serverless VS powertools-lambda-python

I don't know any tutorials that cover all of these, but check out: https://cdkpatterns.com/ for 1) https://docs.aws.amazon.com/cdk/v2/guide/cdk_pipeline.html for 2) 3) and 5) https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-cdk-testing.html for 4)

I would like to see re-useable design patterns advocated similar to the terraform/terragrunt modules concepts. Encouraging pulumi.ComponentResource definitions multi environment structure, best practices. The cdk guys have something similar https://cdkpatterns.com/.

While writing this post, the four stacks combined are 129 lines of source code. With the help of the CDK Constructs that are being used this translates to over 1k lines of code in CloudFormation. We are only using L2 constructs here – there is way more constructs available that you can use in the Constructs Hub and also a lot of guidance regarding the usage of CDK over at CDKPatterns.

I use the "Lambda-lith" pattern, primarily because I am using Rust and deploying via CI/CD (codepipeline).

Here is a read of common lambda design pattern https://github.com/cdk-patterns/serverless/blob/main/the-lambda-trilogy/README.md

Level 3 constructs are opinionated units of reusable code which often deliver a service. Open source level 3 constructs are available from AWS Solutions Constructs - an extension to the CDK, CDK Patterns - a library of well-architected patterns, Construct Hub - a library of open-source constructs and some even come out of the box with the CDK - these are appended with 'patterns' to make them identifiable. Two examples are shown below:

Also I suggest to have a look at AWS Lambda PowerTools, which simplifies adopting best practices with Lambda

Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.

Read more: https://docs.powertools.aws.dev/lambda/python/latest/

Imagine a sample Python application returning "hello world" to simulate a successful call. Each call to the application is captured for billing purposes using EMF. Lambda Powertools is used to reduce the amount of code we need to write.

Below is an example of lambda handler code. It is using the highly recommended Powertools for AWS Lambda library to ensure best practices around tracing, logging, metrics, and more. The function gets passed an SNS topic and Slack webhook URL at creation time (via environment variables) to send notifications to. It parses the passed in event information to determine which hostname had it's status changed and what the new status is and sends out notifications.

It's worth noting that these improvements will also impact other libraries and frameworks that rely on Pydantic, such as FastAPI and AWS Lambda Powertools, which could deliver some transitive performance improvements to various projects that don't directly depend on Pydantic themselves.

I had the pleasure of writing and contributing the Parser utility to a fantastic project on Github called AWS Lambda Powertools. We have used this repository previously in the blog series (parts one to three) for logging, tracing and metrics.

We are going to add aws-lambda-powertools as poetry dependency we need. While this is available as Lambda Layer, we are going to use it as a installed dependency for this exercise.

I truly disliked type hints. Working with CDK started to change that for the better and then I discovered AWS Lambda Powertools. It combines multiple things I am passionate about so I became passionate about using the library and improving it. Lambda Powertools utilizes static typing for several of its main features (Parser, Event Sources and Typing).