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• elffile: ELF Specific File Identification Utility

  - by user9154181

  Solaris 11 has a new standard user level command, /usr/bin/elffile. elffile is a variant of the file utility that is focused exclusively on linker related files: ELF objects, archives, and runtime linker configuration files. All other files are simply identified as "non-ELF". The primary advantage of elffile over the existing file utility is in the area of archives — elffile examines the archive members and can produce a summary of the contents, or per-member details. The impetus to add elffile to Solaris came from the effort to extend the format of Solaris archives so that they could grow beyond their previous 32-bit file limits. That work introduced a new archive symbol table format. Now that there was more than one possible format, I thought it would be useful if the file utility could identify which format a given archive is using, leading me to extend the file utility: % cc -c ~/hello.c % ar r foo.a hello.o % file foo.a foo.a: current ar archive, 32-bit symbol table % ar r -S foo.a hello.o % file foo.a foo.a: current ar archive, 64-bit symbol table In turn, this caused me to think about all the things that I would like the file utility to be able to tell me about an archive. In particular, I'd like to be able to know what's inside without having to unpack it. The end result of that train of thought was elffile. Much of the discussion in this article is adapted from the PSARC case I filed for elffile in December 2010: PSARC 2010/432 elffile Why file Is No Good For Archives And Yet Should Not Be Fixed The standard /usr/bin/file utility is not very useful when applied to archives. When identifying an archive, a user typically wants to know 2 things: Is this an archive? Presupposing that the archive contains objects, which is by far the most common use for archives, what platform are the objects for? Are they for sparc or x86? 32 or 64-bit? Some confusing combination from varying platforms? The file utility provides a quick answer to question (1), as it identifies all archives as "current ar archive". It does nothing to answer the more interesting question (2). To answer that question, requires a multi-step process: Extract all archive members Use the file utility on the extracted files, examine the output for each file in turn, and compare the results to generate a suitable summary description. Remove the extracted files It should be easier and more efficient to answer such an obvious question. It would be reasonable to extend the file utility to examine archive contents in place and produce a description. However, there are several reasons why I decided not to do so: The correct design for this feature within the file utility would have file examine each archive member in turn, applying its full abilities to each member. This would be elegant, but also represents a rather dramatic redesign and re-implementation of file. Archives nearly always contain nothing but ELF objects for a single platform, so such generality in the file utility would be of little practical benefit. It is best to avoid adding new options to standard utilities for which other implementations of interest exist. In the case of the file utility, one concern is that we might add an option which later appears in the GNU version of file with a different and incompatible meaning. Indeed, there have been discussions about replacing the Solaris file with the GNU version in the past. This may or may not be desirable, and may or may not ever happen. Either way, I don't want to preclude it. Examining archive members is an O(n) operation, and can be relatively slow with large archives. The file utility is supposed to be a very fast operation. I decided that extending file in this way is overkill, and that an investment in the file utility for better archive support would not be worth the cost. A solution that is more narrowly focused on ELF and other linker related files is really all that we need. The necessary code for doing this already exists within libelf. All that is missing is a small user-level wrapper to make that functionality available at the command line. In that vein, I considered adding an option for this to the elfdump utility. I examined elfdump carefully, and even wrote a prototype implementation. The added code is small and simple, but the conceptual fit with the rest of elfdump is poor. The result complicates elfdump syntax and documentation, definite signs that this functionality does not belong there. And so, I added this functionality as a new user level command. The elffile Command The syntax for this new command is elffile [-s basic | detail | summary] filename... Please see the elffile(1) manpage for additional details. To demonstrate how output from elffile looks, I will use the following files: FileDescription configA runtime linker configuration file produced with crle dwarf.oAn ELF object /etc/passwdA text file mixed.aArchive containing a mixture of ELF and non-ELF members mixed_elf.aArchive containing ELF objects for different machines not_elf.aArchive containing no ELF objects same_elf.aArchive containing a collection of ELF objects for the same machine. This is the most common type of archive. The file utility identifies these files as follows: % file config dwarf.o /etc/passwd mixed.a mixed_elf.a not_elf.a same_elf.a config: Runtime Linking Configuration 64-bit MSB SPARCV9 dwarf.o: ELF 64-bit LSB relocatable AMD64 Version 1 /etc/passwd: ascii text mixed.a: current ar archive, 32-bit symbol table mixed_elf.a: current ar archive, 32-bit symbol table not_elf.a: current ar archive same_elf.a: current ar archive, 32-bit symbol table By default, elffile uses its "summary" output style. This output differs from the output from the file utility in 2 significant ways: Files that are not an ELF object, archive, or runtime linker configuration file are identified as "non-ELF", whereas the file utility attempts further identification for such files. When applied to an archive, the elffile output includes a description of the archive's contents, without requiring member extraction or other additional steps. Applying elffile to the above files: % elffile config dwarf.o /etc/passwd mixed.a mixed_elf.a not_elf.a same_elf.a config: Runtime Linking Configuration 64-bit MSB SPARCV9 dwarf.o: ELF 64-bit LSB relocatable AMD64 Version 1 /etc/passwd: non-ELF mixed.a: current ar archive, 32-bit symbol table, mixed ELF and non-ELF content mixed_elf.a: current ar archive, 32-bit symbol table, mixed ELF content not_elf.a: current ar archive, non-ELF content same_elf.a: current ar archive, 32-bit symbol table, ELF 64-bit LSB relocatable AMD64 Version 1 The output for same_elf.a is of particular interest: The vast majority of archives contain only ELF objects for a single platform, and in this case, the default output from elffile answers both of the questions about archives posed at the beginning of this discussion, in a single efficient step. This makes elffile considerably more useful than file, within the realm of linker-related files. elffile can produce output in two other styles, "basic", and "detail". The basic style produces output that is the same as that from 'file', for linker-related files. The detail style produces per-member identification of archive contents. This can be useful when the archive contents are not homogeneous ELF object, and more information is desired than the summary output provides: % elffile -s detail mixed.a mixed.a: current ar archive, 32-bit symbol table mixed.a(dwarf.o): ELF 32-bit LSB relocatable 80386 Version 1 mixed.a(main.c): non-ELF content mixed.a(main.o): ELF 64-bit LSB relocatable AMD64 Version 1 [SSE]

