        'memory': 4,
        'storage': 50,
        'region': 'eu-west-1'
    }
}

response = requests.post(f'{API_URL}/blueprints/{blueprint_identifier}/actions/{action_identifier}/runs', json=action_run, headers=headers)

print(response.json())

This will send a message to the Kafka topic.

Now the CloudWatch logs for the Lambda function (Accessible in the AWS console through Lambda→functions→port-execution-lambda→Monitor→Logs→View logs in CloudWatch), will show a log of the latest executions of the Lambda function. It also includes the actual message received, and whether this new VM entity was successfully reported to Port:

Example cloudwatch logs with topic message

Here is an example of the request payload received from Port, inside the Kafka message:

{
  "action": "create_vm",
  "resourceType": "run",
  "status": "TRIGGERED",
  "trigger": {
    "by": {
      "userId": "h2Mf13aRSCYQCUPIcqufoP4XRLwAt8Od@clients",
      "orgId": "org_sYG4DOJZNGy8bYnt",
      "user": {
        "email": "test@test.com",
        "firstName": "test",
        "lastName": "test",
        "id": "auth0|638879fa62c686d381b36ecb"
      }
    },
    "at": "2022-07-14T11:38:10.629Z",
    "origin": "API"
  },
  "context": {
    "entity": null,
    "blueprint": "vm",
    "runId": "run_q2mGf9ZYkFRnZJjB"
  },
  "payload": {
    "entity": null,
    "action": {
      "id": "action_9mFmogL7kQ0fISGx",
      "identifier": "create_vm",
      "title": "Create VM",
      "icon": "Server",
      "userInputs": {
        "properties": {
          "cpu": {
            "type": "number",
            "title": "Number of CPU cores"
          },
          "memory": {
            "type": "number",
            "title": "Size of memory"
          },
          "storage": {
            "type": "number",
            "title": "Size of storage"
          },
          "region": {
            "type": "string",
            "title": "Deployment region",
            "enum": ["eu-west-1", "eu-west-2", "us-west-1", "us-east-1"]
          }
        },
        "required": ["cpu", "memory", "storage", "region"]
      },
      "invocationMethod": { "type": "KAFKA" },
      "trigger": "CREATE",
      "description": "Create a new VM in cloud provider infrastructure",
      "blueprint": "vm",
      "createdAt": "2022-07-14T09:39:01.885Z",
      "createdBy": "h2Mf13aRSCYQCUPIcqufoP4XRLwAt8Od@clients",
      "updatedAt": "2022-07-14T09:39:01.885Z",
      "updatedBy": "h2Mf13aRSCYQCUPIcqufoP4XRLwAt8Od@clients"
    },
    "properties": {
      "cpu": 2,
      "memory": 4,
      "storage": 50,
      "region": "eu-west-1"
    }
  }
}

In addition to seeing the message topic in Cloudwatch, the Lambda function code also creates a new VM entity in Port, according to the provided inputs.

When the action is finished, it will mark the action run as successful. That way, your developers can tell that the provisioning of a new VM has finished successfully.

Next steps

This was a very basic example on how to react to the execution CREATE action. We left placeholder code for you to insert your own custom logic that fits your infrastructure.

Now that you have an execution runner, maybe try exploring our Change Log runner, so that you can react to changes in your Software Catalog.

Setting up a basic execution runner using AWS Lambda

Prerequisites​

Scenario​

Creating the VM blueprint​

Creating the VM CREATE action​

Setting up AWS resources​

Creating a secret for the Lambda​

Creating an execution role​

Creating an AWS Lambda layer​

Creating the Lambda function​

Deploying the Lambda function​

Putting everything together​

Triggering the action​

Next steps​

Prerequisites

Scenario

Creating the VM blueprint

Creating the VM CREATE action

Setting up AWS resources

Creating a secret for the Lambda

Creating an execution role

Creating an AWS Lambda layer

Creating the Lambda function

Deploying the Lambda function

Putting everything together

Triggering the action

Next steps