---

name: aws-lambda compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont
• config description: '"Deploys serverless event-driven applications with Lambda functions, triggers" layers, and VPC integration for cost-effective, auto-scaling compute without server management.' license: MIT maturity: stable metadata: domain: cncf output-format: manifests related-skills: aws-cloudwatch, aws-dynamodb, aws-iam, aws-secrets-manager role: reference scope: infrastructure triggers: lambda, serverless, event-driven, lambda function, api gateway, s3 trigger, sqs, dynamodb stream archetypes:
  • educational
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • non-containerized architecture response_profile: verbosity: medium directive_strength: low abstraction_level: strategic version: "1.0.0"

---

Lambda (AWS Lambda)

Deploy serverless event-driven applications that scale automatically with pay-per-use pricing, VPC integration, and sophisticated trigger and layer management.

TL;DR Checklist

• Use concurrency limits to prevent runaway scaling
• Configure dead-letter queues (DLQ) for failed invocations
• Set appropriate timeout and memory (controls CPU/speed)
• Use Lambda Layers for shared code and libraries
• Enable X-Ray tracing for distributed tracing
• Implement structured logging for CloudWatch Insights
• Use environment variables or Secrets Manager for configuration
• Set up VPC endpoints if accessing VPC resources
• Monitor duration and throttling via CloudWatch
• Use provisioned concurrency for high-traffic functions

---

When to Use

Use Lambda when:

• Building event-driven applications (S3, SNS, SQS, DynamoDB Streams)
• Creating REST APIs with API Gateway
• Processing data asynchronously
• Running scheduled tasks (EventBridge)
• Building microservices with short-lived workloads
• Needing automatic scaling without capacity planning

---

When NOT to Use

Avoid Lambda when:

• Long-running workloads (batch jobs > 15 minutes, use EC2/ECS)
• Needing persistent state or network connections
• Running legacy applications requiring OS access
• Requiring real-time latency (cold starts add overhead)

---

Purpose and Use Cases

Primary Purpose: Provide serverless compute enabling automatic scaling, pay-per-use pricing, and event-driven architectures without server management.

Common Use Cases:

1. REST APIs — API Gateway + Lambda for microservices
2. Data Processing — S3 triggers for image resizing, log analysis
3. Event Processing — SNS/SQS async processing
4. Scheduled Jobs — EventBridge cron triggers
5. Stream Processing — DynamoDB Streams, Kinesis triggers
6. File Operations — S3 object transformations

---

Architecture Design Patterns

Pattern 1: REST API with API Gateway and Lambda

AWSTemplateFormatVersion: '2010-09-09'
Resources:
  # IAM Role for Lambda
  LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
        - arn:aws:iam::aws:policy/service-role/AWSLambdaVPCAccessExecutionRole
      Policies:
        - PolicyName: DynamoDBAccess
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action:
                  - dynamodb:GetItem
                  - dynamodb:PutItem
                  - dynamodb:Query
                  - dynamodb:Scan
                Resource: arn:aws:dynamodb:*:*:table/Users

  # Lambda Function
  APIFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: api-handler
      Runtime: python3.11
      Handler: index.lambda_handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Timeout: 30
      MemorySize: 256
      ReservedConcurrentExecutions: 100
      Environment:
        Variables:
          TABLE_NAME: Users
          LOG_LEVEL: INFO
      Code:
        ZipFile: |
          import json
          import boto3
          import os
          import logging
          from datetime import datetime
          
          logger = logging.getLogger()
          logger.setLevel(os.environ.get('LOG_LEVEL', 'INFO'))
          
          dynamodb = boto3.resource('dynamodb')
          table = dynamodb.Table(os.environ['TABLE_NAME'])
          
          def lambda_handler(event, context):
              """Handle API Gateway requests"""
              try:
                  method = event['httpMethod']
                  path = event['path']
                  
                  logger.info(f"Received {method} {path}")
                  
                  if method == 'POST' and path == '/users':
                      return create_user(event)
                  elif method == 'GET' and path.startswith('/users/'):
                      user_id = path.split('/')[-1]
                      return get_user(user_id)
                  else:
                      return error_response(404, "Not found")
                      
              except Exception as e:
                  logger.error(f"Error: {str(e)}", exc_info=True)
                  return error_response(500, "Internal server error")
          
          def create_user(event):
              """Create new user"""
              try:
                  body = json.loads(event.get('body', '{}'))
                  user_id = body.get('id')
                  name = body.get('name')
                  
                  if not user_id or not name:
                      return error_response(400, "Missing required fields")
                  
                  table.put_item(
                      Item={
                          'id': user_id,
                          'name': name,
                          'created_at': datetime.utcnow().isoformat()
                      }
                  )
                  