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• Oracle Solaris Crash Analysis Tool 5.3 now available

  - by user12609056

  Oracle Solaris Crash Analysis Tool 5.3 The Oracle Solaris Crash Analysis Tool Team is happy to announce the availability of release 5.3.  This release addresses bugs discovered since the release of 5.2 plus enhancements to support Oracle Solaris 11 and updates to Oracle Solaris versions 7 through 10. The packages are available on My Oracle Support - simply search for Patch 13365310 to find the downloadable packages. Release Notes General blast support The blast GUI has been removed and is no longer supported. Oracle Solaris 2.6 Support As of Oracle Solaris Crash Analysis Tool 5.3, support for Oracle Solaris 2.6 has been dropped. If you have systems running Solaris 2.6, you will need to use Oracle Solaris Crash Analysis Tool 5.2 or earlier to read its crash dumps. New Commands Sanity Command Though one can re-run the sanity checks that are run at tool start-up using the coreinfo command, many users were unaware that they were. Though these checks can still be run using that command, a new command, namely sanity, can now be used to re-run the checks at any time. Interface Changes scat_explore -r and -t option The -r option has ben added to scat_explore so that a base directory can be specified and the -t op[tion was added to enable color taggging of the output. The scat_explore sub-command now accepts new options. Usage is: scat --scat_explore [-atv] [-r base_dir] [-d dest] [unix.N] [vmcore.]N Where: -v Verbose Mode: The command will print messages highlighting what it's doing. -a Auto Mode: The command does not prompt for input from the user as it runs. -d dest Instructs scat_explore to save it's output in the directory dest instead of the present working directory. -r base_dir Instructs scat_explore to save it's under the directory base_dir instead of the present working directory. If it is not specified using the -d option, scat_explore names it's output file as "scat_explore_system_name_hostid_lbolt_value_corefile_name." -t Enable color tags. When enabled, scat_explore tags important text with colors that match the level of importance. These colors correspond to the color normally printed when running Oracle Solaris Crash Analysis Tool in interactive mode. Tag Name Definition FATAL An extremely important message which should be investigated. WARNING A warning that may or may not have anything to do with the crash. ERROR An error, usually printer with a suggested command ALERT Used to indicate something the tool discovered. INFO Purely informational message INFO2 A follow-up to an INFO tagged message REDZONE Usually used when prnting memory info showing something is in the kernel's REDZONE. N The number of the crash dump. Specifying unix.N vmcore.N is optional and not required. Example: $ scat --scat_explore -a -v -r /tmp vmcore.0 #Output directory: /tmp/scat_explore_oomph_833a2959_0x28800_vmcore.0 #Tar filename: scat_explore_oomph_833a2959_0x28800_vmcore.0.tar #Extracting crash data... #Gathering standard crash data collections... #Panic string indicates a possible hang... #Gathering Hang Related data... #Creating tar file... #Compressing tar file... #Successful extraction SCAT_EXPLORE_DATA_DIR=/tmp/scat_explore_oomph_833a2959_0x28800_vmcore.0 Sending scat_explore results The .tar.gz file that results from a scat_explore run may be sent using Oracle Secure File Transfer. The Oracle Secure File Transfer User Guide describes how to use it to send a file. The send_scat_explore script now has a -t option for specifying a to address for sending the results. This option is mandatory. Known Issues There are a couple known issues that we are addressing in release 5.4, which you should expect to see soon: Display of timestamps in threads and clock information is incorrect in some cases. There are alignment issues with some of the tables produced by the tool.

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• You're invited : Oracle Solaris Forum, June 19th, Petah Tikva

  - by Frederic Pariente

  The local ISV Engineering will be attending and speaking at the Oracle and ilOUG Solaris Forum next week in Israel. Come meet us there! This free event requires registration, thanks. YOU'RE INVITED Oracle Solaris Forum Date : Tuesday, June 19th, 2012 Time : 14:00 Location :  Dan Academic CenterPetach TikvaIsrael Agenda : Enterprise Manager OPS Center and Oracle Exalogic Elastic CloudSolaris 11NetworkingCustomer Case Study : BMCOpen Systems Curriculum See you there!

