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• C# Simple Twitter Update

  - by mroberts

  For what it's worth a simple twitter update. using System; using System.IO; using System.Net; using System.Text; namespace Server.Actions { public class TwitterUpdate { public string Body { get; set; } public string Login { get; set; } public string Password { get; set; } public override void Execute() { try { //encode user name and password string creds = Convert.ToBase64String(Encoding.ASCII.GetBytes(string.Format("{0}:{1}", this.Login, this.Password))); //encode tweet byte[] tweet = Encoding.ASCII.GetBytes("status=" + this.Body); //setup request HttpWebRequest request = (HttpWebRequest)WebRequest.Create("http://twitter.com/statuses/update.xml"); request.Method = "POST"; request.ServicePoint.Expect100Continue = false; request.Headers.Add("Authorization", string.Format("Basic {0}", creds)); request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = tweet.Length; //write to stream Stream reqStream = request.GetRequestStream(); reqStream.Write(tweet, 0, tweet.Length); reqStream.Close(); //check response HttpWebResponse response = (HttpWebResponse)request.GetResponse(); //... } catch (Exception e) { //... } } } }

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• Oracle IRM Desktop update

  - by martin.abrahams

  Just in time for Christmas, we have made a fresh IRM Desktop build available with a number of valuable enhancements: Office 2010 support Adobe Reader X support Enhanced compatibility with SharePoint Ability to enable the Sealed Email for Lotus Notes integration during IRM Desktop installation The kit is currently available as a patch that you can access by logging in to My Oracle Support and looking for patch 9165540. The patch enables you to download a package containing all 27 language variants of the IRM Desktop. We will be making the kit available from OTN as soon as possible, at which time you will be able to pick a particular language if preferred.

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• How to build Visual Studio Setup projects (.vdproj) with TFS 2010 Build ?

  - by Vishal

  Out of the box, Team Foundation Server 2010 Build does not support building of setup projects (.vdproj). Although, you can modify DefaultTemplate.xaml or create your own in order to achieve this. I had to try bunch of different blog post's and finally got it working with a mixture of all those posts.   Since you don’t have to go through this pain again, I have uploaded the Template which you can use right away : https://skydrive.live.com/redir?resid=65B2671F6B93CDE9!310 Download and CheckIn this template into your source control. Modify your Build Definition to use this template. Unless you have CheckedIn the template, it wont show up in the template selection section in the process task of build definition. In your Visual Studio Solution Configuration Manager, make sure you specify to build the setup project also. You might get this warning in your build result: “The project file “*.vdproj” is not support by MSBuild and cannot be build. Hope it helps. Thanks, Vishal Mody Reference blog posts I had used: http://geekswithblogs.net/jakob/archive/2010/05/14/building-visual-studio-setup-projects-with-tfs-2010-team-build.aspx http://donovanbrown.com/post/I-need-to-build-a-project-that-is-not-supported-by-MSBuild.aspx http://lajak.wordpress.com/2011/02/19/build-biztalk-deployment-framework-projects-using-tfs2010/

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• ODI 11g - Faster Files

  - by David Allan

  Deep in the trenches of ODI development I raised my head above the parapet to read a few odds and ends and then think why don’t they know this? Such as this article here – in the past customers (see forum) were told to use a staging route which has a big overhead for large files. This KM is an example of the great extensibility capabilities of ODI, its quite simple, just a new KM that; improves the out of the box experience – just build the mapping and the appropriate KM is used improves out of the box performance for file to file data movement. This improvement for out of the box handling for File to File data integration cases (from the 11.1.1.5.2 companion CD and on) dramatically speeds up the file integration handling. In the past I had seem some consultants write perl versions of the file to file integration case, now Oracle ships this KM to fill the gap. You can find the documentation for the IKM here. The KM uses pure java to perform the integration, using java.io classes to read and write the file in a pipe – it uses java threading in order to super-charge the file processing, and can process several source files at once when the datastore's resource name contains a wildcard. This is a big step for regular file processing on the way to super-charging big data files using Hadoop – the KM works with the lightweight agent and regular filesystems. So in my design below transforming a bunch of files, by default the IKM File to File (Java) knowledge module was assigned. I pointed the KM at my JDK (since the KM generates and compiles java), and I also increased the thread count to 2, to take advantage of my 2 processors. For my illustration I transformed (can also filter if desired) and moved about 1.3Gb with 2 threads in 140 seconds (with a single thread it took 220 seconds) - by no means was this on any super computer by the way. The great thing here is that it worked well out of the box from the design to the execution without any funky configuration, plus, and a big plus it was much faster than before, So if you are doing any file to file transformations, check it out!

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• EPM Architecture: Reporting and Analysis

  - by Marc Schumacher

  Reporting and Analysis is the basis for all Oracle EPM reporting components. Through the Java based Reporting and Analysis web application deployed on WebLogic, it enables users to browse through reports for all kind of Oracle EPM reporting components. Typical users access the web application by browser through Oracle HTTP Server (OHS). Reporting and Analysis Web application talks to the Reporting and Analysis Agent using CORBA protocol on various ports. All communication to the repository databases (EPM System Registry and Reporting and Analysis database) from web and application layer is done using JDBC. As an additional data store, the Reporting and Analysis Agent uses the file system to lay down individual reports. While the reporting artifacts are stored on the file system, the folder structure and report based security information is stored in the relational database. The file system can be either local or remote (e.g. network share, network file system). If an external user directory is used, Reporting and Analysis services also communicate to this directory. The next post will cover WebAnalysis.

