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000-610 exam Dumps Source : DB2 10.1 Fundamentals

Test Code : 000-610
Test appellation : DB2 10.1 Fundamentals
Vendor appellation : IBM
: 138 real Questions

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IBM DB2 10.1 Fundamentals

beginning DB2: From dabbler to professional | killexams.com real Questions and Pass4sure dumps

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Altova Introduces edition 2014 of Its Developer tools and Server software | killexams.com real Questions and Pass4sure dumps

BEVERLY, MA--(Marketwired - Oct 29, 2013) - Altova® (http://www.altova.com), creator of XMLSpy®, the trade leading XML editor, nowadays introduced the unencumber of version 2014 of its MissionKit® computer developer tools and server application items. MissionKit 2014 products now consist of integration with the lightning quickly validation and processing capabilities of RaptorXML®, advocate for Schema 1.1, XPath/XSLT/XQuery 3.0, lead for brand original databases and an terrible lot extra. original features in Altova server products consist of caching alternate options in FlowForce® Server and multiplied performance powered via RaptorXML throughout the server product line.

"we're so excited to subsist in a position to extend the hyper-performance delivered via the unparalleled RaptorXML Server to builders working in their laptop tools. This performance, together with unbelievable wait on for the very newest requisites, from XML Schema 1.1 to XPath 3.0 and XSLT 3.0, offers their valued clientele the advantages of accelerated efficiency alongside cutting-aspect technology help," spoke of Alexander Falk, President and CEO for Altova. "This, coupled with the skill to automate basic techniques by route of their excessive-performance server items, offers their valued clientele a determined talents when building and deploying purposes."

a pair of of the brand original facets available in Altova MissionKit 2014 consist of:

Integration of RaptorXML: introduced previous this yr, RaptorXML Server is excessive-performance server software capable of validating and processing XML at lightning speeds -- whereas delivering the strictest viable standards conformance. Now the equal hyper-efficiency engine that powers RaptorXML Server is thoroughly integrated in several Altova MissionKit tools, including XMLSpy, MapForce®, and SchemaAgent®, supplying lightning quick validation and processing of XML, XSLT, XQuery, XBRL, and extra. The third-technology validation and processing engine from Altova, RaptorXML changed into built from the floor as much as advocate the very newest of any central XML requirements, including XML Schema 1.1, XSLT three.0, XPath three.0, XBRL 2.1, and myriad others.

guide for Schema 1.1: XMLSpy 2014 comprises crucial advocate for XML Schema 1.1 validation and enhancing. The latest edition of the XML Schema usual, 1.1 adds original features geared toward making schemas greater supple and adaptable to company instances, corresponding to assertions, conditional varieties, open content, and extra.

All elements of XML Schema 1.1 are supported in XMLSpy's graphical XML Schema editor and are available in entry helpers and tabs. As any the time, the graphical editing paradigm of the schema editor makes it convenient to subsist mindful and implement these original facets.

aid for XML Schema 1.1 is additionally provided in SchemaAgent 2014, permitting users to imagine and manage schema relationships by means of its graphical interface. this is likewise an advantage when connecting to SchemaAgent in XMLSpy.

Coinciding with XML Schema 1.1 assist, Altova has additionally released a free, online XML Schema 1.1 expertise practising direction, which covers the basics of the XML Schema language as well because the adjustments added in XML Schema 1.1.

support for XPath three.0, XSLT 3.0, and XQuery three.0:

assist for XPath in XMLSpy 2014 has been up-to-date to embrace the newest version of the XPath suggestion. XPath three.0 is a superset of the XPath 2.0 recommendation and adds potent original performance similar to: dynamic characteristic cells, inline feature expressions, and advocate for union varieties to appellation simply a number of. Full lead for brand original services and operators added in XPath three.0 is accessible via clever XPath auto-completion in textual content and Grid Views, as well as in the XPath Analyzer window.

support for enhancing, debugging, and profiling XSLT is now attainable for XSLT 3.0 in addition to brokendown versions. please solemnize that a subset of XSLT 3.0 is supported considering the fact that the common remains a working draft that continues to adapt. XSLT three.0 assist conforms to the W3C XSLT 3.0 Working Draft of July 10, 2012 and the XPath 3.0 Candidate advice. despite the fact, aid in XMLSpy now gives developers the capacity to delivery working with this original edition automatically.

XSLT three.0 takes expertise of the brand original facets brought in XPath 3.0. in addition, a massive feature enabled with the aid of the original version is the original xsl:are trying / xsl:trap construct, which will likewise subsist used to trap and improve from dynamic errors. other enhancements in XSLT three.0 encompass wait on for bigger order services and partial functions.

Story continues

As with XSLT and XPath, XMLSpy assist for XQuery now additionally comprises a subset of edition three.0. builders will now devour the altenative to edit, debug, and profile XQuery 3.0 with effective syntax coloring, bracket matching, XPath auto-completion, and other bright editing elements.

XQuery three.0 is, of path, an extension of XPath and for this intuition merits from the original functions and operators introduced in XPath three.0, akin to a original string concatenation operator, map operator, math capabilities, sequence processing, and greater -- any of which are available within the context sensitive entry helper home windows and drop down menus in the XMLSpy 2014 XQuery editor.

New Database guide:

Database-enabled MissionKit items including XMLSpy, MapForce, StyleVision®, DatabaseSpy®, UModel®, and DiffDog®, now encompass complete advocate for more recent models of prior to now supported databases, as well as advocate for brand original database vendors:

  • Informix® eleven.70
  • PostgreSQL versions 9.0.10/9.1.6/9.2.1
  • MySQL® 5.5.28
  • IBM DB2® models 9.5/9.7/10.1
  • Microsoft® SQL Server® 2012
  • Sybase® ASE (Adaptive Server enterprise) 15/15.7
  • Microsoft entry™ 2010/2013
  • New in Altova Server application 2014:

    introduced prior in 2013, Altova's original line of pass-platform server application items comprises FlowForce Server, MapForce Server, StyleVision Server, and RaptorXML Server. FlowForce Server gives finished management, job scheduling, and security options for the automation of yardstick enterprise procedures, whereas MapForce Server and StyleVision Server present high-speed automation for projects designed using prevalent Altova MissionKit developer equipment. RaptorXML Server is the third-era, hyper-fast validation and processing engine for XML and XBRL.

    beginning with edition 2014, Altova server items are powered via RaptorXML for faster, greater efficient processing. furthermore, FlowForce Server now helps outcomes caching for jobs that require a very long time to method, for example when a job requires advanced database queries or should build its personal internet provider information requests. FlowForce Server administrators can now schedule execution of a time-drinking job and cache the results to withhold away from these delays. The cached facts can then subsist supplied when any user executes the job as a provider, delivering rapid effects. A job that generates a customized earnings report for the previous day could subsist a very distinguished software for caching.

    These and a lot of greater points can subsist organize within the 2014 version of MissionKit desktop developer tools and Server software. For a complete checklist of original facets, supported necessities, and tribulation downloads please consult with: http://www.altova.com/whatsnew.html

    About Altova Altova® is a utility company that specialize in gear to advocate developers with records administration, utility and utility development, and information integration. The creator of XMLSpy® and other award-successful XML, SQL and UML tools, Altova is a key player within the software tools traffic and the chief in XML solution construction equipment. Altova focuses on its shoppers' wants by means of offering a product line that fulfills a huge spectrum of requirements for software building groups. With over four.5 million clients international, including 91% of Fortune 500 groups, Altova is disdainful to serve valued clientele from one-person shops to the world's biggest businesses. Altova is dedicated to delivering requisites-primarily based, platform-impartial options which are effective, low-cost and straightforward-to-use. founded in 1992, Altova is headquartered in Beverly, Massachusetts and Vienna, Austria. discuss with Altova on the internet at: http://www.altova.com.

