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  • Apple's Java Mac OS X 2012-006 Update

    - by Sharon Zakhour
    The recent Java Mac OS X 2012-006 update from Apple removes the Apple Java 6 plug-in from your Mac. The Mac OS X Install FAQ will be updated with the next 7u release, but you may find the following information useful in the meantime. Q: I have installed Java for OS X 2012-006 and Apple Java 6 can no longer be used for applets or Web Start. How do I get it back? A: The Java for OS X 2012-006 update from Apple uninstalls the Apple-provided Java applet plug-in from all web browsers. You can download the latest version of Java from Oracle, which has improved security, reliability and compatibility. If you prefer to continue using Apple's Java 6 plug-in, you can follow the steps provided in How to re-enable the Apple-provided Java SE 6 applet plug-in and Web Start functionality. Q: After installing Java for OS X 2012-006, can I continue to use Apple's Java 6 alongside the OS X JDK or JRE for Java 7? A: If you want to continue to develop with Java 6 in a Terminal window you can modify the startup script for your favorite command environment. For bash, use this: export JAVA_HOME=`/usr/libexec/java_home -v 1.6` Some applications use /usr/bin/java to invoke Java. After installing Java for OS X 2012-006, /usr/bin/java will find the newest JDK installed, and will use that for all of the Java related command line tools in /usr/bin. You may need to modify those applications to find Java 6, or contact the developer for a newer version of the application. Also, this update removes Apple provided Java Preferences app. For more information on how to determine the default version of Java on your system, see Determining the Installed Version of the JRE in the JRE 7 Installation for Mac OS X page.

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  • RPi and Java Embedded GPIO: Hooking Up Your Wires for Java

    - by hinkmond
    So, you bought your blue jumper wires, your LEDs, your resistors, your breadboard, and your fill of Fry's for the day. How do you hook this cool stuff up to write Java code to blink them LEDs? I'll step you through it. First look at that pinout diagram of the GPIO header that's on your RPi. Find the pins in the corner of your RPi board and make sure to orient it the right way. The upper left corner pin should have the characters "P1" next to it on the board. That pin next to "P1" is your Pin #1 (in the diagram). Then, you can start counting left, right, next row, left, right, next row, left, right, and so on: Pins # 1, 2, next row, 3, 4, next row, 5, 6, and so on. Take one blue jumper wire and connect to Pin # 3 (GPIO0). Connect the other end to a resistor and then the other end of the resistor into the breadboard. Each row of grouped-together holes on a breadboard are connected, so plug in the short-end of a common cathode LED (long-end of a common anode LED) into a hole that is in the same grouping as where the resistor is plugged in. Then, connect the other end of the LED back to Pin # 6 (GND) on the RPi GPIO header. Now you have your first LED connected ready for you to write some Java code to turn it on and off. (As, extra credit you can connect 7 other LEDs the same way to with one lead to Pins # 5, 7, 11, 13, 15, 19 & 21). Whew! That wasn't so bad, was it? Next blog post on this thread will have some Java source code for you to try... Hinkmond

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  • Exalogic 2.0.1 Tea Break Snippets - Creating and using Distribution Groups

    - by The Old Toxophilist
    By default running your Exalogic in a Virtual provides you with, what to Cloud Users, is a single large resource and they can just create vServers and not care about how they are laid down on the the underlying infrastructure. All the Cloud Users will know is that they can create vServers. For example if we have a Quarter Rack (8 Nodes) and our Cloud User creates 8 vServers those 8 vServers may run on 8 distinct nodes or may all run on the same node. Although in many cases we, as Cloud Users, may not be to worried how the Virtualisation Algorithm decides where to place our vServers there are cases where it is extremely important that vServers run on distinct physical compute nodes. For example if we have a Weblogic Cluster we will want the Servers with in the cluster to run on distinct physical node to cover for the situation where one physical node is lost. To achieve this the Exalogic Virtualised implementation provides Distribution Groups that define and anti-aliasing policy that the underlying Virtualisation Algorithm will take into account when placing vServers. It should be noted that Distribution Groups must be created before you create vServers because a vServer can only be added to a Distribution Group at creation time. Creating A Distribution Group To create a Distribution Groups we will first need to select the Account in which we want the Distribution Group to be created. Once we have selected the account we will see the Interface update and Account specific Actions will be displayed within the Action Panes. From the Action pane (or Right-Click on the Account) select the "Create Distribution Group" action. This will initiate the create wizard as follows. Distribution Group Details Within the first Step of the Wizard we can specify the name of the distribution group and this should be unique. In addition we can provide a detailed description of the group. Distribution Group Configuration The second step of the configuration wizard allows you to specify the number of elements that are required within this group and will specify a maximum of the number of nodes within you Exalogic. At this point it is always better to specify a group with spare capacity allowing for future expansion. As vServers are added to group the available slots decrease. Summary Finally the last step of the wizard display a summary of the information entered.

