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000-610 DB2 10.1 Fundamentals

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000-610 exam Dumps Source : DB2 10.1 Fundamentals

Test Code : 000-610
Test name : DB2 10.1 Fundamentals
Vendor name : IBM
: 138 actual Questions

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IBM DB2 10.1 Fundamentals

beginning DB2: From tyro to professional | killexams.com actual Questions and Pass4sure dumps

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Altova Introduces edition 2014 of Its Developer tools and Server software | killexams.com actual 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®, uphold for Schema 1.1, XPath/XSLT/XQuery 3.0, lead for brand fresh databases and an contemptible lot extra. fresh 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 breathe 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 fabulous back 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 distinct talents when structure and deploying purposes."

a couple of of the brand fresh 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 uphold the very newest of everything 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 uphold for XML Schema 1.1 validation and enhancing. The latest edition of the XML Schema usual, 1.1 adds fresh features geared toward making schemas greater springy 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 everything the time, the graphical editing paradigm of the schema editor makes it convenient to breathe mindful and implement these fresh 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 furthermore an edge 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 fresh performance similar to: dynamic characteristic cells, inline feature expressions, and uphold for union varieties to name simply a number of. Full lead for brand fresh services and operators added in XPath three.0 is accessible via sagacious 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. gratify 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 fresh edition automatically.

XSLT three.0 takes expertise of the brand fresh facets brought in XPath 3.0. in addition, a massive feature enabled with the aid of the fresh version is the fresh xsl:are trying / xsl:trap construct, which will furthermore breathe used to trap and better from dynamic errors. other enhancements in XSLT three.0 encompass back 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 hold the selection to edit, debug, and profile XQuery 3.0 with efficient syntax coloring, bracket matching, XPath auto-completion, and other knowing editing elements.

XQuery three.0 is, of path, an extension of XPath and for this understanding merits from the fresh functions and operators introduced in XPath three.0, akin to a fresh string concatenation operator, map operator, math capabilities, sequence processing, and greater -- everything 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 uphold for more recent models of prior to now supported databases, as well as uphold for brand fresh 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 fresh 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 benchmark 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 illustration when a job requires advanced database queries or should Make its personal internet provider information requests. FlowForce Server administrators can now schedule execution of a time-drinking job and cache the results to hold away from these delays. The cached facts can then breathe 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 breathe a very agreeable software for caching.

    These and a lot of greater points can breathe establish within the 2014 version of MissionKit desktop developer tools and Server software. For a complete checklist of fresh facets, supported necessities, and visitation downloads gratify consult with: http://www.altova.com/whatsnew.html

    About Altova Altova® is a utility company that specialize in equipment to uphold 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 industry 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 structure groups. With over four.5 million clients international, including 91% of Fortune 500 groups, Altova is proud 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 breathe the logos of their respective house owners.


    MySQL stored process Programming | killexams.com actual 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 offer 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 fresh capabilities in MySQL, they hold got obtained remarkably shrimp coverage with the aid of engage 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 breathe enough for the more basic stored program construction wants. however for any MySQL developer who wishes to abide in intelligence in-depth the route to Make the most of this fresh functionality in version 5.0, they'll probably want a much more mammoth treatment — and that's precisely what Harrison and Feuerstein hold 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 terminal four chapters cowl language fundamentals; blocks, conditional statements, and iterative programming; SQL; and error coping with. The publication's 2d half, "saved program construction," can breathe 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 name 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 seat of attention on the safety and tuning of saved programs, tuning SQL, optimizing the code, and optimizing the progress 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 observation upon every component to every chapter of every half. Yet the customary nice and utility of the manuscript may furthermore breathe discerned simply through picking only 1 of the aforesaid internet programming languages, and writing some code in that language to muster some MySQL kept tactics and capabilities, to bag consequences from a test database — and setting up everything of this code while relying fully upon the ebook under evaluate. growing some benchmark 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 breathe a useful ebook in developing the most mediocre performance that a programmer would deserve to do into effect.

