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An Antlr4-Based Expression Parser
In this blog, we’ll present a simple arithmetic expression parser implemented through an Antlr4 parser generator. It will be able to take in an input string (such as 2+4+-4+-2*10%9*7) to produce the result (-12.0).
You may be thinking, “Great, but what’s the point?” Well, to answer your question, as simple as this example may seem, the principles involved actually extend to use cases such as DSLs, transpilation, and anything else expressible by grammar rules.
This post has two parts. In part 1, we’ll discuss the background components of a parser. In part 2, we’ll cover building the demo and running it. If you already understand grammar parsing, you could skip part one.
Orchestration – Kubernetes, OpenShift, and Cloud Foundry
Home→Search ResultsOrchestration Big Ideas Kubernetes, OpenShift, and Cloud FoundryThis video discusses platform orchestration from a broad scope. Specifically, Principal Consultant Jaime Niswonger takes a technology-agnostic look at the “big ideas” integral to platform orchestration for the enterprise. He introduces three popular orchestration platforms, Kubernetes, OpenShift, and Cloud Foundry, and discusses scaling container deployments in the enterprise. RecordingThis one-hour video was …
Building a Spring Cloud Native Microservice Application on Azure, Part 1
The big three cloud providers (AWS, Azure, and Google Cloud, in that order) have their various strengths and areas of expertise. Most large organizations though typically pick one cloud provider for their cloud computing needs. This works well if you’re a Java shop that’s on AWS, or a Microsoft shop on Azure. But what if you’re on a large Java project in an organization that wants to use Azure? You’re in luck.
Microsoft Azure has come a long way, and is very supportive of non-Microsoft technologies. The proof though is in the pudding. Which is where this blog post comes in. I take Josh Long’s Bootiful Microservice Services, a great starting point to get a cloud native Spring microservice application up and running, and show how it can be run on Azure.
This first blog post will be all about setting up our basic microservices by walking through the various parts of Josh’s example application, with some best practices and patterns that I’ve found to be effective. Rather than a simplistic ToDo application, we’ll be basing our application off of my favorite bagel shop in New York, Original Bagel Boss in Hicksville, to manage its orders, inventory, etc. If we can run a bagel shop on a Spring application running on Azure, and keep customers happy and full of carbohydrates, then it proves out for applications of a similar size and complexity.
We’ll be staying mostly inside the familiar Java confines, then slowly start working our way out to getting our application deployed to Azure. Then we’ll start introducing additional complexity like Spring Batch jobs, a React front end, etc. A setup this complex will show that Azure is ready for prime time when it comes to running applications in production, even if they are built on non-Microsoft technologies…
Spring Boot and React: Happily Ever After
So you have mastered Spring Boot and started toying around with React. Now you want React to talk to your Boot app as your back-end API. That’s fabulous. You probably already know how to do this, but there is a kicker. You want to package them and start both of them as just one project.
Well, you’re in luck! This blog is going to take a couple of simple projects and combine them into one project. Lace up your boots and get ready to React!
