DEV Community

Sunny Nazar for AWS Community Builders

Posted on • Updated on




AWS Lambda is a serverless computing platform that allows you to run your code in response to events and only pay for the compute time consumed. With Lambda, you can build and deploy applications without worrying about the underlying infrastructure. However, like any other technology, there are best practices that you can follow to ensure that you get the most out of it. In this blog, we'll look at some AWS Lambda best practices.

Best Practices

Right language, Small functions and Trigger type

AWS Lambda natively supports various programming languages like Java, Go, PowerShell, Node.js, C#, Python, and Ruby code. Lambda also provides Runtime API which allows you to use any additional programming languages to create your functions.When choosing a language for your function, please consider your use case and the language's strengths.

AWS Lambda is designed to run small, focused functions. When building your functions, try to keep them as small as possible and focused on a single task. This makes it easier to test, deploy, and maintain your code. Please note that you can run Lambda functions for only 15 minutes.

AWS Lambda supports different trigger types, such as API Gateway, S3, and CloudWatch Events. Choose the right trigger type for your function based on your use case and expected workload.If you follow event-driven architecture that should already help you in choosing right trigger type.

Lambda Layers

If you have code that is shared across multiple functions, please consider using AWS Lambda Layers to manage it. A layer is a ZIP archive that contains libraries, custom runtimes, or other function code. You can use layers to manage dependencies, reduce the size of your function deployment packages, and simplify your code maintenance.

Optimize cold start times

Cold start times can impact the performance of your Lambda functions, especially for infrequently used functions. Optimize your code and use the right runtime to reduce cold start times. Some tips could be :

  • Reduce the size of your deployment package.
  • Use a language that has faster startup time.
  • Use provisioned concurrency.
  • Optimize resource allocation.

Environment Variables for Configuration

When building your functions, you may need to configure them with environment variables, such as API keys or database connection credentials.Use environment variables to store configuration settings instead of hard-coding them in your function's code. This makes it easier to manage your configuration and update it as needed. Best practice is to make use of SSM Parameter Store and Secrets Managers.

Concurrency setting

Configure your Lambda function with the right concurrency settings to handle incoming requests. Below tips will help you to have right settings.

  • Understand your application's requirements: The first step in setting concurrency is to understand your application's requirements. Determine how many requests per second your application needs to handle, and set the concurrency limit accordingly.

  • Use auto-scaling: AWS Lambda can automatically scale the number of concurrent executions based on the number of requests coming in. By enabling auto-scaling, you can ensure that your functions are able to handle bursts of traffic without being overwhelmed.

  • Reserve concurrency: The default value of concurrent Lambda functions in an AWS account in a region is 1000. This means that by default, up to 1000 requests can be processed simultaneously across all Lambda functions in that region. Reserving concurrency allows you to ensure that a certain number of executions are always available, even when other functions are using up the concurrency pool. This can be useful for functions that need to respond quickly to requests, such as real-time applications.

  • Monitor and adjust: It's important to monitor the concurrency usage of your functions and adjust the concurrency limit accordingly. If you're consistently hitting the concurrency limit, consider increasing it. Conversely, if you're consistently underutilizing your concurrency, consider reducing the limit to save costs.

Use the right memory and CPU settings

Configure your Lambda function with the right amount of memory and CPU to ensure optimal performance. This will depend on your workload, so be sure to test your functions under different load conditions and scenarios. Best practice is start with minimum required cpu and memory settings.And as you test your function, adjust these settings accordingly.

Secure your Lambda functions

Use AWS Identity and Access Management (IAM) to restrict access to your Lambda functions (using least privilege access) and use encryption to protect your data at rest and in transit. Also make sure IAM role needed for lambda function follows the least privilage access principal.

Dead Letter Queue (DLQ) and Retries for error handling

Retries in Lambda functions refer to the number of times AWS Lambda will automatically retry a function invocation in case of a function error. By default, AWS Lambda retries function invocations twice, with an exponential backoff in between retries.
A DLQ is a queue where AWS Lambda can send failed or discarded messages, which can be used for further analysis or processing. Configure a DLQ for your Lambda function to handle errors more effectively and prevent data loss. This is particularly useful when using asynchronous event sources like SNS or Kinesis.

Testing, Versioning and Aliases for Deployment

Test your Lambda functions thoroughly before deploying them to production. Use a combination of unit tests, integration tests, and end-to-end tests to ensure that your functions are working as expected.

When deploying your functions, use versioning and aliases to manage your code. Versioning allows you to create and manage multiple versions of your function code, while aliases provide a consistent name for your function's entry point. This makes it easier to manage deployments and rollbacks.

Use a deployment pipeline to automate the process of building, testing, and deploying your Lambda functions. This can help you release new features and updates more frequently and with less risk.

Error Handling, Logging, Monitoring, Tracing

  • AWS Lambda Power tools to simplify your code:
    AWS Lambda Power tools is a set of open-source utilities and libraries that help simplify your code and improve observability. It includes modules for logging, error handling, metrics, and tracing, and can help reduce the amount of boilerplate code you need to write.

  • Monitor Your Functions for Performance and Errors:
    AWS Lambda integrates with CloudWatch Metrics, which allows you to monitor your functions for performance and errors. Make sure that you configure your metrics to track the right metrics and set up alarms to notify you of any issues.

  • Use AWS X-Ray for tracing: Use AWS X-Ray to trace requests through your Lambda function and other AWS services. This can help you identify performance bottlenecks and troubleshoot issues more easily.

  • Use Logging for Debugging:
    When developing your functions, use logging to help you debug issues. AWS Lambda integrates with CloudWatch Logs, which allows you to view and analyze your logs in real-time. Make sure that your logging is comprehensive and includes useful information, such as error messages and input parameters.

Documentation Links


AWS Lambda is a powerful tool for building and deploying serverless applications. By following these best practices, you can ensure that your functions are scalable, secure, and easy to manage. With these best practices, you can build robust and reliable serverless applications on AWS Lambda.

Top comments (4)

indika_wimalasuriya profile image

Thank you for sharing this excellent summary of best practices for Lambda! I particularly appreciate the emphasis on setting appropriate resource limits for functions to ensure optimal performance. Your insights are greatly appreciated.

sunnynazar profile image
Sunny Nazar

Glad that you liked the content !

davo profile image
Davo Galavotti

To be completely honest, this article packs really good advice and I appreciate the OC for writing it. With that said, I got I feeling it was written 100% with ChatGPT with minor adjustments.

davo profile image
Davo Galavotti

The giveaway is the paragraph "Right language, Small functions and Trigger type", where it mentions Node.js, Java, Python, and C#, that's coming straight from the docs, but testing tells us that Rust & Go are faster runtimes than Python, Node and DOTNet, using Max Day Lambda Cold Start Analysis tool.

Rust (prov.al2): 1.541 ms
Python 3.9: 4.925 ms
Python 3.8: 2.677 ms
Python 3.7: 10.359 ms
Node.js 12.x: 10.617 ms
Node.js 14.x: 10.898 ms
Node.js 16.x: 13.881 ms
Node.js 18.x: 15.869 ms