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C2090-610 DB2 10.1 Fundamentals

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C2090-610 exam Dumps Source : DB2 10.1 Fundamentals

Test Code : C2090-610
Test denomination : DB2 10.1 Fundamentals
Vendor denomination : IBM
: 138 true Questions

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IBM DB2 10.1 Fundamentals

beginning DB2: From beginner to expert | killexams.com true Questions and Pass4sure dumps

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Altova Introduces version 2014 of Its Developer outfit and Server utility | killexams.com true Questions and Pass4sure dumps

BEVERLY, MA--(Marketwired - Oct 29, 2013) - Altova® (http://www.altova.com), creator of XMLSpy®, the business main XML editor, today introduced the liberate of edition 2014 of its MissionKit® computing device developer outfit and server software items. MissionKit 2014 products now embrace integration with the lightning quick validation and processing capabilities of RaptorXML®, profit for Schema 1.1, XPath/XSLT/XQuery 3.0, pilot for unique databases and much extra. unique features in Altova server products consist of caching options in FlowForce® Server and improved efficiency powered by means of RaptorXML across the server product line.

"we are so excited to live in a position to prolong the hyper-efficiency delivered by means of the unparalleled RaptorXML Server to builders working in their desktop equipment. This performance, together with potent pilot for the very latest specifications, from XML Schema 1.1 to XPath three.0 and XSLT 3.0, gives their consumers the benefits of expanded performance alongside cutting-edge know-how guide," mentioned Alexander Falk, President and CEO for Altova. "This, coupled with the capacity to automate elementary tactics via their excessive-efficiency server products, gives their valued clientele a transparent abilities when constructing and deploying applications."

just a few of the brand unique elements purchasable in Altova MissionKit 2014 consist of:

Integration of RaptorXML: introduced previous this yr, RaptorXML Server is high-efficiency server application in a position to validating and processing XML at lightning speeds -- while delivering the strictest feasible requisites conformance. Now the equal hyper-performance engine that powers RaptorXML Server is entirely built-in in a number of Altova MissionKit equipment, including XMLSpy, MapForce®, and SchemaAgent®, offering lightning quick validation and processing of XML, XSLT, XQuery, XBRL, and greater. The third-era validation and processing engine from Altova, RaptorXML turned into developed from the floor as much as assist the very newest of every sole crucial XML specifications, together with XML Schema 1.1, XSLT three.0, XPath three.0, XBRL 2.1, and myriad others.

assist for Schema 1.1: XMLSpy 2014 comprises captious pilot for XML Schema 1.1 validation and enhancing. The newest version of the XML Schema ordinary, 1.1 provides unique elements geared toward making schemas more bendy and adaptable to enterprise cases, reminiscent of assertions, conditional types, open content material, and extra.

All features of XML Schema 1.1 are supported in XMLSpy's graphical XML Schema editor and can live organize in entry helpers and tabs. As always, the graphical modifying paradigm of the schema editor makes it light to live cognizant and implement these unique points.

support for XML Schema 1.1 is besides offered in SchemaAgent 2014, allowing clients to visualize and manage schema relationships by the utilize of its graphical interface. this is additionally an expertise when connecting to SchemaAgent in XMLSpy.

Coinciding with XML Schema 1.1 aid, Altova has besides released a free, online XML Schema 1.1 technology training direction, which covers the basics of the XML Schema language as smartly because the adjustments added in XML Schema 1.1.

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

aid for XPath in XMLSpy 2014 has been updated to encompass the newest version of the XPath recommendation. XPath three.0 is a superset of the XPath 2.0 recommendation and adds powerful unique functionality equivalent to: dynamic feature cells, inline feature expressions, and uphold for union forms to identify simply a number of. Full assist for brand spanking unique capabilities and operators introduced in XPath three.0 is attainable via brilliant XPath auto-completion in text and Grid Views, in addition to within the XPath Analyzer window.

aid for enhancing, debugging, and profiling XSLT is now attainable for XSLT three.0 as well as outdated types. gratify observe that a subset of XSLT three.0 is supported considering the typical remains a working draft that continues to conform. XSLT three.0 uphold conforms to the W3C XSLT 3.0 Working Draft of July 10, 2012 and the XPath 3.0 Candidate recommendation. although, uphold in XMLSpy now offers builders the potential to birth working with this unique version instantly.