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• .Net Micro

  - by MarkPearl

  A while back I purchased a RFID scanner that could be connected to a PC and programmed via VS. It was a fun purchase an though the import duties nailed me, I was glad to get the little gadget. Last night while listening to .Net Rocks I heard of another company that sells similar components for .Net Micro. Check out their websites…. TinyClr GHI Electronics .Net Micro Website Trossen Robotics

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• SOA Suite 11g: Unable to start domain (Error occurred during initialization of VM)

  - by Chris Tomkins

  If you have recently installed SOA Suite, created a domain and then tried to start it only to find it fails with the error: Error occurred during initialization of VM Could not reserve enough space for object heap Could not create the Java virtual machine. the solution is to edit the file <domain home>\bin\setSOADomainEnv.cmd/sh (depending on your platform) and modify the line: set DEFAULT_MEM_ARGS=-Xms512m -Xmx1024m to something like: set DEFAULT_MEM_ARGS=-Xms512m -Xmx768m Save the file and then try to start your domain again. Everything should now work at least it does on the Dell Latitude 630 laptop with 4Gb RAM that I have. Technorati Tags: soa suite,11g,java,troubleshooting,problems,domain

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• There's a new kid in town...

  - by Mike Pillittere

  I've had this account for quite a while now, but I've finally got time in my schedule to actually think about posting something here.  Stay tuned, as there's lots going on that I just can't wait to talk about.

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• GLP for Pillar Axiom 600 Storage System Implementation Specialist

  - by uwes

  Now availabe at OPN Competency Center. The guided learning path provides you with an overview of the Pillar Axiom 600 storage system, and the technical details that you need to become a Pillar Axiom 600 Storage System Certified Implementation Specialist.  Learn more, go to: Pillar Axiom 600 Storage System Implementation Specialist.

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• Oracle Accelerate for Midsize Companies Podcast

  Recently, Rebecca Wettemann, Vice President of Research at Nucleus Research, published a report on Oracle Business Accelerators. In this podcast, Rebecca discusses the findings of this report with Jim Lein, Marketing Director with the Oracle Accelerate Program Office. When Nucleus analyzed the actual experiences of customers using Accelerators, analysts found all customers reduced the time to deploy Oracle E-Business Suite, many by more than 50 percent of the time an implementation without Accelerators would have taken.

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• Google Maps in .NET Problem

  - by H(at)Ni

  Hello, I've been struggling with Google maps till I found that someone implemented a wrapper so that you can use Google Map as an ASP.Net user control which is a great effort indeed. You can download it from this link. However, after using it for a while, I've found out that it is storing the Google map object only once in the session and getting it from there whenever needed which was a problem for me that when you update the map in some page, you'll find it updated on another page. So, I've digged deep in the code and updated it so that it stores the map object with a unique identifier that you set it as a property in the user control object like that: this.googleMapCtrl.ControlID = Guid.NewGuid().ToString(); You can download the updated control files from here. Cheers,

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• Virtual Grocery Store

  - by David Dorf

  Because South Korean's are so busy, Tesco decided that its Homeplus grocery chain should offer a virtual alternative in subways.  As you can see in the video below, shoppers passing through a subway station can see a virtual representation of the store and scan items with their mobile phones.  This builds a shopping list which is delivered to their homes later that day. This is a very cool example of leveraging technology to offer a shopping experience that's different from bricks and clicks.

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• EPM 11.1.2 - Issues during configuration when using Oracle DB if not using UTF8

  - by Ahmed A

  If you see issues during configuration when using Oracle DB if not using UTF8: Workaround: a. During configuration of EPM products, a warning message is displayed if the Oracle DB is not UTF8 enabled. If you continue with the configuration, certain products will not work as they will not be able to read the contents in the tables as the format is wrong.b. The Oracle DB must be setup to use AL32UTF8 or a superset that contains AL32UTF8. c. The only difference between AL32UTF8 and UTF8 character sets is that AL32UTF8 stores characters beyond U+FFFF as four bytes (exactly as Unicode defines UTF-8). Oracle’s “UTF8” stores these characters as a sequence of two UTF-16 surrogate characters encoded using UTF-8 (or six bytes per character). Besides this storage difference, another difference is better support for supplementary characters in AL32UTF8 character set.

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• C#/.NET Little Wonders: Interlocked CompareExchange()

