Memanggil AWS Lambda fungsi dalam pipeline di CodePipeline

Ikuti tiga langkah pertama Tutorial: Buat pipeline sederhana (ember S3) untuk membuat bucket Amazon S3, CodeDeploy sumber daya, dan pipeline dua tahap. Pilih opsi Amazon Linux untuk jenis instans Anda. Anda dapat menggunakan nama apa pun yang Anda inginkan untuk pipeline, tetapi langkah-langkah dalam topik ini digunakan MyLambdaTestPipeline.

Pada halaman status pipeline Anda, dalam CodeDeploy tindakan, pilih Detail. Pada halaman detail penerapan untuk grup penyebaran, pilih ID instans dari daftar.

Di EC2 konsol Amazon, pada tab Detail untuk instance, salin alamat IP di IPv4Alamat publik (misalnya,192.0.2.4). Anda menggunakan alamat ini sebagai target fungsi di AWS Lambda.

Masuk ke AWS Management Console dan buka IAM konsol di https://console.aws.amazon.com/iam/.

Pilih Kebijakan, lalu pilih Buat Kebijakan. Pilih JSONtab, lalu tempelkan kebijakan berikut ke dalam bidang.

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

Pilih Tinjau kebijakan.

Pada halaman Kebijakan ulasan, di Nama, ketikkan nama untuk kebijakan (misalnya,CodePipelineLambdaExecPolicy). Dalam Deskripsi, masukkanEnables Lambda to execute code.

Pilih Buat Kebijakan.

Pada halaman dasbor kebijakan, pilih Peran, lalu pilih Buat peran.

Pada halaman Buat peran, pilih Layanan AWS. Pilih Lambda, lalu pilih Berikutnya: Izin.

Pada halaman Lampirkan kebijakan izin, pilih kotak centang di samping CodePipelineLambdaExecPolicy, lalu pilih Berikutnya: Tag. Pilih Berikutnya: Tinjau.

Pada halaman Tinjauan, di Nama peran, masukkan nama, lalu pilih Buat peran.

Masuk ke AWS Management Console dan buka AWS Lambda konsol di https://console.aws.amazon.com/lambda/.

Pada halaman Functions, pilih Create function.

Pilih halaman Buat fungsi, pilih Penulis dari scratch. Dalam nama Fungsi, masukkan nama untuk fungsi Lambda Anda (misalnya,MyLambdaFunctionForAWSCodePipeline). Di Runtime, pilih Node.js 20.x.

Di bawah Peran, pilih Pilih peran yang ada. Di Peran yang ada, pilih peran Anda, lalu pilih Buat fungsi.

Halaman detail untuk fungsi yang Anda buat terbuka.

Salin kode berikut ke dalam kotak kode Fungsi:

import { CodePipelineClient, PutJobSuccessResultCommand, PutJobFailureResultCommand } from "@aws-sdk/client-codepipeline";
import http from 'http';
import assert from 'assert';
 
export const handler = (event, context) => {
 
    const codepipeline = new CodePipelineClient();
 
    // Retrieve the Job ID from the Lambda action
    const jobId = event["CodePipeline.job"].id;
 
    // Retrieve the value of UserParameters from the Lambda action configuration in CodePipeline, in this case a URL which will be
    // health checked by this function.
    const url = event["CodePipeline.job"].data.actionConfiguration.configuration.UserParameters;
 
    // Notify CodePipeline of a successful job
    const putJobSuccess = async function(message) {
        const command = new PutJobSuccessResultCommand({
            jobId: jobId
        });
         try {
             await codepipeline.send(command);
             context.succeed(message);
         } catch (err) {
             context.fail(err); 
         }
    };
 
    // Notify CodePipeline of a failed job
    const putJobFailure = async function(message) {
        const command = new PutJobFailureResultCommand({
            jobId: jobId,
            failureDetails: {
                message: JSON.stringify(message),
                type: 'JobFailed',
                externalExecutionId: context.awsRequestId
            }
        });
        await codepipeline.send(command);
        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
    const 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);
        }
    });
};

Tinggalkan Handler pada nilai default, dan biarkan Peran di default,CodePipelineLambdaExecRole.