    The ebook appears to hold very few features or particular sections in need 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 establish 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 breathe effectively remedied in the next version. Some programming books Make equivalent blunders, but everything over their text, which is even worse. fortunately, many of the code in this engage is neatly formatted, and the variable and application names are commonly descriptive satisfactory.

    one of the crucial publication's fabric could hold 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 hold innovations and suggestions lined with equal skill in other MySQL books, and were not necessary in this one. on the other hand, sloppy developers who churn out lamentable code may quarrel that the terminal chapter, which makes a speciality of most excellent programming practices, could furthermore breathe 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 matter matters is by a long shot 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 assume 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, everything Make 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 prerogative here and there, can't breathe everything bad. Steven Feuerstein is the creator of a couple of well-considered books on Oracle, and it become much to peer him poke some enjoyable on the database heavyweight, in his selection of pattern code to demonstrate the my_replace() function: my_replace( 'we admire 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 discover 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 breathe 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 actual 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 snappily validation and processing capabilities of RaptorXML®, uphold for Schema 1.1, XPath/XSLT/XQuery 3.0, uphold for fresh databases and much more. fresh 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 breathe able to extend the hyper-performance delivered by the unparalleled RaptorXML Server to developers working in their desktop tools. This functionality, along with robust uphold 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 skill to automate essential processes via their high-performance server products, gives their customers a distinct edge when structure and deploying applications."

    A few of the fresh 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 workable standards conformance. Now the selfsame hyper-performance engine that powers RaptorXML Server is fully integrated in several Altova MissionKit tools, including XMLSpy, MapForce®, and SchemaAgent®, delivering lightning snappily 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 uphold the very latest of everything pertinent 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 principal uphold for XML Schema 1.1 validation and editing. The latest version of the XML Schema standard, 1.1 adds fresh features aimed at making schemas more springy and adaptable to industry 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 light to understand and implement these fresh features.

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

    Coinciding with XML Schema 1.1 support, Altova has furthermore 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 fresh functionality such as: dynamic office cells, inline office expressions, and uphold for union types to name just a few. Full uphold for fresh functions and operators added in XPath 3.0 is available through knowing 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. gratify note that a subset of XSLT 3.0 is supported since the benchmark is still a working draft that continues to evolve. XSLT 3.0 uphold conforms to the W3C XSLT 3.0 Working Draft of July 10, 2012 and the XPath 3.0 Candidate Recommendation. However, uphold in XMLSpy now gives developers the skill to start working with this fresh version immediately.

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

    Story continues

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

    XQuery 3.0 is, of course, an extension of XPath and therefore benefits from the fresh functions and operators added in XPath 3.0, such as a fresh string concatenation operator, map operator, math functions, sequence processing, and more -- everything 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 uphold for newer versions of previously supported databases, as well as uphold for fresh 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 fresh 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 industry processes, while MapForce Server and StyleVision Server offer 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 complicated database queries or needs to Make its own Web service data requests. FlowForce Server administrators can now schedule execution of a time-consuming job and cache the results to avert these delays. The cached data can then breathe 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 breathe a agreeable 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 fresh features, supported standards, and visitation downloads gratify 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 progress tools. Altova focuses on its customers' needs by offering a product line that fulfills a broad spectrum of requirements for software progress teams. With over 4.5 million users worldwide, including 91% of Fortune 500 organizations, Altova is proud 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 breathe the trademarks of their respective owners.


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

    Current progress cycles physiognomy many challenges such as an evolving landscape of application architecture (Monolithic to Microservices), the need to frequently deploy features, and fresh IaaS and PaaS environments. This causes many issues throughout the organization, from the progress teams everything the route to operations and management.

    In this blog post, they will elucidate you how you can set up a local system that will uphold MongoDB, MongoDB Ops Manager, and OpenShift. They will walk through the various installation steps and demonstrate how light it is to execute agile application progress with MongoDB and OpenShift.

    MongoDB is the next-generation database that is built for rapid and iterative application development. Its springy data model — the skill 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 back manage a complicated 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 furthermore breathe 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 sustain within a stable, secure, and scalable operating system. Application lifecycle management and agile application progress tooling augment 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 supervene this example, you will need to meet a number of requirements. You will need 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 furthermore 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 furthermore a deployment and orchestration tool. In many respects, aiming to provide big 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 furthermore seeks to unravel other major unsolved IT challenges.

