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Going Full-Stack With Kotlin/JS and Spring Boot

In the dynamic world of web development, Single Page Applications (SPAs) and frameworks like React, Angular, and Vue.js have emerged as the preferred approach for delivering seamless user experiences. With the evolution of the Kotlin language and its recent multiplatform capabilities, new options exist that are worthwhile to evaluate.

In this article, we will explore Kotlin/JS for creating a web application that communicates with a Spring Boot backend which is also written in Kotlin. In order to keep it as simple as possible, we will not bring in any other framework. 

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Competing Consumers With Spring Boot and Hazelcast

The Competing Consumers pattern is a powerful approach that enables multiple consumer applications to process messages from a shared queue in a parallel and distributed manner.  The implementation of this messaging pattern requires the utilization of a point-to-point channel. Senders deliver messages (or work items) to this channel and consumers compete with each other to be the receiver and process the message (i.e., perform some work based on the contents of the message) in an asynchronous and non-blocking manner.

How Does It Work?

Usually, a message broker (e.g., RabbitMQ, ActiveMQ) or a distributed streaming platform (e.g., Apache Kafka) is needed in order to provide the necessary infrastructure to implement this pattern. Producer applications connect to the broker and push messages to a queue (or a Kafka Topic with multiple partitions implemented as point-to-point channels). Then, consumer applications that are also connected to the broker,  pull items from the queue (or belong to the same Kafka Consumer Group and pick up items from a designated partition). Each message is effectively delivered initially to only one available consumer or remains in the queue till a consumer becomes available.

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Auto-Scaling a Spring Boot Native App With Nomad

Horizontal scaling and in particular auto-scaling is a common challenge for many applications no matter their architectural approach (monolithic or microservices) and the deployment environment (cloud or on-premises). Today's Cloud providers provide a rich variety of options and tools to accommodate this critical requirement: to be able to scale services instances up or down, based on specified rules, in an efficient manner in order to: 

  • Avoid unavailability
  • Ensure the quality of service 
  • Optimize resources utilization
  • Avoid unnecessary charges

Many of the out-of-the-box solutions rely on Kubernetes and offer various levels of abstraction to make this process as smooth as possible. However, there are many cases where we don't really need or cannot afford all these cloud offerings as-a-service or we need to operate our own data center on-premises. As presented in a previous article "Look, Ma! No Pods!", HashiCorp Nomad is a simple and flexible deployment tool, able to manage both containers and non-containerized applications running on-prem, cloud or hybrid environments. Lately, Nomad provides auto-scaling functionality via the Nomad Autoscaler Agent. As described in the Nomad Autoscaler Overview, the following strategies are supported:

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Component Tests for Spring Cloud Microservices

Introduction

The shift towards microservices has a direct impact on the testing strategies applied and has introduced a number of complexities that need to be addressed. In fact, microservices require additional levels of testing since we have to deal with multiple independently deployable components.

An excellent explanation of these concepts and the various levels of microservices testing is given by Martin Fowler in his Testing Strategies in a Microservice Architecture presentation. Let's have a look at the revised "test pyramid" from this presentation:

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Look, Ma! No Pods!

Introduction

Release Automation is the cornerstone of modern Software Delivery. Well-established CI/CD pipelines are fundamental to every organization that wants to embrace DevOps/GitOps methodologies in order to achieve:

  • Frequent, rapid release cycles.
  • Fully automated deployment and rollback based on deployment strategies (Rolling Updates, Canary Releases, Blue/Green deployment).
  • Continuous-integration with automated testing.
  • More resources devoted to automated testing than manual testing.

The benefits of CI/CD for businesses undoubtedly should be the first priority no matter what kind of 'Agile' or other methodology is used for project management. Articles like Benefits of Continuous Integration for Businesses and IT Teams or Why the World Needs CI/CD, explain the importance for all stakeholders.

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Manage Hierarchical Data in MongoDB With Spring

Introduction

Modeling Tree Structures is one of the most common requirements often met in software applications. Depending on the size of the data, the information each node keeps, the type of queries performed, and the database storage engines available (or allowed), there are plenty of options to evaluate and choose from.

MongoDB allows various ways to use tree data structures to model large hierarchical or nested data relationships such as [1]:

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Full-Duplex Scalable Client-Server Communication with WebSockets and Spring Boot (Part I)

Introduction

In this article, we will be covering the steps to create a Java WebSocket server, powered by Spring Boot, that communicates with Java clients and supports load balancing across multiple instances. We’ll start by describing a use case scenario, analyzing the requirements, and then we’ll proceed to choose the technology stack that suits the case. We’ll implement the code, and finally test the application to get some performance and durability benchmarks.

Use Case: A Smart Home

The first thought we had when writing this, was to go with the classic demonstration of WebSockets usage that is the chat application. But if you were to search the internet about WebSocket examples, 99% of them are about chat apps. So we needed to come up with another idea, one that would be more fascinating and relevant to today’s technology, and would cover both point-to-point communication, as well as one-to-many broadcasting. And that is a smart home device network.