AWS Lambda Functions

AWS Lambda Functions Skill

Develop high-performance serverless functions with best practices.

Quick Reference

Parameters

Required

Optional

Execution Flow

1. Package code and dependencies
2. Create/update function
3. Configure triggers
4. Set concurrency limits
5. Test invocation
6. Monitor cold starts

Implementation

Create Function

# Create deployment package
zip -r function.zip . -x "*.git*"

# Create function
aws lambda create-function \
  --function-name my-function \
  --runtime python3.12 \
  --architectures arm64 \
  --handler main.handler \
  --role arn:aws:iam::123456789012:role/lambda-role \
  --zip-file fileb://function.zip \
  --memory-size 1024 \
  --timeout 30 \
  --environment "Variables={LOG_LEVEL=INFO}" \
  --tracing-config Mode=Active

Handler Template (Python)

import json
import logging

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

def handler(event, context):
    """
    Lambda handler function.

    Args:
        event: Trigger event data
        context: Runtime context (request_id, memory_limit, etc.)

    Returns:
        Response object or value
    """
    try:
        logger.info(f"Event: {json.dumps(event)}")

        # Business logic here
        result = process_event(event)

        return {
            "statusCode": 200,
            "body": json.dumps(result)
        }
    except Exception as e:
        logger.error(f"Error: {str(e)}")
        return {
            "statusCode": 500,
            "body": json.dumps({"error": str(e)})
        }

Handler Template (Node.js)

export const handler = async (event, context) => {
    console.log('Event:', JSON.stringify(event));

    try {
        const result = await processEvent(event);

        return {
            statusCode: 200,
            body: JSON.stringify(result)
        };
    } catch (error) {
        console.error('Error:', error);
        return {
            statusCode: 500,
            body: JSON.stringify({ error: error.message })
        };
    }
};

Memory/CPU Optimization

Cold Start Mitigation

1. Use arm64: ~34% better price-performance
2. Minimize package: Only required dependencies
3. Use Layers: Shared dependencies
4. Provisioned Concurrency: Pre-warmed functions
5. SnapStart (Java): Cached initialization
6. Avoid VPC: Unless necessary (+1s cold start)

Troubleshooting

Common Issues

Debug Checklist

• Handler path correct (file.function)?
• All dependencies packaged?
• Execution role has permissions?
• Environment variables set?
• Memory sufficient for workload?
• Timeout appropriate?
• VPC has NAT for internet?

CloudWatch Log Patterns

Task timed out after X.XX seconds → Increase timeout
Runtime exited with error: signal: killed → Increase memory
Unable to import module → Check handler path/dependencies
ECONNREFUSED → Check VPC/security group

Test Template

def test_lambda_handler():
    # Arrange
    event = {"key": "value"}
    context = MockContext()

    # Act
    response = handler(event, context)

    # Assert
    assert response["statusCode"] == 200
    body = json.loads(response["body"])
    assert "result" in body

Observability

X-Ray Tracing

from aws_xray_sdk.core import xray_recorder

@xray_recorder.capture('process_data')
def process_data(data):
    # Traced function
    pass

Structured Logging

{
  "level": "INFO",
  "message": "Processing request",
  "request_id": "abc-123",
  "function_name": "my-function",
  "cold_start": true
}

Assets

• assets/lambda-template.py - Python handler template

References

• Lambda Developer Guide
• Lambda Best Practices