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• Connecting SceneBuilder edited FXML to Java code

  - by daniel

  Recently I had to answer several questions regarding how to connect an UI built with the JavaFX SceneBuilder 1.0 Developer Preview to Java Code. So I figured out that a short overview might be helpful. But first, let me state the obvious. What is FXML? To make it short, FXML is an XML based declaration format for JavaFX. JavaFX provides an FXML loader which will parse FXML files and from that construct a graph of Java object. It may sound complex when stated like that but it is actually quite simple. Here is an example of FXML file, which instantiate a StackPane and puts a Button inside it: -- <?xml version="1.0" encoding="UTF-8"?> <?import java.lang.*?> <?import java.util.*?> <?import javafx.scene.control.*?> <?import javafx.scene.layout.*?> <?import javafx.scene.paint.*?> <StackPane prefHeight="150.0" prefWidth="200.0" xmlns:fx="http://javafx.com/fxml"> <children> <Button mnemonicParsing="false" text="Button" /> </children> </StackPane> ... and here is the code I would have had to write if I had chosen to do the same thing programatically: import javafx.scene.control.*; import javafx.scene.layout.*; ... final Button button = new Button("Button"); button.setMnemonicParsing(false); final StackPane stackPane = new StackPane(); stackPane.setPrefWidth(200.0); stackPane.setPrefHeight(150.0); stacPane.getChildren().add(button); As you can see - FXML is rather simple to understand - as it is quite close to the JavaFX API. So OK FXML is simple, but why would I use it?Well, there are several answers to that - but my own favorite is: because you can make it with SceneBuilder. What is SceneBuilder? In short SceneBuilder is a layout tool that will let you graphically build JavaFX user interfaces by dragging and dropping JavaFX components from a library, and save it as an FXML file. SceneBuilder can also be used to load and modify JavaFX scenegraphs declared in FXML. Here is how I made the small FXML file above: Start the JavaFX SceneBuilder 1.0 Developer Preview In the Library on the left hand side, click on 'StackPane' and drag it on the content view (the white rectangle) In the Library, select a Button and drag it onto the StackPane on the content view. In the Hierarchy Panel on the left hand side - select the StackPane component, then invoke 'Edit > Trim To Selected' from the menubar That's it - you can now save, and you will obtain the small FXML file shown above. Of course this is only a trivial sample, made for the sake of the example - and SceneBuilder will let you create much more complex UIs. So, I have now an FXML file. But what do I do with it? How do I include it in my program? How do I write my main class? Loading an FXML file with JavaFX Well, that's the easy part - because the piece of code you need to write never changes. You can download and look at the SceneBuilder samples if you need to get convinced, but here is the short version: Create a Java class (let's call it 'Main.java') which extends javafx.application.Application In the same directory copy/save the FXML file you just created using SceneBuilder. Let's name it "simple.fxml" Now here is the Java code for the Main class, which simply loads the FXML file and puts it as root in a stage's scene. /* * Copyright (c) 2012, Oracle and/or its affiliates. All rights reserved. */ package simple; import java.util.logging.Level; import java.util.logging.Logger; import javafx.application.Application; import javafx.fxml.FXMLLoader; import javafx.scene.Scene; import javafx.scene.layout.StackPane; import javafx.stage.Stage; public class Main extends Application { /** * @param args the command line arguments */ public static void main(String[] args) { Application.launch(Main.class, (java.lang.String[])null); } @Override public void start(Stage primaryStage) { try { StackPane page = (StackPane) FXMLLoader.load(Main.class.getResource("simple.fxml")); Scene scene = new Scene(page); primaryStage.setScene(scene); primaryStage.setTitle("FXML is Simple"); primaryStage.show(); } catch (Exception ex) { Logger.getLogger(Main.class.getName()).log(Level.SEVERE, null, ex); } } } Great! Now I only have to use my favorite IDE to compile the class and run it. But... wait... what does it do? Well nothing. It just displays a button in the middle of a window. There's no logic attached to it. So how do we do that? How can I connect this button to my application logic? Here is how: Connection to code First let's define our application logic. Since this post is only intended to give a very brief overview - let's keep things simple. Let's say that the only thing I want to do is print a message on System.out when the user clicks on my button. To do that, I'll need to register an action handler with my button. And to do that, I'll need to somehow get a handle on my button. I'll need some kind of controller logic that will get my button and add my action handler to it. So how do I get a handle to my button and pass it to my controller? Once again - this is easy: I just need to write a controller class for my FXML. With each FXML file, it is possible to associate a controller class defined for that FXML. That controller class will make the link between the UI (the objects defined in the FXML) and the application logic. To each object defined in FXML we can associate an fx:id. The value of the id must be unique within the scope of the FXML, and is the name of an instance variable inside the controller class, in which the object will be injected. Since I