  - by James Michael Hare

  Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. Two posts ago, I discussed the Interlocked Add(), Increment(), and Decrement() methods (here) for adding and subtracting values in a thread-safe, lightweight manner.  Then, last post I talked about the Interlocked Read() and Exchange() methods (here) for safely and efficiently reading and setting 32 or 64 bit values (or references).  This week, we’ll round out the discussion by talking about the Interlocked CompareExchange() method and how it can be put to use to exchange a value if the current value is what you expected it to be. Dirty reads can lead to bad results Many of the uses of Interlocked that we’ve explored so far have centered around either reading, setting, or adding values.  But what happens if you want to do something more complex such as setting a value based on the previous value in some manner? Perhaps you were creating an application that reads a current balance, applies a deposit, and then saves the new modified balance, where of course you’d want that to happen atomically.  If you read the balance, then go to save the new balance and between that time the previous balance has already changed, you’ll have an issue!  Think about it, if we read the current balance as $400, and we are applying a new deposit of $50.75, but meanwhile someone else deposits $200 and sets the total to $600, but then we write a total of $450.75 we’ve lost $200! Now, certainly for int and long values we can use Interlocked.Add() to handles these cases, and it works well for that.  But what if we want to work with doubles, for example?  Let’s say we wanted to add the numbers from 0 to 99,999 in parallel.  We could do this by spawning several parallel tasks to continuously add to a total: 1: double total = 0; 2:  3: Parallel.For(0, 10000, next => 4: { 5: total += next; 6: }); Were this run on one thread using a standard for loop, we’d expect an answer of 4,999,950,000 (the sum of all numbers from 0 to 99,999).  But when we run this in parallel as written above, we’ll likely get something far off.  The result of one of my runs, for example, was 1,281,880,740.  That is way off!  If this were banking software we’d be in big trouble with our clients.  So what happened?  The += operator is not atomic, it will read in the current value, add the result, then store it back into the total.  At any point in all of this another thread could read a “dirty” current total and accidentally “skip” our add.   So, to clean this up, we could use a lock to guarantee concurrency: 1: double total = 0.0; 2: object locker = new object(); 3:  4: Parallel.For(0, count, next => 5: { 6: lock (locker) 7: { 8: total += next; 9: } 10: }); Which will give us the correct result of 4,999,950,000.  One thing to note is that locking can be heavy, especially if the operation being locked over is trivial, or the life of the lock is a high percentage of the work being performed concurrently.  In the case above, the lock consumes pretty much all of the time of each parallel task – and the task being locked on is relatively trivial. Now, let me put in a disclaimer here before we go further: For most uses, lock is more than sufficient for your needs, and is often the simplest solution!    So, if lock is sufficient for most needs, why would we ever consider another solution?  The problem with locking is that it can suspend execution of your thread while it waits for the signal that the lock is free.  Moreover, if the operation being locked over is trivial, the lock can add a very high level of overhead.  This is why things like Interlocked.Increment() perform so well, instead of locking just to perform an increment, we perform the increment with an atomic, lockless method. As with all things performance related, it’s important to profile before jumping to the conclusion that you should optimize everything in your path.  If your profiling shows that locking is causing a high level of waiting in your application, then it’s time to consider lighter alternatives such as Interlocked. CompareExchange() – Exchange existing value if equal some value So let’s look at how we could use CompareExchange() to solve our problem above.  The general syntax of CompareExchange() is: T CompareExchange<T>(ref T location, T newValue, T expectedValue) If the value in location == expectedValue, then newValue is exchanged.  Either way, the value in location (before exchange) is returned. Actually, CompareExchange() is not one method, but a family of overloaded methods that can take int, long, float, double, pointers, or references.  It cannot take other value types (that is, can’t CompareExchange() two DateTime instances directly).  Also keep in mind that the version that takes any reference type (the generic overload) only checks for reference equality, it does not call any overridden Equals(). So how does this help us?  Well, we can grab the current total, and exchange the new value if total hasn’t changed.  This would look like this: 1: // grab the snapshot 2: double current = total; 3:  4: // if the total hasn’t changed since I grabbed the snapshot, then 5: // set it to the new total 6: Interlocked.CompareExchange(ref total, current + next, current); So what the code above says is: if the amount in total (1st arg) is the same as the amount in current (3rd arg), then set total to current + next (2nd arg).  This check and exchange pair is atomic (and thus thread-safe). This works if total is the same as our snapshot in current, but the problem, is what happens if they aren’t the same?  Well, we know that in either case we will get the previous value of total (before the exchange), back as a result.  Thus, we can test this against our snapshot to see if it was the value we expected: 1: // if the value returned is != current, then our snapshot must be out of date 2: // which means we didn't (and shouldn't) apply current + next 3: if (Interlocked.CompareExchange(ref total, current + next, current) != current) 4: { 5: // ooops, total was not equal to our snapshot in current, what should we do??? 6: } So what do we do if we fail?  That’s up to you and the problem you are trying to solve.  It’s possible you would decide to abort the whole transaction, or perhaps do a lightweight spin and try again.  Let’s try that: 1: double current = total; 2:  3: // make first attempt... 