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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 designation : DB2 10.1 Fundamentals
Vendor designation : IBM
: 138 existent Questions

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IBM DB2 10.1 Fundamentals

starting DB2: From beginner to knowledgeable | killexams.com existent Questions and Pass4sure dumps

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Altova Introduces edition 2014 of Its Developer tackle and Server utility | killexams.com existent Questions and Pass4sure dumps

BEVERLY, MA--(Marketwired - Oct 29, 2013) - Altova® (http://www.altova.com), creator of XMLSpy®, the trade leading XML editor, these days introduced the unlock of edition 2014 of its MissionKit® laptop developer tools and server utility products. MissionKit 2014 products now encompass integration with the lightning quick validation and processing capabilities of RaptorXML®, assist for Schema 1.1, XPath/XSLT/XQuery three.0, aid for unusual databases and a pleasurable deal greater. unusual features in Altova server items encompass caching alternatives in FlowForce® Server and elevated performance powered with the aid of RaptorXML across the server product line.

"we're so excited to be able to prolong the hyper-performance delivered by passage of the unparalleled RaptorXML Server to developers working in their desktop equipment. This functionality, along with effectual aid for the very newest specifications, from XML Schema 1.1 to XPath 3.0 and XSLT three.0, provides their clients the advantages of accelerated performance alongside slicing-side technology support," observed Alexander Falk, President and CEO for Altova. "This, coupled with the means to automate fundamental techniques via their high-efficiency server items, offers their valued clientele a divide potential when edifice and deploying purposes."

a pair of of the brand unusual aspects purchasable in Altova MissionKit 2014 consist of:

Integration of RaptorXML: announced earlier this year, RaptorXML Server is excessive-performance server utility in a position to validating and processing XML at lightning speeds -- while delivering the strictest feasible necessities conformance. Now the very hyper-performance engine that powers RaptorXML Server is absolutely built-in in several Altova MissionKit tools, including XMLSpy, MapForce®, and SchemaAgent®, offering lightning quickly validation and processing of XML, XSLT, XQuery, XBRL, and extra. The third-generation validation and processing engine from Altova, RaptorXML became built from the floor up to aid the very latest of complete material XML requirements, together with XML Schema 1.1, XSLT 3.0, XPath 3.0, XBRL 2.1, and myriad others.

help for Schema 1.1: XMLSpy 2014 includes crucial lead for XML Schema 1.1 validation and enhancing. The latest version of the XML Schema average, 1.1 provides unusual features aimed toward making schemas more bendy and adaptable to business cases, comparable to assertions, conditional types, open content material, and greater.

All facets of XML Schema 1.1 are supported in XMLSpy's graphical XML Schema editor and are available in entry helpers and tabs. As complete the time, the graphical enhancing paradigm of the schema editor makes it smooth to recollect and invoke these unusual elements.

assist for XML Schema 1.1 is moreover offered in SchemaAgent 2014, allowing users to imagine and manage schema relationships by means of its graphical interface. here is moreover an scholarship when connecting to SchemaAgent in XMLSpy.

Coinciding with XML Schema 1.1 help, Altova has additionally released a free, online XML Schema 1.1 know-how training path, which covers the basics of the XML Schema language as well because the adjustments brought in XML Schema 1.1.

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

aid for XPath in XMLSpy 2014 has been up-to-date to involve the newest edition of the XPath suggestion. XPath 3.0 is a superset of the XPath 2.0 suggestion and adds potent unusual performance corresponding to: dynamic characteristic cells, inline characteristic expressions, and assist for union kinds to identify simply a few. Full aid for unusual services and operators brought in XPath three.0 is accessible via knowing XPath auto-completion in text and Grid Views, as well as within the XPath Analyzer window.

assist for modifying, debugging, and profiling XSLT is now available for XSLT 3.0 as well as ripen versions. please note that a subset of XSLT 3.0 is supported considering the detached is noiseless a working draft that continues to conform. XSLT three.0 advocate conforms to the W3C XSLT three.0 Working Draft of July 10, 2012 and the XPath three.0 Candidate recommendation. despite the fact, aid in XMLSpy now offers builders the capacity to start working with this unusual edition instantly.