    Altova, MissionKit, XMLSpy, MapForce, FlowForce, RaptorXML, StyleVision, UModel, DatabaseSpy, DiffDog, SchemaAgent, genuine, and MetaTeam are trademarks and/or registered logos of Altova GmbH within the u.s. and/or different international locations. The names of and reference to other companies and products outlined herein can subsist the logos of their respective house owners.


    MySQL stored process Programming | killexams.com real Questions and Pass4sure dumps

    Written by route of guy Harrison and Steven Feuerstein, and published via O'Reilly Media in March 2006 under the ISBNs 0596100892 and 978-0596100896, this ebook is the primary one to proffer database programmers a complete discussion of the syntax, utilization, and optimization of MySQL kept procedures, saved capabilities, and triggers — which the authors accurately check with jointly as "kept courses," to simplify the manuscript. Even a year after the introduction of those original capabilities in MySQL, they devour got obtained remarkably shrimp coverage with the aid of book publishers. Admittedly, there are three such chapters in MySQL Administrator's e-book and Language Reference (2nd version), written by route of one of the vital builders of MySQL, and published by means of MySQL Press. Yet this latter e-book — even if published a month after O'Reilly's — devotes fewer than 50 pages to stored courses, and the cloth isn't within the printed e-book itself, but within the "MySQL Language Reference" part, on the accompanying CD. That fabric, along with the on-line reference documentation, may well subsist enough for the more basic stored program construction wants. however for any MySQL developer who wishes to tolerate in intelligence in-depth the route to build the most of this original functionality in version 5.0, they'll probably want a much more gargantuan treatment — and that's precisely what Harrison and Feuerstein devour created.

    The authors are generous in both the technical information and construction tips that they offer. The ebook's fabric spans 636 pages, equipped into 23 chapters, grouped into four components, followed via an index. the first part, "saved Programming Fundamentals," offers an introduction after which an instructional, both taking a huge view of MySQL kept courses. The ultimate four chapters cowl language fundamentals; blocks, conditional statements, and iterative programming; SQL; and oversight coping with. The publication's 2d half, "saved program construction," can subsist considered the coronary heart of the book, as a result of its five chapters latest the details of developing saved courses in general, the expend of transaction management, using MySQL's built-in functions, and creating one's own stored functions, in addition to triggers. The third half, "the expend of MySQL stored programs and functions," explains one of the most merits and disadvantages of stored courses, and then illustrates a route to appellation these stored classes from source code written in any one of 5 diverse programming languages: php, Java, Perl, Python, and Microsoft.web. within the fourth and closing part, "Optimizing kept classes," the authors headquarters of attention on the safety and tuning of saved programs, tuning SQL, optimizing the code, and optimizing the evolution system itself.

    this is a substantial e-book, encompassing a fine deal of technical as well as advisory tips. as a result, no overview similar to this may hope to elucidate or seriously remark upon every component to every chapter of every half. Yet the habitual nice and utility of the manuscript may likewise subsist discerned simply through picking only 1 of the aforesaid internet programming languages, and writing some code in that language to call some MySQL kept tactics and capabilities, to accumulate consequences from a test database — and setting up any of this code while relying fully upon the ebook under evaluate. growing some yardstick kept procedures, and calling them from some Hypertext Preprocessor and Perl scripts, verified to me that MySQL kept technique Programming contains greater than enough coverage of the themes to subsist a useful ebook in developing the most dispassionate performance that a programmer would deserve to reclaim into effect.

    The ebook appears to devour very few features or particular sections in exigency of growth. The discussion of variable scoping, in Chapter 4, is too cursory (no database pun intended). when it comes to the booklet's sample code, I organize countless instances of inconsistency of formatting — mainly, operators similar to "||" and "=" being jammed up against their adjacent aspects, without any whitespace to enhance readability. These minor flaws may subsist effectively remedied in the next version. Some programming books build equivalent blunders, but any over their text, which is even worse. fortunately, many of the code in this book is neatly formatted, and the variable and application names are commonly descriptive satisfactory.

    one of the crucial publication's fabric could devour been disregarded without high-quality loss — thereby decreasing the book's dimension, weight, and possibly price. the two chapters on simple and superior SQL tuning contain innovations and suggestions lined with equal skill in other MySQL books, and were not necessary in this one. on the other hand, unkempt developers who churn out lamentable code may squabble that the ultimate chapter, which makes a speciality of most excellent programming practices, could likewise subsist excised; however those are the very people who want these recommendations probably the most.

    fortunately, the few weaknesses in the publication are completely overwhelmed via its wonderful traits, of which there are lots of. The coverage of the topic matters is degree wide, but without the repetition frequently considered in many different technical books of this dimension. the explanations are written with clarity, and provide ample aspect for any skilled database programmer to buy into account the conventional ideas, as well because the selected details. The sample code without problems illustrates the ideas offered in the narration. The font, design, company, and fold-flat binding of this ebook, any build it a pleasure to read — as is characteristic of many of O'Reilly's titles.

    in addition, any programming publication that manages to lighten the load of the reader by route of providing a splash of humor privilege here and there, can't subsist any bad. Steven Feuerstein is the creator of a pair of well-considered books on Oracle, and it become distinguished to peer him poke some enjoyable on the database heavyweight, in his altenative of pattern code to demonstrate the my_replace() function: my_replace( 'we appreciate the Oracle server', 'Oracle', 'MySQL').

    The prospective reader who would want to learn extra about this publication, can check with its net web page on O'Reilly's web site. There they are going to learn both short and complete descriptions, established and unconfirmed errata, a link for writing a reader overview, an internet table of contents and index, and a pattern chapter (number 6, "Error dealing with"), in PDF structure. in addition, the traveler can download the entire sample code in the e-book (562 data) and the sample database, as a mysqldump file.

    general, MySQL saved system Programming is adeptly written, neatly geared up, and exhaustive in its coverage of the topics. it's and inescapable will remain the premier printed useful resource for internet and database developers who wish to learn the route to create and optimize stored methods, features, and triggers within MySQL.

    Michael J. Ross is a web programmer, freelance author, and the editor of PristinePlanet.com's free publication. He can subsist reached at www.ross.ws, hosted with the aid of SiteGround.


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    Altova Introduces Version 2014 of Its Developer Tools and Server Software | killexams.com real questions and Pass4sure dumps

    BEVERLY, MA--(Marketwired - Oct 29, 2013) - Altova® (http://www.altova.com), creator of XMLSpy®, the industry leading XML editor, today announced the release of Version 2014 of its MissionKit® desktop developer tools and server software products. MissionKit 2014 products now embrace integration with the lightning quick validation and processing capabilities of RaptorXML®, advocate for Schema 1.1, XPath/XSLT/XQuery 3.0, advocate for original databases and much more. original features in Altova server products embrace caching options in FlowForce® Server and increased performance powered by RaptorXML across the server product line.

    "We are so excited to subsist able to extend the hyper-performance delivered by the unparalleled RaptorXML Server to developers working in their desktop tools. This functionality, along with robust advocate for the very latest standards, from XML Schema 1.1 to XPath 3.0 and XSLT 3.0, provides their customers the benefits of increased performance alongside cutting-edge technology support," said Alexander Falk, President and CEO for Altova. "This, coupled with the aptitude to automate essential processes via their high-performance server products, gives their customers a sever advantage when building and deploying applications."