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  • Happy Birthday Java EE 6+GlassFish 3!

    - by reza_rahman
    It has been almost exactly three years since Java EE 6 and GlassFish 3 were announced. It's worth pausing a moment to take stock of what has happened since. Both Java EE 6 and GlassFish 3 have been game changers. EE 6 has brought Java EE back in the limelight. To see evidence of that look at presentations like these from independents like Bert Ertman and Paul Bakker: JavaOne 2011: Migrating Spring Applications to Java EE 6 from ertmanb Likewise, the GlassFish community has proven to be a powerful disruptive force in the Java application server landscape. It's impact is evident from this percent growth rate chart from indeed.com of major Java application servers: Please join us in wishing both GlassFish and Java EE a very happy birthday and many more to come with Java EE 7, GlassFish 4 and Oracle's capabale stewardship...

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  • Personal Financial Management – The need for resuscitation

    - by Salil Ravindran
    Until a year or so ago, PFM (Personal Financial Management) was the blue eyed boy of every channel banking head. In an age when bank account portability is still fiction, PFM was expected to incentivise customers to switch banks. It still is, in some emerging economies, but if the state of PFM in matured markets is anything to go by, it is in a state of coma and badly requires resuscitation. Studies conducted around the year show an alarming decline and stagnation in PFM usage in mature markets. A Sept 2012 report by Aite Group – Strategies for PFM Success shows that 72% of users hadn’t used PFM and worse, 58% of them were not kicked about using it. Of the rest who had used it, only half did on a bank site. While there are multiple reasons for this lack of adoption, some are glaringly obvious. While pretty graphs and pie charts are important to provide a visual representation of my income and expense, it is simply not enough to encourage me to return. Static representation of data without any insightful analysis does not help me. Budgeting and Cash Flow is important but when I have an operative account, a couple of savings accounts, a mortgage loan and a couple of credit cards help me with what my affordability is in specific contexts rather than telling me I just busted my budget. Help me with relative importance of each budget category so that I know it is fine to go over budget on books for my daughter as against going over budget on eating out. Budget over runs and spend analysis are post facto and I am informed of my sins only when I return to online banking. That too, only if I decide to come to the PFM area. Fundamentally, PFM should be a part of my banking engagement rather than an analysis tool. It should be contextual so that I can make insight based decisions. So what can be done to resuscitate PFM? Amalgamation with banking activities – In most cases, PFM tools are integrated into online banking pages and they are like chapter 37 of a long story. PFM needs to be a way of banking rather than a tool. Available balances should shift to Spendable Balances. Budget and goal related insights should be integrated with transaction sessions to drive pre-event financial decisions. Personal Financial Guidance - Banks need to think ground level and see if their PFM offering is really helping customers achieve self actualisation. Banks need to recognise that most customers out there are non-proficient about making the best value of their money. Customers return when they know that they are being guided rather than being just informed on their finance. Integrating contextual financial offers and financial planning into PFM is one way ahead. Yet another way is to help customers tag unwanted spending thereby encouraging sound savings habits. Mobile PFM – Most banks have left all those numbers on online banking. With access mostly having moved to devices and the success of apps, moving PFM on to devices will give it a much needed shot in the arm. This is not only about presenting the same wine in a new bottle but also about leveraging the power of the device in pushing real time notifications to make pre-purchase decisions. The pursuit should be to analyse spend, budgets and financial goals real time and push them pre-event on to the device. So next time, I should know that I have over run my eating out budget before walking into that burger joint and not after. Increase participation and collaboration – Peer group experiences and comments are valued above those offered by the bank. Integrating social media into PFM engagement will let customers share and solicit their financial management experiences with their peer group. Peer comparisons help benchmark one’s savings and spending habits with those of the peer group and increases stickiness. While mature markets have gone through this learning in some way over the last one year, banks in maturing digital banking economies increasingly seem to be falling into this trap. Best practices lie in profiling and segmenting customers, being where they are and contextually guiding them to identify and achieve their financial goals. Banks could look at the likes of Simple and Movenbank to draw inpiration from.