XSLT 3.0 takes scholarship of the brand unusual facets brought in XPath three.0. moreover, an incredible feature enabled with the aid of the unusual edition is the unusual xsl:try / xsl:catch assemble, which may moreover be used to trap and recoup from dynamic mistakes. other enhancements in XSLT three.0 involve aid for bigger order capabilities and partial features.

Story continues

As with XSLT and XPath, XMLSpy aid for XQuery now additionally contains a subset of edition three.0. builders will now fill the alternative to edit, debug, and profile XQuery three.0 with advantageous syntax coloring, bracket matching, XPath auto-completion, and other astute editing aspects.

XQuery 3.0 is, of route, an extension of XPath and hence benefits from the brand unusual functions and operators brought in XPath 3.0, akin to a unusual string concatenation operator, map operator, math functions, sequence processing, and more -- complete of which are available within the context sensitive entry helper windows and drop down menus in the XMLSpy 2014 XQuery editor.

New Database guide:

Database-enabled MissionKit products including XMLSpy, MapForce, StyleVision®, DatabaseSpy®, UModel®, and DiffDog®, now consist of comprehensive advocate for more recent versions of prior to now supported databases, as well as lead for unusual 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 commercial enterprise) 15/15.7
  • Microsoft access™ 2010/2013
  • New in Altova Server utility 2014:

    added prior in 2013, Altova's unusual line of move-platform server application products comprises FlowForce Server, MapForce Server, StyleVision Server, and RaptorXML Server. FlowForce Server provides comprehensive administration, job scheduling, and protection options for the automation of fundamental business procedures, whereas MapForce Server and StyleVision Server present high-velocity automation for initiatives designed using customary Altova MissionKit developer equipment. RaptorXML Server is the third-era, hyper-quickly validation and processing engine for XML and XBRL.

    beginning with edition 2014, Altova server items are powered by passage of RaptorXML for quicker, greater efficient processing. furthermore, FlowForce Server now supports effects caching for jobs that require a long time to process, for instance when a job requires complicated database queries or must gain its own internet carrier statistics requests. FlowForce Server directors can now agenda execution of a time-ingesting job and cache the results to stay away from these delays. The cached facts can then be offered when any consumer executes the job as a carrier, offering speedy outcomes. A job that generates a customized earnings file for the ripen day can be a fine software for caching.

    These and many more aspects can be institute in the 2014 version of MissionKit computer developer tools and Server utility. For a complete record of unusual facets, supported necessities, and trial downloads please search counsel from: http://www.altova.com/whatsnew.html

    About Altova Altova® is a application company focusing on tackle to assist builders with information management, software and utility construction, and facts integration. The creator of XMLSpy® and other award-successful XML, SQL and UML equipment, Altova is a key participant within the utility tackle business and the chief in XML solution progress equipment. Altova makes a speciality of its clients' needs via offering a product line that fulfills a huge spectrum of necessities for software construction teams. With over 4.5 million users global, together with ninety one% of Fortune 500 organizations, Altova is supercilious to serve customers from one-grownup stores to the world's largest companies. Altova is committed to supplying specifications-primarily based, platform-independent solutions which are powerful, reasonable and simple-to-use. founded in 1992, Altova is headquartered in Beverly, Massachusetts and Vienna, Austria. talk over with Altova on the internet at: http://www.altova.com.

    Altova, MissionKit, XMLSpy, MapForce, FlowForce, RaptorXML, StyleVision, UModel, DatabaseSpy, DiffDog, SchemaAgent, authentic, and MetaTeam are emblems and/or registered emblems of Altova GmbH in the united states and/or different nations. The names of and reference to other agencies and products mentioned herein may well be the emblems of their respective house owners.


