Best Practices for Writing Python Code for AWS Lambda Function

Overview

Serverless computing has gained immense popularity, and AWS Lambda is a leading platform. When developing AWS Lambda functions with Python, adhering to best practices becomes crucial for efficient, scalable, and maintainable code. In this blog post, we’ll explore key considerations and best practices to ensure your Python code runs smoothly on AWS Lambda.

Pioneers in Cloud Consulting & Migration Services

Reduced infrastructural costs
Accelerated application deployment

Get Started

Introduction

AWS Lambda is a serverless computing service that enables developers to run code without managing servers. It executes functions in response to events, scales automatically, and charges only for actual compute time. AWS Lambda supports various programming languages and seamlessly integrates with other AWS services, facilitating scalable and cost-efficient application development.

Best Practices

Keep Functions Small and Focused

One of the fundamental principles of writing effective AWS Lambda functions is to keep them small and focused on a specific task. This enhances code readability and allows for easier debugging and maintenance. Each function should ideally perform a single, well-defined action.

# Bad practice: A large and complex Lambda function
def complex_handler(event, context):
    # … DO Something
 
# Good practice: Smaller, focused functions
def process_data(event, context):
    # … DO Something 

def generate_report(event, context):
    # … DO Something

# Bad practice: A large and complex Lambda function

def complex_handler(event, context):

# … DO Something

# Good practice: Smaller, focused functions

def process_data(event, context):

# … DO Something

def generate_report(event, context):

# … DO Something

Optimize Dependencies

AWS Lambda functions have size limitations, and minimizing the deployment package size is crucial for faster cold starts and reduced execution time. Include the dependencies necessary for your function to run and avoid unnecessary packages. Additionally, consider using Lambda Layers for storing shared libraries across functions to reduce redundancy.

# Bad practice: Including unnecessary dependencies
import pandas as pd
import numpy as np
import requests
 
def  lambda function(event, context):
    # … DO Something
 
# Good practice: Including only required dependencies
import requests

def lambda_handler(event, context):
    # … DO Something

# Bad practice: Including unnecessary dependencies

import pandas as pd

import numpy as np

import requests

def lambda function(event, context):

# … DO Something

# Good practice: Including only required dependencies

import requests

def lambda_handler(event, context):

# … DO Something

Handle Exceptions Gracefully

When writing AWS Lambda functions, handling exceptions gracefully is essential to avoid unexpected failures. Log relevant information for debugging purposes and consider using custom error messages to provide meaningful feedback. This ensures that even if an error occurs, the function execution details are logged for analysis.

# Bad practice: Not handling exceptions
def lambda_handler(event, context):
    result = 1 / 0         # Raises a ZeroDivisionError
 
# Good practice: Handling exceptions gracefully
def lambda_handler(event, context):
    try:
        result = 1 / 0
    except ZeroDivisionError as e:
        print(f"Error: {str(e)}")

# Bad practice: Not handling exceptions

def lambda_handler(event, context):

result = 1 / 0 # Raises a ZeroDivisionError

# Good practice: Handling exceptions gracefully

def lambda_handler(event, context):

try:

result = 1 / 0

except ZeroDivisionError as e:

print(f"Error: {str(e)}")

Set Proper Environment Variables

AWS Lambda allows you to set environment variables for your functions. Leverage this feature to store configuration values, API keys, or sensitive information. This ensures that your code remains flexible and sensitive information is kept secure.

# Bad practice: Hardcoding sensitive information
def lambda_handler(event, context):
     api_key = 'your-api-key'
    # … DO Something

# Good practice: Using environment variables
import os
def lambda_handler(event, context):
     api_key = os.environ.get('API_KEY')
    # … DO Something

# Bad practice: Hardcoding sensitive information

def lambda_handler(event, context):

api_key = 'your-api-key'

# … DO Something

# Good practice: Using environment variables

import os

def lambda_handler(event, context):

api_key = os.environ.get('API_KEY')

# … DO Something

Minimize Cold Starts

Cold starts can impact the performance of your AWS Lambda functions. To minimize cold starts, consider optimizing your function’s initialization process. This may involve reducing the number of global variables, leveraging connection pooling, and using provisioned concurrency.

