Learn how to run and debug Node.js applications locally with Visual Studio Code, in Part 2 of this series on Node.js serverless functions. Node.js serverless functions on Red Hat OpenShift, Part 2: Debugging locally.
Welcome back to our series on using serverless functions on Red Hat OpenShift. The previous article introduced you to how logging works in Node.js and how to customize what is logged in a Node.js function application. Now, we'll take a look at how to debug Node.js function-based applications. Because debugging is a longer topic, we'll cover it in two parts. This article walks through how to set up and debug function applications locally with Visual Studio Code (VS Code). The next article will show you how to connect and debug function applications running in a container on a cluster.
Note: For an introduction to logging function-based applications, see Node.js serverless functions on Red Hat OpenShift, Part 1: Logging. For an overview of Red Hat OpenShift Serverless Functions, see Create your first serverless function with Red Hat OpenShift Serverless Functions.
To follow along with this article, you will need to install Node.js and download the example application from GitHub. We'll also use VS Code for its easy-to-use built-in debugger.
As with the previous article, we scaffolded this function application with the
kn func command-line interface (CLI) tool. If you are not already familiar with it, you can learn more by reading Create your first serverless function with Red Hat OpenShift Serverless Functions.
Use Git to clone the example repository and then open it up in VS Code. We can see that this Node.js function application is just like any other Node.js application, with an
index.js file where the main function logic is located.
Before we continue, let's put a breakpoint right around line 30, which is inside the
invoke function (see Figure 1).
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In this post, I will go through the process of predicting key performance characteristics and the cost of scale-per-request serverless platforms (like AWS Lambda, IBM Cloud Functions, Azure Functions, and Google Cloud Functions) with different workload intensities (in terms of requests per second) using a performance model.
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