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Docker has revolutionized how Node.js developers create and deploy applications. But developing a Node.js Docker application can be slow and clunky. The main culprit: the process for testing your code in development.
In this article, we’ll show a tutorial and example on how you can use Docker’s host volumes and nodemon to code faster and radically reduce the time you spend testing.
In this tutorial, we will understand how to design SOAP and REST services using Neoload to validate the performance of web services.
Step 1: Create New Project (Go to File -> Click on New -> Enter Project Name -> Click Finish)
Developing a microservice architecture with Java and Spring Boot is quite popular these days. In microservice architecture we hundreds of services and managing services for each service and for each profile which is a quite tedious task. In this article, we will demonstrate the Spring cloud config server using Kotlin.
Spring Boot provided a much-needed spark to the Spring projects.
Just a couple of years ago, serverless was a sparsely used technology, recognized mostly for its cost-efficiency. Today, we’re seeing a radical shift in the attitude towards serverless. Developers have begun to realize that serverless not only minimizes operational overhead but can considerably enhance core applications in production. As cloud vendors continue to develop their serverless offerings, serverless is being used in production more than ever before.
In this blog post, we explain what serverless is, dive into some of its major advantages, and point out some factors contributing to its growing popularity.
Building CI/CD process for the .NET Core application could be complicated especially when you are dealing with Kubernetes and Docker and if you need to include code style analysis, unit test, and code coverage report.
In this article, I am going to explain the process of building a simple CI/CD pipeline for existing .net core application to moving it to Azure Kubernetes Services using Azure DevOps. The final Pipeline will be easy to understand and reusable.
‘Microservices’ is a big buzzword these days among the tech community. Thanks to NetFlix, Uber, and Amazon for making it popular with successful implementations.
Many organizations are now adopting this approach. Organizations are trying to refactor or rewrite their monolithic applications by breaking them into Microservices and there are organizations building products by adopting the microservices approach from scratch.
I am a big fan of in-memory computing and Apache Ignite is one my favorite platform for developing high-performance backend applications. Recently, the Apache Ignite team announced a new extension for developing a Spring Boot application with the Ignite database. The full documentation of the extension is available here.
The main purpose of the extension is to integrate Ignite into Spring Boot application with minimal configuration. This autoconfigure module serves in two different flavors:
In the last few years, there has been a wave of hype that crosses IT-dedicated media back and forth, growing bigger and bigger. And this wave is about microservices.
Countless articles, blog posts, videos, and slideshows dedicated to microservices, how to design them, or how to apply them to existing applications.
One of the joys of my role is that I’m often talking to customers (or potential customers) about their concerns, their plans, and their strategies for moving their business forward based on open source technologies.
When discussing the application development impact on existing developed applications and transitioning to microservices there are five questions that keep popping up in one form or another. They are the same regardless of the size of the organization and seem to become part of strategy discussions later in the process as organizations move towards microservice architectures.
In this tutorial, we are going to expand our examples by deploying a more complex microservice on Platform9 Managed Kubernetes Free Tier (PMKFT). The idea is to make you more comfortable with the platform and to show you how you can leverage it for more advanced scenarios.
In this example, we are going to see the deployment of:
I am going to talk about what has become a very common requirement for web/cloud-applications these days: Authentication in the context of REST API calls using JSON Web Token, or commonly known as JAWT these days.
The idea behind JSON WebToken is to provide authentication in a stateless API world. The tokens are generated with a Key on the basis of Subject (could be a unique field or combination for a user e.g id, username etc.) using an encryption algorithm such as SHA-256 etc.
Event sourcing, aka "the great myth". I've been thinking about writing a series of articles about this for a while, and now it's time to put my hands back on the keyboard.
I thought that with this long period of confinement at least I could have had more time to write some nice articles, but it turns out the reality has been slightly different so far.
In this post, we are going to cover the pod priority class, pod disruption budget, and the relationship of these constructs' with pod eviction. Okay, enough of talking, let’s start with pod priority class.
PriorityClass is a stable Kubernetes object from version 1.14, and it is a part of the scheduling group used for defining a mapping between priority class name and the integer value of the priority. PriorityClass is straightforward to understand; the higher the value of the integer, the higher is the priority. Take, for example, a PriorityClass with an integer value of ten and another with an integer value of twenty; the later one holds a higher priority than the first one.
Health checks are a fundamental part of our APIs. I guess they fall in that category of "non-functional-but-heavily-required" things. More or less like a good part of the infrastructure code.
They don't add business value per se but have an enormous impact for those in IT, like DDD and design patterns. You can normally see them in conjunction with container orchestration or monitoring tools to ensure that the system is alive and kicking.
In this blog post, I want to explain how to automate the deployment of a microservice using a delivery pipeline on IBM Cloud.
Maybe you already know Niklas, Harald, and I made the project called Cloud Native Starter. That project includes a Hands-on workshop that is called “Build a Java Microservice and deploy the Microservice to Kubernetes on IBM Cloud.” In Lab 4, you have to deploy the Authors Microservice to Kubernetes on IBM Cloud. Sometimes we have limited time in workshops. The limited-time is the reason why we want to reduce the manual effort for students in a workshop to a minimum, therefore, we took the IBM Cloud Continuous Delivery, and I created a repeatable way with minimal human interaction. The delivery pipeline contains sequences of stages, which retrieve inputs and run jobs such as builds, and deployments.
Starting from Spring framework 5.0 and Spring Boot 2.0, the framework provides support for asynchronous programming, so does AWS SDK starting with 2.0 version.
In this post, I will be exploring using asynchronous DynamoDB API and Spring Webflux by building a simple reactive REST application. Let's say we need to handle HTTP requests for retrieving or storing some Event (id:string, body: string). The event will be stored in DynamoDB.
In this article, we discuss how to write a Spring Boot microservice based on Spring Jax-Rs Jersey, HATEOAS API, and JerseyTest framework integration. We are going to take the material of our previous article Spring Boot - Microservice - Spring Data REST and HATEOAS Integration and rewrite it for new Spring Jax-Rs Jersey usage.
You may also like: REST API — What Is HATEOAS?
Micronaut is a modern, JVM-based, full-stack framework for building modular, easily testable microservice and serverless applications.
For initial development, we are leveraging a JAX-RS based microservice-starter application similar to implementation using Oracle Helidon.