                  return success_response(201, {'user_id': user_id})
              except Exception as e:
                  logger.error(f"Error creating user: {str(e)}")
                  return error_response(500, "Failed to create user")
          
          def get_user(user_id):
              """Get user by ID"""
              try:
                  response = table.get_item(Key={'id': user_id})
                  
                  if 'Item' not in response:
                      return error_response(404, "User not found")
                  
                  return success_response(200, response['Item'])
              except Exception as e:
                  logger.error(f"Error getting user: {str(e)}")
                  return error_response(500, "Failed to get user")
          
          def success_response(status_code, data):
              return {
                  'statusCode': status_code,
                  'headers': {'Content-Type': 'application/json'},
                  'body': json.dumps(data)
              }
          
          def error_response(status_code, message):
              return {
                  'statusCode': status_code,
                  'headers': {'Content-Type': 'application/json'},
                  'body': json.dumps({'error': message})
              }
      Layers:
        - !Ref DependenciesLayer
      TracingConfig:
        Mode: Active
      Tags:
        - Key: Application
          Value: api

  # Lambda Layer for Dependencies
  DependenciesLayer:
    Type: AWS::Lambda::LayerVersion
    Properties:
      LayerName: api-dependencies
      Description: Shared dependencies for API functions
      Content:
        ZipFile: |
          # In production, use S3 or Code property
      CompatibleRuntimes:
        - python3.11

  # DLQ for Failed Invocations
  DeadLetterQueue:
    Type: AWS::SQS::Queue
    Properties:
      QueueName: api-dlq
      MessageRetentionPeriod: 1209600  # 14 days

  # API Gateway REST API
  RestAPI:
    Type: AWS::ApiGateway::RestApi
    Properties:
      Name: api
      Description: REST API
      EndpointConfiguration:
        Types:
          - REGIONAL

  # Resource: /users
  UsersResource:
    Type: AWS::ApiGateway::Resource
    Properties:
      RestApiId: !Ref RestAPI
      ParentId: !GetAtt RestAPI.RootResourceId
      PathPart: users

  # Resource: /users/{userId}
  UserResource:
    Type: AWS::ApiGateway::Resource
    Properties:
      RestApiId: !Ref RestAPI
      ParentId: !Ref UsersResource
      PathPart: '{userId}'

  # POST /users
  CreateUserMethod:
    Type: AWS::ApiGateway::Method
    Properties:
      RestApiId: !Ref RestAPI
      ResourceId: !Ref UsersResource
      HttpMethod: POST
      AuthorizationType: NONE
      Integration:
        Type: AWS_PROXY
        IntegrationHttpMethod: POST
        Uri: !Sub 'arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${APIFunction.Arn}/invocations'

  # GET /users/{userId}
  GetUserMethod:
    Type: AWS::ApiGateway::Method
    Properties:
      RestApiId: !Ref RestAPI
      ResourceId: !Ref UserResource
      HttpMethod: GET
      AuthorizationType: NONE
      RequestParameters:
        method.request.path.userId: true
      Integration:
        Type: AWS_PROXY
        IntegrationHttpMethod: POST
        Uri: !Sub 'arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${APIFunction.Arn}/invocations'

  # API Gateway permission for Lambda
  APIGatewayInvokePermission:
    Type: AWS::Lambda::Permission
    Properties:
      FunctionName: !Ref APIFunction
      Action: lambda:InvokeFunction
      Principal: apigateway.amazonaws.com
      SourceArn: !Sub 'arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${RestAPI}/*/*'

  # API Deployment
  APIDeployment:
    Type: AWS::ApiGateway::Deployment
    DependsOn:
      - CreateUserMethod
      - GetUserMethod
    Properties:
      RestApiId: !Ref RestAPI
      StageName: prod

  # CloudWatch Log Group
  LambdaLogGroup:
    Type: AWS::Logs::LogGroup
    Properties:
      LogGroupName: !Sub '/aws/lambda/${APIFunction}'
      RetentionInDays: 14

  # CloudWatch Alarms
  LambdaDurationAlarm:
    Type: AWS::CloudWatch::Alarm
    Properties:
      AlarmName: Lambda-Duration-High
      MetricName: Duration
      Namespace: AWS/Lambda
      Statistic: Average
      Period: 300
      EvaluationPeriods: 2
      Threshold: 25000  # 25 seconds
      ComparisonOperator: GreaterThanThreshold
      Dimensions:
        - Name: FunctionName
          Value: !Ref APIFunction

