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C2090-610 - DB2 10.1 Fundamentals - Dump Information

Vendor Name : IBM
Exam Code : C2090-610
Exam Name : DB2 10.1 Fundamentals
Questions and Answers : 138 Q & A
Updated On : February 21, 2019
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IBM C2090-610 Exam (DB2 10.1 Fundamentals) Detailed Information

C2090-610 Test Information / Examination Information


Number of questions : 69
Time allowed in minutes: 90
Required passing score : 66%
Languages : English


C2090-610 Objectives


C2090-610 Questions and Answers

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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 Name : DB2 10.1 Fundamentals
Vendor Name : IBM
Q&A : 138 Real Questions

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IBM DB2 10.1 Fundamentals

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

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

BEVERLY, MA--(Marketwired - Oct 29, 2013) - Altova® (http://www.altova.com), creator of XMLSpy®, the business 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®, guide for Schema 1.1, XPath/XSLT/XQuery three.0, guide for brand new databases and tons greater. New 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 be capable of extend the hyper-efficiency delivered by the unparalleled RaptorXML Server to developers working in our computer equipment. This performance, together with amazing aid for the very latest requisites, from XML Schema 1.1 to XPath three.0 and XSLT three.0, offers our 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 use of our excessive-efficiency server products, offers our shoppers a definite skills when building and deploying applications."

just a few of the new 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 possible 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 fast 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 support the very latest of all relevant 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 important help for XML Schema 1.1 validation and editing. The newest version of the XML Schema commonplace, 1.1 adds new features aimed at making schemas extra bendy and adaptable to business 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 easy to have in mind and put into effect these new 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 also 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 new performance corresponding to: dynamic characteristic cells, inline feature expressions, and help for union types to identify just a number of. Full help for new services and operators brought in XPath 3.0 is available through clever 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. Please be aware that a subset of XSLT three.0 is supported since the typical is still 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, help in XMLSpy now gives developers the potential to start working with this new version instantly.

XSLT three.0 takes skills of the brand new points introduced in XPath three.0. additionally, a huge characteristic enabled by using the new edition is the new xsl:are attempting / xsl:capture assemble, which can be 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 have 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 reason advantages from the new capabilities and operators introduced in XPath three.0, corresponding to a brand new string concatenation operator, map operator, math services, sequence processing, and extra -- all 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 guide for new 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 new 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 business techniques, whereas MapForce Server and StyleVision Server offer excessive-speed automation for projects designed the use 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 make its own net carrier statistics requests. FlowForce Server administrators can now agenda execution of a time-ingesting job and cache the outcomes to keep away from these delays. The cached information can then be offered when any consumer executes the job as a service, offering fast effects. A job that generates a customised sales report for the previous day would be a fine software for caching.

    These and many greater elements can be found within the 2014 version of MissionKit laptop developer equipment and Server utility. For a complete listing of recent features, supported requisites, and trial downloads please consult with: http://www.altova.com/whatsnew.html

    About Altova Altova® is a application company that specialize in equipment to support 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 equipment 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 proud 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 be the logos of their respective homeowners.


    MySQL kept system Programming | killexams.com Real 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 offer database programmers a full discussion of the syntax, usage, and optimization of MySQL stored procedures, saved functions, and triggers — which the authors wisely discuss with mutually as "kept courses," to simplify the manuscript. Even a year after the introduction of those new capabilities in MySQL, they have received remarkably little insurance by way of booklet publishers. Admittedly, there are three such chapters in MySQL Administrator's book 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 be sufficient for the more essential saved program construction needs. however for any MySQL developer who needs to consider in-depth a way to benefit from this new functionality in edition 5.0, they are going to likely need a a whole lot more vast medicine — and that's precisely what Harrison and Feuerstein have created.

    The authors are beneficiant in both the technical counsel and development 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 blunder coping with. The publication's 2d half, "stored program construction," could be 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 call 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 development process itself.

    here's a substantial booklet, encompassing a very good deal of technical in addition to advisory counsel. as a result, no evaluate reminiscent of this can hope to explain or seriously remark upon every part of each chapter of every half. Yet the average exceptional and utility of the manuscript can also be discerned conveniently by means of determining only one of the aforesaid web programming languages, and writing some code in that language to name some MySQL stored methods and features, to get effects from a verify database — and developing all of this code whereas relying fully upon the book beneath review. creating some essential stored processes, and calling them from some personal home page and Perl scripts, proven to me that MySQL stored method Programming includes more than sufficient insurance of the subject matters to be an invaluable book in establishing essentially the most usual performance that a programmer would deserve to implement.