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  • APEX Patchset 4.2.1 erschienen

    - by Carsten Czarski
    Seit Freitag, dem 14.12. steht das APEX Patchset 4.2.1 zur Verfügung. Neben zahlreichen Bugfixes sind auch einige, kleinere Änderungen enthalten: Die JQuery Mobile Bibliothek wurde auf Version 1.2.0 aktualisiert Die AnyChart-Engine wurde auf Version 6.0.11 gehoben. Dadurch stehen "Circular Gauge" Charts auch als HTML5 Diagramme bereit Diagramme in Anwendungen für mobile Endgeräte können sich nun dynamisch der Bildschirmgröße anpassen Details zum Patchset finden sich, wie immer in den Release Notes. Wie bislang unterscheidet sich der Installationsvorgang je nach verwendeter APEX-Version. Wenn noch kein APEX oder eine ältere Version als 4.2 installiert ist, kann die Vollinstallation für APEX 4.2.1 direkt aus dem OTN heruntergeladen und normal installiert werden. Wenn APEX 4.2.0 installiert ist, muss Patch Nr. 14732511 aus MyOracle Support heruntergeladen und eingespielt werden.

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  • Are your merchandise systems limiting growth? Oracle Retail's Merchandise Operations Management could be the answer

    - by user801960
    In this video, Lara Livgard, Director of Oracle Retail Strategy, introduces Oracle Retail Merchandise Operations Management (MOM), a set of integrated, modular solutions that support buying, pricing, inventory management and inventory valuation across a retailer’s channels, countries, and business models. MOM is the backbone of successful retail operations, providing timely and accurate visibility across the entire enterprise and enabling efficient supply-chain execution driven by plans and forecasts. It's modular architecture facilitates tailored and high-value implementations, giving retailers the information they need in order to offer a quality customer experience through a truly integrated multi-channel approach. Further information is available on the Oracle Retail website regarding Merchandise Operations Management.

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  • JAX-RS 2.0, JTA 1.1, JMS 2.0 Replay: Java EE 7 Launch Webinar Technical Breakouts on YouTube

    - by John Clingan
    As stated previously (here) (here), the On-Demand Replay of Java EE 7 Launch Webinar is already available. You can watch the entire Strategy and Technical Keynote there, and all other Technical Breakout sessions as well. We are releasing the next set of Technical Breakout sessions on GlassFishVideos YouTube channel as well. In this series, we are releasing JAX-RS 2.0, JTA 1.1, and JMS 2.0. Here's the JAX-RS 2.0 session: Enjoy watching them over the next few days before we release the next set of videos! And don't forget to download Java EE 7 SDK and try numerous bundled samples. "here), we are releasing the next set of Technical Breakout sessions on GlassFishVideos YouTube channel as well. In this series, the next three videos are released. Here's the JAX-RS 2.0 session: Enjoy watching them over the next few days before we release the next set of videos! And don't forget to download Java EE 7 SDK and try numerous bundled samples.

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  • Data Governance (Veri Yönetisimi)