want to have access to my button, I will need to add an fx:id to my button in FXML, and declare an @FXML variable in my controller class with the same name. In other words - I will need to add fx:id="myButton" to my button in FXML: -- <Button fx:id="myButton" mnemonicParsing="false" text="Button" /> and declare @FXML private Button myButton in my controller class @FXML private Button myButton; // value will be injected by the FXMLLoader Let's see how to do this. Add an fx:id to the Button object Load "simple.fxml" in SceneBuilder - if not already done In the hierarchy panel (bottom left), or directly on the content view, select the Button object. Open the Properties sections of the inspector (right panel) for the button object At the top of the section, you will see a text field labelled fx:id. Enter myButton in that field and validate. Associate a controller class with the FXML file Still in SceneBuilder, select the top root object (in our case, that's the StackPane), and open the Code section of the inspector (right hand side) At the top of the section you should see a text field labelled Controller Class. In the field, type simple.SimpleController. This is the name of the class we're going to create manually. If you save at this point, the FXML will look like this: -- <?xml version="1.0" encoding="UTF-8"?> <?import java.lang.*?> <?import java.util.*?> <?import javafx.scene.control.*?> <?import javafx.scene.layout.*?> <?import javafx.scene.paint.*?> <StackPane prefHeight="150.0" prefWidth="200.0" xmlns:fx="http://javafx.com/fxml" fx:controller="simple.SimpleController"> <children> <Button fx:id="myButton" mnemonicParsing="false" text="Button" /> </children> </StackPane> As you can see, the name of the controller class has been added to the root object: fx:controller="simple.SimpleController" Coding the controller class In your favorite IDE, create an empty SimpleController.java class. Now what does a controller class looks like? What should we put inside? Well - SceneBuilder will help you there: it will show you an example of controller skeleton tailored for your FXML. In the menu bar, invoke View > Show Sample Controller Skeleton. A popup appears, displaying a suggestion for the controller skeleton: copy the code displayed there, and paste it into your SimpleController.java: /** * Sample Skeleton for "simple.fxml" Controller Class * Use copy/paste to copy paste this code into your favorite IDE **/ package simple; import java.net.URL; import java.util.ResourceBundle; import javafx.fxml.FXML; import javafx.fxml.Initializable; import javafx.scene.control.Button; public class SimpleController implements Initializable { @FXML // fx:id="myButton" private Button myButton; // Value injected by FXMLLoader @Override // This method is called by the FXMLLoader when initialization is complete public void initialize(URL fxmlFileLocation, ResourceBundle resources) { assert myButton != null : "fx:id=\"myButton\" was not injected: check your FXML file 'simple.fxml'."; // initialize your logic here: all @FXML variables will have been injected } } Note that the code displayed by SceneBuilder is there only for educational purpose: SceneBuilder does not create and does not modify Java files. This is simply a hint of what you can use, given the fx:id present in your FXML file. You are free to copy all or part of the displayed code and paste it into your own Java class. Now at this point, there only remains to add our logic to the controller class. Quite easy: in the initialize method, I will register an action handler with my button: () { @Override public void handle(ActionEvent event) { System.out.println("That was easy, wasn't it?"); } }); ... -- ... // initialize your logic here: all @FXML variables will have been injected myButton.setOnAction(new EventHandler<ActionEvent>() { @Override public void handle(ActionEvent event) { System.out.println("That was easy, wasn't it?"); } }); ... That's it - if you now compile everything in your IDE, and run your application, clicking on the button should print a message on the console! Summary What happens is that in Main.java, the FXMLLoader will load simple.fxml from the jar/classpath, as specified by 'FXMLLoader.load(Main.class.getResource("simple.fxml"))'. When loading simple.fxml, the loader will find the name of the controller class, as specified by 'fx:controller="simple.SimpleController"' in the FXML. Upon finding the name of the controller class, the loader will create an instance of that class, in which it will try to inject all the objects that have an fx:id in the FXML. Thus, after having created '<Button fx:id="myButton" ... />', the FXMLLoader will inject the button instance into the '@FXML private Button myButton;' instance variable found on the controller instance. This is because The instance variable has an @FXML annotation, The name of the variable exactly matches the value of the fx:id Finally, when the whole FXML has been loaded, the FXMLLoader will call the controller's initialize method, and our code that registers an action handler with the button will be executed. For a complete example, take a look at the HelloWorld SceneBuilder sample. Also make sure to follow the SceneBuilder Get Started guide, which will guide you through a much more complete example. Of course, there are more elegant ways to set up an Event Handler using FXML and SceneBuilder. There are also many different ways to work with the FXMLLoader. But since it's starting to be very late here, I think it will have to wait for another post. I hope you have enjoyed the tour! --daniel

