AWS Serverless & Event-Driven Architecture

This skill provides comprehensive guidance for building serverless applications and event-driven architectures on AWS based on Well-Architected Framework principles.

Integrated MCP Servers

This skill includes 5 MCP servers for serverless development:

AWS Documentation MCP Server

When to use: Always verify AWS service information before implementation

• Search AWS documentation for latest features and best practices
• Check regional availability of AWS services
• Verify service limits and quotas
• Confirm API specifications and parameters
• Access up-to-date AWS service information

AWS Serverless MCP Server

Purpose: Complete serverless application lifecycle with SAM CLI

• Initialize new serverless applications
• Deploy serverless applications
• Test Lambda functions locally
• Generate SAM templates
• Manage serverless application lifecycle

AWS Lambda Tool MCP Server

Purpose: Execute Lambda functions as tools

• Invoke Lambda functions directly
• Test Lambda integrations
• Execute workflows requiring private resource access
• Run Lambda-based automation

AWS Step Functions MCP Server

Purpose: Execute complex workflows and orchestration

• Create and manage state machines
• Execute workflow orchestrations
• Handle distributed transactions
• Implement saga patterns
• Coordinate microservices

Amazon SNS/SQS MCP Server

Purpose: Event-driven messaging and queue management

• Publish messages to SNS topics
• Send/receive messages from SQS queues
• Manage event-driven communication
• Implement pub/sub patterns
• Handle asynchronous processing

When to Use This Skill

Use this skill when:

• Building serverless applications with Lambda
• Designing event-driven architectures
• Implementing microservices patterns
• Creating asynchronous processing workflows
• Orchestrating multi-service transactions
• Building real-time data processing pipelines
• Implementing saga patterns for distributed transactions
• Designing for scale and resilience

AWS Well-Architected Serverless Design Principles

1. Speedy, Simple, Singular

Functions should be concise and single-purpose

// ✅ GOOD - Single purpose, focused function
export const processOrder = async (event: OrderEvent) => {
  // Only handles order processing
  const order = await validateOrder(event);
  await saveOrder(order);
  await publishOrderCreatedEvent(order);
  return { statusCode: 200, body: JSON.stringify({ orderId: order.id }) };
};

// ❌ BAD - Function does too much
export const handleEverything = async (event: any) => {
  // Handles orders, inventory, payments, shipping...
  // Too many responsibilities
};

Keep functions environmentally efficient and cost-aware:

• Minimize cold start times
• Optimize memory allocation
• Use provisioned concurrency only when needed
• Leverage connection reuse

2. Think Concurrent Requests, Not Total Requests

Design for concurrency, not volume

Lambda scales horizontally - design considerations should focus on:

• Concurrent execution limits
• Downstream service throttling
• Shared resource contention
• Connection pool sizing

// Consider concurrent Lambda executions accessing DynamoDB
const table = new dynamodb.Table(this, 'Table', {
  billingMode: dynamodb.BillingMode.PAY_PER_REQUEST, // Auto-scales with load
});

// Or with provisioned capacity + auto-scaling
const table = new dynamodb.Table(this, 'Table', {
  billingMode: dynamodb.BillingMode.PROVISIONED,
  readCapacity: 5,
  writeCapacity: 5,
});

// Enable auto-scaling for concurrent load
table.autoScaleReadCapacity({ minCapacity: 5, maxCapacity: 100 });
table.autoScaleWriteCapacity({ minCapacity: 5, maxCapacity: 100 });

3. Share Nothing

Function runtime environments are short-lived

// ❌ BAD - Relying on local file system
export const handler = async (event: any) => {
  fs.writeFileSync('/tmp/data.json', JSON.stringify(data)); // Lost after execution
};

// ✅ GOOD - Use persistent storage
export const handler = async (event: any) => {
  await s3.putObject({
    Bucket: process.env.BUCKET_NAME,
    Key: 'data.json',
    Body: JSON.stringify(data),
  });
};

State management:

• Use DynamoDB for persistent state
• Use Step Functions for workflow state
• Use ElastiCache for session state
• Use S3 for file storage

