Serverless technologies let us do more with less effort, time and energy. They let us focus on creating user value and let the cloud handle undifferentiated heavy-lifting like scaling and securing the underlying infrastructure that runs our code. Serverless technologies have allowed me to accomplish tasks as a solo engineer that used to take a whole team of engineers to accomplish, and I’m able to complete these tasks in a fraction of the time and cost to my customers.

This is the second post in a 2 part blog series on debugging, monitoring and tracing NodeJS Lambda applications. If you haven’t yet seen part 1, check it out here (it’s a great read!) Now let’s get back into our post with one of the most commonly experienced issues when it comes to Lambda functions, Cold Starts.

In this two post series, we are going to explore some ways to trace and debug NodeJS Lambda applications. Delving into some methods to look further into resources utilized to and some methods to optimize code. AWS Lambda, an event-driven compute service first introduced roughly eight years ago, changed how we build out cloud applications as an industry.

AWS provides multiple ways to deploy containerized applications. From small, ready-made WordPress instances on Lightsail, to managed Kubernetes clusters running hundreds of instances across multiple availability zones. When deciding on the architecture of your application, you should consider building it serverless. Being free from (virtual) server management enables you to focus more on your unique business logic while reducing your operational costs and increasing your speed to market.

The most important thing with building out any application is to think BIG. Build for ten users now and 10,000 users tomorrow. Having infrastructure that scales as your needs do is critical for user adoption—one of the many reasons we love a serverless approach and particularly AWS Lambda. The other part of any growth journey is managing access to organizational cloud infrastructure, especially with rapidly growing organizational development and DevOps teams.

I have been an AWS customer since 2010 and in the early days I, along with just about everyone else on AWS, spent a lot of my time just managing infrastructure. Patching AMIs, configuring load balancers, updating auto-scaling configurations, and so on. It was the sort of thankless task that no one cared about until something went wrong! The very definition of what Werner Vogel often refers to as “undifferentiated heavy-lifting”.

One of the things we love about working in the cloud is the ease and scalability it brings to application development. It enables us to build out applications, APIs and any infrastructure that is needed from prototyping an idea, through to self scaling deployments. Monitoring and troubleshooting production-level serverless applications is always tricky, Especially working across a number of services and the many logs they can produce.

It’s no secret that AWS has an extensive catalog of services which enable organizations to rapidly scale infrastructure. In this fast paced and self scaling cloud native world, observability across all these services has never been more critical. As a long time AWS Technology Partner, it’s always great to speak to our friends at AWS, and most recently, Lumigo CEO Erez Berkner joined AWS on Air to talk about end-to-end observability of the modern cloud application.

Modern applications are designed to leverage cloud native technologies like serverless and containers to run at an unprecedented scale, moving the focus away from machines to the actual service. Lumigo’s observability platform was purpose-built for these evolving cloud environments, and we’ve been delivering the most advanced automated distributed tracing for serverless applications since 2019.