4: if (Interlocked.CompareExchange(ref total, current + i, current) != current) 5: { 6: // if we fail, go into a spin wait, spin, and try again until succeed 7: var spinner = new SpinWait(); 8:  9: do 10: { 11: spinner.SpinOnce(); 12: current = total; 13: } 14: while (Interlocked.CompareExchange(ref total, current + i, current) != current); 15: } 16:  This is not trivial code, but it illustrates a possible use of CompareExchange().  What we are doing is first checking to see if we succeed on the first try, and if so great!  If not, we create a SpinWait and then repeat the process of SpinOnce(), grab a fresh snapshot, and repeat until CompareExchnage() succeeds.  You may wonder why not a simple do-while here, and the reason it’s more efficient to only create the SpinWait until we absolutely know we need one, for optimal efficiency. Though not as simple (or maintainable) as a simple lock, this will perform better in many situations.  Comparing an unlocked (and wrong) version, a version using lock, and the Interlocked of the code, we get the following average times for multiple iterations of adding the sum of 100,000 numbers: 1: Unlocked money average time: 2.1 ms 2: Locked money average time: 5.1 ms 3: Interlocked money average time: 3 ms So the Interlocked.CompareExchange(), while heavier to code, came in lighter than the lock, offering a good compromise of safety and performance when we need to reduce contention. CompareExchange() - it’s not just for adding stuff… So that was one simple use of CompareExchange() in the context of adding double values -- which meant we couldn’t have used the simpler Interlocked.Add() -- but it has other uses as well. If you think about it, this really works anytime you want to create something new based on a current value without using a full lock.  For example, you could use it to create a simple lazy instantiation implementation.  In this case, we want to set the lazy instance only if the previous value was null: 1: public static class Lazy<T> where T : class, new() 2: { 3: private static T _instance; 4:  5: public static T Instance 6: { 7: get 8: { 9: // if current is null, we need to create new instance 10: if (_instance == null) 11: { 12: // attempt create, it will only set if previous was null 13: Interlocked.CompareExchange(ref _instance, new T(), (T)null); 14: } 15:  16: return _instance; 17: } 18: } 19: } So, if _instance == null, this will create a new T() and attempt to exchange it with _instance.  If _instance is not null, then it does nothing and we discard the new T() we created. This is a way to create lazy instances of a type where we are more concerned about locking overhead than creating an accidental duplicate which is not used.  In fact, the BCL implementation of Lazy<T> offers a similar thread-safety choice for Publication thread safety, where it will not guarantee only one instance was created, but it will guarantee that all readers get the same instance.  Another possible use would be in concurrent collections.  Let’s say, for example, that you are creating your own brand new super stack that uses a linked list paradigm and is “lock free”.  We could use Interlocked.CompareExchange() to be able to do a lockless Push() which could be more efficient in multi-threaded applications where several threads are pushing and popping on the stack concurrently. Yes, there are already concurrent collections in the BCL (in .NET 4.0 as part of the TPL), but it’s a fun exercise!  So let’s assume we have a node like this: 1: public sealed class Node<T> 2: { 3: // the data for this node 4: public T Data { get; set; } 5:  6: // the link to the next instance 7: internal Node<T> Next { get; set; } 8: } Then, perhaps, our stack’s Push() operation might look something like: 1: public sealed class SuperStack<T> 2: { 3: private volatile T _head; 4:  5: public void Push(T value) 6: { 7: var newNode = new Node<int> { Data = value, Next = _head }; 8:  9: if (Interlocked.CompareExchange(ref _head, newNode, newNode.Next) != newNode.Next) 10: { 11: var spinner = new SpinWait(); 12:  13: do 14: { 15: spinner.SpinOnce(); 16: newNode.Next = _head; 17: } 18: while (Interlocked.CompareExchange(ref _head, newNode, newNode.Next) != newNode.Next); 19: } 20: } 21:  22: // ... 23: } Notice a similar paradigm here as with adding our doubles before.  What we are doing is creating the new Node with the data to push, and with a Next value being the original node referenced by _head.  This will create our stack behavior (LIFO – Last In, First Out).  Now, we have to set _head to now refer to the newNode, but we must first make sure it hasn’t changed! So we check to see if _head has the same value we saved in our snapshot as newNode.Next, and if so, we set _head to newNode.  This is all done atomically, and the result is _head’s original value, as long as the original value was what we assumed it was with newNode.Next, then we are good and we set it without a lock!  If not, we SpinWait and try again. Once again, this is much lighter than locking in highly parallelized code with lots of contention.  If I compare the method above with a similar class using lock, I get the following results for pushing 100,000 items: 1: Locked SuperStack average time: 6 ms 2: Interlocked SuperStack average time: 4.5 ms So, once again, we can get more efficient than a lock, though there is the cost of added code complexity.  Fortunately for you, most of the concurrent collection you’d ever need are already created for you in the System.Collections.Concurrent (here) namespace – for more information, see my Little Wonders – The Concurent Collections Part 1 (here), Part 2 (here), and Part 3 (here). Summary We’ve seen before how the Interlocked class can be used to safely and efficiently add, increment, decrement, read, and exchange values in a multi-threaded environment.  In addition to these, Interlocked CompareExchange() can be used to perform more complex logic without the need of a lock when lock contention is a concern. The added efficiency, though, comes at the cost of more complex code.  As such, the standard lock is often sufficient for most thread-safety needs.  But if profiling indicates you spend a lot of time waiting for locks, or if you just need a lock for something simple such as an increment, decrement, read, exchange, etc., then consider using the Interlocked class’s methods to reduce wait. Technorati Tags: C#,CSharp,.NET,Little Wonders,Interlocked,CompareExchange,threading,concurrency

