aws-lambda-python-integration

SKILL.md

AWS Lambda Python Integration

Patterns for creating high-performance AWS Lambda functions in Python with optimized cold starts and clean architecture.

Overview

AWS Lambda Python integration with two approaches: AWS Chalice (full-featured framework) and Raw Python (minimal overhead). Both support API Gateway/ALB integration with production-ready configurations.

When to Use

Use this skill when:

Creating new Lambda functions in Python

Migrating existing Python applications to Lambda

Optimizing cold start performance for Python Lambda

Choosing between framework-based and minimal Python approaches

Configuring API Gateway or ALB integration

Setting up deployment pipelines for Python Lambda

Instructions
1. Choose Your Approach

Approach
Cold Start
Best For
Complexity

AWS Chalice
< 200ms
REST APIs, rapid development, built-in routing
Low

Raw Python
< 100ms
Simple handlers, maximum control, minimal dependencies
Low

2. Project Structure

AWS Chalice Structure

my-chalice-app/
├── app.py                    # Main application with routes
├── requirements.txt          # Dependencies
├── .chalice/
│   ├── config.json          # Chalice configuration
│   └── deploy/              # Deployment artifacts
├── chalicelib/              # Additional modules
│   ├── __init__.py
│   └── services.py
└── tests/
    └── test_app.py

Raw Python Structure

my-lambda-function/
├── lambda_function.py       # Handler entry point
├── requirements.txt         # Dependencies
├── template.yaml            # SAM/CloudFormation template
└── src/                     # Additional modules
    ├── __init__.py
    ├── handlers.py
    └── utils.py

3. Implementation Examples

See the References section for detailed implementation guides. Quick examples:

AWS Chalice:

from chalice import Chalice
app = Chalice(app_name='my-api')

@app.route('/')
def index():
    return {'message': 'Hello from Chalice!'}

Raw Python:

def lambda_handler(event, context):
    return {
        'statusCode': 200,
        'body': json.dumps({'message': 'Hello from Lambda!'})
    }

Core Concepts

Cold Start Optimization

Key strategies:

Initialize at module level - Persists across warm invocations

Use lazy loading - Defer heavy imports until needed

Cache boto3 clients - Reuse connections between invocations

See Raw Python Lambda for detailed patterns.

Connection Management

Create clients at module level and reuse:

_dynamodb = None

def get_table():
    global _dynamodb
    if _dynamodb is None:
        _dynamodb = boto3.resource('dynamodb').Table('my-table')
    return _dynamodb

Environment Configuration

class Config:
    TABLE_NAME = os.environ.get('TABLE_NAME')
    DEBUG = os.environ.get('DEBUG', 'false').lower() == 'true'

    @classmethod
    def validate(cls):
        if not cls.TABLE_NAME:
            raise ValueError("TABLE_NAME required")

Best Practices

Memory and Timeout Configuration

Memory: Start with 256MB for simple handlers, 512MB for complex operations

Timeout: Set based on expected processing time

Simple handlers: 3-5 seconds

API with DB calls: 10-15 seconds

Data processing: 30-60 seconds

Dependencies

Keep requirements.txt minimal:

# Core AWS SDK - always needed
boto3>=1.35.0

# Only add what you need
requests>=2.32.0  # If calling external APIs
pydantic>=2.5.0   # If using data validation

Error Handling

Return proper HTTP codes with request ID:

def lambda_handler(event, context):
    try:
        result = process_event(event)
        return {'statusCode': 200, 'body': json.dumps(result)}
    except ValueError as e:
        return {'statusCode': 400, 'body': json.dumps({'error': str(e)})}
    except Exception as e:
        print(f"Error: {str(e)}")  # Log to CloudWatch
        return {'statusCode': 500, 'body': json.dumps({'error': 'Internal error'})}

See Raw Python Lambda for structured error patterns.