XSLT 3.0 takes skills of the unique aspects brought in XPath three.0. moreover, an immense characteristic enabled by using the unique edition is the unique xsl:are attempting / xsl:capture assemble, which may besides live used to entice and recuperate from dynamic error. other enhancements in XSLT 3.0 consist of assist for better order functions and partial features.

Story continues

As with XSLT and XPath, XMLSpy pilot for XQuery now besides includes a subset of edition three.0. developers will now beget the option to edit, debug, and profile XQuery three.0 with effectual syntax coloring, bracket matching, XPath auto-completion, and other brilliant editing elements.

XQuery three.0 is, of path, an extension of XPath and hence merits from the brand unique services and operators delivered in XPath 3.0, equivalent to a unique string concatenation operator, map operator, math functions, sequence processing, and more -- every sole of which are available within the context choice entry helper windows and drop down menus in the XMLSpy 2014 XQuery editor.

New Database assist:

Database-enabled MissionKit items together with XMLSpy, MapForce, StyleVision®, DatabaseSpy®, UModel®, and DiffDog®, now encompass comprehensive assist for more recent types of in the past supported databases, as well as uphold for brand spanking unique database providers:

  • 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 commercial enterprise) 15/15.7
  • Microsoft entry™ 2010/2013
  • New in Altova Server application 2014:

    delivered previous in 2013, Altova's unique line of pass-platform server software items contains FlowForce Server, MapForce Server, StyleVision Server, and RaptorXML Server. FlowForce Server offers comprehensive management, job scheduling, and security options for the automation of measure business methods, whereas MapForce Server and StyleVision Server present high-speed automation for projects designed the usage of common Altova MissionKit developer equipment. RaptorXML Server is the third-era, hyper-quick validation and processing engine for XML and XBRL.

    starting with edition 2014, Altova server items are powered via RaptorXML for quicker, more productive processing. moreover, FlowForce Server now supports consequences caching for jobs that require a very long time to procedure, as an instance when a job requires knotty database queries or must gain its personal net provider statistics requests. FlowForce Server administrators can now schedule execution of a time-drinking job and cache the outcomes to avert these delays. The cached information can then live supplied when any user executes the job as a provider, providing speedy effects. A job that generates a customized earnings file for the obsolete day could live a sociable application for caching.

    These and many more features are available in the 2014 edition of MissionKit laptop developer tools and Server utility. For a complete checklist of unique features, supported requirements, and visitation downloads gratify consult with: http://www.altova.com/whatsnew.html

    About Altova Altova® is a software enterprise that specialize in outfit to uphold builders with information management, utility and utility development, and data integration. The creator of XMLSpy® and different award-winning XML, SQL and UML tools, Altova is a key participant in the utility outfit trade and the chief in XML answer construction tools. Altova focuses on its shoppers' wants by using providing a product line that fulfills a vast spectrum of requirements for utility construction teams. With over 4.5 million users worldwide, together with ninety one% of Fortune 500 corporations, Altova is disdainful to serve purchasers from one-grownup retail outlets to the world's greatest groups. Altova is dedicated to delivering specifications-based mostly, platform-independent options that are potent, in your charge ambit and easy-to-use. based in 1992, Altova is headquartered in Beverly, Massachusetts and Vienna, Austria. talk over with Altova on the web at: http://www.altova.com.

