AWS announced Lambda Runtime API and Lambda Layers, two new AWS Lambda features that enable developers to build custom runtimes, and share and manage common code between functions.
Lambda supposedly makes it easy for developers to run code for virtually any type of application or back-end service.
The Runtime API for AWS Lambda defines a standardised HTTP-based specification which codifies how Lambda and a function’s runtime communicate. It enables users to build custom runtimes that integrate with Lambda to execute functions in response to events.
By leveraging the Runtime API, businesses can use binaries or shell scripts, and their choice of programming languages and language versions.
During an invoke, Lambda will bootstrap the user’s runtime code and communicate with it over Runtime API to execute the function code. With this release, AWS is also publishing a reference implementation of the C++ and Rust runtimes. Users can read more about the Runtime API in the Lambda documentation.
Lambda Layers are a new type of artifact that can contain arbitrary code and data and may be referenced by zero, one, or more functions at the same time.
Lambda functions in a serverless application typically share common dependencies such as SDKs, frameworks, and now runtimes. With layers, users can centrally manage common components across multiple functions enabling better code reuse.
To use layers, businesses simply put their common code in a zip file and upload it to Lambda as a layer. They can then configure their functions to reference it.
When a function is invoked, the layer contents become available to their function code. AWS is also providing a layer which includes the popular NumPy and SciPy scientific libraries for Python. These libraries were prebuilt and optimised for use in Lambda’s execution environment.
By using this layer with Python functions, users can quickly get started with building complex data processing and machine learning applications.
Runtime API and Layers can be used independently. Users can also use them together to publish custom runtimes which can be then shared as layers.
They can also use the AWS Serverless Application Model (SAM) and SAM CLI to test, deploy and manage serverless applications that leverage Runtime API and Layers.
With this release, several of AWS’ technology partners are also launching their offerings on top of Runtime API and Layers.
Customers can author functions in new runtimes and layers, including those which support additional capabilities including application management, monitoring, and security.