    MySQL saved manner Programming | killexams.com existent Questions and Pass4sure dumps

    Written by means of guy Harrison and Steven Feuerstein, and posted by means of O'Reilly Media in March 2006 below the ISBNs 0596100892 and 978-0596100896, this publication is the first one to offer database programmers a replete dialogue of the syntax, utilization, and optimization of MySQL stored approaches, saved features, and triggers — which the authors accurately mention to jointly as "saved courses," to simplify the manuscript. Even a 12 months after the introduction of those unusual capabilities in MySQL, they fill obtained remarkably diminutive coverage by means of publication publishers. Admittedly, there are three such chapters in MySQL Administrator's e reserve and Language Reference (2nd edition), written via some of the developers of MySQL, and posted through MySQL Press. Yet this latter e-book — even though posted a month after O'Reilly's — devotes fewer than 50 pages to kept classes, and the material isn't within the printed publication itself, but within the "MySQL Language Reference" part, on the accompanying CD. That cloth, along with the online reference documentation, may well be satisfactory for the greater yardstick saved software construction needs. but for any MySQL developer who wishes to account in-depth a passage to gain the most of this unusual performance in version 5.0, they're going to seemingly want a plenty more huge medication — and that is the understanding exactly what Harrison and Feuerstein fill created.

    The authors are beneficiant in both the technical suggestions and edifice suggestions that they present. The book's material spans 636 pages, geared up into 23 chapters, grouped into four constituents, followed through an index. the primary half, "kept Programming Fundamentals," offers an introduction and then an academic, each taking a big view of MySQL saved classes. The final 4 chapters cowl language fundamentals; blocks, conditional statements, and iterative programming; SQL; and flounder handling. The ebook's 2d part, "stored application construction," may well be considered the heart of the e-book, because its five chapters current the details of growing kept courses in general, using transaction administration, using MySQL's constructed-in services, and creating one's personal kept services, in addition to triggers. The third part, "using MySQL saved programs and functions," explains probably the most benefits and disadvantages of saved courses, after which illustrates a passage to designation these kept classes from source code written in anyone of five diverse programming languages: php, Java, Perl, Python, and Microsoft.web. in the fourth and closing half, "Optimizing saved courses," the authors focus on the safety and tuning of stored programs, tuning SQL, optimizing the code, and optimizing the edifice technique itself.

    this is a substantial e-book, encompassing a pleasurable deal of technical as well as advisory tips. subsequently, no evaluate akin to this can hope to interpret or seriously remark upon every portion of every chapter of each part. Yet the overall satisfactory and utility of the manuscript can moreover be discerned without hardship through picking out only 1 of the aforesaid web programming languages, and writing some code in that language to designation some MySQL stored methods and features, to rate consequences from a test database — and developing complete of this code whereas relying fully upon the booklet below review. creating some elementary kept procedures, and calling them from some Hypertext Preprocessor and Perl scripts, verified to me that MySQL stored system Programming includes more than satisfactory insurance of the topics to be an invaluable ebook in developing essentially the most usual performance that a programmer would necessity to enforce.

    The ebook appears to fill only a few features or selected sections in want of growth. The dialogue of variable scoping, in Chapter four, is just too cursory (no database pun meant). in terms of the publication's sample code, I institute countless instances of inconsistency of formatting — peculiarly, operators corresponding to "||" and "=" being jammed up towards their adjoining aspects, without any whitespace to enhance readability. These minor flaws can be conveniently remedied within the next version. Some programming books gain equivalent errors, but complete over their textual content, which is even worse. happily, lots of the code during this reserve is neatly formatted, and the variable and program names are frequently descriptive sufficient.