  LambdaErrorsAlarm:
    Type: AWS::CloudWatch::Alarm
    Properties:
      AlarmName: Lambda-Errors
      MetricName: Errors
      Namespace: AWS/Lambda
      Statistic: Sum
      Period: 300
      EvaluationPeriods: 1
      Threshold: 5
      ComparisonOperator: GreaterThanOrEqualToThreshold
      Dimensions:
        - Name: FunctionName
          Value: !Ref APIFunction

  LambdaThrottleAlarm:
    Type: AWS::CloudWatch::Alarm
    Properties:
      AlarmName: Lambda-Throttle
      MetricName: Throttles
      Namespace: AWS/Lambda
      Statistic: Sum
      Period: 300
      EvaluationPeriods: 1
      Threshold: 1
      ComparisonOperator: GreaterThanOrEqualToThreshold
      Dimensions:
        - Name: FunctionName
          Value: !Ref APIFunction

Outputs:
  APIEndpoint:
    Value: !Sub 'https://${RestAPI}.execute-api.${AWS::Region}.amazonaws.com/prod'
    Description: API Gateway endpoint URL

  FunctionArn:
    Value: !GetAtt APIFunction.Arn
    Description: Lambda function ARN

Key Elements:

• Lambda function with proper IAM role and permissions
• Environment variables for configuration
• Lambda Layers for code reuse
• Reserved concurrency to prevent overscaling
• X-Ray tracing for debugging
• Structured logging for CloudWatch Insights
• API Gateway with CORS and request validation
• DLQ for failed invocations
• CloudWatch alarms for duration, errors, throttles

Pattern 2: Async Processing with SQS and DLQ

Resources:
  # SQS Queue
  ProcessingQueue:
    Type: AWS::SQS::Queue
    Properties:
      QueueName: processing-queue
      VisibilityTimeout: 300
      MessageRetentionPeriod: 86400
      RedrivePolicy:
        deadLetterTargetArn: !GetAtt DeadLetterQueue.Arn
        maxReceiveCount: 3

  DeadLetterQueue:
    Type: AWS::SQS::Queue
    Properties:
      QueueName: processing-dlq
      MessageRetentionPeriod: 1209600

  # Lambda for Processing
  ProcessorFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: queue-processor
      Runtime: python3.11
      Handler: index.handler
      Role: !GetAtt ProcessorRole.Arn
      Timeout: 300
      MemorySize: 512
      Code:
        ZipFile: |
          import json
          import logging
          
          logger = logging.getLogger()
          logger.setLevel(logging.INFO)
          
          def handler(event, context):
              """Process SQS messages"""
              processed = []
              failed = []
              
              for record in event['Records']:
                  try:
                      body = json.loads(record['body'])
                      logger.info(f"Processing: {body}")
                      
                      # Process the message
                      result = process_item(body)
                      processed.append(result)
                      
                  except Exception as e:
                      logger.error(f"Failed to process: {str(e)}")
                      failed.append(record['messageId'])
              
              return {
                  'statusCode': 200,
                  'processed': len(processed),
                  'failed': len(failed)
              }
          
          def process_item(item):
              # Business logic here
              return item

  ProcessorRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
      Policies:
        - PolicyName: SQSAccess
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action:
                  - sqs:ReceiveMessage
                  - sqs:DeleteMessage
                  - sqs:GetQueueAttributes
                Resource: !GetAtt ProcessingQueue.Arn

  # Event Source Mapping (SQS -> Lambda)
  EventSourceMapping:
    Type: AWS::Lambda::EventSourceMapping
    Properties:
      EventSourceArn: !GetAtt ProcessingQueue.Arn
      FunctionName: !Ref ProcessorFunction
      BatchSize: 10
      MaximumBatchingWindowInSeconds: 5
      FunctionResponseTypes:
        - ReportBatchItemFailures
      ScalingConfig:
        MaximumConcurrency: 10

Key Elements:

• SQS queue with automatic DLQ routing
• Event source mapping for Lambda
• Batch processing with failure tracking
• MaximumConcurrency to limit parallelism
• Proper error handling and logging