    The book looks to have 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 none whitespace to enrich readability. These minor flaws can be without problems remedied in the next edition. Some programming books make similar mistakes, however all 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 have been overlooked with out remarkable loss — thereby reducing the book's size, weight, and possibly fee. both chapters on primary and advanced SQL tuning include suggestions and proposals lined with equal ability in other MySQL books, and were now not vital in this one. nonetheless, sloppy developers who churn out lamentable code could argue that the remaining chapter, which makes a speciality of finest programming practices, could also be 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 keep 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, all make it a joy to study — as is attribute 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 right here and there, cannot be all unhealthy. Steven Feuerstein is the author of a few smartly-viewed books on Oracle, and it become quality to see him poke some fun at the database heavyweight, in his choice of pattern code to display the my_replace() characteristic: my_replace( 'we love the Oracle server', 'Oracle', 'MySQL').

    The prospective reader who would want to be 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 all 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 certain 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 also be 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 real questions and Pass4sure dumps

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

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

    A few of the new 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 possible 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 fast 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 support the very latest of all relevant 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 important support for XML Schema 1.1 validation and editing. The latest version of the XML Schema standard, 1.1 adds new features aimed at making schemas more flexible 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 easy to understand and implement these new features.

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

    Coinciding with XML Schema 1.1 support, Altova has also 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 include the latest version of the XPath Recommendation. XPath 3.0 is a superset of the XPath 2.0 recommendation and adds powerful new functionality such as: dynamic function cells, inline function expressions, and support for union types to name just a few. Full support for new 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. Please note that a subset of XSLT 3.0 is supported since the standard is still a working draft that continues to evolve. XSLT 3.0 support conforms to the W3C XSLT 3.0 Working Draft of July 10, 2012 and the XPath 3.0 Candidate Recommendation. However, support in XMLSpy now gives developers the ability to start working with this new version immediately.

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

    Story continues

    As with XSLT and XPath, XMLSpy support for XQuery now also includes a subset of version 3.0. Developers will now have 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 new functions and operators added in XPath 3.0, such as a new string concatenation operator, map operator, math functions, sequence processing, and more -- all 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 include complete support for newer versions of previously supported databases, as well as support for new 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 new 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 complex database queries or needs to make its own Web service data requests. FlowForce Server administrators can now schedule execution of a time-consuming job and cache the results to prevent these delays. The cached data can then be provided when any user executes the job as a service, delivering instant results. A job that generates a customized sales report for the previous day would be a good 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 new features, supported standards, and trial downloads please visit: http://www.altova.com/whatsnew.html

    About Altova Altova® is a software company specializing in tools to assist developers with data management, software and application development, and data integration. The creator of XMLSpy® and other award-winning XML, SQL and UML tools, Altova is a key player in the software tools industry and the leader in XML solution 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 proud to serve clients from one-person shops to the world's largest organizations. Altova is committed to delivering standards-based, platform-independent solutions that are powerful, affordable and easy-to-use. Founded in 1992, Altova is headquartered in Beverly, Massachusetts and Vienna, Austria. Visit Altova on the Web at: http://www.altova.com.

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


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

    Current development cycles face many challenges such as an evolving landscape of application architecture (Monolithic to Microservices), the need to frequently deploy features, and new IaaS and PaaS environments. This causes many issues throughout the organization, from the development teams all the way to operations and management.

    In this blog post, we will show you how you can set up a local system that will support MongoDB, MongoDB Ops Manager, and OpenShift. We will walk through the various installation steps and demonstrate how easy it is to do agile application development with MongoDB and OpenShift.

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

    MongoDB Ops Manager (also available as the hosted MongoDB Cloud Manager service) features visualization, custom dashboards, and automated alerting to help 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 also be used to provide seamless no-downtime upgrades, scaling, and backup and restore.

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

    Setting Up Your Test Environment

    In order to follow this example, you will 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 also 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 also 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 also seeks to solve other major unsolved IT challenges.

