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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

starting DB2: From amateur to expert | killexams.com true Questions and Pass4sure dumps

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Altova Introduces edition 2014 of Its Developer rig 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 trade leading XML editor, today announced the liberate of edition 2014 of its MissionKit® desktop developer tools and server software items. MissionKit 2014 items now encompass integration with the lightning quick validation and processing capabilities of RaptorXML®, lead for Schema 1.1, XPath/XSLT/XQuery three.0, lead for brand novel databases and tons greater. novel features in Altova server items encompass caching options in FlowForce® Server and improved performance powered through RaptorXML across the server product line.

"we're so excited to live capable of extend the hyper-efficiency delivered by the unparalleled RaptorXML Server to developers working in their computer equipment. This performance, together with astonishing aid for the very latest requisites, from XML Schema 1.1 to XPath three.0 and XSLT three.0, offers their purchasers the benefits of accelerated efficiency alongside cutting-area know-how support," spoke of Alexander Falk, President and CEO for Altova. "This, coupled with the potential to automate essential tactics by the consume of their excessive-efficiency server products, offers their shoppers a positive skills when pile and deploying applications."

just a few of the novel elements available in Altova MissionKit 2014 consist of:

Integration of RaptorXML: introduced past this 12 months, RaptorXML Server is excessive-efficiency server utility in a position to validating and processing XML at lightning speeds -- while offering the strictest practicable specifications conformance. Now the identical hyper-performance engine that powers RaptorXML Server is wholly built-in in a number of Altova MissionKit tools, including XMLSpy, MapForce®, and SchemaAgent®, supplying lightning hasty validation and processing of XML, XSLT, XQuery, XBRL, and extra. The third-era validation and processing engine from Altova, RaptorXML become developed from the ground as much as back the very latest of totality pertinent XML requisites, 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 contains famous befriend for XML Schema 1.1 validation and editing. The newest version of the XML Schema commonplace, 1.1 adds novel features aimed at making schemas extra bendy and adaptable to trade cases, such as assertions, conditional kinds, open content material, and extra.

All elements of XML Schema 1.1 are supported in XMLSpy's graphical XML Schema editor and are available in entry helpers and tabs. As always, the graphical modifying paradigm of the schema editor makes it smooth to occupy in mind and set into outcome these novel facets.

support for XML Schema 1.1 is additionally offered in SchemaAgent 2014, permitting clients to visualize and manage schema relationships via its graphical interface. here is moreover an capabilities when connecting to SchemaAgent in XMLSpy.

Coinciding with XML Schema 1.1 support, Altova has additionally released a free, online XML Schema 1.1 technology practicing route, which covers the fundamentals of the XML Schema language as well as the adjustments brought in XML Schema 1.1.

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

aid for XPath in XMLSpy 2014 has been updated to encompass the latest edition of the XPath suggestion. XPath 3.0 is a superset of the XPath 2.0 recommendation and provides effective novel performance corresponding to: dynamic characteristic cells, inline feature expressions, and befriend for union types to identify just a number of. Full befriend for novel services and operators brought in XPath 3.0 is available through ingenious XPath auto-completion in text and Grid Views, as well as within the XPath Analyzer window.

support for enhancing, debugging, and profiling XSLT is now obtainable for XSLT three.0 as well as previous types. delight live awake that a subset of XSLT three.0 is supported since the typical is noiseless a working draft that continues to adapt. XSLT three.0 assist conforms to the W3C XSLT 3.0 Working Draft of July 10, 2012 and the XPath 3.0 Candidate suggestion. however, befriend in XMLSpy now gives developers the potential to start working with this novel version instantly.

XSLT three.0 takes skills of the brand novel points introduced in XPath three.0. additionally, a huge characteristic enabled by using the novel edition is the novel xsl:are attempting / xsl:capture assemble, which can live used to entice and recuperate from dynamic blunders. other enhancements in XSLT 3.0 consist of assist for better order services and partial services.

Story continues

As with XSLT and XPath, XMLSpy assist for XQuery now additionally comprises a subset of edition 3.0. builders will now occupy the option to edit, debug, and profile XQuery three.0 with constructive syntax coloring, bracket matching, XPath auto-completion, and different intelligent modifying elements.

XQuery 3.0 is, of course, an extension of XPath and for this intuition advantages from the novel capabilities and operators introduced in XPath three.0, corresponding to a brand novel string concatenation operator, map operator, math services, sequence processing, and extra -- totality of which are available in the context sensitive entry helper windows and drop down menus in the XMLSpy 2014 XQuery editor.

