AWS Lambda Best Practices for Optimal Performance and Efficiency

After years of developing Lambda scripts and serverless applications, it's time to share the best practices we follow to ensure optimal performance and efficiency.

Language Choice and Structure

The choice of interpreted or compiled languages can significantly impact the performance of your AWS Lambda function, particularly in terms of cold start time. While languages like Node.js and Python are generally recommended for their faster cold start times, if you need to use Java, Spring Cloud Functions is the better option than Spring Boot Web Framework due to its efficiency in execution.

Network and VPC Setup

Unless you absolutely need a VPC resource with a private IP, it's best to use the default network environment. Setting up Elastic Network Interfaces (ENI) can add significant time to the cold start process. With the upcoming release of AWS Lambda, expect further improvements in this area.

Clean Up Dependencies

Remove all unnecessary dependencies that are not required to run the function. Keep only those dependencies that are necessary at runtime. This will help reduce the function's footprint and improve startup time.

Optimize and Reuse Resources

Use global/static variables and singleton objects to avoid reinitialization. These objects remain alive until the container goes down, which means subsequent calls do not need to reinitialize them. Define database connections at a global level so they can be reused for subsequent invocations. This can significantly improve your application's performance and reduce the overhead of repeated resource initialization.

Language-Specific Optimization Tips

Java: Use simpler IoC dependency injections like Dagger and Guice instead of Spring to speed up initialization. Additionally, separate your dependency .jar file from the function code to speed up the unpacking/packaging process. Node.js: Keep your function JS file size under 600 characters and use the V8 runtime for better performance. V8 can inline functions whose body size is under 600 characters, including comments.

Environment Variables for Flexibility

AWS Lambda environment variables can be used to define sensitive information like bucket names, RDS instance details, or application keys. These variables are accessible during runtime and provide flexibility for extending the script to other use cases and functions. This also reduces the risk of hardcoding secrets directly in your code.

Efficient Function Initialization

Pre-initialize only necessary packages during the beginning of the Lambda handler function. Use if-else conditions to choose specific outcomes based on input. This reduces the runtime of your application and ultimately leads to lower AWS bills.

Micro Services Approach

Smaller AWS Lambda functions may require more effort to maintain, but they help in analyzing and understanding lambda runtimes and bottlenecks. Breaking down larger functions into smaller microservices can help manage timeouts and disk space errors more effectively. Utilize AWS Lambda limits to prevent function failures.

Monitoring and Error Handling

Set up Lambda metrics and CloudWatch alarms to monitor your functions. This prepares you for handling worst-case scenarios where your script may not function as expected. Robust monitoring ensures issues can be detected and resolved quickly.

DDoS Mitigation and Retry Optimization

Optimize retry logic based on your needs. While not using retries at all or keeping them running indefinitely is not recommended, configure retries to mitigate Distributed Denial of Service (DDoS) attacks. This prevents unnecessary AWS bills due to malicious attacks.

Versioning and Concurrency Management

Version your AWS Lambda functions frequently and use aliases like prod and dev to switch between different environments. Versioning is immutable and allows quick rollbacks to previous versions. Manage concurrency properly to prevent task queue buildup and database connection limits. AWS Lambda limits concurrency region-wise and can be extended through support requests.

By following these best practices, you can significantly enhance the performance and efficiency of your AWS Lambda functions, leading to better resource utilization and cost management.