4. Assume No Hardware Affinity

Applications must be hardware-agnostic

Infrastructure can change without notice:

• Lambda functions can run on different hardware
• Container instances can be replaced
• No assumption about underlying infrastructure

Design for portability:

• Use environment variables for configuration
• Avoid hardware-specific optimizations
• Test across different environments

5. Orchestrate with State Machines, Not Function Chaining

Use Step Functions for orchestration

// ❌ BAD - Lambda function chaining
export const handler1 = async (event: any) => {
  const result = await processStep1(event);
  await lambda.invoke({
    FunctionName: 'handler2',
    Payload: JSON.stringify(result),
  });
};

// ✅ GOOD - Step Functions orchestration
const stateMachine = new stepfunctions.StateMachine(this, 'OrderWorkflow', {
  definition: stepfunctions.Chain
    .start(validateOrder)
    .next(processPayment)
    .next(shipOrder)
    .next(sendConfirmation),
});

Benefits of Step Functions:

• Visual workflow representation
• Built-in error handling and retries
• Execution history and debugging
• Parallel and sequential execution
• Service integrations without code

6. Use Events to Trigger Transactions

Event-driven over synchronous request/response

// Pattern: Event-driven processing
const bucket = new s3.Bucket(this, 'DataBucket');

bucket.addEventNotification(
  s3.EventType.OBJECT_CREATED,
  new s3n.LambdaDestination(processFunction),
  { prefix: 'uploads/' }
);

// Pattern: EventBridge integration
const rule = new events.Rule(this, 'OrderRule', {
  eventPattern: {
    source: ['orders'],
    detailType: ['OrderPlaced'],
  },
});

rule.addTarget(new targets.LambdaFunction(processOrderFunction));

Benefits:

• Loose coupling between services
• Asynchronous processing
• Better fault tolerance
• Independent scaling

7. Design for Failures and Duplicates

Operations must be idempotent

// ✅ GOOD - Idempotent operation
export const handler = async (event: SQSEvent) => {
  for (const record of event.Records) {
    const orderId = JSON.parse(record.body).orderId;

    // Check if already processed (idempotency)
    const existing = await dynamodb.getItem({
      TableName: process.env.TABLE_NAME,
      Key: { orderId },
    });

    if (existing.Item) {
      console.log('Order already processed:', orderId);
      continue; // Skip duplicate
    }

    // Process order
    await processOrder(orderId);

    // Mark as processed
    await dynamodb.putItem({
      TableName: process.env.TABLE_NAME,
      Item: { orderId, processedAt: Date.now() },
    });
  }
};

Implement retry logic with exponential backoff:

async function withRetry<T>(fn: () => Promise<T>, maxRetries = 3): Promise<T> {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await fn();
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await new Promise(resolve => setTimeout(resolve, Math.pow(2, i) * 1000));
    }
  }
  throw new Error('Max retries exceeded');
}

Event-Driven Architecture Patterns

Pattern 1: Event Router (EventBridge)

Use EventBridge for event routing and filtering:

// Create custom event bus
const eventBus = new events.EventBus(this, 'AppEventBus', {
  eventBusName: 'application-events',
});

// Define event schema
const schema = new events.Schema(this, 'OrderSchema', {
  schemaName: 'OrderPlaced',
  definition: events.SchemaDefinition.fromInline({
    openapi: '3.0.0',
    info: { version: '1.0.0', title: 'Order Events' },
    paths: {},
    components: {
      schemas: {
        OrderPlaced: {
          type: 'object',
          properties: {
            orderId: { type: 'string' },
            customerId: { type: 'string' },
            amount: { type: 'number' },
          },
        },
      },
    },
  }),
});

// Create rules for different consumers
new events.Rule(this, 'ProcessOrderRule', {
  eventBus,
  eventPattern: {
    source: ['orders'],
    detailType: ['OrderPlaced'],
  },
  targets: [new targets.LambdaFunction(processOrderFunction)],
});

new events.Rule(this, 'NotifyCustomerRule', {
  eventBus,
  eventPattern: {
    source: ['orders'],
    detailType: ['OrderPlaced'],
  },
  targets: [new targets.LambdaFunction(notifyCustomerFunction)],
});