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• OEL5.5 released

  - by wim.coekaerts

  OEL 5.5 got pushed to ULN last night. we are creating the 5.5 base channel right now - for those that want the convenience of register to a specific update channel only. This is something we have done since the beginning (on ULN). The ISO images will appear on e-delivery in a number of days and for those customers that need urgent ISO access they can file a support SR.

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• Neue Marketing Kits für Hardware

  - by A&C Redaktion

  Zur Vertriebsunterstützung gibt es jetzt auch Oracle Marketing Kits in Deutsch für folgende Hardware-Lösungen: Server & Storage: Improve Database Capacity Management with Oracle Storage and Hybrid Columnar Compression Server & Storage: Accelerating Database Test & Development with Sun ZFS Storage Appliance Server & Storage: Upgrade SAN Storage to Oracle Pillar Axiom Server & Storage: SPARC Refresh with Oracle Solaris Operating System Server & Storage: SPARC Server Refresh: The Next Level of Datacenter Performance with Oracle’s New SPARC Servers Server & Storage: Oracle Server Virtualization Server & Storage: Oracle Desktop Virtualization

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• Today @ OOW: Identity Management for the SoMoClo world

  - by B Shashikumar

  Today at OpenWord, we have a very interesting lineup of Identity Management sessions that discuss how to extend identity management securrley to cloud, mobile and social ecosystems. Here are 3 of the can’t miss identity management sessions today: Identity Management and the Cloud: Security is regularly identified as the #1 barrier to cloud service adoption. Oracle Identity Management is designed to help customers extend and connect core identity services to SaaS applications and systems. This session explores how organizations are using Oracle Identity Management with cloud services and how some customers are offering identity management as a cloud service. Real-time External Authorization for Applications, Middleware and Databases: Externalization of authorization is key to manageability and audit. This session covers enterprise wide authorization solution deployment best practices and real-world examples of using Oracle Entitlements Server—the one-stop standards-compliant authorization solution—for middleware, applications, and data. Delivering Secure WiFi on the Tube as an Olympics Legacy from London 2012: In this session, Virgin Media, the U.K.’s first combined provider of broadband, TV, mobile, and home phone services, shares how it is providing free secure Wi-Fi services to the London Underground, using Oracle Virtual Directory and Oracle Entitlements Server, leveraging back-end legacy systems that were never designed to be externalized. As an Olympics 2012 legacy, the Oracle architecture will form a platform to be consumed by other Virgin Media services such as video on demand. Here is the complete lineup of Identity Management sessions today at OOW.

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• Notifications for Expiring DBSNMP Passwords

  - by Courtney Llamas

  Most user accounts these days have a password profile on them that automatically expires the password after a set number of days.   Depending on your company’s security requirements, this may be as little as 30 days or as long as 365 days, although typically it falls between 60-90 days. For a normal user, this can cause a small interruption in your day as you have to go get your password reset by an admin. When this happens to privileged accounts, such as the DBSNMP account that is responsible for monitoring database availability, it can cause bigger problems. In Oracle Enterprise Manager 12c you may notice the error message “ORA-28002: the password will expire within 5 days” when you connect to a target, or worse you may get “ORA-28001: the password has expired". If you wait too long, your monitoring will fail because the password is locked out. Wouldn’t it be nice if we could get an alert 10 days before our DBSNMP password expired? Thanks to Oracle Enterprise Manager 12c Metric Extensions (ME), you can! See the Oracle Enterprise Manager Cloud Control Administrator’s Guide for more information on Metric Extensions. To create a metric extension, select Enterprise / Monitoring / Metric Extensions, and then click on Create. On the General Properties screen select either Cluster Database or Database Instance, depending on which target you need to monitor.  If you have both RAC and Single instance you may need to create one for each. In this example we will create a Cluster Database metric.  Enter a Name for the ME and a Display Name. Then select SQL for the Adapter.  Adjust the Collection Schedule as desired, for this example we will collect this metric every 1 day. Notice for metric collected every day, we can determine the exact time we want to collect. On the Adapter page, enter the query that you wish to execute.  In this example we will use the query below that specifically checks for the DBSNMP user that is expiring within 10 days. Of course, you can adjust this query to alert for any user that can cause an outage such as an application account or service account such as RMAN. select username, account_status, trunc(expiry_date-sysdate) days_to_expirefrom dba_userswhere username = 'DBSNMP'and expiry_date is not null; The next step is to create the columns to store the data returned from the query.  Click Add and add a column for each of the fields in the same order that data is returned.  The table below will help you complete the column additions. Name Display Name Column Type Value Type Metric Category Unit Username User Name Key String Security AccountStatus Account Status Data String Security DaysToExpire Days Until Expiration Data Number Security Days When creating the DaysToExpire column, you can add a default threshold here for Warning and Critical (say < 10 and 5).  When all columns have been added, click Next. On the Credentials page, you can choose to use the default monitoring credentials or specify new credentials.  We will use the default credentials established for our target (dbsnmp). The next step is to test your Metric Extension.  Click on Add to select a target for testing, then click Select. Now click the button Run Test to execute the test against the selected target(s). We can see in the example below that the Metric Extension has executed and returned a value of 68 days to expire. Click Next to proceed. Review the metric extension in the final screen and click Finish. The metric will be created in Editable status.  Select the metric, click Actions and select Deployable Draft. You can do this once more to move to Published. Finally, we want to apply this metric to a target. When managing many targets, it’s best to add your metric to a template, for details on adding a Metric Extension to a template see the Administrator’s Guide. For this example, we will deploy this to a target directly. Select Actions / Deploy to Targets. Click Add and select the target you wish to deploy to and click Submit.  Once deployment is complete, we can go to the target and view the Metric & Collection Settings to see the new metric and its thresholds.   After some time, you will find the metric has collected and the days to expiration for DBSNMP user can be seen in the All Metrics view.   For metrics collected once per day, you may have to wait up to 24 hours to see the metric and current severity. In the example below, the current severity is Clear (green check) as it is not scheduled to expire within 10 days. To test the notification, we can edit the thresholds for the new metric so they trigger an alert.  Our password expires in 139 days, so we’ll change our Warning to 140 and leave Critical at 5, in our example we also changed the collection time to every 5 minutes.  At the next collection, you’ll find that the current severity changes to a Warning and any related Incident Rules would be triggered to create an Incident or Notification as desired. Now that you get a notification that your DBSNMP passwords is about to expire, you can use OEM Command Line Interface (EM CLI) verb update_db_password to change it at both the database target and the OEM target in one step.  The caveat is you must know the existing password to use the update_db_password command.  To learn more about EM CLI, see the Oracle Enterprise Manager Command Line Interface Guide.  Below is an example of changing the password with the update_db_password verb.  $ ./emcli update_db_password -target_name=emrep -target_type=oracle_database -user_name=dbsnmp -change_at_target=yes -change_all_references=yes Enter value for old_password :Enter value for new_password :Enter value for retype_new_password :Successfully submitted a job to change the password in Enterprise Manager and on the target database: "emrep"Execute "emcli get_jobs -job_id=FA66C1C4D663297FE0437656F20ACC84" to check the status of the job.Search for job name "CHANGE_PWD_JOB_FA66C1C4D662297FE0437656F20ACC84" on the Jobs home page to check job execution details. The subsequent job created will typically run quickly enough that a blackout is not needed, however if you submit a script with many targets to change, your job may run slower so adding a blackout to the script is recommended. $ ./emcli get_jobs -job_id=FA66C1C4D663297FE0437656F20ACC84 Name Type Job ID Execution ID Scheduled Completed TZ Offset Status Status ID Owner Target Type Target Name CHANGE_PWD_JOB_FA66C1C4D662297FE0437656F20ACC84 ChangePassword FA66C1C4D663297FE0437656F20ACC84 FA66C1C4D665297FE0437656F20ACC84 2014-05-28 09:39:12 2014-05-28 09:39:18 GMT-07:00 Succeeded 5 SYSMAN oracle_database emrep After implementing the above Metric Extension and using the EM CLI update_db_password verb, you will be able to stay on top of your DBSNMP password changes without experiencing an unplanned monitoring outage.  