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

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

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

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

    $ sudo yum install ansible Vagrant Install

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

    Vagrant is not included in any standard repository, so we will need to install it. You can install Vagrant by enabling the SCLO repository or you can get it directly from the Vagrant website. We will use 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 we will use VirtualBox for the virtualization software.

    VirtualBox is best done using a repository to ensure you can get updates. To do this you will need to follow these steps:

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

    Once the install is complete you will want to launch VirtualBox and ensure that the Guest Network is on the correct 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 correct domain:

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

    Docker containers are used to package software applications into portable, isolated stores. Developing software with containers helps developers create applications that will run the same way on every platform. However, modern microservice deployments typically use a scheduler such as Kubernetes to run 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 easy to run complex deployments resembling production. This means complex applications can be developed using production grade tools from the very start, meaning developers are unlikely to experience problems stemming from differences in the development and production environments.

    Now let's walk through installation and configuration of the Red Hat CDK. We will create a containerized multi-tier application on the CDK’s OpenShift instance and go through the entire workflow. By the end of this blog post you will know how to run an application on top of OpenShift and will be familiar with the core features of the CDK and OpenShift. Let’s get started…

    Installing the CDK

    The prerequisites for running the CDK are Vagrant and a virtualization client (VirtualBox, VMware Fusion, libvirt). Make sure 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 Development Kit’ under Product Variant, and the appropriate version and architecture. You should download two packages:

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

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

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

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

    $ vagrant plugin list

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

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

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

    $ vagrant box list

    We will use the Vagrantfile that comes shipped with the CDK and has support for OpenShift.

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

    In order to use the landrush plugin to configure the DNS we 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 be replaced) :

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

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

    Save the changes and start the virtual machine :

    $ vagrant up

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

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

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

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

    Next, we create a new application using one of the built-in ‘Instant Apps’. Instant Apps are predefined application templates that pull specific images. These are an easy way to quickly get 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, we will use the source code from the OpenShift GitHub repository located here. If you want to follow 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 important to us – GITHUB_WEBHOOK_SECRET and APPLICATION_DOMAIN:

  • GITHUB_WEBHOOK_SECRET: this field allows us to create a secret to use with the GitHub webhook for automatic builds. You don’t need to specify this, but you’ll need to remember the value later if you do.
  • APPLICATION_DOMAIN: this field will determine where we can access our application. This value must include the Top Level 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, we can ‘Create’ our application. This brings us to an information page which gives us some helpful CLI commands as well as our webhook URL. Copy this URL as we will use it later on.

    OpenShift will then pull the code from GitHub, find the appropriate Docker image in the Red Hat repository, and also 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 look like this:

    In order to use the Landrush plugin, there is additional steps that are required to configure dnsmasq. To do that you will need to do 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! Our application has now been built and deployed on our local OpenShift environment. To complete the Continuous Deployment pipeline we just need to add a webhook into our GitHub repository we specified above, which will automatically update the running application.

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

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

    To test out the pipeline, we need to make a change to our project and push a commit to the repo.

    Any easy way to do this is to edit the views/index.html file, e.g: (Note that you can also do this through the GitHub web interface if you’re feeling lazy). Commit and push this change to the GitHub repo, and we can see a new build is triggered automatically within the web console. Once the build completes, if we again open our application we should see the updated front page.

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

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

    For our example application, we have a Pod running the application. Pods can be scaled up/down from the OpenShift interface.

    Replication Controllers

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

    Services

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

    Deployments

    With every new code commit (assuming you set-up the GitHub webhooks) OpenShift will update your application. New pods will be started with the help of replication controllers running your new application version. The old pods will be deleted. OpenShift deployments can perform rollbacks and provide various deploy strategies. It’s hard to overstate the advantages of being able to run a production environment in development and the efficiencies gained from the fast feedback cycle of a Continuous Deployment pipeline.

    In this post, we have shown how to use the Red Hat CDK to achieve both of these goals within a short-time frame and now have a Node.js and MongoDB application running in containers, deployed using the OpenShift PaaS. This is a great way to quickly get 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, we will create the virtual machines that will be required to set up the replica set. We will not walk through all of the steps of setting up Red Hat as this is prerequisite knowledge.

    What we will be doing is creating a base RHEL 7.2 minimal install and then using the VirtualBox interface to clone the images. We will do this so that we can easily install the replica set using the MongoDB Automation Agent.