Pattern 2: Queue-Based Processing (SQS)

Use SQS for reliable asynchronous processing:

// Standard queue for at-least-once delivery
const queue = new sqs.Queue(this, 'ProcessingQueue', {
  visibilityTimeout: Duration.seconds(300),
  retentionPeriod: Duration.days(14),
  deadLetterQueue: {
    queue: dlq,
    maxReceiveCount: 3,
  },
});

// FIFO queue for ordered processing
const fifoQueue = new sqs.Queue(this, 'OrderedQueue', {
  fifo: true,
  contentBasedDeduplication: true,
  deduplicationScope: sqs.DeduplicationScope.MESSAGE_GROUP,
});

// Lambda consumer
new lambda.EventSourceMapping(this, 'QueueConsumer', {
  target: processingFunction,
  eventSourceArn: queue.queueArn,
  batchSize: 10,
  maxBatchingWindow: Duration.seconds(5),
});

Pattern 3: Pub/Sub (SNS + SQS Fan-Out)

Implement fan-out pattern for multiple consumers:

// Create SNS topic
const topic = new sns.Topic(this, 'OrderTopic', {
  displayName: 'Order Events',
});

// Multiple SQS queues subscribe to topic
const inventoryQueue = new sqs.Queue(this, 'InventoryQueue');
const shippingQueue = new sqs.Queue(this, 'ShippingQueue');
const analyticsQueue = new sqs.Queue(this, 'AnalyticsQueue');

topic.addSubscription(new subscriptions.SqsSubscription(inventoryQueue));
topic.addSubscription(new subscriptions.SqsSubscription(shippingQueue));
topic.addSubscription(new subscriptions.SqsSubscription(analyticsQueue));

// Each queue has its own Lambda consumer
new lambda.EventSourceMapping(this, 'InventoryConsumer', {
  target: inventoryFunction,
  eventSourceArn: inventoryQueue.queueArn,
});

Pattern 4: Saga Pattern with Step Functions

Implement distributed transactions:

const reserveFlight = new tasks.LambdaInvoke(this, 'ReserveFlight', {
  lambdaFunction: reserveFlightFunction,
  outputPath: '$.Payload',
});

const reserveHotel = new tasks.LambdaInvoke(this, 'ReserveHotel', {
  lambdaFunction: reserveHotelFunction,
  outputPath: '$.Payload',
});

const processPayment = new tasks.LambdaInvoke(this, 'ProcessPayment', {
  lambdaFunction: processPaymentFunction,
  outputPath: '$.Payload',
});

// Compensating transactions
const cancelFlight = new tasks.LambdaInvoke(this, 'CancelFlight', {
  lambdaFunction: cancelFlightFunction,
});

const cancelHotel = new tasks.LambdaInvoke(this, 'CancelHotel', {
  lambdaFunction: cancelHotelFunction,
});

// Define saga with compensation
const definition = reserveFlight
  .next(reserveHotel)
  .next(processPayment)
  .addCatch(cancelHotel.next(cancelFlight), {
    resultPath: '$.error',
  });

new stepfunctions.StateMachine(this, 'BookingStateMachine', {
  definition,
  timeout: Duration.minutes(5),
});

Pattern 5: Event Sourcing

Store events as source of truth:

// Event store with DynamoDB
const eventStore = new dynamodb.Table(this, 'EventStore', {
  partitionKey: { name: 'aggregateId', type: dynamodb.AttributeType.STRING },
  sortKey: { name: 'version', type: dynamodb.AttributeType.NUMBER },
  stream: dynamodb.StreamViewType.NEW_IMAGE,
});

// Lambda function stores events
export const handleCommand = async (event: any) => {
  const { aggregateId, eventType, eventData } = event;

