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The repo cloud-native-starter contains an end-to-end sample application that demonstrates how to develop your first cloud-native applications. Two of the microservices have been developed with Java EE and MicroProfile. To simplify the creation of new Java EE microservices, I've added another very simple service that can be used as template for new services.
In this blog post, I'll show you how to configure a Quarkus application using the specific application.properties file as well as the Microprofile Config API. To break it into more details you will learn:
In this example we have one REST Endpoint that uses a repository to persist and retrieve books from a database:
Over the past few weeks I've worked on an example application that demonstrates how Java EE developers can get started with microservices. The application is a full end-to-end sample which includes a web application, business logic, authentication, and now also persistence. It runs on Kubernetes and Istio and there are scripts to easily deploy it.
Get the cloud-native-starter code from GitHub.
I've been working on an example that demonstrates how to get started with cloud-native applications as a Java developer. The example is supposed to be a full end-to-end sample application which includes the topics of authentication and authorization, since that functionality is required by most applications. This article describes how to check authorization in microservices implemented with Java EE and Eclipse MicroProfile.
Get the code of the example application cloud-native-starter from GitHub.
One area of RESTful web API design that's quite frequently overlooked is how to report errors and problems, either related to the business or application. The proper usage of the HTTP status codes comes to mind first, and although quite handy, it is often not informative enough. Let us take 400 Bad Request as an example. Yes, it clearly states that the request is problematic, but what exactly is wrong?
The RESTful architectural style does not dictate what should be done in this case and so everyone is inventing their own styles, conventions, and specifications. It could be as simple as including an error message in the response or as shortsighted as copy/pasting long stack traces (in case of Java or .NET, to name a few cultprits). There is no shortage of ideas but luckily, we have at least some guidance available in the form of RFC 7807: Problem Details for HTTP APIs. Despite the fact that it is not an official specification but a draft (still), it outlines some good common principles on the problem at hand and this is what we are going to talk about in this post.
Managing application configuration in a traditional monolith is pretty straight forward. The configuration is usually externalized in a properties files on the same server as the application. If you need to update the configuration you simply amend the properties file and restart the application. Things can get a little tricker with microservices, but why is that?
Microservices are composed of many small, autonomous services, each with their own configuration. Rather than a centralised properties file (like the monolith), configuration is scattered across multiple services, running on multiple servers. In a production environment, where you likely have multiple instances of each service, configuration management can become a hefty task.
GraphQL is an API that was invented and open sourced by Facebook as a better replacement for REST. It can be understood as Querby language for APIs, which enables declarative data fetching by exposing a single endpoint and responds to queries. In REST, there is always a dedicated endpoint for each type of request and can't be customized.
In GraphQL, the client decides what data they need and that's the reason the client sends a query (payload) to the server and the server sends the data back as per the query request. There is where they get the name GraphQL
I've previously posted about defining Consumer Driven Contracts using the OpenAPI specification and Spring Cloud Contract. This approach works well because you can tie your contracts to your API specification, and also wire in a request validator into your contract tests.
But one problem that remained is typically your OpenAPI specification will span multiple services. Thus, when generating contract tests, you'd get tests generated for all services and not just a specific service.
Most developers are creating microservices these days and deploying to cloud platforms. Pivotal Cloud Foundry (PCF) is one of the most well known cloud platforms. When we talk about deploying applications on PCF, mostly they would be long-running processes which never end, like web applications, SPAs, or REST-based services. PCF monitors all these long-running instances and if one goes down it spins up the new instance to replace the failed one. This works fine where the process is expected to run continuously but for a batch process, it's overkill. The container will be running all the time with no CPU usage and the cost will add up. Many developers have an ambiguity that PCF cannot run a batch application which can just be initiated based on a request. But that is not correct.
Spring Batch enables us to create a batch application and provides many out-of-the-box features to reduce boilerplate code. Recently, Spring Cloud Task has been added in the list of projects to create short-running processes. With both of these options, we can create microservices, deploy them on PCF, and then stop them so that PCF doesn't try to self heal them. And with the help of PCF Scheduler, we can schedule the task to run them at a certain time of the day. Let's see in this article how we can do that with very few steps.
This tutorial is the second part of a series: Building microservices through Event Driven Architecture.
