Java Challengers #4: Comparing Java objects with equals() and hashcode()

In this Java Challenger you’ll learn how equals() and hashcode() combine to make object comparisons efficient and easy in your Java programs. Simply put, these methods work together to verify if two objects have the same values.  

Without equals() and hashcode() we would have to create very large “if” comparisons, comparing every field from an object. This would make code really confusing and hard to read. Together, these two methods help us create more flexible and cohesive code.

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Removed from JDK 11, JavaFX 11 arrives as a standalone module

JavaFX 11, the first standalone release of the Java-based rich client technology, is now available. Oracle is removing JavaFX from the Java Development Kit (JDK) 11, given an overall desire to pull out noncore modules from the JDK and retire them or stand them up as independent modules.

The open source JavaFX 11 provides a client application platform for desktop, mobile, and embedded systems. JavaFX is a runtime available as a platform-specific SDK, as jmod files, and as a set of Maven central artifacts. With the JDK no longer including JavaFX, developers must explicitly include JavaFX modules in applications.

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How to transition to a microservices architecture

By this point, you know what microservices are and how they work. Now it’s time to get down to brass tacks: namely, the very critical topic of how to approach the transition to microservices.

The need for microservices transition

A monolithic application is very large (in terms of lines of code) and complex (in terms of functions interdependencies, data, etc.), serving hundreds of thousands of users across geographical regions and requiring several developers and IT engineers. A monolithic app may look something like Figure 1.

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Business can’t win without developers, but you need more

Money can’t buy you happiness, but developers just might. According to a new survey from Stripe, companies finally recognize that access to engineering talent is a bigger inhibitor to growth than access to capital. In fact, as fed up as enterprises may be with their outdated IT infrastructure, they’re convinced that if they can just find good developers, most other problems will prove secondary.

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Oracle forges a Java microservices framework

Oracle has introduced Project Helidon, an open source microservices framework for Java.

Helidon features a collection of Java libraries for writing microservices that will run on a web core powered by the Netty network application framework. The project also includes Helidon Reactive WebServer, which provides a functional programming model to run on Netty. Cloud application development is supported, along with health checks, metrics, tracing, and fault tolerance.

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What is the JRE? Introduction to the Java Runtime Environment

Together, the Java Development Kit (JDK), the Java Virtual Machine (JVM), and the Java Runtime Environment (JRE) form a powerful trifecta of Java platform components for developing and running Java applications. I’ve previously introduced the JDK and JVM, and in this quick tutorial you’ll learn about the JRE, which is the runtime environment for Java.

Practically speaking, a runtime environment is a piece of software that is designed to run other software. As the runtime environment for Java, the JRE contains the Java class libraries, the Java class loader, and the Java Virtual Machine. In this system:

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(Insider Story)
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10 machine learning APIs developers will love

It wasn’t too long ago that you needed to put on a white lab coat to work with artificial intelligence. The science was arcane, complex, and something that very few human intelligences could grok.

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Machine learning: When to use each method and technique

You’re probably hearing more and more about machine learning, a subset of artificial intelligence. But what exactly can you do with machine learning?

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Kubeflow brings Kubernetes to machine learning workloads

Now in beta, the open source Kubeflow project aims to help deploy a machine learning stack on the Kubernetes container orchestration system.

The Kubeflow machine learning toolkit project is intended to help deploy machine learning workloads across multiple nodes but where breaking up and distributing a workload can add computational overhead and complexity. Kubernetes itself is tasked with making it easier to manage distributed workloads, while Kubeflow centers on making the running of these workloads portable, scalable, and simple. Scripts and configuration files are part of the project. Users can customize their configuration and run scripts to deploy containers to a chosen environment.

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