Elevate Your Lambda Functions with Container Runtimes

Since its release in 2014, AWS Lambda has continued to be a game-changer in software development, thanks to its serverless nature and its rich feature set. Developers seeking to enhance portability, improve local testing fidelity, or leverage existing containerized applications may find container runtimes for Lambda to be a valuable solution. This article explores how container runtimes can serve as a powerful alternative to traditional Lambda layers in specific use cases, offering more flexibility in managing complex dependencies and potentially simplifying the deployment process.

AWS Lambda layers are an excellent tool for extending your functions' capabilities. They allow you to:

Whether you're adding common utilities, external libraries, or custom runtime dependencies, layers provide a clean and efficient solution for many serverless applications. While Lambda layers are invaluable for enhancing the functionality of your Lambda functions, there are instances where they may fall short. In such scenarios, it becomes crucial to consider alternative approaches. What options are available when Lambda layers do not meet your needs?

While working on a Lambda function that needed to connect to a PostgreSQL RDS instance, I encountered an interesting challenge. After setting up my Lambda layers with the necessary packages for connecting to my Postgres instance, I ran my Lambda function and it failed.

I double-checked my Lambda layers to ensure I had created them correctly and even ended up re-creating the layer just to be safe. However, when I re-deployed the newly created Lambda layer and executed the function a second time, to my surprise, the code execution still failed.

After researching the matter, I discovered that the in-console Python runtime environment didn't include the dependencies needed to implement PostgreSQL connectors properly.

After spending considerable time trying to find a solution, I stumbled upon the custom container runtime page in the Lambda documentation. I realized that I could create my own runtime to mitigate the dependency issue altogether.

After reading through the documentation, creating my image, and deploying it in Lambda, it was time to test the solution. Success! I was able to connect to the RDS instance and display the query results in my application.

Now I'll walk you through the steps required to create your own Lambda container runtime. Note: This guide is based on the PostgreSQL use case mentioned in this article. You are welcome to clone this solution from GitHub and change it to suit your development needs. AWS Samples: PostgreSQL Lambda Container Runtime

Here's a quick comparison of Lambda Layers vs. Container Runtimes:

While container runtimes offer more flexibility, they may introduce slightly longer cold start times and require more initial setup. Consider your specific needs when choosing between the two approaches. You can also use provisioned concurrency to mitigate cold starts.

Whether you choose Lambda layers or container runtimes, the goal is to streamline your development process and ensure consistency across environments. Start by evaluating your project's specific needs, then experiment with both approaches to find the best fit. Remember, the right tool can significantly reduce development time and improve the quality of your serverless applications.

Lambda functions are incredibly versatile tools that enable you to accomplish your tasks in various ways. One such approach is utilizing custom container runtimes, which proved to be an effective solution when other methods didn’t work for my PostgreSQL-connected Lambda function.