Serverless Computing Patterns

Serverless Computing Patterns

Building serverless applications — from function design and event sources through cold-start optimization, observability, and cost management.

When to Use

• Event-driven data processing pipelines
• APIs with variable traffic patterns
• Scheduled batch jobs and cron replacements
• Webhook handlers and integrations
• Prototyping and rapid iteration

Function Design

# Handler pattern (AWS Lambda + API Gateway)
def handler(event, context):
    """
    Standard Lambda handler for API Gateway HTTP API.
    """
    try:
        # Parse request
        path = event.get('rawPath', '/')
        method = event.get('requestContext', {}).get('http', {}).get('method', 'GET')
        body = json.loads(event.get('body', '{}')) if event.get('body') else {}
        
        # Business logic
        result = process_request(method, path, body)
        
        return {
            'statusCode': 200,
            'headers': {'Content-Type': 'application/json'},
            'body': json.dumps(result)
        }
    except Exception as e:
        return {'statusCode': 500, 'body': json.dumps({'error': str(e)})}

Common Pitfalls

1. Cold starts — functions spin up from zero on infrequent invocations; use provisioned concurrency
2. Timeout limits — Lambda max 15 min; design for the limit or use Step Functions
3. Stateless assumption — no local filesystem state between invocations; use S3/EFS
4. Over-fragmentation — one function per endpoint = management nightmare; group related logic
5. Cost surprises — high invocation rates cost more than fixed servers; estimate first

Verification Checklist

• Cold start time < 500ms (or acceptable for use case)
• Function timeout matches expected execution time
• Error handling with DLQ for async invocations
• Tracing/monitoring configured (X-Ray, CloudWatch)
• Least-privilege IAM roles per function
• Environment variables for configuration (not code)
• Versioning and aliases for safe deployments