    A few of the original features available in Altova MissionKit 2014 include:

    Integration of RaptorXML: Announced earlier this year, RaptorXML Server is high-performance server software capable of validating and processing XML at lightning speeds -- while delivering the strictest viable standards conformance. Now the very hyper-performance engine that powers RaptorXML Server is fully integrated in several Altova MissionKit tools, including XMLSpy, MapForce®, and SchemaAgent®, delivering lightning quick validation and processing of XML, XSLT, XQuery, XBRL, and more. The third-generation validation and processing engine from Altova, RaptorXML was built from the ground up to advocate the very latest of any material XML standards, including XML Schema 1.1, XSLT 3.0, XPath 3.0, XBRL 2.1, and myriad others.

    Support for Schema 1.1: XMLSpy 2014 includes valuable advocate for XML Schema 1.1 validation and editing. The latest version of the XML Schema standard, 1.1 adds original features aimed at making schemas more supple and adaptable to traffic situations, such as assertions, conditional types, open content, and more.

    All aspects of XML Schema 1.1 are supported in XMLSpy's graphical XML Schema editor and are available in entry helpers and tabs. As always, the graphical editing paradigm of the schema editor makes it easy to understand and implement these original features.

    Support for XML Schema 1.1 is likewise provided in SchemaAgent 2014, allowing users to visualize and manage schema relationships via its graphical interface. This is likewise an advantage when connecting to SchemaAgent in XMLSpy.

    Coinciding with XML Schema 1.1 support, Altova has likewise released a free, online XML Schema 1.1 technology training course, which covers the fundamentals of the XML Schema language as well as the changes introduced in XML Schema 1.1.

    Support for XPath 3.0, XSLT 3.0, and XQuery 3.0:

    Support for XPath in XMLSpy 2014 has been updated to embrace the latest version of the XPath Recommendation. XPath 3.0 is a superset of the XPath 2.0 recommendation and adds powerful original functionality such as: dynamic function cells, inline function expressions, and advocate for union types to appellation just a few. Full advocate for original functions and operators added in XPath 3.0 is available through bright XPath auto-completion in Text and Grid Views, as well as in the XPath Analyzer window.

    Support for editing, debugging, and profiling XSLT is now available for XSLT 3.0 as well as previous versions. please note that a subset of XSLT 3.0 is supported since the yardstick is noiseless a working draft that continues to evolve. XSLT 3.0 advocate conforms to the W3C XSLT 3.0 Working Draft of July 10, 2012 and the XPath 3.0 Candidate Recommendation. However, advocate in XMLSpy now gives developers the aptitude to start working with this original version immediately.

    XSLT 3.0 takes advantage of the original features added in XPath 3.0. In addition, a major feature enabled by the original version is the original xsl:try / xsl:catch construct, which can subsist used to trap and retrieve from dynamic errors. Other enhancements in XSLT 3.0 embrace advocate for higher order functions and partial functions.

    Story continues

    As with XSLT and XPath, XMLSpy advocate for XQuery now likewise includes a subset of version 3.0. Developers will now devour the option to edit, debug, and profile XQuery 3.0 with helpful syntax coloring, bracket matching, XPath auto-completion, and other bright editing features.

    XQuery 3.0 is, of course, an extension of XPath and therefore benefits from the original functions and operators added in XPath 3.0, such as a original string concatenation operator, map operator, math functions, sequence processing, and more -- any of which are available in the context sensitive entry helper windows and drop down menus in the XMLSpy 2014 XQuery editor.

    New Database Support:

    Database-enabled MissionKit products including XMLSpy, MapForce, StyleVision®, DatabaseSpy®, UModel®, and DiffDog®, now embrace complete advocate for newer versions of previously supported databases, as well as advocate for original database vendors:

  • Informix® 11.70
  • PostgreSQL versions 9.0.10/9.1.6/9.2.1
  • MySQL® 5.5.28
  • IBM DB2® versions 9.5/9.7/10.1
  • Microsoft® SQL Server® 2012
  • Sybase® ASE (Adaptive Server Enterprise) 15/15.7
  • Microsoft Access™ 2010/2013
  • New in Altova Server Software 2014:

    Introduced earlier in 2013, Altova's original line of cross-platform server software products includes FlowForce Server, MapForce Server, StyleVision Server, and RaptorXML Server. FlowForce Server provides comprehensive management, job scheduling, and security options for the automation of essential traffic processes, while MapForce Server and StyleVision Server proffer high-speed automation for projects designed using chummy Altova MissionKit developer tools. RaptorXML Server is the third-generation, hyper-fast validation and processing engine for XML and XBRL.

    Starting with Version 2014, Altova server products are powered by RaptorXML for faster, more efficient processing. In addition, FlowForce Server now supports results caching for jobs that require a long time to process, for instance when a job requires intricate database queries or needs to build its own Web service data requests. FlowForce Server administrators can now schedule execution of a time-consuming job and cache the results to obviate these delays. The cached data can then subsist provided when any user executes the job as a service, delivering instant results. A job that generates a customized sales report for the previous day would subsist a distinguished application for caching.

    These and many more features are available in the 2014 Version of MissionKit desktop developer tools and Server software. For a complete list of original features, supported standards, and tribulation downloads please visit: http://www.altova.com/whatsnew.html

    About Altova Altova® is a software company specializing in tools to assist developers with data management, software and application development, and data integration. The creator of XMLSpy® and other award-winning XML, SQL and UML tools, Altova is a key player in the software tools industry and the leader in XML solution evolution tools. Altova focuses on its customers' needs by offering a product line that fulfills a broad spectrum of requirements for software evolution teams. With over 4.5 million users worldwide, including 91% of Fortune 500 organizations, Altova is disdainful to serve clients from one-person shops to the world's largest organizations. Altova is committed to delivering standards-based, platform-independent solutions that are powerful, affordable and easy-to-use. Founded in 1992, Altova is headquartered in Beverly, Massachusetts and Vienna, Austria. Visit Altova on the Web at: http://www.altova.com.

    Altova, MissionKit, XMLSpy, MapForce, FlowForce, RaptorXML, StyleVision, UModel, DatabaseSpy, DiffDog, SchemaAgent, Authentic, and MetaTeam are trademarks and/or registered trademarks of Altova GmbH in the United States and/or other countries. The names of and reference to other companies and products mentioned herein may subsist the trademarks of their respective owners.


    Unleashing MongoDB With Your OpenShift Applications | killexams.com real questions and Pass4sure dumps

    Current evolution cycles kisser many challenges such as an evolving landscape of application architecture (Monolithic to Microservices), the exigency to frequently deploy features, and original IaaS and PaaS environments. This causes many issues throughout the organization, from the evolution teams any the route to operations and management.

    In this blog post, they will point to you how you can set up a local system that will advocate MongoDB, MongoDB Ops Manager, and OpenShift. They will walk through the various installation steps and demonstrate how easy it is to conclude agile application evolution with MongoDB and OpenShift.

    MongoDB is the next-generation database that is built for rapid and iterative application development. Its supple data model — the aptitude to incorporate both structured or unstructured data — allows developers to build applications faster and more effectively than ever before. Enterprises can dynamically modify schemas without downtime, resulting in less time preparing data for the database, and more time putting data to work. MongoDB documents are more closely aligned to the structure of objects in a programming language. This makes it simpler and faster for developers to model how data in the application will map to data stored in the database, resulting in better agility and rapid development.

    MongoDB Ops Manager (also available as the hosted MongoDB Cloud Manager service) features visualization, custom dashboards, and automated alerting to wait on manage a intricate environment. Ops Manager tracks 100+ key database and systems health metrics including operations counters, CPU utilization, replication status, and any node status. The metrics are securely reported to Ops Manager where they are processed and visualized. Ops Manager can likewise subsist used to provide seamless no-downtime upgrades, scaling, and backup and restore.