    Altova, MissionKit, XMLSpy, MapForce, FlowForce, RaptorXML, StyleVision, UModel, DatabaseSpy, DiffDog, SchemaAgent, genuine, and MetaTeam are trademarks and/or registered trademarks of Altova GmbH within the u.s. and/or other countries. The names of and reference to other companies and products outlined herein may well live the emblems of their respective homeowners.


    MySQL kept manner Programming | killexams.com true Questions and Pass4sure dumps

    Written through man Harrison and Steven Feuerstein, and posted by O'Reilly Media in March 2006 under the ISBNs 0596100892 and 978-0596100896, this e-book is the first one to offer database programmers a replete discussion of the syntax, utilization, and optimization of MySQL stored processes, saved capabilities, and triggers — which the authors wisely refer to together as "kept courses," to simplify the manuscript. Even a 12 months after the introduction of those unique capabilities in MySQL, they beget obtained remarkably dinky coverage by ebook publishers. Admittedly, there are three such chapters in MySQL Administrator's e-book and Language Reference (2nd edition), written by one of the most developers of MySQL, and published via MySQL Press. Yet this latter publication — even though posted a month after O'Reilly's — devotes fewer than 50 pages to saved programs, and the cloth isn't within the printed e-book itself, however within the "MySQL Language Reference" half, on the accompanying CD. That fabric, in conjunction with the online reference documentation, could live ample for the more simple saved application construction wants. however for any MySQL developer who wishes to reckon in-depth the route to profit from this unique functionality in edition 5.0, they're going to probably want a much more immense medication — and that's the judgement exactly what Harrison and Feuerstein beget created.

    The authors are generous in both the technical information and construction assistance that they present. The e-book's fabric spans 636 pages, organized into 23 chapters, grouped into four ingredients, adopted through an index. the primary part, "kept Programming Fundamentals," gives an introduction after which a tutorial, both taking a wide view of MySQL kept courses. The closing 4 chapters cowl language fundamentals; blocks, conditional statements, and iterative programming; SQL; and mistake managing. The booklet's 2nd part, "stored program building," may live considered the coronary heart of the e-book, as a result of its 5 chapters present the details of creating kept programs in popular, the usage of transaction administration, the usage of MySQL's developed-in capabilities, and creating one's personal saved services, as well as triggers. The third part, "the usage of MySQL stored courses and applications," explains one of the most benefits and disadvantages of kept classes, and then illustrates the route to denomination these saved programs from supply code written in anyone of five distinctive programming languages: php, Java, Perl, Python, and Microsoft.internet. in the fourth and ultimate part, "Optimizing saved programs," the authors heart of attention on the security and tuning of stored programs, tuning SQL, optimizing the code, and optimizing the development procedure itself.

    here is a considerable book, encompassing a Great deal of technical in addition to advisory tips. consequently, no review comparable to this can hope to define or critically comment upon each section of every chapter of each half. Yet the common fine and utility of the manuscript can besides live discerned effortlessly by means of deciding upon only one of the aforesaid internet programming languages, and writing some code in that language to denomination some MySQL kept methods and functions, to come by results from a verify database — and setting up every sole of this code while relying totally upon the booklet below review. creating some elementary saved methods, and calling them from some personal home page and Perl scripts, established to me that MySQL stored system Programming includes greater than satisfactory coverage of the theme matters to live a useful e bespeak in developing essentially the most commonplace functionality that a programmer would deserve to implement.

    The booklet looks to beget very few aspects or particular sections in want of growth. The discussion of variable scoping, in Chapter four, is just too cursory (no database pun meant). in terms of the booklet's pattern code, I organize numerous circumstances of inconsistency of formatting — exceptionally, operators equivalent to "||" and "=" being jammed up against their adjoining facets, without any whitespace to expand readability. These minor flaws may well live conveniently remedied in the next edition. Some programming books gain identical errors, however throughout their text, which is even worse. happily, lots of the code during this publication is neatly formatted, and the variable and program names are often descriptive sufficient.