    one of the vital booklet's material could fill been overlooked without grandiose loss — thereby cutting back the ebook's size, weight, and possibly expense. both chapters on simple and advanced SQL tuning involve innovations and recommendations lined with equal skill in other MySQL books, and had been no longer mandatory during this one. then again, slipshod builders who churn out lamentable code may wrangle that the final chapter, which focuses on foremost programming practices, may even be excised; but these are the very people who necessity those techniques probably the most.

    fortunately, the few weaknesses in the e-book are completely overwhelmed by passage of its high-quality traits, of which there are many. The insurance of the topics is quite extensive, however devoid of the repetition regularly viewed in lots of other technical books of this size. the explanations are written with clarity, and supply satisfactory detail for any experienced database programmer to understand the time-honored ideas, as well as the specific particulars. The pattern code easily illustrates the ideas introduced within the narration. The font, layout, organization, and fold-flat binding of this e-book, complete gain it a pleasure to read — as is characteristic of lots of O'Reilly's titles.

    moreover, any programming booklet that manages to lighten the weight of the reader by passage of offering a palpate of humor privilege here and there, can't be complete bad. Steven Feuerstein is the creator of several well-considered books on Oracle, and it was grandiose to note him poke some fun on the database heavyweight, in his alternative of pattern code to demonstrate the my_replace() function: my_replace( 'we adore the Oracle server', 'Oracle', 'MySQL').

    The potential reader who would want to be taught extra about this publication, can consult its web page on O'Reilly's web page. There they're going to find each brief and replete descriptions, established and unconfirmed errata, a hyperlink for writing a reader evaluate, an internet desk of contents and index, and a pattern chapter (quantity 6, "Error handling"), in PDF layout. moreover, the traveler can download the entire sample code in the booklet (562 information) and the sample database, as a mysqldump file.

    overall, MySQL saved method Programming is adeptly written, neatly equipped, and exhaustive in its coverage of the topics. it's and certain will remain the premier printed resource for web and database builders who wish to find out how to create and optimize kept processes, functions, and triggers inside MySQL.

    Michael J. Ross is an internet programmer, freelance creator, and the editor of PristinePlanet.com's free e-newsletter. He may moreover be reached at www.ross.ws, hosted by means of SiteGround.


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    Altova Introduces Version 2014 of Its Developer Tools and Server Software | killexams.com existent 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 involve integration with the lightning lickety-split validation and processing capabilities of RaptorXML®, advocate for Schema 1.1, XPath/XSLT/XQuery 3.0, advocate for unusual databases and much more. unusual features in Altova server products involve caching options in FlowForce® Server and increased performance powered by RaptorXML across the server product line.

    "We are so excited to be 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 ability to automate essential processes via their high-performance server products, gives their customers a divide advantage when edifice and deploying applications."

    A few of the unusual 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 practicable 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 lickety-split 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 complete 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 vital advocate for XML Schema 1.1 validation and editing. The latest version of the XML Schema standard, 1.1 adds unusual features aimed at making schemas more elastic and adaptable to business 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 smooth to understand and implement these unusual features.

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

    Coinciding with XML Schema 1.1 support, Altova has moreover 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 involve the latest version of the XPath Recommendation. XPath 3.0 is a superset of the XPath 2.0 recommendation and adds powerful unusual functionality such as: dynamic duty cells, inline duty expressions, and advocate for union types to designation just a few. Full advocate for unusual 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. 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 ability to start working with this unusual version immediately.

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

    Story continues

    As with XSLT and XPath, XMLSpy advocate for XQuery now moreover includes a subset of version 3.0. Developers will now fill 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 unusual functions and operators added in XPath 3.0, such as a unusual string concatenation operator, map operator, math functions, sequence processing, and more -- complete 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 involve complete advocate for newer versions of previously supported databases, as well as advocate for unusual 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 unusual 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 business processes, while MapForce Server and StyleVision Server offer high-speed automation for projects designed using intimate 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 knotty database queries or needs to gain 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 be 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 be a pleasurable 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 unusual features, supported standards, and trial 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 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 supercilious 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 be the trademarks of their respective owners.