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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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• The latest Oracle Social Network News from Open World

  - by me

  Highlights Oracle and Partners showcase the latest development around  Oracle Social Network  (OSN) Integration of OSN Social Fabric into Business Applications like Finance, HCM and Customer Experience Partners like Cisco WebEx, Avaya, Weemo, Lingotek and HarQen showcase OSN integration Oracle shares details around internal OSN deployment Please visit us at 2413 Moscone South  Exhibition Hall  and  experience a live OSN demo Social Fabric  Oracle Social Network socializes your Applications, Process and Content within your Enterprise. Here are some examples what is shown at Oracle Open World. Socialize the Finance department Enable Finance departments to collaborate instantly during quarter close with real-time information access Enable finance professionals in the back office to easily interact with the rest of the company Provide privacy when discussing sensitive financial results within Conversations  Socialize Human Capital Management (HCM) Promotes attainable performance goals that achieve the business objectives of the enterprise Capture expertise across the network Continuous feedback loop provided that results in productivity and innovation improvement tied to higher employee engagement OSN and Customer Experience Find the person with the best skills to assist with the issue Real-time collaboration in  context of the issue Track an Agent’s collaboration contributions Identify and contribute relevant knowledge back to the system Cisco/Webex integration The Web Conferencing tool of your choice can be integrated with OSN. In the example below you can see the integration of the Cisco WebEx solution into OSN. and sure - this works on mobile devices as well  OSN @ Oracle Oracle has deployed OSN as part of the internal Fusion CRM application rollout. After just 4 month we can see impressive usage patterns.

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• An update on PeopleSoft Supplier Relationship Management 9.0

  Listen to Alexandra Dhillon, Principal Product Strategy Manager PeopleSoft applications updates listeners on why PeopleSoft SRM 9.0 is a significant focus area for PeopleSoft, how customers can benefit from using Release 9.0 today and future enhancements for this area.

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• The Other Side of XBRL

  - by john.orourke(at)oracle.com

  With the United States SEC's mandate for XBRL filings entering its third year, and impacting over 7000 additional companies in 2011, there's a lot of buzz in the industry about how companies should address the new reporting requirements.  Should they outsource the XBRL tagging process to a third party publisher, handle the process in-house with a bolt-on XBRL tool, or should they integrate XBRL tagging with the financial close and reporting process?  Oracle is recommending the latter approach, in fact  here's a link to a recent webcast that I did with CFO.com on this topic: http://www.cfo.com/webcasts/index.cfm/l_eventarchive/14548560 But production of XBRL-based filings is only half of the story. The other half is consumption of XBRL by regulators, academics, financial analysts and investors.  As I mentioned in my December article on the XBRL US conference, the feedback from these groups is that they are not really leveraging XBRL for analysis of companies due to a lack of tools and historic XBRL-based data on public companies.   The good news here is that the historic data problem is getting better as large, accelerated filers enter their third year of XBRL filings.  And the situation is getting better on the reporting and analysis tools side of the equation as well - and Oracle is leading the way. In early January, Oracle released the Oracle XBRL Extension for Oracle Database 11g.  This is a "no cost option" on top of the latest Oracle Database 11.2.0.2.0 release. With this added functionality organizations will have the ability to create one or more back-end XBRL repositories based on Oracle Database, which provide XBRL storage and query-ability with a set of XBRL-specific services.  The XBRL Extension to Oracle XML DB integrates easily with Oracle Business Intelligence Suite Enterprise Edition (OBIEE) for analytics and with interactive development environments (IDEs) and design tools for creating and editing XBRL taxonomies. The Oracle XBRL Extension to Oracle Database 11g should be attractive to regulators, stock exchanges, universities and other organizations that need to collect, analyze and disseminate XBRL-based filings.  It should also be attractive to organizations that produce XBRL filings, and need a way to store and compare their own XBRL-based financial filings to those of their peers and competitors. If you would like more information, here's a link to a web page on the Oracle Technology Network with the details about Oracle XBRL Extension for Oracle Database 11g, including data sheet, white paper, presentation, demos and other information: http://www.oracle.com/technetwork/database/features/xmldb/index-087631.html