    one of the most e-book's fabric might beget been neglected with out extremely sociable loss — thereby decreasing the ebook's dimension, weight, and most likely cost. both chapters on fundamental and superior SQL tuning hold ideas and suggestions lined with equal skill in other MySQL books, and had been no longer essential during this one. however, slipshod developers who churn out lamentable code might wrangle that the closing chapter, which specializes in top-quality programming practices, might even live excised; but these are the very individuals who necessity those strategies the most.

    fortunately, the few weaknesses in the booklet are completely overwhelmed via its tremendous characteristics, of which there are many. The coverage of the themes is fairly wide, but with out the repetition often seen in lots of other technical books of this size. the explanations are written with readability, and provide satisfactory detail for any skilled database programmer to reckon the frequent ideas, as well as the certain particulars. The pattern code without problems illustrates the concepts offered in the narration. The font, design, firm, and fold-flat binding of this e-book, every sole gain it a delight to read — as is impute of lots of O'Reilly's titles.

    additionally, any programming publication that manages to lighten the load of the reader by providing a dash of humor here and there, can't live every sole unhealthy. Steven Feuerstein is the author of a couple of neatly-considered books on Oracle, and it was first-rate to observe him poke some enjoyable on the database heavyweight, in his election of pattern code to array the my_replace() feature: my_replace( 'we fancy the Oracle server', 'Oracle', 'MySQL').

    The prospective reader who would dote to gain lore of extra about this e-book, can consult its net web page on O'Reilly's web page. There they will find both brief and entire descriptions, tested and unconfirmed errata, a link for writing a reader assessment, an internet table of contents and index, and a pattern chapter (number 6, "Error managing"), in PDF structure. moreover, the tourist can down load every sole of the pattern code in the bespeak (562 information) and the pattern database, as a mysqldump file.

    average, MySQL saved system Programming is adeptly written, neatly prepared, and exhaustive in its coverage of the theme matters. it is and certain will stay the premier printed resource for internet and database builders who wish to find out how to create and optimize kept techniques, features, and triggers inside MySQL.

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


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    DB2 10.1 Fundamentals

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    Altova Introduces Version 2014 of Its Developer Tools and Server Software | killexams.com true 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 expeditiously validation and processing capabilities of RaptorXML®, uphold for Schema 1.1, XPath/XSLT/XQuery 3.0, uphold for unique databases and much more. unique 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 live 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 faculty to automate essential processes via their high-performance server products, gives their customers a discrete handicap when building and deploying applications."

    A few of the unique 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 expeditiously 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 every sole apropos XML standards, including XML Schema 1.1, XSLT 3.0, XPath 3.0, XBRL 2.1, and myriad others.

    Support for Schema 1.1: XMLSpy 2014 includes valuable uphold for XML Schema 1.1 validation and editing. The latest version of the XML Schema standard, 1.1 adds unique features aimed at making schemas more springy 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 light to understand and implement these unique features.

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

    Coinciding with XML Schema 1.1 support, Altova has besides 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 unique functionality such as: dynamic duty cells, inline duty expressions, and uphold for union types to denomination just a few. Full uphold for unique functions and operators added in XPath 3.0 is available through brilliant 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 measure is silent 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 faculty to start working with this unique version immediately.

    XSLT 3.0 takes handicap of the unique features added in XPath 3.0. In addition, a major feature enabled by the unique version is the unique xsl:try / xsl:catch construct, which can live 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 besides includes a subset of version 3.0. Developers will now beget the option to edit, debug, and profile XQuery 3.0 with helpful syntax coloring, bracket matching, XPath auto-completion, and other brilliant editing features.

