This article is going to introduce you to Spring Boot with GraphQL. We’ll walk through a simple beer app to show you what it can do. So you have built this really sweet API with all the gets, puts, and deletes you can think of. Your baby is just beautiful the way it is, right? Well, maybe developer Joe thinks …
This article will set up a basic Spring Boot app that incorporates Apache Camel to move some sample files around.
If you are like me, you find that flat-file processing can be pretty dry. Considering what Apache Camel does, its name is very fitting. While there are plenty of reasons for the name, it definitely makes sense that Apache Camel does a lot of lugging things around for you…
When the Microservices approach became popular a few years ago, many companies rushed to build their own microservices or to convert their legacy applications into microservices. Over the years, companies have implemented an abundance of microservices, mostly with Spring Boot. Each of them manage their own configurations across deployment environments like Dev, Test, and Prod.
Due to the nature of a complex business process, there are many common configurations (e.g. databases, queues, email servers, and FTP servers, etc.) used in the distributed services. This can result in services having redundant and confusing configs on a distributed system. It can become challenging to update the configs for too many services on a distributed system across multiple environments.
Thankfully, Spring Cloud Config provides the implementation to successfully resolve these issues. It provides server-side and client-side support for externalized configuration in a distributed system. With the Config Server, you have a central place to manage external properties for applications across all environments. The concepts on both client and server map identically to the Spring Environment and PropertySource abstractions, so they fit very well with Spring applications.
In this post, I’ll demonstrate Config Server and Client with example code. I’ll show you how to use Git or a local repository as a central place to store all the config files. The diagram below illustrates how the distributed client services (e.g. Investment Position/Price/Reporting Data service) fetch their configuration from the Config Server, which in turn retrieves them from one central place.
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…
AWS SQS (Simple Queue Service) can provide developers with flexibility and scalability when building microservice application(s). In this quick start tutorial, we will demonstrate how to configure a FIFO queue for a fictional online marketplace.
What Is A FIFO Queue?
A FIFO (first in, first out) queue is used when the order of items or events is critical, or where duplicates items in the queue are not permitted. For example:
– Prevent a user from buying an item that isn’t available on a marketplace.