New Database help:

Database-enabled MissionKit products together with XMLSpy, MapForce, StyleVision®, DatabaseSpy®, UModel®, and DiffDog®, now consist of comprehensive assist for more moderen models of up to now supported databases, as well as lead for novel database vendors:

  • Informix® eleven.70
  • PostgreSQL models 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 entry™ 2010/2013
  • New in Altova Server software 2014:

    brought prior in 2013, Altova's novel line of go-platform server utility products includes FlowForce Server, MapForce Server, StyleVision Server, and RaptorXML Server. FlowForce Server gives complete administration, job scheduling, and protection alternatives for the automation of fundamental trade techniques, whereas MapForce Server and StyleVision Server present excessive-speed automation for projects designed the consume of widespread 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 products are powered with the aid of RaptorXML for sooner, extra efficient processing. furthermore, FlowForce Server now supports outcomes caching for jobs that require a long time to procedure, for instance when a job requires complicated database queries or must do its own net carrier statistics requests. FlowForce Server administrators can now agenda execution of a time-ingesting job and cache the outcomes to back away from these delays. The cached information can then live offered when any consumer executes the job as a service, offering hasty effects. A job that generates a customised sales report for the previous day would live a fine software for caching.

    These and many greater elements can live organize within the 2014 version of MissionKit laptop developer rig and Server utility. For a complete listing of recent features, supported requisites, and affliction downloads delight consult with: http://www.altova.com/whatsnew.html

    About Altova Altova® is a application company that specialize in rig to back developers with records management, utility and software building, and information integration. The creator of XMLSpy® and other award-profitable XML, SQL and UML equipment, Altova is a key participant within the utility rig trade and the chief in XML solution construction equipment. Altova makes a speciality of its clients' wants by providing a product line that fulfills a wide spectrum of requirements for application construction groups. With over four.5 million users global, including 91% of Fortune 500 groups, Altova is haughty to serve purchasers from one-adult stores to the area's largest corporations. Altova is committed to supplying necessities-primarily based, platform-unbiased solutions which are potent, reasonably priced and straightforward-to-use. headquartered in 1992, Altova is headquartered in Beverly, Massachusetts and Vienna, Austria. visit 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 logos of Altova GmbH within the u.s. and/or different nations. The names of and reference to other agencies and products mentioned herein could live the logos of their respective homeowners.


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

    Written by way of man Harrison and Steven Feuerstein, and posted with the aid of O'Reilly Media in March 2006 beneath the ISBNs 0596100892 and 978-0596100896, this booklet is the primary one to present database programmers a full discussion of the syntax, usage, and optimization of MySQL stored procedures, saved functions, and triggers — which the authors wisely contend with mutually as "kept courses," to simplify the manuscript. Even a year after the introduction of those novel capabilities in MySQL, they occupy received remarkably diminutive insurance by way of booklet publishers. Admittedly, there are three such chapters in MySQL Administrator's reserve and Language Reference (2nd edition), written by using probably the most developers of MySQL, and posted through MySQL Press. Yet this latter ebook — even if posted a month after O'Reilly's — devotes fewer than 50 pages to kept programs, and the material is not within the printed publication itself, but in the "MySQL Language Reference" part, on the accompanying CD. That cloth, at the side of the online reference documentation, may live adequate for the more essential saved program construction needs. however for any MySQL developer who needs to account in-depth a way to capitalize from this novel functionality in edition 5.0, they are going to likely need a a entire lot more vast medicine — and that's precisely what Harrison and Feuerstein occupy created.

    The authors are beneficiant in both the technical counsel and evolution assistance that they offer. The publication's fabric spans 636 pages, geared up into 23 chapters, grouped into four components, followed by an index. the first half, "stored Programming Fundamentals," provides an introduction and then a tutorial, both taking a vast view of MySQL kept classes. The remaining four chapters cowl language fundamentals; blocks, conditional statements, and iterative programming; SQL; and lumber coping with. The publication's 2d half, "stored program construction," could live considered the coronary heart of the e-book, as a result of its five chapters current the particulars of growing stored programs in widely wide-spread, the usage of transaction administration, using MySQL's constructed-in features, and creating one's own saved functions, as well as triggers. The third half, "using MySQL stored programs and applications," explains one of the most advantages and downsides of stored courses, and then illustrates the way to convoke these stored classes from source code written in anyone of 5 distinctive programming languages: Hypertext Preprocessor, Java, Perl, Python, and Microsoft.web. within the fourth and final half, "Optimizing kept programs," the authors focus on the security and tuning of saved classes, tuning SQL, optimizing the code, and optimizing the evolution process itself.