Di Pengaturan dasar, untuk Timeout, masukkan 20 detik.

Pilih Simpan.

Masuk ke AWS Management Console dan buka CodePipeline konsol di http://console.aws.amazon.com/codesuite/codepipeline/home.

Pada halaman Selamat Datang, pilih pipeline yang Anda buat.

Pada halaman tampilan pipeline, pilih Edit.

Pada halaman Edit, pilih + Tambahkan tahap untuk menambahkan tahap setelah tahap penerapan dengan CodeDeploy tindakan. Masukkan nama untuk panggung (misalnya,LambdaStage), dan pilih Tambahkan tahap.

Pilih + Tambahkan grup tindakan. Di Edit tindakan, di Nama tindakan, masukkan nama untuk tindakan Lambda Anda (misalnya,MyLambdaAction). Di Provider, pilih AWS Lambda. Dalam nama Fungsi, pilih atau masukkan nama fungsi Lambda Anda (misalnya,MyLambdaFunctionForAWSCodePipeline). Dalam parameter Pengguna, tentukan alamat IP untuk EC2 instans Amazon yang Anda salin sebelumnya (misalnya,http://192.0.2.4), lalu pilih Selesai.

Pada halaman Edit tindakan, pilih Simpan.

Pada halaman detail pipeline, pilih Rilis perubahan. Ini menjalankan revisi terbaru yang tersedia di setiap lokasi sumber yang ditentukan dalam aksi sumber melalui pipeline.

Saat tindakan Lambda selesai, pilih tautan Detail untuk melihat aliran log untuk fungsi di Amazon CloudWatch, termasuk durasi acara yang ditagih. Jika fungsi gagal, CloudWatch log memberikan informasi tentang penyebabnya.

Buka konsol Lambda dan pilih Buat fungsi.

Pilih halaman Buat fungsi, pilih Penulis dari scratch. Di nama Fungsi, masukkan nama untuk fungsi Lambda Anda.

Di Runtime, pilih Python 2.7.

Di bawah Pilih atau buat peran eksekusi, pilih Gunakan peran yang ada. Di Peran yang ada, pilih peran Anda, lalu pilih Buat fungsi.

Halaman detail untuk fungsi yang Anda buat terbuka.

Salin kode berikut ke dalam kotak kode Fungsi:

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', 'UPDATE_COMPLETE_CLEANUP_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."

Biarkan Handler pada nilai default, dan biarkan Peran pada nama yang Anda pilih atau buat sebelumnya,CodePipelineLambdaExecRole.

Dalam pengaturan Dasar, untuk Timeout, ganti default 3 detik dengan20.

Pilih Simpan.

Dari CodePipeline konsol, edit pipeline untuk menambahkan fungsi sebagai tindakan dalam tahap di pipeline Anda. Pilih Edit untuk tahap pipeline yang ingin Anda ubah, dan pilih Tambahkan grup tindakan. Pada halaman Edit tindakan, dalam nama Tindakan, masukkan nama untuk tindakan Anda. Di penyedia Tindakan, pilih Lambda.

Di bawah Input artefak, pilihMyTemplate. Di UserParameters, Anda harus memberikan JSON string dengan tiga parameter:

Gunakan kurung kurawal ({}) dan pisahkan parameter dengan koma. Misalnya, untuk membuat tumpukan bernama MyTestStack, untuk pipa dengan artefak input MyTemplate, masuk UserParameters, masukkan: {"stack”:”MyTestStack“, "file” :"template-package/template.json”, "artefak”:”MyTemplate"}.

Simpan perubahan Anda ke pipeline, lalu lepaskan perubahan secara manual untuk menguji tindakan dan fungsi Lambda.