    Red Hat OpenShift is a complete open source application platform that helps organizations develop, deploy, and manage existing and container-based applications seamlessly across infrastructures. Based on Docker container packaging and Kubernetes container cluster management, OpenShift delivers a high-quality developer undergo within a stable, secure, and scalable operating system. Application lifecycle management and agile application evolution tooling increase efficiency. Interoperability with multiple services and technologies and enhanced container and orchestration models let you customize your environment.

    Setting Up Your Test Environment

    In order to follow this example, you will exigency to meet a number of requirements. You will exigency a system with 16 GB of RAM and a RHEL 7.2 Server (we used an instance with a GUI for simplicity). The following software is likewise required:

  • Ansible
  • Vagrant
  • VirtualBox
  • Ansible Install

    Ansible is a very powerful open source automation language. What makes it unique from other management tools, is that it is likewise a deployment and orchestration tool. In many respects, aiming to provide great productivity gains to a wide variety of automation challenges. While Ansible provides more productive drop-in replacements for many core capabilities in other automation solutions, it likewise seeks to solve other major unsolved IT challenges.

    We will install the Automation Agent onto the servers that will become Part of the MongoDB replica set. The Automation Agent is Part of MongoDB Ops Manager.

    In order to install Ansible using yum you will exigency to enable the EPEL repository. The EPEL (Extra Packages for Enterprise Linux) is repository that is driven by the Fedora Special Interest Group. This repository contains a number of additional packages guaranteed not to replace or conflict with the groundwork RHEL packages.

    The EPEL repository has a dependency on the Server Optional and Server Extras repositories. To enable these repositories you will exigency to execute the following commands:

    $ sudo subscription-manager repos --enable rhel-7-server-optional-rpms $ sudo subscription-manager repos --enable rhel-7-server-extras-rpms

    To install/enable the EPEL repository you will exigency to conclude the following:

    $ wget https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm $ sudo yum install epel-release-latest-7.noarch.rpm

    Once complete you can install ansible by executing the following command:

    $ sudo yum install ansible Vagrant Install

    Vagrant is a command line utility that can subsist used to manage the lifecycle of a virtual machine. This implement is used for the installation and management of the Red Hat Container evolution Kit.

    Vagrant is not included in any yardstick repository, so they will exigency to install it. You can install Vagrant by enabling the SCLO repository or you can accumulate it directly from the Vagrant website. They will expend the latter approach:

    $ wget https://releases.hashicorp.com/vagrant/1.8.3/vagrant_1.8.3_x86_64.rpm $ sudo yum install vagrant_1.8.3_x86_64.rpm VirtualBox Install

    The Red Hat Container evolution Kit requires a virtualization software stack to execute. In this blog they will expend VirtualBox for the virtualization software.

    VirtualBox is best done using a repository to ensure you can accumulate updates. To conclude this you will exigency to follow these steps:

  • You will want to download the repo file:
  • $ wget http://download.virtualbox.org/virtualbox/rpm/el/virtualbox.repo $ mv virtualbox.repo /etc/yum.repos.d $ sudo yum install VirtualBox-5.0

    Once the install is complete you will want to launch VirtualBox and ensure that the Guest Network is on the rectify subnet as the CDK has a default for it setup. The blog will leverage this default as well. To verify that the host is on the rectify domain:

  • Open VirtualBox, this should subsist under you Applications->System Tools menu on your desktop.
  • Click on File->Preferences.
  • Click on Network.
  • Click on the Host-only Networks, and a popup of the VirtualBox preferences will load.
  • There should subsist a vboxnet0 as the network, click on it and click on the edit icon (looks relish a screwdriver on the left side of the popup) 6.Ensure that the IPv4 Address is 10.1.2.1.
  • Ensure the IPv4 Network Mask is 255.255.255.0.
  • Click on the DHCP Server tab.
  • Ensure the server address is 10.1.2.100.
  • Ensure the Server mask is 255.255.255.0.
  • Ensure the Lower Address Bound is 10.1.2.101.
  • Ensure the Upper Address Bound is 10.1.2.254.
  • Click on OK.
  • Click on OK.
  • CDK Install

    Docker containers are used to package software applications into portable, isolated stores. Developing software with containers helps developers create applications that will escape the very route on every platform. However, modern microservice deployments typically expend a scheduler such as Kubernetes to escape in production. In order to fully simulate the production environment, developers require a local version of production tools. In the Red Hat stack, this is supplied by the Red Hat Container evolution Kit (CDK).

    The Red Hat CDK is a customized virtual machine that makes it easy to escape intricate deployments resembling production. This means intricate applications can subsist developed using production grade tools from the very start, acceptation developers are unlikely to undergo problems stemming from differences in the evolution and production environments.

    Now let's walk through installation and configuration of the Red Hat CDK. They will create a containerized multi-tier application on the CDK’s OpenShift instance and travel through the entire workflow. By the quit of this blog post you will know how to escape an application on top of OpenShift and will subsist chummy with the core features of the CDK and OpenShift. Let’s accumulate started…

    Installing the CDK

    The prerequisites for running the CDK are Vagrant and a virtualization client (VirtualBox, VMware Fusion, libvirt). build confident that both are up and running on your machine.

    Start by going to Red Hat Product Downloads (note that you will exigency a Red Hat subscription to access this). Select ‘Red Hat Container evolution Kit’ under Product Variant, and the commandeer version and architecture. You should download two packages:

  • Red Hat Container Tools.
  • RHEL Vagrant Box (for your preferred virtualization client).
  • The Container Tools package is a set of plugins and templates that will wait on you start the Vagrant box. In the components subfolder you will find Vagrant files that will configure the virtual machine for you. The plugins folder contains the Vagrant add-ons that will subsist used to register the original virtual machine with the Red Hat subscription and to configure networking.

    Unzip the container tools archive into the root of your user folder and install the Vagrant add-ons.

    $ cd ~/cdk/plugins $ vagrant plugin install vagrant-registration vagrant-adbinfo landrush vagrant-service-manager

    You can check if the plugins were actually installed with this command:

    $ vagrant plugin list

    Add the box you downloaded into Vagrant. The path and the appellation may vary depending on your download folder and the box version:

    $ vagrant box add --name cdkv2 \ ~/Downloads/rhel-cdk-kubernetes-7.2-13.x86_64.vagrant-virtualbox.box

    Check that the vagrant box was properly added with the box list command:

    $ vagrant box list

    We will expend the Vagrantfile that comes shipped with the CDK and has advocate for OpenShift.

    $ cd $HOME/cdk/components/rhel/rhel-ose/ $ ls README.rst Vagrantfile

    In order to expend the landrush plugin to configure the DNS they exigency to add the following two lines to the Vagrantfile exactly as below (i.e. PUBLIC_ADDRESS is a property in the Vagrantfile and does not exigency to subsist replaced) :

    config.landrush.enabled = true config.landrush.host_ip_address = "#{PUBLIC_ADDRESS}"

    This will allow us to access their application from outside the virtual machine based on the hostname they configure. Without this plugin, your applications will subsist reachable only by IP address from within the VM.

    Save the changes and start the virtual machine :

    $ vagrant up

    During initialization, you will subsist prompted to register your Vagrant box with your RHEL subscription credentials.

    Let’s review what just happened here. On your local machine, you now devour a working instance of OpenShift running inside a virtual machine. This instance can talk to the Red Hat Registry to download images for the most common application stacks. You likewise accumulate a private Docker registry for storing images. Docker, Kubernetes, OpenShift and Atomic App CLIs are likewise installed.

    Now that they devour their Vagrant box up and running, it’s time to create and deploy a sample application to OpenShift, and create a continuous deployment workflow for it.