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• Computer Networks UNISA - Chap 8 &ndash; Wireless Networking

  - by MarkPearl

  After reading this section you should be able to Explain how nodes exchange wireless signals Identify potential obstacles to successful transmission and their repercussions, such as interference and reflection Understand WLAN architecture Specify the characteristics of popular WLAN transmission methods including 802.11 a/b/g/n Install and configure wireless access points and their clients Describe wireless MAN and WAN technologies, including 802.16 and satellite communications The Wireless Spectrum All wireless signals are carried through the air by electromagnetic waves. The wireless spectrum is a continuum of the electromagnetic waves used for data and voice communication. The wireless spectrum falls between 9KHZ and 300 GHZ. Characteristics of Wireless Transmission Antennas Each type of wireless service requires an antenna specifically designed for that service. The service’s specification determine the antenna’s power output, frequency, and radiation pattern. A directional antenna issues wireless signals along a single direction. An omnidirectional antenna issues and receives wireless signals with equal strength and clarity in all directions The geographical area that an antenna or wireless system can reach is known as its range Signal Propagation LOS (line of sight) uses the least amount of energy and results in the reception of the clearest possible signal. When there is an obstacle in the way, the signal may… pass through the object or be obsrobed by the object or may be subject to reflection, diffraction or scattering. Reflection – waves encounter an object and bounces off it. Diffraction – signal splits into secondary waves when it encounters an obstruction Scattering – is the diffusion or the reflection in multiple different directions of a signal Signal Degradation Fading occurs as a signal hits various objects. Because of fading, the strength of the signal that reaches the receiver is lower than the transmitted signal strength. The further a signal moves from its source, the weaker it gets (this is called attenuation) Signals are also affected by noise – the electromagnetic interference) Interference can distort and weaken a wireless signal in the same way that noise distorts and weakens a wired signal. Frequency Ranges Older wireless devices used the 2.4 GHZ band to send and receive signals. This had 11 communication channels that are unlicensed. Newer wireless devices can also use the 5 GHZ band which has 24 unlicensed bands Narrowband, Broadband, and Spread Spectrum Signals Narrowband – a transmitter concentrates the signal energy at a single frequency or in a very small range of frequencies Broadband – uses a relatively wide band of the wireless spectrum and offers higher throughputs than narrowband technologies The use of multiple frequencies to transmit a signal is known as spread-spectrum technology. In other words a signal never stays continuously within one frequency range during its transmission. One specific implementation of spread spectrum is FHSS (frequency hoping spread spectrum). Another type is known as DSS (direct sequence spread spectrum) Fixed vs. Mobile Each type of wireless communication falls into one of two categories Fixed – the location of the transmitted and receiver do not move (results in energy saved because weaker signal strength is possible with directional antennas) Mobile – the location can change WLAN (Wireless LAN) Architecture There are two main types of arrangements Adhoc – data is sent directly between devices – good for small local devices Infrastructure mode – a wireless access point is placed centrally, that all devices connect with 802.11 WLANs The most popular wireless standards used on contemporary LANs are those developed by IEEE’s 802.11 committee. Over the years several distinct standards related to wireless networking have been released. Four of the best known standards are also referred to as Wi-Fi. They are…. 802.11b 802.11a 802.11g 802.11n These four standards share many characteristics. i.e. All 4 use half duplex signalling Follow the same access method Access Method 802.11 standards specify the use of CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) to access a shared medium. Using CSMA/CA before a station begins to send data on an 802.11 network, it checks for existing wireless transmissions. If the source node detects no transmission activity on the network, it waits a brief period of time and then sends its transmission. If the source does detect activity, it waits a brief period of time before checking again. The destination node receives the transmission and, after verifying its accuracy, issues an acknowledgement (ACT) packet to the source. If the source receives the ACK it assumes the transmission was successful, – if it does not receive an ACK it assumes the transmission failed and sends it again. Association Two types of scanning… Active – station transmits a special frame, known as a prove, on all available channels within its frequency range. When an access point finds the probe frame, it issues a probe response. Passive – wireless station listens on all channels within its frequency range for a special signal, known as a beacon frame, issued from an access point – the beacon frame contains information necessary to connect to the point. Re-association occurs when a mobile user moves out of one access point’s range and into the range of another. Frames Read page 378 – 381 about frames and specific 802.11 protocols Bluetooth Networks Sony Ericson originally invented the Bluetooth technology in the early 1990s. In 1998 other manufacturers joined Ericsson in the Special Interest Group (SIG) whose aim was to refine and standardize the technology. Bluetooth was designed to be used on small networks composed of personal communications devices. It has become popular wireless technology for communicating among cellular telephones, phone headsets, etc. Wireless WANs and Internet Access Refer to pages 396 – 402 of the textbook for details.

