qldb-simple-demo VS dazn-lambda-powertools

To try this out yourself, check out the backend folder in qldb simple demo on GitHub.

The full source code can be found at QLDB Simple Demo, but lets walk through the main steps involved.

For those of you that want to take a look at the CDC Outbox style pattern with QLDB and Kinesis, Matthew Lewis has created a demo available on github which highlights this process. It is also available as a running reference example that you can play around with at demo.qldbguide.com.

I'm a big fan of TypeScript and in fact co-authored a book about it. I don't find myself using Java or Python much, so while I've been interested in Lambda Powertools, I never tried it out until now. Lambda Powertools TypeScript joins middy and DAZN Lambda Powertools in the Lambda tooling space for the Node.js runtime. Two things that differentiate Lambda Powertools TypeScript from comparable libraries are it is sponsored by AWS and it supports decorators.

The next natural step was to look at middleware such as Middy to decorate the main business logic, and allow these tasks to be defined once and used consistently. I remember listening to a Comic Relief talk about their Lambda wrapper back in 2019. Since then, I was introduced by Yan Cui to DAZN Lambda Powertools, which was the inspiration for this new project.

You might be interested in the logger/correlation ID features of https://github.com/getndazn/dazn-lambda-powertools - been super useful for us so far.

Nevertheless, log files are an important resource to debug problems and to get deeper insights into the behavior of a serverless system. Every logged detail might help to identify issues and to fix bugs and problems. Structured logging is important as log files can be analyzed much easier (e.g. with AWS CloudWatch Insights) which will save time and engineering costs. The dazn-lambda-powertools library provides a logger that supports structured logging for Node.js, the AWS Lambda Powertools offer the same for Python and Java.