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

    In order to install Ansible using yum you will need 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 combat with the base RHEL packages.

    The EPEL repository has a dependency on the Server Optional and Server Extras repositories. To enable these repositories you will need 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 need to execute 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 breathe used to manage the lifecycle of a virtual machine. This implement is used for the installation and management of the Red Hat Container progress Kit.

    Vagrant is not included in any benchmark repository, so they will need to install it. You can install Vagrant by enabling the SCLO repository or you can bag 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 progress 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 bag updates. To execute this you will need to supervene 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 reform 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 reform domain:

  • Open VirtualBox, this should breathe 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 breathe a vboxnet0 as the network, click on it and click on the edit icon (looks like 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 accelerate the selfsame route on every platform. However, modern microservice deployments typically expend a scheduler such as Kubernetes to accelerate 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 progress Kit (CDK).

    The Red Hat CDK is a customized virtual machine that makes it light to accelerate complicated deployments resembling production. This means complicated applications can breathe developed using production grade tools from the very start, import developers are unlikely to sustain problems stemming from differences in the progress 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 Go through the entire workflow. By the finish of this blog post you will know how to accelerate an application on top of OpenShift and will breathe chummy with the core features of the CDK and OpenShift. Let’s bag started…

    Installing the CDK

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

    Start by going to Red Hat Product Downloads (note that you will need a Red Hat subscription to access this). Select ‘Red Hat Container progress Kit’ under Product Variant, and the confiscate 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 back 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 breathe used to register the fresh 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 name 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 uphold 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 need 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 need to breathe 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 breathe reachable only by IP address from within the VM.

    Save the changes and start the virtual machine :

    $ vagrant up

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

    Let’s review what just happened here. On your local machine, you now hold 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 furthermore bag a private Docker registry for storing images. Docker, Kubernetes, OpenShift and Atomic App CLIs are furthermore installed.

    Now that they hold 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 breathe 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 breathe openshift-dev/devel. You can furthermore expend your Red Hat credentials to login. In the console, they create a fresh project:

    Next, they create a fresh application using one of the built-in ‘Instant Apps’. Instant Apps are predefined application templates that drag specific images. These are an light route to quickly bag 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 supervene along with the webhook steps later, you’ll need 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 principal to us – GITHUB_WEBHOOK_SECRET and APPLICATION_DOMAIN:

  • GITHUB_WEBHOOK_SECRET: this bailiwick allows us to create a furtive to expend with the GitHub webhook for automatic builds. You don’t need to specify this, but you’ll need to remember the value later if you do.
  • APPLICATION_DOMAIN: this bailiwick will determine where they can access their application. This value must embrace the Top even 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 drag the code from GitHub, find the confiscate Docker image in the Red Hat repository, and furthermore 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 spy like this:

    In order to expend the Landrush plugin, there is additional steps that are required to configure dnsmasq. To execute that you will need to execute 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 need 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 need a route of routing from the public internet to the Vagrant machine running on your host. An light route to achieve this is to expend a third party forwarding service such as ultrahook or ngrok. They need 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 Go to Settings -> Webhooks & services -> Add webhook. Under Payload URL enter the URL that the forwarding service gave you, plus the furtive (if you specified one when setting up the OpenShift project). If your webhook is configured correctly you should remark something like this:

    To test out the pipeline, they need to Make a change to their project and shove a confide to the repo.

    Any light route to execute this is to edit the views/index.html file, e.g: (Note that you can furthermore execute this through the GitHub web interface if you’re zeal lazy). confide and shove this change to the GitHub repo, and they can remark a fresh build is triggered automatically within the web console. Once the build completes, if they again open their application they should remark the updated front page.

    We now hold Continuous Deployment configured for their application. Throughout this blog post, we’ve used the OpenShift web interface. However, they could hold performed the selfsame 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 breathe deployed to a node together. A pod represents the smallest unit that can breathe deployed and managed in OpenShift. The pod will breathe assigned its own IP address. everything of the containers in the pod will share local storage and networking.

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

    For their illustration application, they hold a Pod running the application. Pods can breathe scaled up/down from the OpenShift interface.