    - by Arda Eralp
    Data governance,veri ile ilgili islemler için bir sorumluluklar sistemidir. Bu sistemin temelini ise politikalar, standartlar ve prosedürler olusturur. Sistem politikalar, standartlar ve prosedürler sayesinde verinin ne zaman, hangi sartlar altinda, hangi eylemlerde, hangi yöntemler ile kimler tarafindan kullanilacagina karar verir. Sistemin kurumda basarili bir sekilde islemesi için öncelikle kurumda farkindalik saglanmasi gereklidir. Farkindalik saglandiktan sonra ise kurum governance ve mimari kültürünü benimsemelidir. Ancak bu sartlar altinda sistem basarili bir sekilde isleyebilecektir. Bu sebeplerden dolayidir ki data governance kisa bir süreç degil, aksine kurum varligini sürdürdügü sürece isleyecek olan bir süreçtir. Bu durum bize data governance in bir proje degil bir program oldugunu açiklamaktadir. Programin baslangicinda kurumun ihtiyaçlarinin netlesmesi ve farkindaligin saglanmasi temeldir. Hedef kitle ise, veri ile dogrudan ve ya dolayli olarak iliski içerisinde olan herkesdir. Bu sebeple programin baslangicinda hedef kitleyi içeren ekipler ile toplantilar düzenlenecektir. Bu toplantilar sayesinde hem farkindalik saglanacak hemde ekiplerin ihtiyaçlari birebir ekipler tarafindan aktarilarak netlesecektir. Hedef kitlenin ihtiyaçlari netlestirildikten sonra ise devamli isleyecek olan bu sürecin planlamasi yapilacaktir. Bu sürecin planlanmasinda ihtiyaçlarin önceliklendirilmesi gerekmektedir. Sebebi ise her ekibin ihtiyaçlarinin farkli olabilecegi ve bütün ihtiyaçlara ayni anda karsilik verilemeyebileceginin öngörülmesidir. Bu öngörünün temeli ise ekiplerin ihtiyaçlarinin birbirleriyle olan baglantisidir. Süreç planlamasinda ihtiyaçlarin önceliklendirilmesinin ardindan kurumun büyüklügünün gözönünde bulundurulmasi gerekmedir. Kurumun büyüklügünün önemi ise eger kurum bir bütün olarak ayni anda govern edilemeyecek kadar büyük ise ihtiyaçlari öncelikli olarak bulunan ekipler ile govern edilmesine baslanarak sürecin belirli bir hiz ile bütün kurumda isler hale getirilmesini saglamaktir. Ihtiyaçlar belirlendikten ve ilgili ekipler seçildikten sonra artik programin planlanmasina geçilebilecek. Programin planlama asamasinda öncelikli olarak sürecin asamalarini kontrol edecek ve süreç kurum içerisinde isleyise geçtiginde kontrolü saglayacak olan Data Governance Office in planlanmasidir. Office in planlanmasiyla birlikte süreçteki roller ve bu rollerin sorumluluklari belirlenecektir. Planlama asamasinda Data governance office, roller ve sorumluluklar, güvenlik ve veri saklanan sistemler ele alinacak konulardir. Planlama asamasi tamamlandiginda ise belirlenen ekipler ve ihtiyaçlar dogrultusunda programin isleyis asamasina geçilebilecektir. Isleyis kisminda ekibin ihtiyaçlari dogrultusunda güvenlik konusunda ve veri saklanan sistemler üzerinde çalismalar yapilacaktir. Bu yapilan çalismalar bir süreç olarak dökümante edilecek ve süreç sona erdiginde baska bir ekiple baska bir ihtiyaç dogrultusunda çalisma yapilarak ayni süreç isletilecek ve böylece kurum içesinde ilgili süreçte standartlasma saglanacaktir. Güvenlik konusunda verinin erisim güvenligi ve kullanim güvenligi ele alinacaktir. Veri saklanan sistemler üzerindeki çalismalar ise saklanan sistemlerin program dahilinde belirlenen standartlar ile olusturulmasi ve yönetilmesi saglanacaktir. Isleyis kisminin ardindan ise programin izleme kismina geçilecektir. Bu kisimda artik Data Governance Office olusmus, politikalar, standartlar ve prosedürler belirlenmistir. Ve Data Governance Office çalisanlari rolleri ve sorumluluklari dahilinde programin isleyisini izleyecek ve gerek gördügünde politikalar standartlar ve prosedürler üzerinde degisiklikler yapacaklardir.

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  • Oracle BPM Marketing Update

    - by JuergenKress
    Thanks to Ajay Khanna from the global marketing team for the comprehensive BPM marketing overview: Content and Collateral Whitepaper: What's New in Oracle BPM Suite 11g: Review By Bruce Silver Business Driven Process Management Analyst Report: [Ovum] SWOT Assessment: Oracle BPM Suite 11g Solution Brief: Managing Unpredictability with BPM for Adaptive Case Management Solution brief: BPM in the Public Sector: Increasing Efficiency and Responsiveness Datasheet: Automating Financial Reports Approval with Oracle Process Accelerators Financial Services Loan Origination Business Account Opening Electronic Forms Management Public Sector Incident Reporting Oracle Process Accelerators for Horizontal Solutions Employee Onboarding References: BPM Suite Customers in Action Video: Avea Legal Department runs Better with BPM University of Melbourne Improves Efficiency with Oracle BPM Press: San Joaquin County Leverages Oracle to Deliver Better Services to its 650,000 Residents On-Demand Assets Webcast: New Directions with Business-Driven BPM - New Oracle BPM Suite Extend Your Applications with Oracle Business Process Management Screen Cast: Customer Experience on Your Mind? Think BPM + Social + Mobile Video: Introducing Oracle BPM Suite Assessment Tool : BPM Maturity Self Assessment Blog Series Transforming Public Sector With Process Excellence New Oracle Process Accelerators in Financial Services & Telco Blog: Detect, Analyze, Act Fast with BPM Part I - Manage Processes, the way Octopus does Part II - Perry Mason and the Case of the Unstructured Process Part III - Managing the Unstructured, the Flexible and the Adaptive Resource Kits BPM Resource Kit Financial Services: BPM in Financial Services Public Sector: Transforming Public Sector with Process Excellence SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Facebook Wiki Mix Forum Technorati Tags: BPM,bpm marketing,SOA Community,Oracle SOA,Oracle BPM,Community,OPN,Jürgen Kress