  // Get current version
  const items = await dynamodb.query({
    TableName: process.env.EVENT_STORE,
    KeyConditionExpression: 'aggregateId = :id',
    ExpressionAttributeValues: { ':id': aggregateId },
    ScanIndexForward: false,
    Limit: 1,
  });

  const nextVersion = items.Items?.[0]?.version + 1 || 1;

  // Append new event
  await dynamodb.putItem({
    TableName: process.env.EVENT_STORE,
    Item: {
      aggregateId,
      version: nextVersion,
      eventType,
      eventData,
      timestamp: Date.now(),
    },
  });
};

// Projections read from event stream
eventStore.grantStreamRead(projectionFunction);

Serverless Architecture Patterns

Pattern 1: API-Driven Microservices

REST APIs with Lambda backend:

const api = new apigateway.RestApi(this, 'Api', {
  restApiName: 'microservices-api',
  deployOptions: {
    throttlingRateLimit: 1000,
    throttlingBurstLimit: 2000,
    tracingEnabled: true,
  },
});

// User service
const users = api.root.addResource('users');
users.addMethod('GET', new apigateway.LambdaIntegration(getUsersFunction));
users.addMethod('POST', new apigateway.LambdaIntegration(createUserFunction));

// Order service
const orders = api.root.addResource('orders');
orders.addMethod('GET', new apigateway.LambdaIntegration(getOrdersFunction));
orders.addMethod('POST', new apigateway.LambdaIntegration(createOrderFunction));

Pattern 2: Stream Processing

Real-time data processing with Kinesis:

const stream = new kinesis.Stream(this, 'DataStream', {
  shardCount: 2,
  retentionPeriod: Duration.days(7),
});

// Lambda processes stream records
new lambda.EventSourceMapping(this, 'StreamProcessor', {
  target: processFunction,
  eventSourceArn: stream.streamArn,
  batchSize: 100,
  maxBatchingWindow: Duration.seconds(5),
  parallelizationFactor: 10,
  startingPosition: lambda.StartingPosition.LATEST,
  retryAttempts: 3,
  bisectBatchOnError: true,
  onFailure: new lambdaDestinations.SqsDestination(dlq),
});

Pattern 3: Async Task Processing

Background job processing:

// SQS queue for tasks
const taskQueue = new sqs.Queue(this, 'TaskQueue', {
  visibilityTimeout: Duration.minutes(5),
  receiveMessageWaitTime: Duration.seconds(20), // Long polling
  deadLetterQueue: {
    queue: dlq,
    maxReceiveCount: 3,
  },
});

// Lambda worker processes tasks
const worker = new lambda.Function(this, 'TaskWorker', {
  // ... configuration
  reservedConcurrentExecutions: 10, // Control concurrency
});

new lambda.EventSourceMapping(this, 'TaskConsumer', {
  target: worker,
  eventSourceArn: taskQueue.queueArn,
  batchSize: 10,
  reportBatchItemFailures: true, // Partial batch failure handling
});

Pattern 4: Scheduled Jobs

Periodic processing with EventBridge:

// Daily cleanup job
new events.Rule(this, 'DailyCleanup', {
  schedule: events.Schedule.cron({ hour: '2', minute: '0' }),
  targets: [new targets.LambdaFunction(cleanupFunction)],
});

// Process every 5 minutes
new events.Rule(this, 'FrequentProcessing', {
  schedule: events.Schedule.rate(Duration.minutes(5)),
  targets: [new targets.LambdaFunction(processFunction)],
});

Pattern 5: Webhook Processing

Handle external webhooks:

// API Gateway endpoint for webhooks
const webhookApi = new apigateway.RestApi(this, 'WebhookApi', {
  restApiName: 'webhooks',
});

const webhook = webhookApi.root.addResource('webhook');
webhook.addMethod('POST', new apigateway.LambdaIntegration(webhookFunction, {
  proxy: true,
  timeout: Duration.seconds(29), // API Gateway max
}));

// Lambda handler validates and queues webhook
export const handler = async (event: APIGatewayProxyEvent) => {
  // Validate webhook signature
  const isValid = validateSignature(event.headers, event.body);
  if (!isValid) {
    return { statusCode: 401, body: 'Invalid signature' };
  }