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• OPN Developer Services for Solaris Developers

  - by user13333379

  Independent Software Vendors (ISVs) who develop applications for Solaris 11 can exploit a number of interesting services as long as they are OPN Members with a Gold (or above) status and a Solaris Knowledge specialization: Free access to a Solaris development cloud with preconfigured Solaris developer zones through the apply for the: Oracle Exastack Remote Labs to get free access to Solaris development environments for SPARC and x86. Free access to patches and support information through MOS for Oracle Solaris, Oracle Solaris Studio, Oracle Solaris Cluster including updates for development systems  apply for the Oracle Solaris Development Initiative. Free email developer support for all questions around Oracle Solaris, Oracle Solaris Studio, Oracle Solaris Cluster and Oracle technologies integrating with Solaris 11 apply for the Solaris Adoption Technical Assistance.  

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• Oracle Solaris Remote Lab (OSRL) Fact Sheet

  - by user13333379

  The Oracle Solaris Remote Lab allows independent software vendors (ISVs) to test and qualify their applications in a self service Solaris cloud. ISVs who are Oracle Partner Network Gold members with a specialization in the Solaris knowledge zone can apply for free access in OPN. The lab offers the following features to it's users: Lifetime of project: 45 days (extensions granted on demand)  Up to 5 virtual machines in a private network  Virtual Machine technology: Solaris zones  Resources per VM processor support: SPARC or x86  OS version: OracleSolaris 11.0 4GB physical memory  4GB swap space  10GB local filesystem storage  10GB network filesystem (NFS) mounted on all virtual machines Networking configuration The only external network routes are to Partner's other Virtual Machines  No network routing to the Internet  The SMB (CIFS) sharing protocol is not available between Virtual Machines  Device Access  Applications that assume the existence of /devices will not run in a Virtual Machine  Applications that use eeprom to modify SPARC eeprom setting will not run in a Virtual Machine The following utilities do not work properly in Virtual Machines:  add_drv, disks, prtconf, prtdiag, rem_dev Access technology: Secure Global Desktop, file up and download root access within VM Available VM templates (both processor architectures) Oracle Database 11g Release 2 (11.2.0.3) for Solaris with Oracle Enterprise Manager 11g Weblogic 12c  SAMP: Apache http server, PHP, MySQL, phpadmin on all templates and images: Oracle Solaris Studio 12.3 for application development  More resources: Online application for Oracle Solaris remote Lab Developer Webinar about the Oracle Solaris Remote Lab Everything an Oracle Solaris Developer needs...

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• Oracle Solaris Remote Lab (OSRL) Fact Sheet

  - by user13333379

  The Oracle Solaris Remote Lab allows independent software vendors (ISVs) to test and qualify their applications in a self service Solaris cloud. ISVs who are Oracle Partner Network Gold members with a specialization in the Solaris knowledge zone can apply for free access in OPN. The lab offers the following features to it's users: Lifetime of project: 45 days (extensions granted on demand)  Up to 5 virtual machines in a private network  Virtual Machine technology: Solaris zones  Resources per VM processor support: SPARC or x86  OS version: OracleSolaris 11.0 4GB physical memory  4GB swap space  10GB local filesystem storage  10GB network filesystem (NFS) mounted on all virtual machines Networking configuration The only external network routes are to Partner's other Virtual Machines  No network routing to the Internet  The SMB (CIFS) sharing protocol is not available between Virtual Machines  Device Access  Applications that assume the existence of /devices will not run in a Virtual Machine  Applications that use eeprom to modify SPARC eeprom setting will not run in a Virtual Machine The following utilities do not work properly in Virtual Machines:  add_drv, disks, prtconf, prtdiag, rem_dev Access technology: Secure Global Desktop, file up and download root access within VM Available VM templates (both processor architectures) Oracle Database 11g Release 2 (11.2.0.3) for Solaris with Oracle Enterprise Manager 11g Weblogic 12c  SAMP: Apache http server, PHP, MySQL, phpadmin on all templates and images: Oracle Solaris Studio 12.3 for application development  More resources: Online application for Oracle Solaris remote Lab Developer Webinar about the Oracle Solaris Remote Lab Everything an Oracle Solaris Developer needs...

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• Solaris Day in NY and Boston

  - by unixman

  Hey all, -- We're hosting yet another Solaris event in New York -- this one will be on November 29th and focused on some key in-depth technologies in Solaris 11, which had just been released earlier this month.  Speakers include Dave Miner, Glenn Brunette and Jeff Victor.  It starts in the morning and goes through lunch; check out the agenda from the below link. Topics include: new and improved installation and package management experience, virtualization, ZFS and security.Please check it out and come join us! The RSVP link is belowhttp://www.oracle.com/go/?&Src=7239490&Act=34&pcode=NAFM10128512MPP016 Additionally, if you are in the Boston area, an identical event will be held in Burlington the following day, on November 30th. The RSVP link for that is http://www.oracle.com/us/dm/h2fy11/21285-nafm10128512mpp013-oem-525338.html Hope to see you there!

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• New Solaris 11 Customer Maintenance Lifecycle blog

  - by user12244672

  Hi Folks, On the basis that you can't have too much of a good thing, I've started a 2nd blog, the Solaris11Life blog , to enable me to blog about all aspects of the Solaris 11 Customer Maintenance Lifecycle, including policies, best practices, resource links, clarifications, and anything else which I hope you may find useful. In my first post, I share my Solaris 11 Customer Maintenance Lifecycle presentation, which I gave at Oracle Open World and the recent Deutsche Oracle Anwendergruppe (DOAG) conference. I'll be posting lots more there in the coming week as time allows, including secret handshake stuff on how to interpret IPS FMRI version strings. In future, I'll post any Solaris 11 Customer Maintenance Lifecycle related material on the Solaris11Life blog, http://blogs.oracle.com/Solaris11Life , and any Solaris 10 or below material here on the Patch Corner blog, http://blogs.oracle.com/patch . Best Wishes, Gerry.

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• Healthcare Mobile Database Synchronization Demonstration

  - by Jim Connors

  Like many of you, I learn best by getting my hands dirty.  When confronted with the task of understanding a new set of products and technologies and figuring out how they might apply to a vertical industry like healthcare, I set out to create a demonstration.  The video that follows aims to show how the Oracle embedded software portfolio can be applied to a healthcare application.  The demonstration utilizes among others, Java SE Embedded, Berkeley DB, Apache Tomcat, Oracle 11gR2 and Oracle Database Mobile Server. Eric Jensen gives a great critique and description of the demo here.  To sum it up, we aim to show how live medical data can be collected on a medical device, stored in a local database, synchronized to a master database and furthermore propagated to a mobile phone (Android) application.  Come take a look!