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  • Introduction to Oracle ADF

    - by Arda Eralp
    The Oracle Application Development Framework (Oracle ADF) is an end-to-end application framework that builds on Java Platform, Enterprise Edition (Java EE) standards and open-source technologies. You can use Oracle ADF to implement enterprise solutions that search, display, create, modify, and validate data using web, wireless, desktop, or web services interfaces. Because of its declarative nature, Oracle ADF simplifies and accelerates development by allowing users to focus on the logic of application creation rather than coding details. Used in tandem, Oracle JDeveloper 11g and Oracle ADF give you an environment that covers the full development lifecycle from design to deployment, with drag-and-drop data binding, visual UI design, and team development features built in. In line with community best practices, applications you build using the Fusion web technology stack achieve a clean separation of business logic, page navigation, and user interface by adhering to a model-view-controller architecture. MVC architecture: The model layer represents the data values related to the current page The view layer contains the UI pages used to view or modify that data The controller layer processes user input and determines page navigation The business service layer handles data access and encapsulates business logic Each ADF module fits in the Fusion web application architecture. The core module in the framework is ADF Model, a data binding facility. The ADF Model layer enables a unified approach to bind any user interface to any business service, without the need to write code. The other modules that make up a Fusion web application technology stack are: ADF Business Components, which simplifies building business services. ADF Faces rich client, which offers a rich library of AJAX-enabled UI components for web applications built with JavaServer Faces (JSF). ADF Controller, which integrates JSF with ADF Model. The ADF Controller extends the standard JSF controller by providing additional functionality, such as reusable task flows that pass control not only between JSF pages, but also between other activities, for instance method calls or other task flows.

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  • ADF How-To #4: Adding a View Criteria and a Search Panel

    - by Vik Kumar
    In this week's How-To we are explaining how to add a view criteria to VO and then use it to create a Search Panel via customization. The detailed steps can be found here . We have also prepared a video walking you through the steps, available via our Youtube Channel. For any questions or comments, please use the comments section below or visit our OTN forum. We are always looking for topic suggestions for additional How-Tos.

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  • SOA Suite 11g Developers Cookbook Published

    - by Antony Reynolds
    SOA Suite 11g Developers Cookbook Available Just realized that I failed to mention that Matt & mine’s most recent book, the SOA Suite 11g Developers Cookbook was published over Christmas last year! In some ways this was an easier book to write than the Developers Guide, the hard bit was deciding what recipes to include.  Once we had decided that the writing of the book was pretty straight forward. The book focuses on areas that we felt we had neglected in the Developers Guide, and so there is more about Java integration and OSB, both of which we see a lot of questions about when working with customers. Amazon has a couple of reviews. Table of Contents Chapter 1: Building an SOA Suite ClusterChapter 2: Using the Metadata Service to Share XML ArtifactsChapter 3: Working with TransactionsChapter 4: Mapping DataChapter 5: Composite Messaging PatternsChapter 6: OSB Messaging PatternsChapter 7: Integrating OSB with JSONChapter 8: Compressed File Adapter PatternsChapter 9: Integrating Java with SOA SuiteChapter 10: Securing Composites and Calling Secure Web ServicesChapter 11: Configuring the Identity ServiceChapter 12: Configuring OSB to Use Foreign JMS QueuesChapter 13: Monitoring and Management

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  • Using R to Analyze G1GC Log Files

    - by user12620111
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  Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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  • Mobile Apps: An Ongoing Revolution