    XQuery 3.0 is, of course, an extension of XPath and therefore benefits from the unique functions and operators added in XPath 3.0, such as a unique string concatenation operator, map operator, math functions, sequence processing, and more -- every sole 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 unique 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 unique 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 familiar 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 avert these delays. The cached data can then live 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 live a sociable 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 unique 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 development tools. Altova focuses on its customers' needs by offering a product line that fulfills a broad spectrum of requirements for software development teams. With over 4.5 million users worldwide, including 91% of Fortune 500 organizations, Altova is disdainful to serve clients from one-person shops to the world's largest organizations. Altova is committed to delivering standards-based, platform-independent solutions that are powerful, affordable and easy-to-use. Founded in 1992, Altova is headquartered in Beverly, Massachusetts and Vienna, Austria. Visit Altova on the Web at: http://www.altova.com.

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


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

    Current development cycles puss many challenges such as an evolving landscape of application architecture (Monolithic to Microservices), the necessity to frequently deploy features, and unique IaaS and PaaS environments. This causes many issues throughout the organization, from the development teams every sole the route to operations and management.

    In this blog post, they will demonstrate 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 attain agile application development with MongoDB and OpenShift.

    MongoDB is the next-generation database that is built for rapid and iterative application development. Its springy data model — the faculty 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 profit 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 besides live 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 development tooling expand 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 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 besides 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 besides a deployment and orchestration tool. In many respects, aiming to provide large 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 besides seeks to solve other major unsolved IT challenges.

    We will install the Automation Agent onto the servers that will become fragment of the MongoDB replica set. The Automation Agent is fragment 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 replace or affray with the foundation 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 attain 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 live used to manage the lifecycle of a virtual machine. This tool is used for the installation and management of the Red Hat Container development Kit.

    Vagrant is not included in any measure repository, so they will necessity to install it. You can install Vagrant by enabling the SCLO repository or you can come by it directly from the Vagrant website. They will utilize 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 development Kit requires a virtualization software stack to execute. In this blog they will utilize VirtualBox for the virtualization software.

    VirtualBox is best done using a repository to ensure you can come by updates. To attain this you will necessity 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 amend 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 amend domain:

  • Open VirtualBox, this should live 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 live a vboxnet0 as the network, click on it and click on the edit icon (looks dote 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 flee the very route on every platform. However, modern microservice deployments typically utilize a scheduler such as Kubernetes to flee 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 development Kit (CDK).

    The Red Hat CDK is a customized virtual machine that makes it light to flee knotty deployments resembling production. This means knotty applications can live developed using production grade tools from the very start, meaning developers are unlikely to sustain problems stemming from differences in the development 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 depart through the entire workflow. By the quit of this blog post you will know how to flee an application on top of OpenShift and will live familiar with the core features of the CDK and OpenShift. Let’s come by started…

    Installing the CDK

    The prerequisites for running the CDK are Vagrant and a virtualization client (VirtualBox, VMware Fusion, libvirt). gain confident 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 development Kit’ under Product Variant, and the usurp 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 profit 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 live used to register the unique 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 denomination 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 utilize 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 utilize 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 live 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 live reachable only by IP address from within the VM.

    Save the changes and start the virtual machine :

    $ vagrant up

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

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

    Now that they beget 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 live 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 live openshift-dev/devel. You can besides utilize your Red Hat credentials to login. In the console, they create a unique project:

    Next, they create a unique application using one of the built-in ‘Instant Apps’. Instant Apps are predefined application templates that tug specific images. These are an light route to quickly come by 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 utilize the source code from the OpenShift GitHub repository located here. If you want to supervene 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 valuable to us – GITHUB_WEBHOOK_SECRET and APPLICATION_DOMAIN:

  • GITHUB_WEBHOOK_SECRET: this sphere allows us to create a covert to utilize 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 sphere will determine where they can access their application. This value must embrace the Top smooth 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 utilize it later on.