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• You're invited : Oracle Solaris Forum, Dec 18th, Petah Tikva

  - by Frederic Pariente

  The local ISV Engineering will be attending and speaking at the Oracle and ilOUG Solaris Forum next week in Israel. Come meet us there! This free event requires registration, thanks. YOU'RE INVITED Oracle Solaris Forum Date : Tuesday, December 18th, 2012 Time : 14:00 Location :  Dan Academic CenterPetach TikvaIsrael Agenda : New Features in Solaris 11.1SPARC T4 & T5Solaris 11 Serviceability See you there!

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• Using R to Analyze G1GC Log Files

  - by user12620111

  Using R to Analyze G1GC Log Files body, td { font-family: sans-serif; background-color: white; font-size: 12px; margin: 8px; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1 { font-size:2.2em; } h2 { font-size:1.8em; } h3 { font-size:1.4em; } h4 { font-size:1.0em; } h5 { font-size:0.9em; } h6 { font-size:0.8em; } a:visited { color: rgb(50%, 0%, 50%); } pre { margin-top: 0; max-width: 95%; border: 1px solid #ccc; white-space: pre-wrap; } pre code { display: block; padding: 0.5em; } code.r, code.cpp { background-color: #F8F8F8; } table, td, th { border: none; } blockquote { color:#666666; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { 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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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• SPARC Solaris Momentum

  - by Mike Mulkey-Oracle

  Following up on the Oracle Solaris 11.2 launch on April 29th, if you were able to watch the launch event, you saw Mark Hurd state that Oracle will be No. 1 in high-end computing systems "in a reasonable time frame”.  "This is not a 3-year vision," he continued.Well, According to IDC's latest 1QCY14 Tracker, Oracle has regained the #1 UNIX Shipments Marketshare! You can see the report and read about it here: Oracle regains the #1 UNIX Shipments Marketshare, but suffice to say that SPARC Solaris is making strong gains on the competition.  If you have seen the public roadmap through 2019 of Oracle's commitment to continue to deliver on this technology, you can see that Mark Hurd’s comment was not to be taken lightly.  We feel the systems tide turning in Oracle's direction and are working hard to show our partner community the value of being a part of the SPARC Solaris momentum.We are now planning for the Solaris 11.2 GA in late summer (11.2 beta is available now), as well as doing early preparations for Oracle OpenWorld 2014 on September 28th.  Stay tuned there!Here is a sampling of the coverage highlights around the Oracle Solaris 11.2 launch:“Solaris is still one of the most advanced platforms in the enterprise.” – ITBusinessEdge“Oracle is serious about clouds now, just as its customers are, whether they are building them in their own datacenters or planning to use public clouds.” – EnterpriseTech"Solaris is more about a layer of an integrated system than an operating system.” — ZDNet

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• ZFS Basics

  - by user12614620

  Stage 1 basics: creating a pool # zpool create $NAME $REDUNDANCY $DISK1_0..N [$REDUNDANCY $DISK2_0..N]... $NAME = name of the pool you're creating. This will also be the name of the first filesystem and, by default, be placed at the mountpoint "/$NAME" $REDUNDANCY = either mirror or raidzN, and N can be 1, 2, or 3. If you leave N off, then it defaults to 1. $DISK1_0..N = the disks assigned to the pool. Example 1: zpool create tank mirror c4t1d0 c4t2d0 name of pool: tank redundancy: mirroring disks being mirrored: c4t1d0 and c4t2d0 Capacity: size of a single disk Example 2: zpool create tank raidz c4t1d0 c4t2d0 c4t3d0 c4t4d0 c4t5d0 Here the redundancy is raidz, and there are five disks, in a 4+1 (4 data, 1 parity) config. This means that the capacity is 4 times the disk size. If the command used "raidz2" instead, then the config would be 3+2. Likewise, "raidz3" would be a 2+3 config. Example 3: zpool create tank mirror c4t1d0 c4t2d0 mirror c4t3d0 c4t4d0 This is the same as the first mirror example, except there are two mirrors now. ZFS will stripe data across both mirrors, which means that writing data will go a bit faster. Note: you cannot create a mirror of two raidzs. You can create a raidz of mirrors, but to do that requires trickery.

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• Take Two: Comparing JVMs on ARM/Linux