During this journey, I will implement the domain model: EduSync.Speech.Domain,
For cloud-native applications, Kubernetes and Istio deliver a lot of important functionality out of the box, for example, certain metrics, which are independent from the actual service implementations, can be collected automatically.
However, other metrics are application specific, for example, how many times certain methods have been invoked and how long they take. Eclipse MicroProfile allows Java developers to easily expose these metrics. Check out the list of available Java annotations.
Zuul is part of the Spring Cloud Netflix package and allows redirect REST requests to perform various types of filters.
In almost any project where we use microservices, it is desirable that all communications between those microservices go through a communal place so that the inputs and outputs are recorded and can implement security or redirect requests, depending on various parameters.
Redis can be widely used in microservices architecture. It is probably one of the few popular software solutions that may be leveraged by your application in so many different ways. Depending on the requirements, it can act as a primary database, cache, or message broker. While it is also a key/value store we can use it as a configuration server or discovery server in your microservices architecture. Although it is usually defined as an in-memory data structure, we can also run it in persistent mode.
Today, I'm going to show you some examples of using Redis with microservices built on top of Spring Boot and Spring Cloud frameworks. These applications will communicate between each other asynchronously using Redis Pub/Sub, using Redis as a cache or primary database, and finally using Redis as a configuration server. Here's the picture that illustrates the described architecture.
Quarkus is a “next-generation Kubernetes native Java framework” which is available as open source. Quarkus promises fast boot times and low memory usages. This makes Quarkus a perfect fit for Java workloads running as microservices on Kubernetes as well as Java workloads running as serverless functions.
Read the article Why Quarkus to learn how Quarkus works. In a nutshell, Quarkus compiles Java source code in native binaries via GraalVM. The Quarkus framework helps developers to easily build applications that can leverage the GraalVM benefits.
Previously, I blogged about how to implement and document REST APIs in JavaEE applications with Eclipse MicroProfile. In this article, I describe the inverse scenario — how services can invoke other services via REST APIs over HTTP.
MicroProfile comes with a REST Client which defines a type-safe client programming model. The REST Client makes it easier to convert between the JSON data and Java objects in both directions.
Welcome back! If you missed Part 1, you can check it out here. Now, let's learn how to run and test those apps!
We will start the Derby network server with authentication. If Derby is installed in derby_install_dir then change directory to <derby_install_dir>/bin and first execute:
The Micronaut Framework provides support for Kotlin built upon the Kapt compiler plugin. It also implements the most popular cloud-native patterns, like distributed configuration, service discovery, and client-side load balancing. These features allow you to include an application that's been built on top of Micronaut into an existing microservices-based system. The most popular example of such an approach may be integration with the Spring Cloud ecosystem. If you have already used Spring Cloud, it is very likely you built your microservices-based architecture using the Eureka discovery server and Spring Cloud Config as a configuration server. Beginning with version 1.1, Micronaut supports both these popular tools as part of the Spring Cloud project. That's good news because, in version 1.0, the only supported distributed solution was Consul, and there was no way to use Eureka discovery together with Consul's property source (running them together ends with an exception).
In this article, you will learn how to:
The Micronaut framework was introduced as an alternative to Spring Boot for building microservice applications. At first glance, it is very similar to Spring. It also implements patterns like dependency injection and inversion of control based on annotations, however, it uses JSR-330 (java.inject) to do it. It has been designed specifically for building serverless functions, Android applications, and low memory-footprint microservices. This means that it should have a faster startup time, lower memory usage, and be easier to unit test than competing frameworks. However, today I don't want to focus on those characteristics of Micronaut. I'm going to show you how to build a simple microservices-based system using this framework. You can easily compare it with Spring Boot and Spring Cloud by reading my previous article about the same subject Quick Guide to Microservices with Spring Boot 2.0, Eureka and Spring Cloud. Does Micronaut have a chance to gain the same popularity as Spring Boot? Let's find out.
Our sample system consists of three independent microservices that communicate with each other. All of them integrate with Consul in order to fetch shared configurations. After startup, every single service will register itself in Consul. The applications organization-service and department-service call endpoints exposed by other microservices using the Micronaut declarative HTTP client. The traces from communication are sent to Zipkin. The source code of the sample applications is available on GitHub in the sample-micronaut-microservices repository.