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

    Current progress cycles visage many challenges such as an evolving landscape of application architecture (Monolithic to Microservices), the necessity to frequently deploy features, and unusual IaaS and PaaS environments. This causes many issues throughout the organization, from the progress teams complete the passage to operations and management.

    In this blog post, they will expose 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 smooth it is to enact agile application progress with MongoDB and OpenShift.

    MongoDB is the next-generation database that is built for rapid and iterative application development. Its elastic data model — the ability 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 aid manage a knotty 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 moreover be 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 experience within a stable, secure, and scalable operating system. Application lifecycle management and agile application progress tooling multiply 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 succeed this example, you will necessity to meet a number of requirements. You will necessity 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 moreover 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 moreover 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 moreover seeks to decipher 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 necessity 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 supersede or combat 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 necessity 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 necessity to enact 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 be used to manage the lifecycle of a virtual machine. This utensil is used for the installation and management of the Red Hat Container progress Kit.

    Vagrant is not included in any yardstick repository, so they will necessity to install it. You can install Vagrant by enabling the SCLO repository or you can rate 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 rate updates. To enact this you will necessity to succeed 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 remedy 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 remedy domain:

  • Open VirtualBox, this should be 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 be 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 quicken the very passage on every platform. However, modern microservice deployments typically expend a scheduler such as Kubernetes to quicken 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 smooth to quicken knotty deployments resembling production. This means knotty applications can be developed using production grade tools from the very start, significance developers are unlikely to experience 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 discontinuance of this blog post you will know how to quicken an application on top of OpenShift and will be intimate with the core features of the CDK and OpenShift. Let’s rate started…

    Installing the CDK

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

    Start by going to Red Hat Product Downloads (note that you will necessity a Red Hat subscription to access this). Select ‘Red Hat Container progress Kit’ under Product Variant, and the appropriate 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 aid 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 be used to register the unusual 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 designation 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 necessity 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 necessity to be 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 be reachable only by IP address from within the VM.

    Save the changes and start the virtual machine :

    $ vagrant up

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

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

    Now that they fill 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 be 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 be openshift-dev/devel. You can moreover expend your Red Hat credentials to login. In the console, they create a unusual project:

    Next, they create a unusual application using one of the built-in ‘Instant Apps’. Instant Apps are predefined application templates that haul specific images. These are an smooth passage to quickly rate 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 succeed along with the webhook steps later, you’ll necessity 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 vital to us – GITHUB_WEBHOOK_SECRET and APPLICATION_DOMAIN:

  • GITHUB_WEBHOOK_SECRET: this province allows us to create a clandestine to expend with the GitHub webhook for automatic builds. You don’t necessity to specify this, but you’ll necessity to recollect the value later if you do.
  • APPLICATION_DOMAIN: this province will determine where they can access their application. This value must involve 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 haul the code from GitHub, find the appropriate Docker image in the Red Hat repository, and moreover 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 notice relish this:

    In order to expend the Landrush plugin, there is additional steps that are required to configure dnsmasq. To enact that you will necessity to enact 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 necessity 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 necessity a passage of routing from the public internet to the Vagrant machine running on your host. An smooth passage to achieve this is to expend a third party forwarding service such as ultrahook or ngrok. They necessity 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 clandestine (if you specified one when setting up the OpenShift project). If your webhook is configured correctly you should note something relish this:

    To test out the pipeline, they necessity to gain a change to their project and push a relegate to the repo.

    Any smooth passage to enact this is to edit the views/index.html file, e.g: (Note that you can moreover enact this through the GitHub web interface if you’re ardor lazy). relegate and push this change to the GitHub repo, and they can note a unusual build is triggered automatically within the web console. Once the build completes, if they again open their application they should note the updated front page.