  - by user12608080

  Although the intent of the previous article, entitled Comparing JVMs on ARM/Linux, was to introduce and highlight the availability of the HotSpot server compiler (referred to as c2) for Java SE-Embedded ARM v7,  it seems, based on feedback, that everyone was more interested in the OpenJDK comparisons to Java SE-E.  In fact there were two main concerns: The fact that the previous article compared Java SE-E 7 against OpenJDK 6 might be construed as an unlevel playing field because version 7 is newer and therefore potentially more optimized. That the generic compiler settings chosen to build the OpenJDK implementations did not put those versions in a particularly favorable light. With those considerations in mind, we'll institute the following changes to this version of the benchmarking: In order to help alleviate an additional concern that there is some sort of benchmark bias, we'll use a different suite, called DaCapo.  Funded and supported by many prestigious organizations, DaCapo's aim is to benchmark real world applications.  Further information about DaCapo can be found at http://dacapobench.org. At the suggestion of Xerxes Ranby, who has been a great help through this entire exercise, a newer Linux distribution will be used to assure that the OpenJDK implementations were built with more optimal compiler settings.  The Linux distribution in this instance is Ubuntu 11.10 Oneiric Ocelot. Having experienced difficulties getting Ubuntu 11.10 to run on the original D2Plug ARMv7 platform, for these benchmarks, we'll switch to an embedded system that has a supported Ubuntu 11.10 release.  That platform is the Freescale i.MX53 Quick Start Board.  It has an ARMv7 Coretex-A8 processor running at 1GHz with 1GB RAM. We'll limit comparisons to 4 JVM implementations: Java SE-E 7 Update 2 c1 compiler (default) Java SE-E 6 Update 30 (c1 compiler is the only option) OpenJDK 6 IcedTea6 1.11pre 6b23~pre11-0ubuntu1.11.10.2 CACAO build 1.1.0pre2 OpenJDK 6 IcedTea6 1.11pre 6b23~pre11-0ubuntu1.11.10.2 JamVM build-1.6.0-devel Certain OpenJDK implementations were eliminated from this round of testing for the simple reason that their performance was not competitive.  The Java SE 7u2 c2 compiler was also removed because although quite respectable, it did not perform as well as the c1 compilers.  Recall that c2 works optimally in long-lived situations.  Many of these benchmarks completed in a relatively short period of time.  To get a feel for where c2 shines, take a look at the first chart in this blog. The first chart that follows includes performance of all benchmark runs on all platforms.  Later on we'll look more at individual tests.  In all runs, smaller means faster.  The DaCapo aficionado may notice that only 10 of the 14 DaCapo tests for this version were executed.  The reason for this is that these 10 tests represent the only ones successfully completed by all 4 JVMs.  Only the Java SE-E 6u30 could successfully run all of the tests.  Both OpenJDK instances not only failed to complete certain tests, but also experienced VM aborts too. One of the first observations that can be made between Java SE-E 6 and 7 is that, for all intents and purposes, they are on par with regards to performance.  While it is a fact that successive Java SE releases add additional optimizations, it is also true that Java SE 7 introduces additional complexity to the Java platform thus balancing out any potential performance gains at this point.  We are still early into Java SE 7.  We would expect further performance enhancements for Java SE-E 7 in future updates. In comparing Java SE-E to OpenJDK performance, among both OpenJDK VMs, Cacao results are respectable in 4 of the 10 tests.  The charts that follow show the individual results of those four tests.  Both Java SE-E versions do win every test and outperform Cacao in the range of 9% to 55%. For the remaining 6 tests, Java SE-E significantly outperforms Cacao in the range of 114% to 311% So it looks like OpenJDK results are mixed for this round of benchmarks.  In some cases, performance looks to have improved.  But in a majority of instances, OpenJDK still lags behind Java SE-Embedded considerably. Time to put on my asbestos suit.  Let the flames begin...

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• Tip #15: How To Debug Unit Tests During Maven Builds

  - by ByronNevins

  It must be really really hard to step through unit tests in a debugger during a maven build.  Right? Wrong! Here is how i do it: 1) Set up these environmental variables: MAVEN_OPTS=-Xmx1024m -Xms256m -XX:MaxPermSize=512mMAVEN_OPTS_DEBUG=-Xmx1024m -Xms256m -XX:MaxPermSize=512m  -Xdebug (no line break here!!)  -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=9999MAVEN_OPTS_REG=-Xmx1024m -Xms256m -XX:MaxPermSize=512m 2) create 2 scripts or aliases like so:  maveny.bat: set MAVEN_OPTS=%MAVEN_OPTS_DEBUG% mavenn.bat: set MAVEN_OPTS=%MAVEN_OPTS_REG%    To debug do this: run maveny.bat run mvn install attach your debugger to port 9999 (set breakpoints of course) When maven gets to the unit test phase it will hit your breakpoint and wait for you. When done debugging simply run mavenn.bat Notes If it takes a while to do the build then you don't really need to set the suspend=y flag. If you set the suspend=n flag then you can just leave it -- but only one maven build can run at a time because of the debug port conflict.

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• Developer Webinar Today: "Writing Solaris 11 Device Drivers"

  - by user13333379

  Oracle's Solaris Organization is pleased to announce a Technical Webinar for Developers on Oracle Solaris 11: "Writing Solaris 11 Device Drivers" By Bill Knoche (Principal Software Engineer) today June 5, 2012 9:00 AM PDT This bi-weekly webinar series (every other Tuesday @ 9 a.m. PT) is designed for ISVs, IHVs, and Application Developers who want a deep-dive overview about how they can deploy Oracle Solaris 11 into their application environments. This series will provide you the unique opportunity to learn directly from Oracle Solaris ISV Engineers and will include LIVE Q&A via chat with subject matter experts from each topic area. Any OTN member can register for this free webinar here. 