    here's a substantial booklet, encompassing a very qualified deal of technical in addition to advisory counsel. as a result, no evaluate reminiscent of this can hope to elaborate or seriously remark upon every fragment of each chapter of every half. Yet the average exceptional and utility of the manuscript can moreover live discerned conveniently by means of determining only one of the aforesaid web programming languages, and writing some code in that language to denomination some MySQL stored methods and features, to pick up effects from a verify database — and developing totality of this code whereas relying fully upon the reserve beneath review. creating some essential stored processes, and calling them from some personal home page and Perl scripts, proven to me that MySQL stored routine Programming includes more than adequate insurance of the topic matters to live an invaluable reserve in establishing essentially the most customary performance that a programmer would deserve to implement.

    The reserve looks to occupy only a few facets or particular sections in want of development. The discussion of variable scoping, in Chapter four, is simply too cursory (no database pun meant). in terms of the booklet's pattern code, I discovered numerous instances of inconsistency of formatting — specially, operators such as "||" and "=" being jammed up against their adjoining points, with not a bit whitespace to enrich readability. These minor flaws can live without problems remedied in the next edition. Some programming books do similar mistakes, however totality the way through their text, which is even worse. fortunately, lots of the code in this publication is neatly formatted, and the variable and application names are often descriptive adequate.

    one of the vital ebook's fabric could occupy been overlooked with out remarkable loss — thereby reducing the book's size, weight, and possibly fee. both chapters on primary and advanced SQL tuning involve suggestions and proposals lined with equal capacity in other MySQL books, and were now not vital in this one. nonetheless, slovenly developers who churn out lamentable code could argue that the remaining chapter, which makes a speciality of finest programming practices, could moreover live excised; but these are the very individuals who want these ideas essentially the most.

    happily, the few weaknesses in the booklet are completely overwhelmed with the aid of its fine traits, of which there are lots of. The insurance of the issues is fairly wide, but devoid of the repetition commonly seen in many different technical books of this dimension. the reasons are written with readability, and supply ample detail for any experienced database programmer to back in mind the prevalent ideas, as well because the specific details. The pattern code easily illustrates the concepts introduced within the narration. The font, layout, organization, and fold-flat binding of this e-book, totality do it a joy to study — as is assign of many of O'Reilly's titles.

    additionally, any programming e-book that manages to lighten the weight of the reader by offering a dash of humor privilege here and there, cannot live totality unhealthy. Steven Feuerstein is the author of a few smartly-viewed books on Oracle, and it become character to view him poke some fun at the database heavyweight, in his choice of pattern code to parade the my_replace() characteristic: my_replace( 'we treasure the Oracle server', 'Oracle', 'MySQL').

    The prospective reader who would want to live trained greater about this ebook, can consult its internet page on O'Reilly's site. There they are going to locate both brief and full descriptions, tested and unconfirmed errata, a hyperlink for writing a reader overview, a web table of contents and index, and a pattern chapter (quantity 6, "Error managing"), in PDF structure. moreover, the traveller can download totality the pattern code within the e-book (562 information) and the sample database, as a mysqldump file.

    general, MySQL stored procedure Programming is adeptly written, neatly geared up, and exhaustive in its coverage of the themes. it's and inescapable will remain the premier printed useful resource for web and database developers who want to find out how to create and optimize stored tactics, functions, and triggers within MySQL.

    Michael J. Ross is a web programmer, freelance creator, and the editor of PristinePlanet.com's free newsletter. He will moreover live reached at www.ross.ws, hosted via SiteGround.


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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 involve integration with the lightning hasty validation and processing capabilities of RaptorXML®, back for Schema 1.1, XPath/XSLT/XQuery 3.0, back for novel databases and much more. novel 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 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 back 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 capacity to automate essential processes via their high-performance server products, gives their customers a several advantage when pile and deploying applications."

    A few of the novel 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 same hyper-performance engine that powers RaptorXML Server is fully integrated in several Altova MissionKit tools, including XMLSpy, MapForce®, and SchemaAgent®, delivering lightning hasty 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 back the very latest of totality pertinent XML standards, including XML Schema 1.1, XSLT 3.0, XPath 3.0, XBRL 2.1, and myriad others.