Logging

Use structured logging for CloudWatch Insights:

import logging, json
logger = logging.getLogger()
logger.setLevel(logging.INFO)

# Structured log
logger.info(json.dumps({
    'eventType': 'REQUEST',
    'requestId': context.aws_request_id,
    'path': event.get('path')
}))

See Raw Python Lambda for advanced patterns.

Deployment Options

Quick Start

Validation Checkpoint: Always run serverless print or sam validate before deploying to catch configuration errors early.

Serverless Framework:

# serverless.yml
service: my-python-api
provider:
  name: aws
  runtime: python3.12  # or python3.11
functions:
  api:
    handler: lambda_function.lambda_handler
    events:
      - http:
          path: /{proxy+}
          method: ANY

AWS SAM:

# template.yaml
AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Resources:
  ApiFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: ./
      Handler: lambda_function.lambda_handler
      Runtime: python3.12  # or python3.11
      Events:
        ApiEvent:
          Type: Api
          Properties:
            Path: /{proxy+}
            Method: ANY

AWS Chalice:

chalice new-project my-api
cd my-api
chalice local 8080  # Test locally before deploying
chalice deploy --stage dev

Validation Checkpoint: Test locally with chalice local or sam local invoke before deploying to production.

For complete deployment configurations including CI/CD, environment-specific settings, and advanced SAM/Serverless patterns, see Serverless Deployment.

Constraints and Warnings

Lambda Limits

Deployment package: 250MB unzipped maximum (50MB zipped)

Memory: 128MB to 10GB

Timeout: 15 minutes maximum

Concurrent executions: 1000 default (adjustable)

Environment variables: 4KB total size

Python-Specific Considerations

Cold start: Python has excellent cold start performance; avoid heavy imports at module level

Dependencies: Keep requirements.txt minimal; use Lambda Layers for shared dependencies

Native dependencies: Must be compiled for Amazon Linux 2 (x86_64 or arm64)

Common Pitfalls

Importing heavy libraries at module level - Defer to function level if not always needed

Not handling Lambda context - Use context.get_remaining_time_in_millis() for timeout awareness

Not validating input - Always validate and sanitize event data

Printing sensitive data - Be careful with logs and CloudWatch

Error Recovery: If deployment fails, check CloudWatch logs for initialization errors and run sam logs to diagnose issues.

Security Considerations

Never hardcode credentials; use IAM roles and environment variables

Validate all input data

Use least privilege IAM policies

Enable CloudTrail for audit logging

References

For detailed guidance on specific topics:

AWS Chalice - Complete Chalice setup, routing, middleware, deployment

Raw Python Lambda - Minimal handler patterns, module caching, packaging

Serverless Deployment - Serverless Framework, SAM, CI/CD pipelines

Testing Lambda - pytest, moto, SAM Local, localstack

Examples

Example 1: Create an AWS Chalice REST API

Input:

Create a Python Lambda REST API using AWS Chalice for a todo application

Process:

Initialize Chalice project with chalice new-project

Configure routes for CRUD operations

Set up DynamoDB integration

Configure deployment stages

Deploy with chalice deploy

Output:

Complete Chalice project structure

REST API with CRUD endpoints

DynamoDB table configuration

Deployment configuration

Example 2: Optimize Cold Start for Raw Python

Input:

My Python Lambda has slow cold start, how do I optimize it?

Process:

Analyze imports and initialization code

Move heavy imports inside functions (lazy loading)

Cache boto3 clients at module level

Remove unnecessary dependencies

Use provisioned concurrency if needed

Output:

Refactored code with lazy loading

Optimized cold start < 100ms

Dependency analysis

Example 3: Deploy with GitHub Actions

Input:

Configure CI/CD for Python Lambda with SAM

Process:

Create GitHub Actions workflow

Set up Python environment and dependencies

Run pytest with coverage

Package with SAM

Deploy to dev/prod stages

Output:

Complete .github/workflows/deploy.yml

Multi-stage pipeline

Integrated test automation

Version

Version: 1.0.0