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PostgreSQL as an Extensible RDBMS

This article by Usama Dar , the author of the book PostgreSQL Server Programming - Second Edition , explains the process of creating a new operator, overloading it, optimizing it, creating index access methods, and much more. PostgreSQL is an extensible database. I hope you've learned this much by now. It is extensible by virtue of the design that it has. As discussed before, PostgreSQL uses a catalog-driven design. In fact, PostgreSQL is more catalog-driven than most of the traditional relational databases. The key benefit here is that the catalogs can be changed or added to, in order to modify or extend the database functionality. PostgreSQL also supports dynamic loading, that is, a user-written code can be provided as a shared library, and PostgreSQL will load it as required.

Creating a Brick Breaking Game

Have you ever thought about procedurally generated levels? Have you thought about how this could be done, how their logic works, and how their resources are managed? With our example bricks game, you will get to the core point of generating colors procedurally for each block, every time the level gets loaded. Physics has always been a huge and massively important topic in the process of developing a game. However, a brick breaking game can be made in many ways and using the many techniques that the engine can provide, but I choose to make it a physics-based game to cover the usage of the new, unique, and amazing component that Epic has recently added to its engine. The Projectile component is a physics-based component for which you can tweak many attributes to get a huge variation of behaviors that you can use with any game genre. By the end of this article by Muhammad A.Moniem , the author of Learning Unreal Engine iOS Game Development , you will be able to: Build your first multicomponent blueprints Understand more about the game modes Script a touch input Understand the Projectile component in depth Build a simple emissive material Use the dynamic material instances Start using the construction scripts Detect collisions Start adding sound effects to the game Restart a level Have a fully functional gameplay

Introducing Splunk

In this article by Betsy Page Sigman , author of the book Splunk Essentials , Splunk , whose "name was inspired by the process of exploring caves, or splunking, helps analysts, operators, programmers, and many others explore data from their organizations by obtaining, analyzing, and reporting on it. This multinational company, cofounded by Michael Baum, Rob Das, and Erik Swan, has a core product called " Splunk Enterprise . This manages searches, inserts, deletes, and filters, and analyzes big data that is generated by machines, as well as other types of data. "They also have a free version that has most of the capabilities of Splunk Enterprise and is an excellent learning tool.

Going beyond Zabbix agents

In this article by Andrea Dalle Vacche and Stefano Kewan Lee , author of Zabbix Network Monitoring Essentials , we will learn the different possibilities Zabbix offers to the enterprising network administrator. There are certainly many advantages in using Zabbix's own agents and protocol when it comes to monitoring Windows and Unix operating systems or the applications that run on them. However, when it comes to network monitoring, the vast majority of monitored objects are network appliances of various kinds, where it's often impossible to install and run a dedicated agent of any type. This by no means implies that you'll be unable to fully leverage Zabbix's power to monitor your network. Whether it's a simple ICMP echo request, an SNMP query, an SNMP trap, netflow logging, or a custom script, there are many possibilities to extract meaningful data from your network. This section will show you how to set up these different methods of gathering data, and give you a few examples on how to use them.

Basics of Programming in Julia

 In this article by Ivo Balbaert , author of the book Getting Started with Julia Programming , we will explore how Julia interacts with the outside world, reading from standard input and writing to standard output, files, networks, and databases. Julia provides asynchronous networking I/O using the libuv library. We will see how to handle data in Julia. We will also discover the parallel processing model of Julia. In this article, the following topics are covered: Working with files (including the CSV files) Using DataFrames

Performance Considerations

In this article by Dayong Du , the author of Apache Hive Essentials , we will look at the different performance considerations when using Hive. Although Hive is built to deal with big data, we still cannot ignore the importance of performance. Most of the time, a better Hive query can rely on the smart query optimizer to find the best execution strategy as well as the default setting best practice from vendor packages. However, as experienced users, we should learn more about the theory and practice of performance tuning in Hive, especially when working in a performance-based project or environment. We will start from utilities available in Hive to find potential issues causing poor performance. Then, we introduce the best practices of performance considerations in the areas of queries and job.