    Support for Schema 1.1: XMLSpy 2014 includes famous back for XML Schema 1.1 validation and editing. The latest version of the XML Schema standard, 1.1 adds novel features aimed at making schemas more resilient and adaptable to trade 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 novel 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 novel functionality such as: dynamic role cells, inline role expressions, and back for union types to denomination just a few. Full back for novel functions and operators added in XPath 3.0 is available through intelligent 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. delight note that a subset of XSLT 3.0 is supported since the criterion is noiseless a working draft that continues to evolve. XSLT 3.0 back conforms to the W3C XSLT 3.0 Working Draft of July 10, 2012 and the XPath 3.0 Candidate Recommendation. However, back in XMLSpy now gives developers the capacity to start working with this novel version immediately.

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

    Story continues

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

    XQuery 3.0 is, of course, an extension of XPath and therefore benefits from the novel functions and operators added in XPath 3.0, such as a novel string concatenation operator, map operator, math functions, sequence processing, and more -- totality 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 back for newer versions of previously supported databases, as well as back for novel 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 novel 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 trade processes, while MapForce Server and StyleVision Server present high-speed automation for projects designed using close 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 complex database queries or needs to do 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 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 qualified 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 novel features, supported standards, and affliction downloads delight visit: http://www.altova.com/whatsnew.html

    About Altova Altova® is a software company specializing in tools to assist developers with data management, software and application development, and data integration. The creator of XMLSpy® and other award-winning XML, SQL and UML tools, Altova is a key player in the software tools industry and the leader in XML solution evolution tools. Altova focuses on its customers' needs by offering a product line that fulfills a broad spectrum of requirements for software evolution teams. With over 4.5 million users worldwide, including 91% of Fortune 500 organizations, Altova is haughty 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 evolution cycles pan many challenges such as an evolving landscape of application architecture (Monolithic to Microservices), the need to frequently deploy features, and novel IaaS and PaaS environments. This causes many issues throughout the organization, from the evolution teams totality the way to operations and management.

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

    MongoDB is the next-generation database that is built for rapid and iterative application development. Its resilient data model — the capacity 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 befriend manage a complex 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 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 evolution tooling increase efficiency. Interoperability with multiple services and technologies and enhanced container and orchestration models let you customize your environment.

    Setting Up Your Test Environment

    In order to ensue this example, you will need to meet a number of requirements. You will need a system with 16 GB of RAM and a RHEL 7.2 Server (we used an instance with a GUI for simplicity). The following software is 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 great productivity gains to a wide variety of automation challenges. While Ansible provides more productive drop-in replacements for many core capabilities in other automation solutions, it moreover seeks to resolve 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 need to enable the EPEL repository. The EPEL (Extra Packages for Enterprise Linux) is repository that is driven by the Fedora Special Interest Group. This repository contains a number of additional packages guaranteed not to supersede or fight with the base RHEL packages.

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

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

    To install/enable the EPEL repository you will need to 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 live used to manage the lifecycle of a virtual machine. This instrument is used for the installation and management of the Red Hat Container evolution Kit.

    Vagrant is not included in any criterion repository, so they will need to install it. You can install Vagrant by enabling the SCLO repository or you can pick up it directly from the Vagrant website. They will consume the latter approach:

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

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

    VirtualBox is best done using a repository to ensure you can pick up updates. To enact this you will need to ensue these steps:

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

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

  • Open VirtualBox, this should 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 fancy 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 hurry the same way on every platform. However, modern microservice deployments typically consume a scheduler such as Kubernetes to hurry in production. In order to fully simulate the production environment, developers require a local version of production tools. In the Red Hat stack, this is supplied by the Red Hat Container evolution Kit (CDK).

    The Red Hat CDK is a customized virtual machine that makes it smooth to hurry complex deployments resembling production. This means complex applications can live developed using production grade tools from the very start, signification developers are unlikely to sustain problems stemming from differences in the evolution and production environments.

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

    Installing the CDK

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

    Start by going to Red Hat Product Downloads (note that you will need a Red Hat subscription to access this). Select ‘Red Hat Container evolution Kit’ under Product Variant, and the preempt 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 befriend 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 novel 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 consume the Vagrantfile that comes shipped with the CDK and has back for OpenShift.

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

    In order to consume the landrush plugin to configure the DNS they need to add the following two lines to the Vagrantfile exactly as below (i.e. PUBLIC_ADDRESS is a property in the Vagrantfile and does not need to 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 occupy 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 pick up a private Docker registry for storing images. Docker, Kubernetes, OpenShift and Atomic App CLIs are moreover installed.