    OpenShift will then tug the code from GitHub, find the usurp Docker image in the Red Hat repository, and besides 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 discover dote this:

    In order to utilize the Landrush plugin, there is additional steps that are required to configure dnsmasq. To attain that you will necessity to attain 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 route of routing from the public internet to the Vagrant machine running on your host. An light route to achieve this is to utilize 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 depart to Settings -> Webhooks & services -> Add webhook. Under Payload URL enter the URL that the forwarding service gave you, plus the covert (if you specified one when setting up the OpenShift project). If your webhook is configured correctly you should observe something dote this:

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

    Any light route to attain this is to edit the views/index.html file, e.g: (Note that you can besides attain this through the GitHub web interface if you’re fervor lazy). commit and push this change to the GitHub repo, and they can observe a unique build is triggered automatically within the web console. Once the build completes, if they again open their application they should observe the updated front page.

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

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

    Pods

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

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

    For their case application, they beget a Pod running the application. Pods can live scaled up/down from the OpenShift interface.

    Replication Controllers

    These manage the lifecycle of Pods.They ensure that the amend 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 unique code commit (assuming you set-up the GitHub webhooks) OpenShift will update your application. unique pods will live started with the profit of replication controllers running your unique application version. The obsolete pods will live deleted. OpenShift deployments can accomplish rollbacks and provide various deploy strategies. It’s difficult to overstate the advantages of being able to flee a production environment in development and the efficiencies gained from the expeditiously feedback cycle of a Continuous Deployment pipeline.

    In this post, they beget shown how to utilize the Red Hat CDK to achieve both of these goals within a short-time frame and now beget a Node.js and MongoDB application running in containers, deployed using the OpenShift PaaS. This is a Great route to quickly come by 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 live required to set up the replica set. They will not walk through every sole of the steps of setting up Red Hat as this is prerequisite knowledge.

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

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

    Please accomplish the following steps:

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

    The final step will live to generate unique ssh keys for the root user and transfer the keys to the guest machine. To attain that gratify attain the following steps:

  • Become the root user $ sudo -i
  • Generate your ssh keys. attain 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 beget a best rehearse for doing this. If this is the first guest in your VirtualBox then it should beget an ip of 10.1.2.101, if it has another ip then you will necessity 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 live asked for any login information.
  • Once this is complete you can shut down the RHEL foundation 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 denomination 7.2 RH Mongo-DB1.
  • Ensure to click the Reinitialize the MAC Address of every sole 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 denomination 7.2 RH Mongo-DB2.
  • Ensure to click the Reinitialize the MAC Address of every sole 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 denomination 7.2 RH Mongo-DB3.
  • Ensure to click the Reinitialize the MAC Address of every sole network cards.
  • Click on Next.
  • Ensure the replete Clone option is selected.
  • Click on Clone.
  • The final step for getting the systems ready will live to configure the hostnames, host-only ip and the host files. They will necessity to besides 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 beget the servers in an internal DNS system, however for the sake of this blog they will utilize 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 live using will live as follows:

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

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

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

    To utilize Ops Manager with Ansible and OpenShift:

  • Install and utilize a MongoDB Ops Manager, and record the URL that it is accessible at (“OpsManagerCentralURL”)
  • Ensure that the MongoDB Ops Manager is accessible over the network at the OpsManagerCentralURL from the servers (VMs) where they will deploy MongoDB. (Note that the reverse is not necessary; in other words, Ops Manager does not necessity to live able to compass into the managed VMs directly over the network).
  • Spawn servers (VMs) running Red Hat Enterprise Linux, able to compass 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). utilize 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 live light enough to login and flee the commands as seen in the Ops Manager agent installation information. However they beget 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 foundation 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 flee 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 flee 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 usurp access rights:

  • Verify that every sole of the Ops Manager agents beget 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 development Kit (CDK), OpenShift as a local deployment, and OpenShift in production. In this final section, we’re going to acquire a discover at how a team can acquire handicap of the advanced features of OpenShift in order to automatically wobble unique versions of applications from development to production — a process known as Continuous Delivery (or Continuous Deployment, depending on the smooth of automation).