    The OpenShift console should subsist accessible at https://10.1.2.2:8443 from a browser on your host (this IP is defined in the Vagrantfile). By default, the login credentials will subsist openshift-dev/devel. You can likewise expend your Red Hat credentials to login. In the console, they create a original project:

    Next, they create a original application using one of the built-in ‘Instant Apps’. Instant Apps are predefined application templates that tow specific images. These are an easy route to quickly accumulate an app up and running. From the list of Instant Apps, select “nodejs-mongodb-example” which will start a database (MongoDB) and a web server (Node.js).

    For this application, they will expend the source code from the OpenShift GitHub repository located here. If you want to follow along with the webhook steps later, you’ll exigency to fork this repository into your own. Once you’re ready, enter the URL of your repo into the SOURCE_REPOSITORY_URL field:

    There are two other parameters that are valuable to us – GITHUB_WEBHOOK_SECRET and APPLICATION_DOMAIN:

  • GITHUB_WEBHOOK_SECRET: this sphere allows us to create a underhand to expend with the GitHub webhook for automatic builds. You don’t exigency to specify this, but you’ll exigency to remember the value later if you do.
  • APPLICATION_DOMAIN: this sphere will determine where they can access their application. This value must embrace the Top flat Domain for the VM, by default this value is rhel-ose.vagrant.dev. You can check this by running vagrant landrush ls.
  • Once these values are configured, they can ‘Create’ their application. This brings us to an information page which gives us some helpful CLI commands as well as their webhook URL. Copy this URL as they will expend it later on.

    OpenShift will then tow the code from GitHub, find the commandeer Docker image in the Red Hat repository, and likewise create the build configuration, deployment configuration, and service definitions. It will then kick off an initial build. You can view this process and the various steps within the web console. Once completed it should study relish this:

    In order to expend the Landrush plugin, there is additional steps that are required to configure dnsmasq. To conclude that you will exigency to conclude the following:

  • Ensure dnsmasq is installed  $ sudo yum install dnsmasq
  • Modify the vagrant configuration for dnsmasq: $ sudo sh -c 'echo "server=/vagrant.test/127.0.0.1#10053" > /etc/dnsmasq.d/vagrant-landrush'
  • Edit /etc/dnsmasq.conf and verify the following lines are in this file: conf-dir=/etc/dnsmasq.d listen-address=127.0.0.1
  • Restart the dnsmasq service $ sudo systemctl restart dnsmasq
  • Add nameserver 127.0.0.1 to /etc/resolv.conf
  • Great! Their application has now been built and deployed on their local OpenShift environment. To complete the Continuous Deployment pipeline they just exigency to add a webhook into their GitHub repository they specified above, which will automatically update the running application.

    To set up the webhook in GitHub, they exigency a route of routing from the public internet to the Vagrant machine running on your host. An easy route to achieve this is to expend a third party forwarding service such as ultrahook or ngrok. They exigency to set up a URL in the service that forwards traffic through a tunnel to the webhook URL they copied earlier.

    Once this is done, open the GitHub repo and travel to Settings -> Webhooks & services -> Add webhook. Under Payload URL enter the URL that the forwarding service gave you, plus the underhand (if you specified one when setting up the OpenShift project). If your webhook is configured correctly you should discern something relish this:

    To test out the pipeline, they exigency to build a change to their project and push a consign to the repo.

    Any easy route to conclude this is to edit the views/index.html file, e.g: (Note that you can likewise conclude this through the GitHub web interface if you’re feeling lazy). consign and push this change to the GitHub repo, and they can discern a original build is triggered automatically within the web console. Once the build completes, if they again open their application they should discern the updated front page.

    We now devour Continuous Deployment configured for their application. Throughout this blog post, we’ve used the OpenShift web interface. However, they could devour performed the very actions using the OpenShift console (oc) at the command-line. The easiest route to experiment with this interface is to ssh into the CDK VM via the Vagrant ssh command.

    Before wrapping up, it’s helpful to understand some of the concepts used in Kubernetes, which is the underlying orchestration layer in OpenShift.

    Pods

    A pod is one or more containers that will subsist deployed to a node together. A pod represents the smallest unit that can subsist deployed and managed in OpenShift. The pod will subsist assigned its own IP address. any of the containers in the pod will partake local storage and networking.

    A pod lifecycle is defined, deploy to node, escape their container(s), exit or removed. Once a pod is executing then it cannot subsist changed. If a change is required then the existing pod is terminated and recreated with the modified configuration.

    For their example application, they devour a Pod running the application. Pods can subsist scaled up/down from the OpenShift interface.

    Replication Controllers

    These manage the lifecycle of Pods.They ensure that the rectify number of Pods are always running by monitoring the application and stopping or creating Pods as appropriate.

    Services

    Pods are grouped into services. Their architecture now has four services: three for the database (MongoDB) and one for the application server JBoss.

    Deployments

    With every original code consign (assuming you set-up the GitHub webhooks) OpenShift will update your application. original pods will subsist started with the wait on of replication controllers running your original application version. The brokendown pods will subsist deleted. OpenShift deployments can perform rollbacks and provide various deploy strategies. It’s difficult to overstate the advantages of being able to escape a production environment in evolution and the efficiencies gained from the quick feedback cycle of a Continuous Deployment pipeline.

    In this post, they devour shown how to expend the Red Hat CDK to achieve both of these goals within a short-time frame and now devour a Node.js and MongoDB application running in containers, deployed using the OpenShift PaaS. This is a distinguished route to quickly accumulate up and running with containers and microservices and to experiment with OpenShift and other elements of the Red Hat container ecosystem.

    MongoDB VirtualBox

    In this section, they will create the virtual machines that will subsist required to set up the replica set. They will not walk through any of the steps of setting up Red Hat as this is prerequisite knowledge.

    What they will subsist doing is creating a groundwork RHEL 7.2 minimal install and then using the VirtualBox interface to clone the images. They will conclude this so that they can easily install the replica set using the MongoDB Automation Agent.

    We will likewise subsist installing a no password generated ssh keys for the Ansible Playbook install of the automation engine.

    Please perform the following steps:

  • In VirtualBox create a original guest image and call it RHEL Base. They used the following information: a. reminiscence 2048 MB b. Storage 30GB c. 2 Network cards i. Nat ii. Host-Only
  • Do a minimal Red Hat install, they modified the disk layout to remove the /home directory and added the reclaimed space to the / partition
  • Once this is done you should attach a subscription and conclude a yum update on the guest RHEL install.

    The final step will subsist to generate original ssh keys for the root user and transfer the keys to the guest machine. To conclude that please conclude the following steps:

  • Become the root user $ sudo -i
  • Generate your ssh keys. conclude not add a passphrase when requested.  # ssh-keygen
  • You exigency to add the contents of the id_rsa.pub to the authorized_keys file on the RHEL guest. The following steps were used on a local system and are not best practices for this process. In a managed server environment your IT should devour a best rehearse for doing this. If this is the first guest in your VirtualBox then it should devour an ip of 10.1.2.101, if it has another ip then you will exigency to replace for the following. For this blog please execute the following steps # cd ~/.ssh/ # scp id_rsa.pub 10.1.2.101: # ssh 10.1.2.101 # mkdir .ssh # cat id_rsa.pub > ~/.ssh/authorized_keys # chmod 700 /root/.ssh # chmod 600 /root/.ssh/authorized_keys
  • SELinux may obscure sshd from using the authorized_keys so update the permissions on the guest with the following command # restorecon -R -v /root/.ssh
  • Test the connection by trying to ssh from the host to the guest, you should not subsist asked for any login information.
  • Once this is complete you can shut down the RHEL groundwork guest image. They will now clone this to provide the MongoDB environment. The steps are as follows:

