/aws-lambda-python-integration

name: aws-lambda-python-integration
description: Provides AWS Lambda integration patterns for Python with cold start optimization. Use when deploying Python functions to AWS Lambda, choosing between AWS Chalice and raw Python approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless Python applications. Triggers include "create lambda python", "deploy python lambda", "chalice lambda aws", "python lambda cold start", "aws lambda python performance", "python serverless framework".
allowed-tools: Read, Write, Edit, Bash, Glob, Grep

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](#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:

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

See [Raw Python Lambda](references/raw-python-lambda.md#cold-start-optimization) 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](references/raw-python-lambda.md#error-handling) 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](references/raw-python-lambda.md#logging) 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