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

    Please perform the following steps:

  • In VirtualBox create a new guest image and call it RHEL Base. We used the following information: a. Memory 2048 MB b. Storage 30GB c. 2 Network cards i. Nat ii. Host-Only
  • Do a minimal Red Hat install, we 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 do a yum update on the guest RHEL install.

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

  • Become the root user $ sudo -i
  • Generate your ssh keys. Do 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 have a best practice for doing this. If this is the first guest in your VirtualBox then it should have an ip of 10.1.2.101, if it has another ip then you will need to replace for the following. For this blog please execute the following steps # cd ~/.ssh/ # scp id_rsa.pub 10.1.2.101: # ssh 10.1.2.101 # mkdir .ssh # cat id_rsa.pub > ~/.ssh/authorized_keys # chmod 700 /root/.ssh # chmod 600 /root/.ssh/authorized_keys
  • SELinux may 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 be asked for any login information.
  • Once this is complete you can shut down the RHEL Base guest image. We will now clone this to provide the MongoDB environment. The steps are as follows:

  • Right click on the RHEL guest OS and select Clone.
  • Enter the Name 7.2 RH Mongo-DB1.
  • Ensure to click the Reinitialize the MAC Address of all 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 Name 7.2 RH Mongo-DB2.
  • Ensure to click the Reinitialize the MAC Address of all 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 Name 7.2 RH Mongo-DB3.
  • Ensure to click the Reinitialize the MAC Address of all network cards.
  • Click on Next.
  • Ensure the Full Clone option is selected.
  • Click on Clone.
  • The final step for getting the systems ready will be to configure the hostnames, host-only ip and the host files. We will need to also ensure that the systems can communicate on the port for MongoDB, so we 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 have the servers in an internal DNS system, however for the sake of this blog we will use hosts files for the purpose of names. We want to edit the /etc/hosts file on the three MongoDB guests as well as the hosts.

    The information we will be using will be as follows:

    To do so on each of the guests do 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 our case the interface was enp0s8: # sudo vi /etc/sysconfig/network-scripts/ifcfg-enp0s8
  • You will want to change the ONBOOT and BOOTPROTO to the following and add the three lines for IP address, netmask, and Broadcast. Note: the IP address should be based upon the table above. They should match the info below: ONBOOT=yes BOOTPROTO=static IPADDR=10.1.2.10 NETMASK-255.255.255.0 BROADCAST=10.1.2.255
  • Disable the firewall with: # systemctl stop firewalld # systemctl disable firewalld
  • Edit the hostname using the appropriate values from the table above.  # hostnamectl set-hostname "mongo-db1" --static
  • Edit the hosts file adding the following to etc/hosts, you should also do this on the guest: 10.1.2.10 mongo-db1 10.1.2.11 mongo-db2 10.1.2.12 mongo-db3
  • Restart the guest.
  • Try to SSH by hostname.
  • Also, try pinging each guest by hostname from guests and host.
  • Ops Manager

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

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

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

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

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

    To use Ops Manager with Ansible and OpenShift:

  • Install and use 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 we will deploy MongoDB. (Note that the reverse is not necessary; in other words, Ops Manager does not need to be able to reach into the managed VMs directly over the network).
  • Spawn servers (VMs) running Red Hat Enterprise Linux, able to reach 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). Use the Ops Manager UI (or REST 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 we want to install the automation agent it would be easy enough to login and run the commands as seen in the Ops Manager agent installation information. However we have 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 run 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 run 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 appropriate access rights:

  • Verify that all of the Ops Manager agents have 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 take a look at how a team can take advantage of the advanced features of OpenShift in order to automatically move new versions of applications from development to production — a process known as Continuous Delivery (or Continuous Deployment, depending on the level of automation).

    OpenShift supports different setups depending on organizational requirements. Some organizations may run a completely separate cluster for each environment (e.g. dev, staging, production) and others may use a single cluster for several environments. If you run a separate OpenShift PaaS for each environment, they will each have 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 run on one OpenShift cluster through the platform’s support for resource isolation, which allows nodes to be dedicated to specific environments. This means you will have one OpenShift cluster with common masters for all environments, but dedicated nodes assigned to specific environments. This allows for scenarios such as only allowing production projects to run on the more powerful / expensive nodes.