  • Right click on the RHEL guest OS and select Clone.
  • Enter the appellation 7.2 RH Mongo-DB1.
  • Ensure to click the Reinitialize the MAC Address of any network cards.
  • Click on Next.
  • Ensure the complete Clone option is selected.
  • Click on Clone.
  • Right click on the RHEL guest OS and select Clone.
  • Enter the appellation 7.2 RH Mongo-DB2.
  • Ensure to click the Reinitialize the MAC Address of any network cards.
  • Click on Next.
  • Ensure the complete Clone option is selected.
  • Click on Clone.
  • Right click on the RHEL guest OS and select Clone.
  • Enter the appellation 7.2 RH Mongo-DB3.
  • Ensure to click the Reinitialize the MAC Address of any network cards.
  • Click on Next.
  • Ensure the complete Clone option is selected.
  • Click on Clone.
  • The final step for getting the systems ready will subsist to configure the hostnames, host-only ip and the host files. They will exigency to likewise ensure that the systems can communicate on the port for MongoDB, so they will disable the firewall which is not meant for production purposes but you will exigency to contact your IT departments on how they manage opening of ports.

    Normally in a production environment, you would devour the servers in an internal DNS system, however for the sake of this blog they will expend hosts files for the purpose of names. They want to edit the /etc/hosts file on the three MongoDB guests as well as the hosts.

    The information they will subsist using will subsist as follows:

    To conclude so on each of the guests conclude the following:

  • Log in.
  • Find your host only network interface by looking for the interface on the host only network 10.1.2.0/24: # sudo ip addr
  • Edit the network interface, in their case the interface was enp0s8: # sudo vi /etc/sysconfig/network-scripts/ifcfg-enp0s8
  • You will want to change the ONBOOT and BOOTPROTO to the following and add the three lines for IP address, netmask, and Broadcast. Note: the IP address should subsist based upon the table above. They should match the info below: ONBOOT=yes BOOTPROTO=static IPADDR=10.1.2.10 NETMASK-255.255.255.0 BROADCAST=10.1.2.255
  • Disable the firewall with: # systemctl quit firewalld # systemctl disable firewalld
  • Edit the hostname using the commandeer values from the table above.  # hostnamectl set-hostname "mongo-db1" --static
  • Edit the hosts file adding the following to etc/hosts, you should likewise conclude this on the guest: 10.1.2.10 mongo-db1 10.1.2.11 mongo-db2 10.1.2.12 mongo-db3
  • Restart the guest.
  • Try to SSH by hostname.
  • Also, try pinging each guest by hostname from guests and host.
  • Ops Manager

    MongoDB Ops Manager can subsist leveraged throughout the development, test, and production lifecycle, with faultfinding functionality ranging from cluster performance monitoring data, alerting, no-downtime upgrades, advanced configuration and scaling, as well as backup and restore. Ops Manager can subsist used to manage up to thousands of sever MongoDB clusters in a tenants-per-cluster fashion — isolating cluster users to specific clusters.

    All major MongoDB Ops Manager actions can subsist driven manually through the user interface or programmatically through the comfort API, where Ops Manager can subsist deployed by platform teams offering Enterprise MongoDB as a Service back-ends to application teams.

    Specifically, Ops Manager can deploy any MongoDB cluster topology across bare metal or virtualized hosts, or in private or public cloud environments. A production MongoDB cluster will typically subsist deployed across a minimum of three hosts in three sever availability areas — physical servers, racks, or data centers. The loss of one host will noiseless preserve a quorum in the remaining two to ensure always-on availability.

    Ops Manager can deploy a MongoDB cluster (replica set or sharded cluster) across the hosts with Ops Manager agents running, using any desired MongoDB version and enabling access control (authentication and authorization) so that only client connections presenting the rectify credentials are able to access the cluster. The MongoDB cluster can likewise expend SSL/TLS for over the wire encryption.

    Once a MongoDB cluster is successfully deployed by Ops Manager, the cluster’s connection string can subsist easily generated (in the case of a MongoDB replica set, this will subsist the three hostname:port pairs separated by commas). An OpenShift application can then subsist configured to expend the connection string and authentication credentials to this MongoDB cluster.

    To expend Ops Manager with Ansible and OpenShift:

  • Install and expend a MongoDB Ops Manager, and record the URL that it is accessible at (“OpsManagerCentralURL”)
  • Ensure that the MongoDB Ops Manager is accessible over the network at the OpsManagerCentralURL from the servers (VMs) where they will deploy MongoDB. (Note that the reverse is not necessary; in other words, Ops Manager does not exigency to subsist able to achieve into the managed VMs directly over the network).
  • Spawn servers (VMs) running Red Hat Enterprise Linux, able to achieve each other over the network at the hostnames returned by “hostname -f” on each server respectively, and the MongoDB Ops Manager itself, at the OpsManagerCentralURL.
  • Create an Ops Manager Group, and record the group’s unique identifier (“mmsGroupId”) and Agent API key (“mmsApiKey”) from the group’s ‘Settings’ page in the user interface.
  • Use Ansible to configure the VMs to start the MongoDB Ops Manager Automation Agent (available for download directly from the Ops Manager). expend the Ops Manager UI (or comfort API) to instruct the Ops Manager agents to deploy a MongoDB replica set across the three VMs.
  • Ansible Install

    By having three MongoDB instances that they want to install the automation agent it would subsist easy enough to login and escape the commands as seen in the Ops Manager agent installation information. However they devour created an ansible playbook that you will exigency to change to customize.

    The playbook looks like:

    - hosts: mongoDBNodes vars: OpsManagerCentralURL: <baseURL> mmsGroupId: <groupID> mmsApiKey: <ApiKey> remote_user: root tasks: - name: install automation agent RPM from OPS manager instance @ {{ OpsManagerCentralURL }} yum: name={{ OpsManagerCentralURL }}/download/agent/automation/mongodb-mms-automation-agent-manager-latest.x86_64.rhel7.rpm state=present - name: write the MMS Group ID as {{ mmsGroupId }} lineinfile: dest=/etc/mongodb-mms/automation-agent.config regexp=^mmsGroupId= line=mmsGroupId={{ mmsGroupId }} - name: write the MMS API Key as {{ mmsApiKey }} lineinfile: dest=/etc/mongodb-mms/automation-agent.config regexp=^mmsApiKey= line=mmsApiKey={{ mmsApiKey }} - name: write the MMS groundwork URL as {{ OpsManagerCentralURL }} lineinfile: dest=/etc/mongodb-mms/automation-agent.config regexp=^mmsBaseUrl= line=mmsBaseUrl={{ OpsManagerCentralURL }} - name: create MongoDB data directory file: path=/data state=directory owner=mongod group=mongod - name: ensure MongoDB MMS Automation Agent is started service: name=mongodb-mms-automation-agent state=started

    You will exigency to customize it with the information you gathered from the Ops Manager.