# Bad practice: Initializing resources unnecessarily
 import boto3
s3 = boto3.client('s3')
def lambda_handler(event, context):
    # … DO Something
 
# Good practice: Initializing resources judiciously
import boto3
def lambda_handler(event, context):
    s3 = boto3.client('s3')
    # … DO Something

# Bad practice: Initializing resources unnecessarily

import boto3

s3 = boto3.client('s3')

def lambda_handler(event, context):

# … DO Something

# Good practice: Initializing resources judiciously

import boto3

def lambda_handler(event, context):

s3 = boto3.client('s3')

# … DO Something

Use the Correct Python Runtime

Choose the Python runtime that best fits your application’s requirements. AWS Lambda supports multiple Python runtimes, and selecting the appropriate version ensures compatibility with your dependencies.

Enable Logging

Logging is a crucial aspect of monitoring and debugging AWS Lambda functions. Use the built-in print statements for basic logging and consider integrating a logging library for more advanced logging capabilities. Amazon CloudWatch Logs can be used to centralize and analyze logs.

# Bad practice: Not logging important information
def lambda_handler(event, context):
    # … DO Something
 
 
# Good practice: Using logging for better visibility
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
 
def lambda_handler(event, context):
    logger.info("Function execution started.")
    # … DO Something

# Bad practice: Not logging important information

def lambda_handler(event, context):

# … DO Something

# Good practice: Using logging for better visibility

import logging

logger = logging.getLogger()

logger.setLevel(logging.INFO)

def lambda_handler(event, context):

logger.info("Function execution started.")

# … DO Something

Conclusion

Writing Python code for AWS Lambda functions involves best practices focusing on code organization, performance optimization, error handling, and security. By following these best practices, you can create AWS Lambda functions that are efficient, maintainable, and well-suited for various serverless applications.

Remember that AWS Lambda continually evolves, so staying updated on the latest features and best practices is crucial for maximizing the benefits of serverless computing on AWS.

Drop a query if you have any questions regarding AWS Lambda functions and we will get back to you quickly.

Making IT Networks Enterprise-ready – Cloud Management Services

Accelerated cloud migration
End-to-end view of the cloud environment

Get Started

About CloudThat

CloudThat is an award-winning company and the first in India to offer cloud training and consulting services worldwide. As a Microsoft Solutions Partner, AWS Advanced Tier Training Partner, and Google Cloud Platform Partner, CloudThat has empowered over 850,000 professionals through 600+ cloud certifications winning global recognition for its training excellence including 20 MCT Trainers in Microsoft’s Global Top 100 and an impressive 12 awards in the last 8 years. CloudThat specializes in Cloud Migration, Data Platforms, DevOps, IoT, and cutting-edge technologies like Gen AI & AI/ML. It has delivered over 500 consulting projects for 250+ organizations in 30+ countries as it continues to empower professionals and enterprises to thrive in the digital-first world.

FAQs

1. Why is modularization important for AWS Lambda functions?

ANS: – Modularization is crucial for AWS Lambda functions for several reasons. It promotes code reusability, making managing and testing individual components easier. It also helps stay within the deployment package size limits AWS Lambda imposes.

2. How can I minimize the size of my deployment package and optimize dependencies?

ANS: – Minimize your deployment package size by including only necessary dependencies and using virtual environments. Create a requirements.txt file to list dependencies and use tools like pip to install them. This helps reduce the size of the package and improves cold start times.

3. What's the significance of handling exceptions gracefully in AWS Lambda functions?

ANS: – Handling exceptions gracefully is essential for AWS Lambda functions because it contributes to effective error logging and troubleshooting. Use try-except blocks to catch and log exceptions, providing meaningful error messages. This helps in identifying and addressing issues quickly.

4. Why should I use environment variables in AWS Lambda functions?

ANS: – Environment variables in AWS Lambda functions are useful for storing sensitive information such as API keys or credentials. This practice enhances security by preventing the exposure of sensitive data in your codebase. Environment variables can be easily managed and updated without modifying the code.

WRITTEN BY Raghavendra Santosh Kulkarni

Raghavendra is a skilled Full Stack Developer with expertise in a wide range of technologies. He has a strong working knowledge of AWS and is always looking to learn about new and emerging technologies. In addition to his technical skills, Raghavendra is a highly motivated and dedicated professional, committed to delivering high quality work.