Time Travelling with Spring

This article by Sujoy Acharya , the author of the book Mockito for Spring , delves into the details Time Travelling with Spring. Spring 4.0 is the Java 8-enabled latest release of the Spring Framework. In this article, we'll discover the major changes in the Spring 4.x release and the four important features of the Spring 4 framework. We will cover the following topics in depth: @RestController AsyncRestTemplate Async tasks Caching

MapReduce functions

 In this article, by John Zablocki , author of the book, Couchbase Essentials , you will be acquainted to MapReduce and how you'll use it to create secondary indexes for our documents. At its simplest, MapReduce is a programming pattern used to process large amounts of data that is typically distributed across several nodes in parallel. In the NoSQL world, MapReduce implementations may be found on many platforms from MongoDB to Hadoop, and of course, Couchbase. Even if you're new to the NoSQL landscape, it's quite possible that you've already worked with a form of MapReduce. The inspiration for MapReduce in distributed NoSQL systems was drawn from the functional programming concepts of map and reduce. While purely functional programming languages haven't quite reached mainstream status, languages such as Python, C#, and JavaScript all support map and reduce operations.

Speeding Vagrant Development With Docker

In this article by Chad Thompson , author of Vagrant Virtual Development Environment Cookbook , we will learn that many software developers are familiar with using Vagrant ( to distribute and maintain development environments. In most cases, Vagrant is used to manage virtual machines running in desktop hypervisor software such as VirtualBox or the VMware Desktop product suites. (VMware Fusion for OS X and VMware Desktop for Linux and Windows environments.) More recently, Docker ( has become increasingly popular for deploying containers—Linux processes that can run in a single operating system environment yet be isolated from one another. In practice, this means that a container includes the runtime environment for an application, down to the operating system level. While containers have been popular for deploying applications, we can also use them for desktop development. Vagrant can use Docker in a couple of ways: As a target for running a process defined by Vagrant with the Vagrant provider. As a complete development environment for building and testing containers within the context of a virtual machine. This allows you to build a complete production-like container deployment environment with the Vagrant provisioner. In this example, we'll take a look at how we can use the Vagrant provider to build and run a web server. Running our web server with Docker will allow us to build and test our web application without the added overhead of booting and provisioning a virtual machine.

Basic SQL Server Administration

 In this article by Donabel Santos , the author of PowerShell for SQL Server Essentials , we will look at how to accomplish typical SQL Server administration tasks by using PowerShell. Many of the tasks that we will see can be accomplished by using SQL Server Management Objects ( SMO ). As we encounter new SMO classes, it is best to verify the properties and methods of that class using Get-Help , or by directly visiting the TechNet or MSDN website.

Packaged Elegance

In this article by John Farrar , author of the book KnockoutJS Web development , we will see how templates drove us to a more dynamic, creative platform. The next advancement in web development was custom HTML components. KnockoutJS allows us to jump right in with some game-changing elegance for designers and developers. In this article, we will focus on: An introduction to components Bring Your Own Tags ( BYOT ) Enhancing attribute handling Making your own libraries Asynchronous module definition ( AMD )—on demand resource loading This entire article is about packaging your code for reuse. Using these techniques, you can make your code more approachable and elegant.

SciPy for Signal Processing

In this article by Sergio J. Rojas G. and Erik A Christensen , authors of the book Learning SciPy for Numerical and Scientific Computing , Second Edition , we will focus on the usage of some most commonly used routines that are included in SciPy modules— scipy.signal , scipy.ndimage , and scipy.fftpack , which are used for signal processing, multidimensional image processing, and computing Fourier transforms, respectively. We define a signal as data that measures either a time-varying or spatially varying phenomena. Sound or electrocardiograms are excellent examples of time-varying quantities, while images embody the quintessential spatially varying cases. Moving images are treated with the techniques of both types of signals, obviously. The field of signal processing treats four aspects of this kind of data: its acquisition, quality improvement, compression, and feature extraction. SciPy has many routines to treat effectively tasks in any of the four fields. All these are included in two low-level modules ( scipy.signal being the main module, with an emphasis on time-varying data, and scipy.ndimage , for images). Many of the routines in these two modules are based on Discrete Fourier Transform of the data. SciPy has an extensive package of applications and definitions of these background algorithms, scipy.fftpack , which we will start covering first.

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