JavaWorld News
Most of us ought to know the acronym LAMP, used to describe web services stacks made with Linux, the Apache web server, the MySQL database server, and either PHP, Perl, or Python.
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GitHub has introduced a workflow tool called GitHub Actions to its popular code-sharing site, to allow continuous integration/continuous deployment (CI/CD) right from GitHub itself.
Using the tool, which is now in limited beta, developers can build, deploy, and update software projects on either GitHub or an external system without having to run code themselves. Workflows and infrastructure deployments can be expressed as code.
Actions adds customizable workflow capabilities to GitHub.com, so developers build and share code containers to run a software development workflow, even across multiple clouds. Other examples of tasks that can be done with actions include packaging an NPM module or sending an SMS alert.
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Request handling is the bread and butter of Java web application development. In order to respond to requests from the network, a Java web application must first determine what code will respond to the request URL, then marshal a response. Every technology stack has a way of accomplishing request-response handling. In Java, we use servlets (and the Java Servlet API) for this purpose. Think of a servlet as a tiny server whose job is to accept requests and issue responses.
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One of the last computing chores to be sucked into the cloud is data analysis. Perhaps it’s because scientists are naturally good at programming and so they enjoy having a machine on their desks. Or maybe it’s because the lab equipment is hooked up directly to the computer to record the data. Or perhaps it’s because the data sets can be so large that it’s time-consuming to move them.
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Swift, Apple’s language for MacOS and iOS development, is challenging for a permanent position in the Top 10 of the Tiobe index of programming language popularity, but it has competition from Google’s Go language (Golang) and the R language. All three languages, however, face obstacles to their ascendance in the index.
The four-year-old Swift was in tenth place in the index in October 2018. Swift has ranked this high before but it never scaled above tenth place. Swift’s inabiity to build Google Android applications has had developers instead using cross-platform frameworks, capping Swift’s rach, software quality services vendor Tiobe said.
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We’re awash in increasingly sophisticated machine learning technologies. Too bad so few people know how to use them.
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In the first half of this JavaWorld introduction to Apache Kafka, you developed a couple of small-scale producer/consumer applications using Kafka. From these exercises you should be familiar with the basics of the Apache Kafka messaging system. In this second half, you’ll learn how to use partitions to distribute load and scale your application horizontally, handling up to millions of messages per day. You’ll also learn how Kafka uses message offsets to track and manage complex message processing, and how to protect your Apache Kafka messaging system against failure should a consumer go down. We’ll develop the example application from Part 1 for both publish-subscribe and point-to-point use cases.
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When the big data movement started it was mostly focused on batch processing. Distributed data storage and querying tools like MapReduce, Hive, and Pig were all designed to process data in batches rather than continuously. Businesses would run multiple jobs every night to extract data from a database, then analyze, transform, and eventually store the data. More recently enterprises have discovered the power of analyzing and processing data and events as they happen, not just once every few hours. Most traditional messaging systems don’t scale up to handle big data in realtime, however. So engineers at LinkedIn built and open-sourced Apache Kafka: a distributed messaging framework that meets the demands of big data by scaling on commodity hardware.
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The Eclipse Foundation now has received all Java EE (Enterprise Edition) reference implementation components from Oracle, as part of the foundation’s takeover of the enterprise Java platform.
Oracle has contributed 100 percent of EE and GlassFish application server components to the foundation. GlassFish has served as a reference implementation of Java EE, which has been renamed Jakarta EE under Eclipse’s jurisdiction. The foundation said that it now had all the components in hand, which have been published to GitHub repositories. What this means is progress of the individual projects under Eclipse’s enterprise Java effort now is largely under control of the projects themselves.
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Today, there are many data sources—such at IoT devices, user interaction events from mobile applications, financial service transactions, and health monitoring systems—that broadcast critical information in real time. Developers working with these data sources need to think about the architecture to capture real time streaming data at varying scales and complexities.
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