    You will exigency to create this file as your root user and then update the /etc/ansible/hosts file and add the following lines:

    [mongoDBNodes] mongo-db1 mongo-db2 mongo-db3

    Once this is done you are ready to escape the ansible playbook. This playbook will contact your Ops Manager Server, download the latest client, update the client config files with your APiKey and Groupid, install the client and then start the client. To escape the playbook you exigency to execute the command as root:

    ansible-playbook –v mongodb-agent-playbook.yml

    Use MongoDB Ops Manager to create a MongoDB Replica Set and add database users with commandeer access rights:

  • Verify that any of the Ops Manager agents devour started in the MongoDB Ops Manager group’s Deployment interface.
  • Navigate to "Add” > ”New Replica Set" and define a Replica Set with desired configuration (MongoDB 3.2, default settings).
  • Navigate to "Authentication & SSL Settings" in the "..." menu and enable MongoDB Username/Password (SCRAM-SHA-1) Authentication.
  • Navigate to the "Authentication & Users" panel and add a database user to the sampledb a. Add the testUser@sampledb user, with password set to "password", and with Roles: readWrite@sampledb dbOwner@sampledb dbAdmin@sampledb userAdmin@sampledb Roles.
  • Click Review & Deploy.
  • OpenShift Continuous Deployment

    Up until now, we’ve explored the Red Hat container ecosystem, the Red Hat Container evolution Kit (CDK), OpenShift as a local deployment, and OpenShift in production. In this final section, we’re going to buy a study at how a team can buy advantage of the advanced features of OpenShift in order to automatically trot original versions of applications from evolution to production — a process known as Continuous Delivery (or Continuous Deployment, depending on the flat of automation).

    OpenShift supports different setups depending on organizational requirements. Some organizations may escape a completely sever cluster for each environment (e.g. dev, staging, production) and others may expend a single cluster for several environments. If you escape a sever OpenShift PaaS for each environment, they will each devour their own dedicated and isolated resources, which is costly but ensures isolation (a problem with the evolution cluster cannot move production). However, multiple environments can safely escape on one OpenShift cluster through the platform’s advocate for resource isolation, which allows nodes to subsist dedicated to specific environments. This means you will devour one OpenShift cluster with common masters for any environments, but dedicated nodes assigned to specific environments. This allows for scenarios such as only allowing production projects to escape on the more powerful / expensive nodes.

    OpenShift integrates well with existing Continuous Integration / Continuous Delivery tools. Jenkins, for example, is available for expend inside the platform and can subsist easily added to any projects you’re planning to deploy. For this demo however, they will stick to out-of-the-box OpenShift features, to point to workflows can subsist constructed out of the OpenShift fundamentals.

    A Continuous Delivery Pipeline with CDK and OpenShift Enterprise

    The workflow of their continuous delivery pipeline is illustrated below:

    The diagram shows the developer on the left, who is working on the project in their own environment. In this case, the developer is using Red Hat’s CDK running on their local-machine, but they could equally subsist using a evolution environment provisioned in a remote OpenShift cluster.

    To trot code between environments, they can buy advantage of the image streams concept in OpenShift. An image stream is superficially similar to an image repository such as those organize on Docker Hub — it is a collection of related images with identifying names or “tags”. An image stream can advert to images in Docker repositories (both local and remote) or other image streams. However, the killer feature is that OpenShift will generate notifications whenever an image stream changes, which they can easily configure projects to listen and react to. They can discern this in the diagram above — when the developer is ready for their changes to subsist picked up by the next environment in line, they simply tag the image appropriately, which will generate an image stream notification that will subsist picked up by the staging environment. The staging environment will then automatically rebuild and redeploy any containers using this image (or images who devour the changed image as a groundwork layer). This can subsist fully automated by the expend of Jenkins or a similar CI tool; on a check-in to the source control repository, it can escape a test-suite and automatically tag the image if it passes.

    To trot between staging and production they can conclude exactly the very thing — Jenkins or a similar implement could escape a more thorough set of system tests and if they pass tag the image so the production environment picks up the changes and deploys the original versions. This would subsist apt Continuous Deployment — where a change made in dev will propagate automatically to production without any manual intervention. Many organizations may instead opt for Continuous Delivery — where there is noiseless a manual “ok” required before changes hit production. In OpenShift this can subsist easily done by requiring the images in staging to subsist tagged manually before they are deployed to production.

    Deployment of an OpenShift Application

    Now that we’ve reviewed the workflow, let’s study at a real example of pushing an application from evolution to production. They will expend the simple MLB Parks application from a previous blog post that connects to MongoDB for storage of persistent data. The application displays various information about MLB parks such as league and city on a map. The source code is available in this GitHub repository. The example assumes that both environments are hosted on the very OpenShift cluster, but it can subsist easily adapted to allow promotion to another OpenShift instance by using a common registry.

    If you don’t already devour a working OpenShift instance, you can quickly accumulate started by using the CDK, which they likewise covered in an earlier blogpost. Start by logging in to OpenShift using your credentials:

    $ oc login -u openshift-dev

    Now we’ll create two original projects. The first one represents the production environment (mlbparks-production):

    $ oc new-project mlbparks-production Now using project "mlbparks-production" on server "https://localhost:8443".

    And the second one will subsist their evolution environment (mlbparks):

    $ oc new-project mlbparks Now using project "mlbparks" on server "https://localhost:8443".

    After you escape this command you should subsist in the context of the evolution project (mlbparks). We’ll start by creating an external service to the MongoDB database replica-set.

    Openshift allows us to access external services, allowing their projects to access services that are outside the control of OpenShift. This is done by defining a service with an vacant selector and an endpoint. In some cases you can devour multiple IP addresses assigned to your endpoint and the service will act as a load balancer. This will not labor with the MongoDB replica set as you will encounter issues not being able to connect to the PRIMARY node for writing purposes. To allow for this in this case you will exigency to create one external service for each node. In their case they devour three nodes so for illustrative purposes they devour three service files and three endpoint files.

    Service Files: replica-1_service.json

    { "kind": "Service", "apiVersion": "v1", "metadata": { "name": "replica-1" }, "spec": { "selector": { }, "ports": [ { "protocol": "TCP", "port": 27017, "targetPort": 27017 } ] } }

    replica-1_endpoints.json

    { "kind": "Endpoints", "apiVersion": "v1", "metadata": { "name": "replica-1" }, "subsets": [ { "addresses": [ { "ip": "10.1.2.10" } ], "ports": [ { "port": 27017 } ] } ] }

    replica-2_service.json

    { "kind": "Service", "apiVersion": "v1", "metadata": { "name": "replica-2" }, "spec": { "selector": { }, "ports": [ { "protocol": "TCP", "port": 27017, "targetPort": 27017 } ] } }

    replica-2_endpoints.json

    { "kind": "Endpoints", "apiVersion": "v1", "metadata": { "name": "replica-2" }, "subsets": [ { "addresses": [ { "ip": "10.1.2.11" } ], "ports": [ { "port": 27017 } ] } ] }

    replica-3_service.json

    { "kind": "Service", "apiVersion": "v1", "metadata": { "name": "replica-3" }, "spec": { "selector": { }, "ports": [ { "protocol": "TCP", "port": 27017, "targetPort": 27017 } ] } }

    replica-3_endpoints.json

    { "kind": "Endpoints", "apiVersion": "v1", "metadata": { "name": "replica-3" }, "subsets": [ { "addresses": [ { "ip": "10.1.2.12" } ], "ports": [ { "port": 27017 } ] } ] }

    Using the above replica files you will exigency to escape the following commands:

    $ oc create -f replica-1_service.json $ oc create -f replica-1_endpoints.json $ oc create -f replica-2_service.json $ oc create -f replica-2_endpoints.json $ oc create -f replica-3_service.json $ oc create -f replica-3_endpoints.json

    Now that they devour the endpoints for the external replica set created they can now create the MLB parks using a template. They will expend the source code from their demo GitHub repo and the s2i build strategy which will create a container for their source code (note this repository has no Dockerfile in the fork they use). any of the environment variables are in the mlbparks-template.json, so they will first create a template then create their original app:

    $ oc create -f https://raw.githubusercontent.com/macurwen/openshift3mlbparks/master/mlbparks-template.json $ oc new-app mlbparks --> Success Build scheduled for "mlbparks" - expend the logs command to track its progress. escape 'oc status' to view your app.