    - by Steve Walker
    a guest post from Suhas Uliyar, VP Mobile Strategy, Product Management, Oracle The rise of smartphone apps have proved transformational for businesses, increasing the productivity of employees while simultaneously creating some seriously cool end user experiences. But this is a revolution that is only just beginning. Over the next few years, apps will change everything about the way enterprises work as well as overhauling the experiences of customers. The spark for this revolution is simplicity. Simplicity has already proved important for the front-end of apps, which are now often as compelling and intuitive as consumer apps. Businesses will encourage this trend, both to further increase employee productivity and to attract ‘digital natives’ (as employees and customers). With the variety of front-end development tools available already, this should be a simple mission for developers to accomplish – but front-end simplicity alone is not enough for the enterprise mobile revolution. Without the right content even the most user-friendly app is useless. Yet when it comes to integrating apps with ‘back-end’ systems to enable this content, developers often face a complex, costly and time-consuming task. Then there is security: how can developers strike a balance between complying with enterprise security policies and keeping the user experience simple? Complexity has acted as a brake on innovation, with integration and security compliance swallowing enterprise resources. This is why the simplification of integration, security and scalability is so important: it frees time and money for revolutionary innovation. The key is to put in place a complete and unified SOA integration platform that runs across the entire enterprise and enables organizations to easily integrate and connect applications across IT environments. The platform must also be capable of abstracting apps from the underlying OS and enabling a ‘write-once, run- anywhere’ capability for mobile devices - essential for BYOD environments and integrating third-party apps. Mobile Back-end-as-a-Service can also be very important in streamlining back-end integration. Mobile services offered through the cloud can simplify mobile application development with a standard approach to dealing with complex server-side programming and integration issues. This allows the business to innovate at its own pace while providing developers with a choice of tools to speed development and integration. Finally, there is security, which must be done in a way that encourages users to make the most of their mobile devices and applications. As mobile users, we want convenience and that is why we generally approve of businesses that adopt BYOD policies. Enterprises can safely encourage BYOD as they can separate, protect, and wipe corporate applications by installing a secure ‘container’ around corporate applications on any mobile device. BYOD management also means users’ personal applications and data can be kept separate from the enterprise information – giving them the confidence they need to embrace the use of their devices for corporate apps. Enterprises that place mobility at the heart of what they do will fundamentally transform their businesses and leap ahead of the competition. As businesses take to mobile platforms that simplify integration, security and scalability we will see a blossoming of innovation that will drive new levels of user convenience and create new ways of working that we are only beginning to imagine.

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  • Video Did Not Kill the Podcast Star

    - by Justin Kestelyn
    Who says video killed the podcast star? We're seeing more favorites out there than ever before. For example, the OTN team is proud to be supporters of the Java Spotlight Podcasts, straight from the official Java Evangelist Team at Oracle (lots of great insider info); the OurSQL: The MySQL Database Podcasts, produced by MySQL maven (and Oracle ACE Director) Sheeri Cabral; and The GlassFish Podcast, always a reliable source. And we'd add The Java Posse and The Basement Coders to our personal playlist. And although we're on a video kick ourselves at the moment, you can still get the audio of our TechCast Live shows, if you think we have "faces for radio."

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  • Bouygues Telecom Gains a 360-Degree Overview of Customers and Improves Offer Acceptance Rates

    - by user511693
    With more than 10 million mobile customers and 500,000 landline customers, the mission of Bouygues Telecom is to become the premier mobile, landline, television, and internet brand in France, by focusing on customer acquisition, advice, service, and support. Project challenges included: Leverage every customer relationship and increase customer loyalty through personalized offers or promotions on landline or mobile phone contracts Build on marketing campaigns and take advantage of incoming contacts from the company’s call center, Web, and retail stores Improve acceptance rates of communication service offers “Thanks to Oracle’s Siebel CRM solutions and Oracle Real-Time Decisions, we can now meet customer requests faster, personalize offers to improve the services we provide, and gain feedback on responses to offers. This enhances our knowledge of our customers before our next contact with them, whether through the Web site, call center, or our Club retail stores.” – Eric Dobremer, IT Manager - Grand Public CRM Development, Bouygues Telecom Read about results here.

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  • Oracle Weblogic 12c Launch

    - by Robert Baumgartner
    Am 1. Dezember 2011 wird Oracle WebLogic Server 12c weltweit vorgestellt. Um 19:00 findet ein Execuite Overview mit Hasan Rizvi, Senior Vice President, Product Development, statt. Um 20:00 findet ein Developer Deep-Dive mit Will Lyons, Director, Oracle WebLogic Server Product Management, statt. The new release of Oracle WebLogic Server is: • Designed to help customers seamlessly move into the public or private cloud with an open, standards-based platform • Built to drive higher value for customers’ current infrastructure and significantly reduce development time and cost • Enhanced with transformational platforms and technologies such as Java EE 6, Oracle’s Active GridLink for RAC, Oracle Traffic Director, and Oracle Virtual Assembly Builder Hier geht es zur Anmeldung: Anmeldung

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  • How To: Drag & Drop in einer APEX Kalenderregion

    - by carstenczarski
    Wussten Sie schon, dass Sie Einträge in einer APEX-Kalenderregion mit Drag & Drop verschieben können - ganz wie in bekannten Anwendungen wie Microsoft Outlook oder Mozilla Thunderbird? Und dazu müssen Sie noch nicht einmal aufwändiges JavaScript programmieren - APEX bringt nahezu alles nötige out-of-the-box mit. Lesen Sie in unserem aktuellen Tipp, wie Sie Drag & Drop in einer APEX-Kalenderregion aktivieren - und das in wenigen Minuten. Übrigens: Schon zum APEX-Entwicklertag angemeldet ...?