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

    A Continuous Delivery Pipeline with CDK and OpenShift Enterprise

    The workflow of our continuous delivery pipeline is illustrated below:

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

    To move code between environments, we can take advantage of the image streams concept in OpenShift. An image stream is superficially similar to an image repository such as those found 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 we can easily configure projects to listen and react to. We can see this in the diagram above — when the developer is ready for their changes to be picked up by the next environment in line, they simply tag the image appropriately, which will generate an image stream notification that will be picked up by the staging environment. The staging environment will then automatically rebuild and redeploy any containers using this image (or images who have the changed image as a base layer). This can be fully automated by the use of Jenkins or a similar CI tool; on a check-in to the source control repository, it can run a test-suite and automatically tag the image if it passes.

    To move between staging and production we can do exactly the same thing — Jenkins or a similar tool could run 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 new versions. This would be true Continuous Deployment — where a change made in dev will propagate automatically to production without any manual intervention. Many organizations may instead opt for Continuous Delivery — where there is still a manual “ok” required before changes hit production. In OpenShift this can be easily done by requiring the images in staging to be tagged manually before they are deployed to production.

    Deployment of an OpenShift Application

    Now that we’ve reviewed the workflow, let’s look at a real example of pushing an application from development to production. We will use 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 be easily adapted to allow promotion to another OpenShift instance by using a common registry.

    If you don’t already have a working OpenShift instance, you can quickly get started by using the CDK, which we also covered in an earlier blogpost. Start by logging in to OpenShift using your credentials:

    $ oc login -u openshift-dev

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

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

    And the second one will be our development environment (mlbparks):

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

    After you run this command you should be 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 our 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 have multiple IP addresses assigned to your endpoint and the service will act as a load balancer. This will not work 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 our case we have three nodes so for illustrative purposes we have 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 run 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 we have the endpoints for the external replica set created we can now create the MLB parks using a template. We will use the source code from our demo GitHub repo and the s2i build strategy which will create a container for our source code (note this repository has no Dockerfile in the branch we use). All of the environment variables are in the mlbparks-template.json, so we will first create a template then create our new app:

    $ oc create -f https://raw.githubusercontent.com/macurwen/openshift3mlbparks/master/mlbparks-template.json $ oc new-app mlbparks --> Success Build scheduled for "mlbparks" - use the logs command to track its progress. Run '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 have the application up and running (accessible at the hostname found in the pod of the web ui) built from an image stream.

    We can get the name of the image created by the build with the help 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 Pull 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 we know the image ID, we can create a tag that marks it as ready for use 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 our image. This is because we want the production tag to be tied to this particular version. If we hadn’t done this, production would automatically track changes to latest, which would include untested code.

    To allow the production project to pull the image from the development repository, we need to grant pull rights to the service account associated with production environment. Note that mlbparks-production is the name of the production project:

    $ oc policy add-role-to-group system:image-puller \ system:serviceaccounts:mlbparks-production \ --namespace=mlbparks To verify that the new policy is in place, we can check the rolebindings: $ oc get 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 we have an image that can be deployed to the production environment. Let’s switch the current project to the production one:

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

    To start the database we’ll use 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 part we’ll be using the image stream created in the development project that was tagged “production”:

    $ oc new-app mlbparks/mlbparks:production --> Found image 5621fed (11 minutes old) in image stream "mlbparks in project mlbparks" under tag :production for "mlbparks/mlbparks:production" * This image will be deployed in deployment config "mlbparks" * Port 8080/tcp will be load balanced by service "mlbparks" --> Creating resources with label app=mlbparks ... DeploymentConfig "mlbparks" created Service "mlbparks" created --> Success Run '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 ability to both automatically move new items to production, but we will also show how we can update an application without having to update the MongoDB schema. We have created a branch of the code in which we 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 new 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 we want to add a new element to in our application to be persisted to the database, we would need to make the changes in the code as well as have a DBA manually update the schema at the database. The following code is an example of how we can modify the application code without manually making changes to the MongoDB schema.

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

    Once the build finishes running, a deployment task will start that will replace the running container. Once the new version is deployed, you should be able to see East under Toronto for example.

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

    OK, we’re happy with the change, let’s tag it ready for production. Again, run oc to get the ID of the image tagged latest, which we 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 new image to the production environment.

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


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