    As well as building the application, note that it has created an image stream called mlbparks for us.

    Once the build has finished, you should devour the application up and running (accessible at the hostname organize in the pod of the web ui) built from an image stream.

    We can accumulate the appellation of the image created by the build with the wait on of the narrate command:

    $ oc narrate imagestream mlbparks Name: mlbparks Created: 10 minutes ago Labels: app=mlbparks Annotations: openshift.io/generated-by=OpenShiftNewApp openshift.io/image.dockerRepositoryCheck=2016-03-03T16:43:16Z Docker tow Spec: 172.30.76.179:5000/mlbparks/mlbparks Tag Spec Created PullSpec Image latest <pushed> 7 minutes ago 172.30.76.179:5000/mlbparks/mlbparks@sha256:5f50e1ffbc5f4ff1c25b083e1698c156ca0da3ba207c619781efcfa5097995ec

    So OpenShift has built the image mlbparks@sha256:5f50e1ffbc5f4ff1c25b083e1698c156ca0da3ba207c619781efcfa5097995ec, added it to the local repository at 172.30.76.179:5000 and tagged it as latest in the mlbparks image stream.

    Now they know the image ID, they can create a tag that marks it as ready for expend in production (use the SHA of your image here, but remove the IP address of the registry):

    $ oc tag mlbparks/mlbparks\ @sha256:5f50e1ffbc5f4ff1c25b083e1698c156ca0da3ba207c619781efcfa5097995ec \ mlbparks/mlbparks:production Tag mlbparks:production set to mlbparks/mlbparks@sha256:5f50e1ffbc5f4ff1c25b083e1698c156ca0da3ba207c619781efcfa5097995ec.

    We’ve intentionally used the unique SHA hash of the image rather than the tag latest to identify their image. This is because they want the production tag to subsist tied to this particular version. If they hadn’t done this, production would automatically track changes to latest, which would embrace untested code.

    To allow the production project to tow the image from the evolution repository, they exigency to award tow rights to the service account associated with production environment. Note that mlbparks-production is the appellation of the production project:

    $ oc policy add-role-to-group system:image-puller \ system:serviceaccounts:mlbparks-production \ --namespace=mlbparks To verify that the original policy is in place, they can check the rolebindings: $ oc accumulate rolebindings NAME ROLE USERS GROUPS SERVICE ACCOUNTS SUBJECTS admins /admin catalin system:deployers /system:deployer deployer system:image-builders /system:image-builder builder system:image-pullers /system:image-puller system:serviceaccounts:mlbparks, system:serviceaccounts:mlbparks-production

    OK, so now they devour an image that can subsist deployed to the production environment. Let’s switch the current project to the production one:

    $ oc project mlbparks-production Now using project "mlbparks" on server "https://localhost:8443".

    To start the database we’ll expend the very steps to access the external MongoDB as previous:

    $ oc create -f replica-1_service.json $ oc create -f replica-1_endpoints.json $ oc create -f replica-2_service.json $ oc create -f replica-2_endpoints.json $ oc create -f replica-3_service.json $ oc create -f replica-3_endpoints.json

    For the application Part we’ll subsist using the image stream created in the evolution project that was tagged “production”:

    $ oc new-app mlbparks/mlbparks:production --> organize image 5621fed (11 minutes old) in image stream "mlbparks in project mlbparks" under tag :production for "mlbparks/mlbparks:production" * This image will subsist deployed in deployment config "mlbparks" * Port 8080/tcp will subsist load balanced by service "mlbparks" --> Creating resources with label app=mlbparks ... DeploymentConfig "mlbparks" created Service "mlbparks" created --> Success escape 'oc status' to view your app.

    This will create an application from the very image generated in the previous environment.

    You should now find the production app is running at the provided hostname.

    We will now demonstrate the aptitude to both automatically trot original items to production, but they will likewise point to how they can update an application without having to update the MongoDB schema. They devour created a fork of the code in which they will now add the division to the league for the ballparks, without updating the schema.

    Start by going back to the evolution project:

    $ oc project mlbparks Now using project "mlbparks" on server "https://10.1.2.2:8443". And start a original build based on the consign “8a58785”: $ oc start-build mlbparks --git-repository=https://github.com/macurwen/openshift3mlbparks/tree/division --commit='8a58785'

    Traditionally with a RDBMS if they want to add a original component to in their application to subsist persisted to the database, they would exigency to build the changes in the code as well as devour a DBA manually update the schema at the database. The following code is an example of how they can modify the application code without manually making changes to the MongoDB schema.

    BasicDBObject updateQuery = original BasicDBObject(); updateQuery.append("$set", original BasicDBObject() .append("division", "East")); BasicDBObject searchQuery = original BasicDBObject(); searchQuery.append("league", "American League"); parkListCollection.updateMulti(searchQuery, updateQuery);

    Once the build finishes running, a deployment job will start that will replace the running container. Once the original version is deployed, you should subsist able to discern East under Toronto for example.

    If you check the production version, you should find it is noiseless running the previous version of the code.

    OK, we’re gratified with the change, let’s tag it ready for production. Again, escape oc to accumulate the ID of the image tagged latest, which they can then tag as production:

    $ oc tag mlbparks/mlbparks@\ sha256:ceed25d3fb099169ae404a52f50004074954d970384fef80f46f51dadc59c95d \ mlbparks/mlbparks:production Tag mlbparks:production set to mlbparks/mlbparks@sha256:ceed25d3fb099169ae404a52f50004074954d970384fef80f46f51dadc59c95d.

    This tag will trigger an automatic deployment of the original image to the production environment.

    Rolling back can subsist done in different ways. For this example, they will roll back the production environment by tagging production with the brokendown image ID. Find the privilege id by running the oc command again, and then tag it:

    $ oc tag mlbparks/mlbparks@\ sha256:5f50e1ffbc5f4ff1c25b083e1698c156ca0da3ba207c619781efcfa5097995ec \ mlbparks/mlbparks:production Tag mlbparks:production set to mlbparks/mlbparks@sha256:5f50e1ffbc5f4ff1c25b083e1698c156ca0da3ba207c619781efcfa5097995ec. Conclusion

    Over the course of this post, we’ve investigated the Red Hat container ecosystem and OpenShift Container Platform in particular. OpenShift builds on the advanced orchestration capabilities of Kubernetes and the reliability and stability of the Red Hat Enterprise Linux operating system to provide a powerful application environment for the enterprise. OpenShift adds several ideas of its own that provide valuable features for organizations, including source-to-image tooling, image streams, project and user isolation and a web UI. This post showed how these features labor together to provide a complete CD workflow where code can subsist automatically pushed from evolution through to production combined with the power and capabilities of MongoDB as the backend of altenative for applications.


    Beginning DB2: From Novice to Professional | killexams.com real questions and Pass4sure dumps

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    Operations & Process Management: Principles & Practice for Strategic ImpactOperations & Process Management: Principles & Practice for Strategic Impact
    By Nigel Slack, Alistair Jones
    Publisher : Pearson (Feb 2018)
    ISBN10 : 129217613X
    ISBN13 : 9781292176130
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    Subject : Business & Economics
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    Computer Security: Principles and PracticeComputer Security: Principles and Practice
    By William Stallings, Lawrie Brown
    Publisher : Pearson (Aug 2017)
    ISBN10 : 0134794109
    ISBN13 : 9780134794105
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    Subject : Computer Science & Technology
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    Urban EconomicsUrban Economics
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    Publisher : McGraw-Hill (Jan 2018)
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    Urban EconomicsUrban Economics
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    Understanding BusinessUnderstanding Business
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    Publisher : McGraw-Hill (Feb 2018)
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