Pattern 3: VPC Lambda with Database Access

Resources:
  # Lambda in VPC accessing RDS
  VPCFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: vpc-db-function
      Runtime: python3.11
      Handler: index.handler
      Role: !GetAtt VPCFunctionRole.Arn
      VpcConfig:
        SecurityGroupIds:
          - sg-0123456789abcdef0
        SubnetIds:
          - subnet-0123456789abcdef0
          - subnet-0123456789abcdef1
      Environment:
        Variables:
          DB_HOST: prod-database.c9akciq32.us-east-1.rds.amazonaws.com
          DB_USER: admin
          DB_PASSWORD: !Sub '{{resolve:secretsmanager:rds-password:SecretString:password}}'
          DB_NAME: production
      Code:
        ZipFile: |
          import psycopg2
          import os
          
          def handler(event, context):
              conn = psycopg2.connect(
                  host=os.environ['DB_HOST'],
                  user=os.environ['DB_USER'],
                  password=os.environ['DB_PASSWORD'],
                  database=os.environ['DB_NAME']
              )
              
              cursor = conn.cursor()
              cursor.execute("SELECT COUNT(*) FROM users")
              count = cursor.fetchone()[0]
              
              cursor.close()
              conn.close()
              
              return {'statusCode': 200, 'count': count}

  VPCFunctionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaVPCAccessExecutionRole

Key Elements:

• VPC configuration for private subnet access
• ENI creation for database connectivity
• Security group for database access
• Secrets Manager for password management

---

Integration Approaches

1. Integration with API Gateway

Lambda + API Gateway creates:

• REST and HTTP APIs
• Request/response transformation
• Request validation
• CORS support

2. Integration with EventBridge

EventBridge + Lambda enables:

• Scheduled cron jobs
• Event-driven processing
• Cross-service event routing
• Third-party integrations

3. Integration with S3 Events

S3 + Lambda triggers:

• Image transformations
• Log processing
• File validation
• Data pipeline workflows

4. Integration with DynamoDB Streams

DynamoDB Streams + Lambda enables:

• Real-time data synchronization
• Cross-service event propagation
• Cache invalidation
• Audit logging

---

Common Pitfalls

❌ Pitfall 1: No Timeout Configuration

Problem: Function hangs indefinitely; Lambda charged until timeout.

Solution:

• Set appropriate timeout (default 3s, max 900s)
• Account for database connection time
• Monitor duration metrics

❌ Pitfall 2: Unlimited Concurrency

Problem: Lambda scales infinitely; causes runaway costs and downstream throttling.

Solution:

• Set ReservedConcurrentExecutions
• Account for concurrent database connections
• Monitor throttle metrics

❌ Pitfall 3: No Dead Letter Queue

Problem: Failed invocations silently discarded; no way to retry.

Solution:

• Configure DLQ for async invocations
• Monitor DLQ for failures
• Implement retry logic with exponential backoff

❌ Pitfall 4: Cold Start Delays

Problem: New instances incur latency; affects user experience.

Solution:

• Reduce code size and dependencies
• Use Provisioned Concurrency for critical functions
• Optimize imports and initialization

❌ Pitfall 5: Hardcoded Secrets

Problem: Database passwords visible in code; security breach.

Solution:

• Use environment variables
• Store in Secrets Manager
• Use IAM roles for AWS service access

❌ Pitfall 6: No X-Ray Tracing

Problem: Slow requests hard to debug; no visibility into downstream services.

Solution:

• Enable X-Ray tracing
• Instrument HTTP clients and AWS SDK
• Analyze service maps for bottlenecks

---

Best Practices Summary

Category	Best Practice
Concurrency	Set reserved concurrency; use provisioned for critical functions
Error Handling	Use DLQ; implement retries; log errors
Timeout	Set appropriate timeout; account for downstream services
Secrets	Use Secrets Manager; never hardcode
Monitoring	Enable X-Ray; set CloudWatch alarms
Layers	Use layers for shared code and dependencies

---

Related Skills

Skill	Purpose
cncf-aws-iam	Lambda execution roles and permissions
cncf-aws-sqs	Async invocation with queues
cncf-aws-dynamodb	Event source from DynamoDB Streams
cncf-aws-cloudwatch	Function monitoring and logging

---

Core Workflow

1. Assess Requirements — Understand the use case, scale, integration needs, and existing infrastructure. Checkpoint: Document requirements, constraints, and success criteria.

2. Design Architecture — Plan component interactions, data flow, and deployment strategy using cloud-native best practices. Checkpoint: Verify the architecture addresses all requirements and follows CNCF conventions.

3. Implement & Configure — Create manifests, configurations, and deployment scripts. Include resource limits, health checks, and observability hooks. Checkpoint: Validate all YAML against schema and test in a staging environment.

4. Deploy & Monitor — Apply manifests to the cluster, verify component health, and confirm observability is working. Checkpoint: Confirm all pods/services are running, probes passing, and metrics/alerts configured.

---

Constraints

MUST DO

• Include at least one complete working YAML manifest example
• Note when content is auto-generated vs. manually verified
• Reference relevant CNCF project documentation

MUST NOT DO

• Deploy manifests without testing in a staging environment first
• Use deprecated API versions (e.g., apps/v1beta1)
• Omit resource limits and requests in Kubernetes manifests