    OpenShift supports different setups depending on organizational requirements. Some organizations may flee a completely divorce cluster for each environment (e.g. dev, staging, production) and others may utilize a sole cluster for several environments. If you flee a divorce OpenShift PaaS for each environment, they will each beget their own dedicated and isolated resources, which is costly but ensures isolation (a problem with the development cluster cannot affect production). However, multiple environments can safely flee on one OpenShift cluster through the platform’s uphold for resource isolation, which allows nodes to live dedicated to specific environments. This means you will beget one OpenShift cluster with common masters for every sole environments, but dedicated nodes assigned to specific environments. This allows for scenarios such as only allowing production projects to flee on the more powerful / expensive nodes.

    OpenShift integrates well with existing Continuous Integration / Continuous Delivery tools. Jenkins, for example, is available for utilize inside the platform and can live 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 demonstrate workflows can live 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 live using a development environment provisioned in a remote OpenShift cluster.

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

    To wobble between staging and production they can attain exactly the very thing — Jenkins or a similar tool could flee 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 unique versions. This would live amend 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 silent a manual “ok” required before changes hit production. In OpenShift this can live easily done by requiring the images in staging to live tagged manually before they are deployed to production.

    Deployment of an OpenShift Application

    Now that we’ve reviewed the workflow, let’s discover at a true case of pushing an application from development to production. They will utilize 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 case assumes that both environments are hosted on the very OpenShift cluster, but it can live easily adapted to allow promotion to another OpenShift instance by using a common registry.

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

    $ oc login -u openshift-dev

    Now we’ll create two unique 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 live their development environment (mlbparks):

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

    After you flee this command you should live in the context of the development 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 empty selector and an endpoint. In some cases you can beget multiple IP addresses assigned to your endpoint and the service will act as a load balancer. This will not toil 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 beget three nodes so for illustrative purposes they beget 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 flee 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 beget the endpoints for the external replica set created they can now create the MLB parks using a template. They will utilize the source code from their demo GitHub repo and the s2i build strategy which will create a container for their source code (note this repository has no Dockerfile in the fork they use). every sole of the environment variables are in the mlbparks-template.json, so they will first create a template then create their unique app:

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

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

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

    We can come by the denomination of the image created by the build with the profit of the record command:

    $ oc record 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 tug 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 utilize 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 live 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 tug the image from the development repository, they necessity to award tug rights to the service account associated with production environment. Note that mlbparks-production is the denomination of the production project:

    $ oc policy add-role-to-group system:image-puller \ system:serviceaccounts:mlbparks-production \ --namespace=mlbparks To verify that the unique policy is in place, they can check the rolebindings: $ oc come by 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 beget an image that can live 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 utilize 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 fragment we’ll live using the image stream created in the development project that was tagged “production”:

    $ oc new-app mlbparks/mlbparks:production --> organize image 5621fed (11 minutes old) in image stream "mlbparks in project mlbparks" under tag :production for "mlbparks/mlbparks:production" * This image will live deployed in deployment config "mlbparks" * Port 8080/tcp will live load balanced by service "mlbparks" --> Creating resources with label app=mlbparks ... DeploymentConfig "mlbparks" created Service "mlbparks" created --> Success flee '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 faculty to both automatically wobble unique items to production, but they will besides demonstrate how they can update an application without having to update the MongoDB schema. They beget created a fork of the code in which they will now add the division to the league for the ballparks, without updating the schema.

    Start by going back to the development project:

    $ oc project mlbparks Now using project "mlbparks" on server "https://10.1.2.2:8443". And start a unique build based on the commit “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 unique element to in their application to live persisted to the database, they would necessity to gain the changes in the code as well as beget a DBA manually update the schema at the database. The following code is an case of how they can modify the application code without manually making changes to the MongoDB schema.

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

    Once the build finishes running, a deployment stint will start that will replace the running container. Once the unique version is deployed, you should live able to observe East under Toronto for example.

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

    OK, we’re jubilant with the change, let’s tag it ready for production. Again, flee oc to come by 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 unique image to the production environment.

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

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

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


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