    Replication Controllers

    These manage the lifecycle of Pods.They ensure that the reform 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 fresh code confide (assuming you set-up the GitHub webhooks) OpenShift will update your application. fresh pods will breathe started with the back of replication controllers running your fresh application version. The brokendown pods will breathe deleted. OpenShift deployments can perform rollbacks and provide various deploy strategies. It’s hard to overstate the advantages of being able to accelerate a production environment in progress and the efficiencies gained from the snappily feedback cycle of a Continuous Deployment pipeline.

    In this post, they hold shown how to expend the Red Hat CDK to achieve both of these goals within a short-time frame and now hold a Node.js and MongoDB application running in containers, deployed using the OpenShift PaaS. This is a much route to quickly bag 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 breathe required to set up the replica set. They will not walk through everything of the steps of setting up Red Hat as this is prerequisite knowledge.

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

    We will furthermore breathe 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 fresh guest image and muster it RHEL Base. They used the following information: a. remembrance 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 execute a yum update on the guest RHEL install.

    The final step will breathe to generate fresh ssh keys for the root user and transfer the keys to the guest machine. To execute that gratify execute the following steps:

  • Become the root user $ sudo -i
  • Generate your ssh keys. execute not add a passphrase when requested.  # ssh-keygen
  • You need 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 hold a best practice for doing this. If this is the first guest in your VirtualBox then it should hold an ip of 10.1.2.101, if it has another ip then you will need to replace for the following. For this blog gratify 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 obstruct 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 breathe asked for any login information.
  • Once this is complete you can shut down the RHEL base 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 name 7.2 RH Mongo-DB1.
  • Ensure to click the Reinitialize the MAC Address of everything 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 name 7.2 RH Mongo-DB2.
  • Ensure to click the Reinitialize the MAC Address of everything 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 name 7.2 RH Mongo-DB3.
  • Ensure to click the Reinitialize the MAC Address of everything network cards.
  • Click on Next.
  • Ensure the complete Clone option is selected.
  • Click on Clone.
  • The final step for getting the systems ready will breathe to configure the hostnames, host-only ip and the host files. They will need to furthermore 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 need to contact your IT departments on how they manage opening of ports.

    Normally in a production environment, you would hold 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 breathe using will breathe as follows:

    To execute so on each of the guests execute 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 breathe 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 discontinue firewalld # systemctl disable firewalld
  • Edit the hostname using the confiscate values from the table above.  # hostnamectl set-hostname "mongo-db1" --static
  • Edit the hosts file adding the following to etc/hosts, you should furthermore execute 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 breathe 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 breathe used to manage up to thousands of distinct MongoDB clusters in a tenants-per-cluster style — isolating cluster users to specific clusters.

    All major MongoDB Ops Manager actions can breathe driven manually through the user interface or programmatically through the relaxation API, where Ops Manager can breathe 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 breathe deployed across a minimum of three hosts in three distinct availability areas — physical servers, racks, or data centers. The loss of one host will still 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 reform credentials are able to access the cluster. The MongoDB cluster can furthermore expend SSL/TLS for over the wire encryption.

    Once a MongoDB cluster is successfully deployed by Ops Manager, the cluster’s connection string can breathe easily generated (in the case of a MongoDB replica set, this will breathe the three hostname:port pairs separated by commas). An OpenShift application can then breathe 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 invert is not necessary; in other words, Ops Manager does not need to breathe able to gain into the managed VMs directly over the network).
  • Spawn servers (VMs) running Red Hat Enterprise Linux, able to gain 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 relaxation 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 breathe light enough to login and accelerate the commands as seen in the Ops Manager agent installation information. However they hold created an ansible playbook that you will need 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 base 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 need to customize it with the information you gathered from the Ops Manager.

    You will need 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 accelerate 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 accelerate the playbook you need 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 confiscate access rights:

  • Verify that everything of the Ops Manager agents hold 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 progress Kit (CDK), OpenShift as a local deployment, and OpenShift in production. In this final section, we’re going to assume a spy at how a team can assume edge of the advanced features of OpenShift in order to automatically trot fresh versions of applications from progress to production — a process known as Continuous Delivery (or Continuous Deployment, depending on the even of automation).