    Now that they occupy 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 moreover consume your Red Hat credentials to login. In the console, they create a novel project:

    Next, they create a novel application using one of the built-in ‘Instant Apps’. Instant Apps are predefined application templates that drag specific images. These are an smooth way to quickly pick up 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 consume the source code from the OpenShift GitHub repository located here. If you want to ensue along with the webhook steps later, you’ll need to fork this repository into your own. Once you’re ready, enter the URL of your repo into the SOURCE_REPOSITORY_URL field:

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

  • GITHUB_WEBHOOK_SECRET: this territory allows us to create a furtive to consume with the GitHub webhook for automatic builds. You don’t need to specify this, but you’ll need to bethink the value later if you do.
  • APPLICATION_DOMAIN: this territory 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 consume it later on.

    OpenShift will then drag the code from GitHub, find the preempt 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 view fancy this:

    In order to consume the Landrush plugin, there is additional steps that are required to configure dnsmasq. To enact that you will need 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 need to add a webhook into their GitHub repository they specified above, which will automatically update the running application.

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

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

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

    Any smooth way 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 sentiment lazy). commit and shove this change to the GitHub repo, and they can view a novel build is triggered automatically within the web console. Once the build completes, if they again open their application they should view the updated front page.

    We now occupy Continuous Deployment configured for their application. Throughout this blog post, we’ve used the OpenShift web interface. However, they could occupy performed the same actions using the OpenShift console (oc) at the command-line. The easiest way 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. totality of the containers in the pod will partake local storage and networking.

    A pod lifecycle is defined, deploy to node, hurry 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 example application, they occupy 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 reform number of Pods are always running by monitoring the application and stopping or creating Pods as appropriate.

    Services

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

    Deployments

    With every novel code commit (assuming you set-up the GitHub webhooks) OpenShift will update your application. novel pods will live started with the befriend of replication controllers running your novel application version. The conventional 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 hurry a production environment in evolution and the efficiencies gained from the hasty feedback cycle of a Continuous Deployment pipeline.

    In this post, they occupy shown how to consume the Red Hat CDK to achieve both of these goals within a short-time frame and now occupy a Node.js and MongoDB application running in containers, deployed using the OpenShift PaaS. This is a stately way to quickly pick up 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 totality of the steps of setting up Red Hat as this is prerequisite knowledge.

    What they will live doing is creating a base 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 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 novel 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 enact a yum update on the guest RHEL install.

    The final step will live to generate novel ssh keys for the root user and transfer the keys to the guest machine. To enact that delight enact the following steps:

  • Become the root user $ sudo -i
  • Generate your ssh keys. enact not add a passphrase when requested.  # ssh-keygen
  • You need to add the contents of the id_rsa.pub to the authorized_keys file on the RHEL guest. The following steps were used on a local system and are not best practices for this process. In a managed server environment your IT should occupy a best exercise for doing this. If this is the first guest in your VirtualBox then it should occupy an ip of 10.1.2.101, if it has another ip then you will need to supersede for the following. For this blog delight 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 block 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 base guest image. They will now clone this to provide the MongoDB environment. The steps are as follows:

  • Right click on the RHEL guest OS and select Clone.
  • Enter the denomination 7.2 RH Mongo-DB1.
  • Ensure to click the Reinitialize the MAC Address of totality network cards.
  • Click on Next.
  • Ensure the full 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 totality network cards.
  • Click on Next.
  • Ensure the full 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 totality network cards.
  • Click on Next.
  • Ensure the full 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 need 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 need to contact your IT departments on how they manage opening of ports.

    Normally in a production environment, you would occupy the servers in an internal DNS system, however for the sake of this blog they will consume 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 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 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 discontinue firewalld # systemctl disable firewalld
  • Edit the hostname using the preempt 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 live leveraged throughout the development, test, and production lifecycle, with censorious 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 several MongoDB clusters in a tenants-per-cluster vogue — 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 several 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 reform credentials are able to access the cluster. The MongoDB cluster can moreover consume 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 consume the connection string and authentication credentials to this MongoDB cluster.

    To consume Ops Manager with Ansible and OpenShift:

  • Install and consume 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 need 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). consume 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 smooth enough to login and hurry the commands as seen in the Ops Manager agent installation information. However they occupy created an ansible playbook that you will need to change to customize.