    We now fill Continuous Deployment configured for their application. Throughout this blog post, we’ve used the OpenShift web interface. However, they could fill performed the very actions using the OpenShift console (oc) at the command-line. The easiest passage 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 be deployed to a node together. A pod represents the smallest unit that can be deployed and managed in OpenShift. The pod will be assigned its own IP address. complete of the containers in the pod will participate local storage and networking.

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

    For their instance application, they fill a Pod running the application. Pods can be scaled up/down from the OpenShift interface.

    Replication Controllers

    These manage the lifecycle of Pods.They ensure that the remedy 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 unusual code relegate (assuming you set-up the GitHub webhooks) OpenShift will update your application. unusual pods will be started with the aid of replication controllers running your unusual application version. The ripen pods will be deleted. OpenShift deployments can discharge rollbacks and provide various deploy strategies. It’s hard to overstate the advantages of being able to quicken a production environment in progress and the efficiencies gained from the lickety-split feedback cycle of a Continuous Deployment pipeline.

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

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

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

    Please discharge the following steps:

  • In VirtualBox create a unusual guest image and call it RHEL Base. They used the following information: a. memory 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 enact a yum update on the guest RHEL install.

    The final step will be to generate unusual ssh keys for the root user and transfer the keys to the guest machine. To enact that please enact the following steps:

  • Become the root user $ sudo -i
  • Generate your ssh keys. enact not add a passphrase when requested.  # ssh-keygen
  • You necessity 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 fill a best exercise for doing this. If this is the first guest in your VirtualBox then it should fill an ip of 10.1.2.101, if it has another ip then you will necessity to supersede 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 hide 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 be 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 designation 7.2 RH Mongo-DB1.
  • Ensure to click the Reinitialize the MAC Address of complete network cards.
  • Click on Next.
  • Ensure the replete Clone option is selected.
  • Click on Clone.
  • Right click on the RHEL guest OS and select Clone.
  • Enter the designation 7.2 RH Mongo-DB2.
  • Ensure to click the Reinitialize the MAC Address of complete network cards.
  • Click on Next.
  • Ensure the replete Clone option is selected.
  • Click on Clone.
  • Right click on the RHEL guest OS and select Clone.
  • Enter the designation 7.2 RH Mongo-DB3.
  • Ensure to click the Reinitialize the MAC Address of complete network cards.
  • Click on Next.
  • Ensure the replete Clone option is selected.
  • Click on Clone.
  • The final step for getting the systems ready will be to configure the hostnames, host-only ip and the host files. They will necessity to moreover 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 necessity to contact your IT departments on how they manage opening of ports.

    Normally in a production environment, you would fill 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 be using will be as follows:

    To enact so on each of the guests enact 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 be 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 appropriate values from the table above.  # hostnamectl set-hostname "mongo-db1" --static
  • Edit the hosts file adding the following to etc/hosts, you should moreover enact 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 be leveraged throughout the development, test, and production lifecycle, with critical functionality ranging from cluster performance monitoring data, alerting, no-downtime upgrades, advanced configuration and scaling, as well as backup and restore. Ops Manager can be used to manage up to thousands of divide MongoDB clusters in a tenants-per-cluster style — isolating cluster users to specific clusters.

    All major MongoDB Ops Manager actions can be driven manually through the user interface or programmatically through the ease API, where Ops Manager can be 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 be deployed across a minimum of three hosts in three divide 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 remedy credentials are able to access the cluster. The MongoDB cluster can moreover expend SSL/TLS for over the wire encryption.

    Once a MongoDB cluster is successfully deployed by Ops Manager, the cluster’s connection string can be easily generated (in the case of a MongoDB replica set, this will be the three hostname:port pairs separated by commas). An OpenShift application can then be 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 transpose is not necessary; in other words, Ops Manager does not necessity to be 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 ease 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 be smooth enough to login and quicken the commands as seen in the Ops Manager agent installation information. However they fill created an ansible playbook that you will necessity 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 necessity to customize it with the information you gathered from the Ops Manager.

