AWS Lambda Python Integration

Overview

1. AWS Chalice Framework - Full-featured framework with built-in routing, dependency injection, and local testing server
2. Raw Python - Minimal overhead approach with maximum flexibility and control

When to Use

• 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

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

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

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

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

Core Concepts

Cold Start Optimization

1. Initialize at module level - Persists across warm invocations
2. Use lazy loading - Defer heavy imports until needed
3. Cache boto3 clients - Reuse connections between invocations

Connection Management

_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

requirements.txt

# 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

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'})}

Logging

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')
}))

Deployment Options

Quick Start

# 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

# 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

chalice new-project my-api
cd my-api
chalice deploy --stage dev

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, but 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

1. Importing heavy libraries at module level - Defer to function level if not always needed
2. Not handling Lambda context - Use

   context.get_remaining_time_in_millis()

   for timeout awareness
3. Not validating input - Always validate and sanitize event data
4. Printing sensitive data - Be careful with logs and CloudWatch

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

• 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

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

1. Initialize Chalice project with

   chalice new-project

2. Configure routes for CRUD operations
3. Set up DynamoDB integration
4. Configure deployment stages
5. Deploy with

   chalice deploy

• Complete Chalice project structure
• REST API with CRUD endpoints
• DynamoDB table configuration
• Deployment configuration

Example 2: Optimize Cold Start for Raw Python

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

1. Analyze imports and initialization code
2. Move heavy imports inside functions (lazy loading)
3. Cache boto3 clients at module level
4. Remove unnecessary dependencies
5. Use provisioned concurrency if needed

• Refactored code with lazy loading
• Optimized cold start < 100ms
• Dependency analysis

Example 3: Deploy with GitHub Actions

Configure CI/CD for Python Lambda with SAM

1. Create GitHub Actions workflow
2. Set up Python environment and dependencies
3. Run pytest with coverage
4. Package with SAM
5. Deploy to dev/prod stages

• Complete

  .github/workflows/deploy.yml

• Multi-stage pipeline
• Integrated test automation

Version