Tag Archives: Microservices

Several traditional ORMs do not fully honor Java module encapsulation (JPMS). This often means a lot of unnecessary files and classes are used when deploying microservices. The latest major release 3.2 of open-sourceSpeedment solves this problem by introducing module system support allowing efficient deployment of cloud applications and providing a more stringent API. In this article, you will learn what’s ...

Publish-Subscribe Messaging systems play an important role in any enterprise architecture as it enables reliable integration without tightly coupling the applications. The ability to share data between decoupled systems is not a problem that is easily tackled. Consider an enterprise with multiple applications that are being built independently, with different languages and platforms. It needs to share data and processes ...

In the previous posts we build few micro services (Order Service, Product Service, Customer Service) for an order management system using Docker and Spring Boot. We used Netflix libraries to manage, discover and load balance the micro services. Managing these micro services and their multiple containers could get a bit tricky; especially in terms of Scalability, Observability, Security, Monitoring and ...

1. Introduction In this last part of the tutorial we are going to talk about the topic where all the observability pillars come together: monitoring and alerting. For many, this subject belongs strictly to operations and the only way you know it is somehow working is when you are on-call and get pulled in. Table Of Contents 1. Introduction 2. ...

Building a recommendation system with Spark is a simple task. Spark’s machine learning library already does all the hard work for us. In this study I will show you how to build a scalable application for Big Data using the following technologies: Scala LanguageSpark with Machine LearningAkka with ActorsCassandra A recommendation system is an information filtering mechanism that attempts to ...

Microservice architectures are not free lunch! Microservices need to be decoupled, flexible, operationally transparent, data aware and elastic. Most material from last years only discusses point-to-point architectures with tightly coupled and non-scalable technologies like REST / HTTP. This blog post takes a look at cutting edge technologies like Apache Kafka, Kubernetes, Envoy, Linkerd and Istio to implement a cloud-native service ...

1. Introduction This part of the tutorial is going to conclude the observability discussions by dissecting its last pillar, distributed tracing. Distributed tracing, also called distributed request tracing, is a method used to profile and monitor applications, especially those built using a microservices architecture. Distributed tracing helps pinpoint where failures occur and what causes poor performance. – https://opentracing.io/docs/overview/what-is-tracing/ In distributed ...

When discussing the development impact on existing applications while 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. These articles cover questions that everyone ...

When discussing the development impact on existing applications while 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. These articles cover questions that everyone ...