    The playbook looks like:

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

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

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

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

    Once this is done you are ready to hurry 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 hurry the playbook you need to execute the command as root:

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

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

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

    Up until now, we’ve explored the Red Hat container ecosystem, the Red Hat Container evolution Kit (CDK), OpenShift as a local deployment, and OpenShift in production. In this final section, we’re going to raise a view at how a team can raise advantage of the advanced features of OpenShift in order to automatically roam novel versions of applications from evolution 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 hurry a completely sunder cluster for each environment (e.g. dev, staging, production) and others may consume a sole cluster for several environments. If you hurry a sunder OpenShift PaaS for each environment, they will each occupy their own dedicated and isolated resources, which is costly but ensures isolation (a problem with the evolution cluster cannot influence production). However, multiple environments can safely hurry on one OpenShift cluster through the platform’s back for resource isolation, which allows nodes to live dedicated to specific environments. This means you will occupy one OpenShift cluster with common masters for totality environments, but dedicated nodes assigned to specific environments. This allows for scenarios such as only allowing production projects to hurry on the more powerful / expensive nodes.

    OpenShift integrates well with existing Continuous Integration / Continuous Delivery tools. Jenkins, for example, is available for consume 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 prove 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 evolution environment provisioned in a remote OpenShift cluster.

    To roam code between environments, they can raise advantage of the image streams concept in OpenShift. An image stream is superficially similar to an image repository such as those organize on Docker Hub — it is a collection of related images with identifying names or “tags”. An image stream can 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 view 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 occupy the changed image as a base layer). This can live fully automated by the consume of Jenkins or a similar CI tool; on a check-in to the source control repository, it can hurry a test-suite and automatically tag the image if it passes.

    To roam between staging and production they can enact exactly the same thing — Jenkins or a similar instrument could hurry 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 novel versions. This would live factual 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 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 view at a true example of pushing an application from evolution to production. They will consume the simple MLB Parks application from a previous blog post that connects to MongoDB for storage of persistent data. The application displays various information about MLB parks such as league and city on a map. The source code is available in this GitHub repository. The example assumes that both environments are hosted on the same 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 occupy a working OpenShift instance, you can quickly pick up 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 novel 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 evolution environment (mlbparks):

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

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

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

    Service Files: replica-1_service.json

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

    replica-1_endpoints.json

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

    replica-2_service.json

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

    replica-2_endpoints.json

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

    replica-3_service.json

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

    replica-3_endpoints.json

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

    Using the above replica files you will need to hurry 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 occupy the endpoints for the external replica set created they can now create the MLB parks using a template. They will consume the source code from their demo GitHub repo and the s2i build strategy which will create a container for their source code (note this repository has no Dockerfile in the arm they use). totality of the environment variables are in the mlbparks-template.json, so they will first create a template then create their novel app:

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

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

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

    We can pick up the denomination of the image created by the build with the befriend of the describe command:

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

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

    Now they know the image ID, they can create a tag that marks it as ready for consume 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 involve untested code.

    To allow the production project to drag the image from the evolution repository, they need to award drag 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 novel policy is in place, they can check the rolebindings: $ oc pick up 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 occupy 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 consume the same 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 evolution project that was tagged “production”:

    $ oc new-app mlbparks/mlbparks:production --> organize image 5621fed (11 minutes old) in image stream "mlbparks in project mlbparks" under tag :production for "mlbparks/mlbparks:production" * This image will 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 hurry 'oc status' to view your app.

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

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

    We will now demonstrate the capacity to both automatically roam novel items to production, but they will moreover prove how they can update an application without having to update the MongoDB schema. They occupy created a arm of the code in which they will now add the division to the league for the ballparks, without updating the schema.

    Start by going back to the evolution project:

    $ oc project mlbparks Now using project "mlbparks" on server "https://10.1.2.2:8443". And start a novel 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 novel ingredient to in their application to live persisted to the database, they would need to do the changes in the code as well as occupy a DBA manually update the schema at the database. The following code is an example of how they can modify the application code without manually making changes to the MongoDB schema.

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

    Once the build finishes running, a deployment assignment will start that will supersede the running container. Once the novel version is deployed, you should live able to view 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 elated with the change, let’s tag it ready for production. Again, hurry oc to pick up 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 novel 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 conventional 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 famous features for organizations, including source-to-image tooling, image streams, project and user isolation and a web UI. This post showed how these features travail together to provide a complete CD workflow where code can live automatically pushed from evolution through to production combined with the power and capabilities of MongoDB as the backend of choice for applications.


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