Gradle 3.1: Composite Builds, Here you Go!

Gradle 3.1: Composite Builds, Here you Go!

Tired of having your changing dependencies slow you down? Gradle 3.1’s composite builds provide you with a process of changing and refreshing your dependencies.

Our journey with Gradle started one and half years ago with this presentation. Everybody in the company just loved its expressive and easy-to-understand structure and realized that a plugin model was the wrong level of abstraction. Instead, language-based approaches were the right one in terms of their flexibility for the long term.

So it didn’t take long for almost 50 members of the development team to change the whole build infrastructure with Gradle. Gradle doesn’t just throw away the foundation that other build tools brought. Instead, it builds up easily and more powerfully on top of others while remaining 100% compatible with them. Therefore, this made Gradle not an alternative but an upgrade for us.

While we were migrating projects, simple pom.xml projects required only a few steps, such as; “gradle init,” but others needed more effort. Everything was good with Gradle except for one thing — “composite builds.”

And finally, our most-requested feature came out with Gradle 3.1, and now we love Gradle more than ever.

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High Performance Streaming Data Processing Implementation and Analysis

High Performance Streaming Data Processing Implementation and Analysis

Introduction

This article is more like a proof of concept of an implementation of microservice development, based on a real world implementation of a specific project build on top of integration of two stock markets, Nasdaq and BIST. The project requires different type of approaches and know-how which can be grouped into two categories, stream data api implementation and data analysis. Both sides have different challenges as they have different requirements.
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Theme, User Story, Epic

Theme, User Story, Epic

After Scrum methodology settled into the organizational culture, there are some issues that cause confusion and communication problems. First of all, we should not forget that the aim of all these descriptions are only for creating a common language in the organization. That is why, if you already have the common terminology for these definitions, it is alright to keep going with them. In this article I address the well accepted description of these concepts.

Just before defining the three main term, I want to emphasise two basic concepts;

Task & Feature

In real Scrum lifecycle, while creating Product Backlog, requirements are collected by Product Owner while asking the question of “What”. Each answer of this question is handled as Feature and associated with each other. This correlated packages of Features generate Wish List of the project.

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Docker Containers With Gradle Application Plugin

After trying several different approaches, we came up with what we think is the most elegant way of integrating Docker into our build tool Gradle.

Docker and Gradle have been around for a while, and there are many tutorials, blog posts, etc. related to best practices. After trying several different approaches, we came up with what we think is an elegant way of integrating Docker with our build tool Gradle. What follows is a simple and elegant integration of the two technologies.

First, let’s simply start with why we chose and how we can use Gradle’s Application plugin without getting into Docker yet. The Application plugin works hand in hand with Groovy, Scala, and Java plugins to create an executable JVM application. Using the Application plugin itself also implies application of the Distribution plugin. So, when it comes to deciding what plugin to use for making executables, the Application plugin is the most official way of doing things.

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Getting Started Deep Learning Revolution with Java

AI and deep learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. It is the technology behind self-driven cars, intelligent personal assistant computers, and decision support systems. Deep learning algorithms are being used across a broad range of industries. As the fundamental driver of AI, being able to tackle deep learning with Java is going to be a vital and valuable skill, not only within the tech world, but also for the wider global economy that depends upon knowledge and insight for growth and success.

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Ruby on Rails Development Life-cycle on Docker Containers

Before the containers have been integrated with the applications, the RoR application’s deployments are managed either manually or maintaining more handy tool such as Capistrano. Either way, there are a couple of required procedures needs to be applied on every new change set of the source code wanted to be deployed as a version.

Administration point of view, RoR applications are threated as file based applications, similar to PHP.  Unlike Java or Go, there is not one binary/archived deployable artifact. Therefore, every single change set contains several files and directories which leads the deployment ( directly or indirectly ) a process between SCM and the destination servers. Capistrano handles this quite well, especially In case of any deployment error, there is this automated rollback capability which tries to keep all the target nodes in the cluster on the same version.
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Type-Safe and Clean Coding: The Benefits of Type-Inference

Type-Safe and Clean Coding: The Benefits of Type-Inference

Type-interference is a great programming feature that helps coders write clean, readable code in a reasonable amount of time. Learn more here.

Writing type-safe language while maintaining less boilerplate code is an important aspect of programming languages in terms of developer’s productivity. Because type-safe code is less error-prone and less boilerplate code leads to more readable code, both together means reduced development time. Type-inference is a great programming language feature that maintains this balance.

Developers usually read more often than write. Thus, even if the source code will end up being processed by the computer, most of the time our focus is putting it into more human-readable form. At some point, we pay attention to the UX principles, like:

  • Humans have a limited attention span, so source code should help to spend this attention wisely. Information comes at a cost, so the longer the code is, the more overwhelming it is to read.
  • Sometimes, less means more. Short code may look brilliant, but it amplifies the time that others must spend on it. We should provide just enough information. Implicit values, if we do not abuse them, are the fix for this.

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Reactive Microservices and Service Discovery with Vert.x

Vert.x can lend a hand with helping your microservices find each other. See how to get it set up and what it can do for your software.

Remember the Unix philosophy “Do one thing and do it well?” That is the philosophy of microservices. In software development, it is a common practice that when the same functionality is seen in the different parts of the application, it is abstracted away as another component.

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Launching Vert.x Dynamically

Launching Vert.x Dynamically

This article shows you how to launch Vert.x, the toolkit for creating reactive apps on the JVM, in a dynamic way.

Vert.x started back in 2011 and it was one of the first projects to push the reactive microsystem model of the modern applications that need to handle a lot of concurrency. Since back then, people have developed best practices from writing good quality code using Rxfied Vert.x, RxJava’s Observable, and JoinObservable to its deployment using Docker, Kubernetes, or Swarm. Vert.x does not restrict developers to obey certain rules and standards, therefore, it is a better fit for our current Agile environments and Lean Entreprises. Thus, Developers like us, who are keen on freedom, can try new ways of doing things. With that in mind, we did not want to launch our microservices in statically defined ways. So in this article, I want to introduce how we launch the Vert.x in a dynamic way and in the coming days we want to publish series of articles about how we use brand new methods related to things like service discovery and deployment.

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Location Transparency With Vert.x

See how Vert.X, its Service Directory component and its eventbus work to get services talking to one another on both single or multiple JVMs.

In my previous article, I explained the Service Discovery in Vert.x and introduced an example of transparent remoting using Service Discovery. Transparent Remoting is a remote method invocation that looks like a local method invocation. In the other words, we have a plain Java interface and its proxy implementation at the client side. In the meantime, we have stub at the server side, where the actual implementation runs. With Service Discovery in Vert.x, we can obtain service references using its service name so that we no longer need to care whether a service runs locally or remotely. However, Location Transparency in Vert.x is a very important topic, and I am going to explain it in detail in this article.

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