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• How to leverage the internal HTTP endpoint available on Azure web roles?

  - by Alfredo Delsors

  Imagine you have a Web application using an in-memory collection that changes occasionally but is used very often. The collection gets loaded from storage on the Application_Start global.asax event and is updated whenever its content changes. If you want to deploy this application on Azure you need to keep in mind that more than one instance of the application can be running at any time and therefore you need to provide some mechanism to keep all instances informed with the latest changes. Because the communication through internal endpoints between Azure role instances is at no cost, a good solution can be maintaining the information on Azure Storage Tables, reading its contents on the Application_Start event and populating its changes to all other instances using the internal HTTP port available on Azure Web Roles. You need to follow these steps to leverage the internal HTTP endpoint available on Azure web roles to maintain all instances up to date. 1.   Define an internal HTTP endpoint in the Web Role properties, for example InternalHttpEndpoint   2.   Add a new WCF service to the Web Role, for example NotificationService.svc 3.   Disable multiple site bindings in web.config: <serviceHostingEnvironment multipleSiteBindingsEnabled="false"> 4.   Add a method on the new service to receive notifications from other role instances. namespace Service { [ServiceContract] public interface INotificationService { [OperationContract(IsOneWay = true)] void Notify(Information info); } } 5.   Declare a class that inherits from System.ServiceModel.Activation.ServiceHostFactory and override the method CreateServiceHost to host the internal endpoint. public class InternalServiceFactory : ServiceHostFactory { protected override ServiceHost CreateServiceHost(Type serviceType, Uri[] baseAddresses) { var internalEndpointAddress = string.Format( "http://{0}/NotificationService.svc", RoleEnvironment.CurrentRoleInstance.InstanceEndpoints["InternalHttpEndpoint"].IPEndpoint); ServiceHost host = new ServiceHost( typeof(NotificationService), new Uri(internalEndpointAddress)); BasicHttpBinding binding = new BasicHttpBinding(SecurityMode.None); host.AddServiceEndpoint( typeof(INotificationService), binding, internalEndpointAddress); return host; } } Note that you can use SecurityMode.None because the internal endpoint is private to the instances of the service. 6.   Edit the markup of the service right clicking the svc file and selecting "View markup" to add the new factory as the factory to be used to create the service <%@ ServiceHost Language="C#" Debug="true" Factory="Service.InternalServiceFactory" Service="Service.NotificationService" CodeBehind="NotificationService.svc.cs" %> 7.   Now you can notify changes to other instances using this code: var current = RoleEnvironment.CurrentRoleInstance; var endPoints = current.Role.Instances .Where(instance => instance != current) .Select(instance => instance.InstanceEndpoints["InternalHttpEndpoint"]); foreach (var ep in endPoints) { EndpointAddress address = new EndpointAddress( String.Format("http://{0}/NotificationService.svc", ep.IPEndpoint)); BasicHttpBinding binding = new BasicHttpBinding(SecurityMode.None); var factory = new ChannelFactory<INotificationService>(binding); INotificationService instance = factory.CreateChannel(address); instance.Notify(changedinfo); }

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• Base Pages and Interfaces for ASP.NET Pages

  - by geekrutherford

  For quite a while I have been using the concept of base pages when developing pages in ASP.NET applications. It is a wonderful method for exposing common functions to all of your applications pages and also overriding certain events for various purposes (i.e. dynamic themes).  Recently I found out a new developer will be joining my team. This prompted me to review the applications code for readability and ease of maintenance. I began adding comments through out the code behind for all pages within the application. While doing so I noted that I had used common method names for such things as loading data, configuring controls, applying filters, etc.   Bringing a new developer on board, I wanted to make the transition as seamless as possible while also ensuring they follow existing coding practices we already have in place. While I could have created virtual methods for the common page methods allowing them to overridden, what I really needed was a way to ensure the new developer implemented the same methods for each and every page. Thus I created an interface to force the issue.   Now, every page not only inherits the base page class but also implements an interface. This provides every page not only common functions and overridden page events but also imposes rules for implementing certain common methods :-)   Interface   public interface BasePageInterface { /// Configures page based on users security permissions. void CheckPermissions(); /// Configures Filter Form control for current page.  /// Ensure you have set the FilteredGrid and PageAjaxManager properties of the FilterForm control in PageLoad!!!  void ConfigureFilters(); /// Sets event handlers and default settings for controls on the current page. void ConfigureControls(); /// Exports data bound to grid in selected format. void ExportGridData(ExportFormat fmt); /// Loads data and binds to grid. /// Columns are turned on/off in grid depending on tab selected and users permissions.  void LoadData(); }   Page code-behind class definition:   public partial class MyPage : BasePage, BasePageInterface Note, you could not use an abstract class to accomplish this considering C# does not allow for multiple inheritance.  Nor could the base page class be abstract since it needs to inherit from the System.Web.UI.Page class in order to override page events.

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