    You will necessity 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 quicken 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 quicken the playbook you necessity 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 appropriate access rights:

  • Verify that complete of the Ops Manager agents fill 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 pinch a notice at how a team can pinch advantage of the advanced features of OpenShift in order to automatically scuttle unusual 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 quicken a completely divide cluster for each environment (e.g. dev, staging, production) and others may expend a sole cluster for several environments. If you quicken a divide OpenShift PaaS for each environment, they will each fill their own dedicated and isolated resources, which is costly but ensures isolation (a problem with the progress cluster cannot impress production). However, multiple environments can safely quicken on one OpenShift cluster through the platform’s advocate for resource isolation, which allows nodes to be dedicated to specific environments. This means you will fill one OpenShift cluster with common masters for complete environments, but dedicated nodes assigned to specific environments. This allows for scenarios such as only allowing production projects to quicken 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 be 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 expose workflows can be 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 be using a progress environment provisioned in a remote OpenShift cluster.

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

    To scuttle between staging and production they can enact exactly the very thing — Jenkins or a similar utensil could quicken 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 unusual versions. This would be true 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 be easily done by requiring the images in staging to be tagged manually before they are deployed to production.

    Deployment of an OpenShift Application

    Now that we’ve reviewed the workflow, let’s notice at a existent instance 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 instance assumes that both environments are hosted on the very OpenShift cluster, but it can be easily adapted to allow promotion to another OpenShift instance by using a common registry.

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

    $ oc login -u openshift-dev

    Now we’ll create two unusual 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 be their progress environment (mlbparks):

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

    After you quicken this command you should be 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 barren selector and an endpoint. In some cases you can fill multiple IP addresses assigned to your endpoint and the service will act as a load balancer. This will not drudgery 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 necessity to create one external service for each node. In their case they fill three nodes so for illustrative purposes they fill 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 necessity to quicken 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 fill 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 offshoot they use). complete of the environment variables are in the mlbparks-template.json, so they will first create a template then create their unusual 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. quicken 'oc status' to view your app.

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

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

    We can rate the designation of the image created by the build with the aid of the limn command:

    $ oc limn 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 haul 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 be tied to this particular version. If they hadn’t done this, production would automatically track changes to latest, which would involve untested code.

    To allow the production project to haul the image from the progress repository, they necessity to accord haul rights to the service account associated with production environment. Note that mlbparks-production is the designation of the production project:

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

    $ oc new-app mlbparks/mlbparks:production --> institute image 5621fed (11 minutes old) in image stream "mlbparks in project mlbparks" under tag :production for "mlbparks/mlbparks:production" * This image will be deployed in deployment config "mlbparks" * Port 8080/tcp will be load balanced by service "mlbparks" --> Creating resources with label app=mlbparks ... DeploymentConfig "mlbparks" created Service "mlbparks" created --> Success quicken '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 ability to both automatically scuttle unusual items to production, but they will moreover expose how they can update an application without having to update the MongoDB schema. They fill created a offshoot 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 unusual build based on the relegate “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 unusual component to in their application to be persisted to the database, they would necessity to gain the changes in the code as well as fill a DBA manually update the schema at the database. The following code is an instance of how they can modify the application code without manually making changes to the MongoDB schema.

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

    Once the build finishes running, a deployment task will start that will supersede the running container. Once the unusual version is deployed, you should be able to note 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 joyful with the change, let’s tag it ready for production. Again, quicken oc to rate 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 unusual image to the production environment.

    Rolling back can be done in different ways. For this example, they will roll back the production environment by tagging production with the ripen 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 vital features for organizations, including source-to-image tooling, image streams, project and user isolation and a web UI. This post showed how these features drudgery together to provide a complete CD workflow where code can be automatically pushed from progress through to production combined with the power and capabilities of MongoDB as the backend of election for applications.


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