    OpenShift supports different setups depending on organizational requirements. Some organizations may accelerate a completely part cluster for each environment (e.g. dev, staging, production) and others may expend a lone cluster for several environments. If you accelerate a part OpenShift PaaS for each environment, they will each hold their own dedicated and isolated resources, which is costly but ensures isolation (a problem with the progress cluster cannot move production). However, multiple environments can safely accelerate on one OpenShift cluster through the platform’s uphold for resource isolation, which allows nodes to breathe dedicated to specific environments. This means you will hold one OpenShift cluster with common masters for everything environments, but dedicated nodes assigned to specific environments. This allows for scenarios such as only allowing production projects to accelerate 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 breathe 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 elucidate workflows can breathe 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 breathe using a progress environment provisioned in a remote OpenShift cluster.

    To trot code between environments, they can assume edge of the image streams concept in OpenShift. An image stream is superficially similar to an image repository such as those establish on Docker Hub — it is a collection of related images with identifying names or “tags”. An image stream can refer 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 remark this in the diagram above — when the developer is ready for their changes to breathe picked up by the next environment in line, they simply tag the image appropriately, which will generate an image stream notification that will breathe picked up by the staging environment. The staging environment will then automatically rebuild and redeploy any containers using this image (or images who hold the changed image as a base layer). This can breathe fully automated by the expend of Jenkins or a similar CI tool; on a check-in to the source control repository, it can accelerate a test-suite and automatically tag the image if it passes.

    To trot between staging and production they can execute exactly the selfsame thing — Jenkins or a similar implement could accelerate 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 fresh versions. This would breathe accurate 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 still a manual “ok” required before changes hit production. In OpenShift this can breathe easily done by requiring the images in staging to breathe tagged manually before they are deployed to production.

    Deployment of an OpenShift Application

    Now that we’ve reviewed the workflow, let’s spy at a actual illustration of pushing an application from progress 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 illustration assumes that both environments are hosted on the selfsame OpenShift cluster, but it can breathe easily adapted to allow promotion to another OpenShift instance by using a common registry.

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

    $ oc login -u openshift-dev

    Now we’ll create two fresh 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 breathe their progress environment (mlbparks):

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

    After you accelerate this command you should breathe in the context of the progress 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 vacuous selector and an endpoint. In some cases you can hold 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 need to create one external service for each node. In their case they hold three nodes so for illustrative purposes they hold 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 need to accelerate 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 hold 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 arm they use). everything of the environment variables are in the mlbparks-template.json, so they will first create a template then create their fresh 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. accelerate 'oc status' to view your app.

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

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

    We can bag the name of the image created by the build with the back 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 drag 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 breathe 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 drag the image from the progress repository, they need to concede drag rights to the service account associated with production environment. Note that mlbparks-production is the name of the production project:

    $ oc policy add-role-to-group system:image-puller \ system:serviceaccounts:mlbparks-production \ --namespace=mlbparks To verify that the fresh policy is in place, they can check the rolebindings: $ oc bag 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 hold an image that can breathe 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 selfsame 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 piece we’ll breathe using the image stream created in the progress project that was tagged “production”:

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

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

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

    We will now demonstrate the skill to both automatically trot fresh items to production, but they will furthermore elucidate how they can update an application without having to update the MongoDB schema. They hold created a arm 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 progress project:

    $ oc project mlbparks Now using project "mlbparks" on server "https://10.1.2.2:8443". And start a fresh build based on the confide “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 fresh factor to in their application to breathe persisted to the database, they would need to Make the changes in the code as well as hold a DBA manually update the schema at the database. The following code is an illustration of how they can modify the application code without manually making changes to the MongoDB schema.

    BasicDBObject updateQuery = fresh BasicDBObject(); updateQuery.append("$set", fresh BasicDBObject() .append("division", "East")); BasicDBObject searchQuery = fresh 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 fresh version is deployed, you should breathe able to remark East under Toronto for example.

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

    OK, we’re lucky with the change, let’s tag it ready for production. Again, accelerate oc to bag 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 fresh image to the production environment.

    Rolling back can breathe done in different ways. For this example, they will roll back the production environment by tagging production with the brokendown image ID. Find the prerogative 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 principal 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 breathe automatically pushed from progress through to production combined with the power and capabilities of MongoDB as the backend of selection for applications.


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