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#1 Lambda Layers
Lambda Layers allow developers to centrally manage code and data which is shared across multiple functions. Instead of packaging and deploying this shared code together with all the functions using it, developers can put common components in a ZIP file and upload it as a Lambda Layer. These Layers can be used within an AWS account, shared between accounts, or shared publicly within the developer community.
Developers can include additional files or data for their functions including binaries such as FFmpeg or ImageMagick, or dependencies, such as NumPy for Python. These layers are added to your function’s zip file when published.
Layers can also be versioned to manage updates, which will make each version immutable. When a version is deleted or its permissions are revoked, a developer won’t be able to create new functions; however, functions that used it previously will continue to work.
Lamba layers helps in making the function code smaller and more focused on what the application has to build. In addition to faster deployments, because less code must be packaged and uploaded, code dependencies can be reused.
#2 Lambda Runtime API
This is a simple interface to use any programming language, or a specific language version, for developing functions. Here, runtimes can be shared as layers, which allows developers to work with a programming language of their choice when authoring Lambda functions. Developers using the Runtime API will have to bundle the same with their application artifact or as a Lambda layer that the application uses.
When creating or updating a function, users can select a custom runtime. The function must include (in its code or in a layer) an executable file called bootstrap, that will be responsible for the communication between code and the Lambda environment.
The Runtime API will depict how AWS will support new languages in Lambda. A notable feature of the C++ runtime is its simplicity and expressiveness of interpreted languages while maintaining a good performance and low memory footprint. The Rust runtime makes it easy to write highly performant Lambda functions in Rust.
#3 Application Load Balancers to invoke Lambda functions to serve HTTP(S) requests
This new functionality will enable users to access serverless applications from any HTTP client, including web browsers. Users can also route requests to different Lambda functions based on the requested content. Application Load Balancer will be used as a common HTTP endpoint to both simplify operations and monitor applications that use servers and serverless computing.
#4 Ruby is now a supported language for AWS Lambda
Developers can use Lambda functions as idiomatic Ruby code, and run them on AWS. The AWS SDK for Ruby is included in the Lambda execution environment by default making it easy and quick for functions to directly interact with the AWS resources directly. Ruby on Lambda can be used either through the AWS Management Console or the AWS SAM CLI. This will ensure developers benefit from the reduced operational overhead, scalability, availability, and pay-per-use pricing of Lambda.
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