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  • Easy Made Easier - Networking

    - by dragonfly
        In my last post, I highlighted the feature of the Appliance Manager Configurator to auto-fill some fields based on previous field values, including host names based on System Name and sequential IP addresses from the first IP address entered. This can make configuration a little faster and a little less subject to data entry errors, particularly if you are doing the configuration on the Oracle Database Appliance itself.     The Oracle Database Appliance Appliance Manager Configurator is available for download here. But why would you download it, if it comes pre-installed on the Oracle Database Appliance? A common reason for customers interested in this new Engineered System is to get a good idea of how easy it is to configure. Beyond that, you can save the resulting configuration as a file, and use it on an Oracle Database Appliance. This allows you to verify the data entered in advance, and in the comfort of your office. In addition, the topic of this post is another strong reason to download and use the Appliance Manager Configurator prior to deploying your Oracle Database Appliance.     The most common source of hiccups in deploying an Oracle Database Appliance, based on my experiences with a variety of customers, involves the network configuration. It is during Step 11, when network validation occurs, that these come to light, which is almost half way through the 24 total steps, and can be frustrating, whether it was a typo, DNS mis-configuration or IP address already in use. This is why I recommend as a best practice taking advantage of the Appliance Manager Configurator prior to deploying an Oracle Database Appliance.     Why? Not only do you get the benefit of being able to double check your entries before you even start on the Oracle Database Appliance, you can also take advantage of the Network Validation step. This is the final step before you review all the data and can save it to a text file. It can be skipped, if you aren't ready or are not connected to the network that the Oracle Database Appliance will be on. My recommendation, though, is to run the Appliance Manager Configurator on your laptop, enter the data or re-load a previously saved file of the data, and then connect to the network that the Oracle Database Appliance will be on. Now run the Network Validation. It will check to make sure that the host names you entered are in DNS and do resolve to the IP addresses you specifiied. It will also ping the IP Addresses you specified, so that you can verify that no other machine is already using them (yes, that has happened at customer sites).     After you have completed the validation, as seen in the screen shot below, you can review the results and move on to saving your settings to a file for use on your Oracle Database Appliance, or if there are errors, you can use the Back button to return to the appropriate screen and correct the data. Once you are satisfied with the Network Validation, just check the Skip/Ignore Network Validation checkbox at the top of the screen, then click Next. Is the Network Validation in the Appliance Manager Configurator required? No, but it can save you time later. I should also note that the Network Validation screen is not part of the Appliance Manager Configurator that currently ships on the Oracle Database Appliance, so this is the easiest way to verify your network configuration.     I hope you are finding this series of posts useful. My next post will cover some aspects of the windowing environment that gets run by the 'startx' command on the Oracle Database Appliance, since this is needed to run the Appliance Manager Configurator via a direct connected monitor, keyboard and mouse, or via the ILOM. If it's been a while since you've used an OpenWindows environment, you'll want to check it out.

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  • Oracle University New Courses (Week 14)

    - by swalker
    Oracle University released the following new (versions of) courses recently: Database Oracle Data Modeling and Relational Database Design (4 days) Fusion Middleware Oracle Directory Services 11g: Administration (5 days) Oracle Unified Directory 11g: Services Deployment Essentials (2 days) Oracle GoldenGate 11g Management Pack: Overview (1 day) Business Intelligence & Datawarehousing Oracle Database 11g: Data Mining Techniques (2 days) Oracle Solaris Oracle Solaris 10 System Administration for HP-UX Administrators (5 days) E-Business Suite R12.x Oracle Time and Labor Fundamentals Get in contact with your local Oracle University team for more details and course dates. Stay Connected to Oracle University: LinkedIn OracleMix Twitter Facebook Google+

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  • Upcoming Customer WebCast: Adapters and JCA Transport in Oracle Service Bus 11g