  // Queue for async processing
  await sqs.sendMessage({
    QueueUrl: process.env.QUEUE_URL,
    MessageBody: event.body,
  });

  // Return immediately
  return { statusCode: 202, body: 'Accepted' };
};

Best Practices

Error Handling

Implement comprehensive error handling:

export const handler = async (event: SQSEvent) => {
  const failures: SQSBatchItemFailure[] = [];

  for (const record of event.Records) {
    try {
      await processRecord(record);
    } catch (error) {
      console.error('Failed to process record:', record.messageId, error);
      failures.push({ itemIdentifier: record.messageId });
    }
  }

  // Return partial batch failures for retry
  return { batchItemFailures: failures };
};

Dead Letter Queues

Always configure DLQs for error handling:

const dlq = new sqs.Queue(this, 'DLQ', {
  retentionPeriod: Duration.days(14),
});

const queue = new sqs.Queue(this, 'Queue', {
  deadLetterQueue: {
    queue: dlq,
    maxReceiveCount: 3,
  },
});

// Monitor DLQ depth
new cloudwatch.Alarm(this, 'DLQAlarm', {
  metric: dlq.metricApproximateNumberOfMessagesVisible(),
  threshold: 1,
  evaluationPeriods: 1,
  alarmDescription: 'Messages in DLQ require attention',
});

Observability

Enable tracing and monitoring:

new NodejsFunction(this, 'Function', {
  entry: 'src/handler.ts',
  tracing: lambda.Tracing.ACTIVE, // X-Ray tracing
  environment: {
    POWERTOOLS_SERVICE_NAME: 'order-service',
    POWERTOOLS_METRICS_NAMESPACE: 'MyApp',
    LOG_LEVEL: 'INFO',
  },
});

Using MCP Servers Effectively

AWS Serverless MCP Usage

Lifecycle management:

• Initialize new serverless projects
• Generate SAM templates
• Deploy applications
• Test locally before deployment

Lambda Tool MCP Usage

Function execution:

• Test Lambda functions directly
• Execute automation workflows
• Access private resources
• Validate integrations

Step Functions MCP Usage

Workflow orchestration:

• Create state machines for complex workflows
• Execute distributed transactions
• Implement saga patterns
• Coordinate microservices

SNS/SQS MCP Usage

Messaging operations:

• Test pub/sub patterns
• Send test messages to queues
• Validate event routing
• Debug message processing

Additional Resources

This skill includes comprehensive reference documentation based on AWS best practices:

• Serverless Patterns: references/serverless-patterns.md

  • Core serverless architectures and API patterns
  • Data processing and integration patterns
  • Orchestration with Step Functions
  • Anti-patterns to avoid

• Event-Driven Architecture Patterns: references/eda-patterns.md

  • Event routing and processing patterns
  • Event sourcing and saga patterns
  • Idempotency and error handling
  • Message ordering and deduplication

• Security Best Practices: references/security-best-practices.md

  • Shared responsibility model
  • IAM least privilege patterns
  • Data protection and encryption
  • Network security with VPC

• Observability Best Practices: references/observability-best-practices.md

  • Three pillars: metrics, logs, traces
  • Structured logging with Lambda Powertools
  • X-Ray distributed tracing
  • CloudWatch alarms and dashboards

• Performance Optimization: references/performance-optimization.md

  • Cold start optimization techniques
  • Memory and CPU optimization
  • Package size reduction
  • Provisioned concurrency patterns

• Deployment Best Practices: references/deployment-best-practices.md

  • CI/CD pipeline design
  • Testing strategies (unit, integration, load)
  • Deployment strategies (canary, blue/green)
  • Rollback and safety mechanisms

External Resources:

• AWS Well-Architected Serverless Lens: https://docs.aws.amazon.com/wellarchitected/latest/serverless-applications-lens/
• ServerlessLand.com: Pre-built serverless patterns
• AWS Serverless Workshops: https://serverlessland.com/learn?type=Workshops

For detailed implementation patterns, anti-patterns, and code examples, refer to the comprehensive references in the skill directory.