    - by MariaSalzberger
    There is an upcoming webcast planned for September 19th that will show how to implement services using a JCA adapter in Oracle Service Bus 11g. The session will help to utilize existing resources like samples and information centers for adapters in the context of Oracle Service Bus. Topics covered in the webcast are: JCA Transport Overview / Inbound and Outbound scenarios using JCA adapters Implementation of an end-to-end use case using an inbound file adapter and and an outbound database adapter in Oracle Service Bus It will show how to find information on supported adapters in a certain version of OSB 11g Available adapter samples for OSB and SOA How to use SOA adapter samples for Oracle Service Bus A live demo of an adapter sample implementation in Oracle Service Bus Information Centers for adapters and Oracle Service Bus information The presentation recording can by found here after the webcast. Select "Oracle Fusion Middleware" as product. (https://support.oracle.com/rs?type=doc&id=740966.1) The schedule for future webcasts can be found in the above mentioned document as well.

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  • Ubiquitous BIP

    - by Tim Dexter
    The last number I heard from Mike and the PM team was that BIP is now embedded in more than 40 oracle products. That's a lot of products to keep track of and to help out with new releases, etc. Its interesting to see how internal Oracle product groups have integrated BIP into their products. Just as you might integrate BIP they have had to make a choice about how to integrate. 1. Library level - BIP is a pure java app and at the bottom of the architecture are a group of java libraries that expose APIs that you can use. they fall into three main areas, data extraction, template processing and formatting and delivery. There are post processing capabilities but those APIs are embedded withing the template processing libraries. Taking this integration route you are going to need to manage templates, data extraction and processing. You'll have your own UI to allow users to control all of this for themselves. Ultimate control but some effort to build and maintain. I have been trawling some of the products during a coffee break. I found a great post on the reporting capabilities provided by BIP in the records management product within WebCenter Content 11g. This integration falls into the first category, content manager looks after the report artifacts itself and provides you the UI to manage and run the reports. 2. Web Service level - further up in the stack is the web service layer. This is sitting on the BI Publisher server as a set of services, runReport and scheduleReport are the main protagonists. However, you can also manage the reports and users (locally managed) on the server and the catalog itself via the services layer.Taking this route, you still need to provide the user interface to choose reports and run them but the creation and management of the reports is all handled by the Publisher server. I have worked with a few customer on this approach. The web services provide the ability to retrieve a list of reports the user can access; then the parameters and LOVs for the selected report and finally a service to submit the report on the server. 3. Embedded BIP server UI- the final level is not so well supported yet. You can currently embed a report and its various levels of surrounding  'chrome' inside another html based application using a URL. Check the docs here. The look and feel can be customized but again, not easy, nor documented. I have messed with running the server pages inside an IFRAME, not bad, but not great. Taking this path should present the least amount of effort on your part to get BIP integrated but there are a few gotchas you need to get around. So a reasonable amount of choices with varying amounts of effort involved. There is another option coming soon for all you ADF developers out there, the ability to drop a BIP report into your application pages. But that's for another post.

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  • Customers Deploying Sun Oracle Database Machine

    - by kimberly.billings
    Philippine Savings Bank (PS Bank) recently deployed the Sun Oracle Database Machine to underpin its enterprise-wide analytics platform. Now, the response times for queries and requests that used to take from three hours to several days is completed in less than one minute with near real-time updates. Read the press release. EFU General Insurance also announced this week that they have deployed the Sun Oracle Database Machine. With Oracle, EFU will be able to open more sales channels via the Web and facilitate integration with other companies. As a result, more quality services can be offered to its customers via the Web because of the more agile and reliable IT infrastructure. In addition, a centralized IT environment will offer the EFU management a real time view of key information, enabling EFU to analyze business trends and make timely decisions. Read the press release. Let us know about your Sun Oracle Database deployment! var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-13185312-1"); pageTracker._trackPageview(); } catch(err) {}

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  • Java Developers: Open-source Modules, Great Tools, Opportunity.

    - by Paul Sorensen
    The role of Java developer may just be better than ever. An excellent article in Java Magazine discusses the availability of web-based tools that help development teams more effectively manage their projects and modules. If you are a Java developer you should definitely read this article. I especially like the Expert Opinions scattered throughout the article. These highlight real-world usage of the latest and greatest development tools.  As you consider steps to move your career forward, consider Java certification. Oracle has over 15 unique Java certification credentials available. The process of becoming certified in Java and preparing for your exams will require you to study, learn and practice (code). All of this activity will help you sharpen your skills and increase your working knowledge of Java - making you a better developer and more valuable member of your team. You can use the Certification Finder on the Oracle certification homepage to find a Java certification that is right for you. Thanks! 

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