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• ZFS for Database Log Files

  - by user12620111

  I've been troubled by drop outs in CPU usage in my application server, characterized by the CPUs suddenly going from close to 90% CPU busy to almost completely CPU idle for a few seconds. Here is an example of a drop out as shown by a snippet of vmstat data taken while the application server is under a heavy workload. # vmstat 1  kthr      memory            page            disk          faults      cpu  r b w   swap  free  re  mf pi po fr de sr s3 s4 s5 s6   in   sy   cs us sy id  1 0 0 130160176 116381952 0 16 0 0 0 0  0  0  0  0  0 207377 117715 203884 70 21 9  12 0 0 130160160 116381936 0 25 0 0 0 0 0  0  0  0  0 200413 117162 197250 70 20 9  11 0 0 130160176 116381920 0 16 0 0 0 0 0  0  1  0  0 203150 119365 200249 72 21 7  8 0 0 130160176 116377808 0 19 0 0 0 0  0  0  0  0  0 169826 96144 165194 56 17 27  0 0 0 130160176 116377800 0 16 0 0 0 0  0  0  0  0  1 10245 9376 9164 2  1 97  0 0 0 130160176 116377792 0 16 0 0 0 0  0  0  0  0  2 15742 12401 14784 4 1 95  0 0 0 130160176 116377776 2 16 0 0 0 0  0  0  1  0  0 19972 17703 19612 6 2 92  14 0 0 130160176 116377696 0 16 0 0 0 0 0  0  0  0  0 202794 116793 199807 71 21 8  9 0 0 130160160 116373584 0 30 0 0 0 0  0  0 18  0  0 203123 117857 198825 69 20 11 This behavior occurred consistently while the application server was processing synthetic transactions: HTTP requests from JMeter running on an external machine. I explored many theories trying to explain the drop outs, including: Unexpected JMeter behavior Network contention Java Garbage Collection Application Server thread pool problems Connection pool problems Database transaction processing Database I/O contention Graphing the CPU %idle led to a breakthrough: Several of the drop outs were 30 seconds apart. With that insight, I went digging through the data again and looking for other outliers that were 30 seconds apart. In the database server statistics, I found spikes in the iostat "asvc_t" (average response time of disk transactions, in milliseconds) for the disk drive that was being used for the database log files. Here is an example:                     extended device statistics     r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 2053.6    0.0 8234.3  0.0  0.2    0.0    0.1   0  24 c3t60080E5...F4F6d0s0     0.0 2162.2    0.0 8652.8  0.0  0.3    0.0    0.1   0  28 c3t60080E5...F4F6d0s0     0.0 1102.5    0.0 10012.8  0.0  4.5    0.0    4.1   0  69 c3t60080E5...F4F6d0s0     0.0   74.0    0.0 7920.6  0.0 10.0    0.0  135.1   0 100 c3t60080E5...F4F6d0s0     0.0  568.7    0.0 6674.0  0.0  6.4    0.0   11.2   0  90 c3t60080E5...F4F6d0s0     0.0 1358.0    0.0 5456.0  0.0  0.6    0.0    0.4   0  55 c3t60080E5...F4F6d0s0     0.0 1314.3    0.0 5285.2  0.0  0.7    0.0    0.5   0  70 c3t60080E5...F4F6d0s0 Here is a little more information about my database configuration: The database and application server were running on two different SPARC servers. Storage for the database was on a storage array connected via 8 gigabit Fibre Channel Data storage and log file were on different physical disk drives Reliable low latency I/O is provided by battery backed NVRAM Highly available: Two Fibre Channel links accessed via MPxIO Two Mirrored cache controllers The log file physical disks were mirrored in the storage device Database log files on a ZFS Filesystem with cutting-edge technologies, such as copy-on-write and end-to-end checksumming Why would I be getting service time spikes in my high-end storage? First, I wanted to verify that the database log disk service time spikes aligned with the application server CPU drop outs, and they did: At first, I guessed that the disk service time spikes might be related to flushing the write through cache on the storage device, but I was unable to validate that theory. After searching the WWW for a while, I decided to try using a separate log device: # zpool add ZFS-db-41 log c3t60080E500017D55C000015C150A9F8A7d0 The ZFS log device is configured in a similar manner as described above: two physical disks mirrored in the storage array. This change to the database storage configuration eliminated the application server CPU drop outs: Here is the zpool configuration: # zpool status ZFS-db-41   pool: ZFS-db-41  state: ONLINE  scan: none requested config:         NAME                                     STATE         ZFS-db-41                                ONLINE           c3t60080E5...F4F6d0  ONLINE         logs           c3t60080E5...F8A7d0  ONLINE Now, the I/O spikes look like this:                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1053.5    0.0 4234.1  0.0  0.8    0.0    0.7   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1131.8    0.0 4555.3  0.0  0.8    0.0    0.7   0  76 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1167.6    0.0 4682.2  0.0  0.7    0.0    0.6   0  74 c3t60080E5...F8A7d0s0     0.0  162.2    0.0 19153.9  0.0  0.7    0.0    4.2   0  12 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1247.2    0.0 4992.6  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0     0.0   41.0    0.0   70.0  0.0  0.1    0.0    1.6   0   2 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1241.3    0.0 4989.3  0.0  0.8    0.0    0.6   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1193.2    0.0 4772.9  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0 We can see the steady flow of 4k writes to the ZIL device from O_SYNC database log file writes. The spikes are from flushing the transaction group. Like almost all problems that I run into, once I thoroughly understand the problem, I find that other people have documented similar experiences. Thanks to all of you who have documented alternative approaches. Saved for another day: now that the problem is obvious, I should try "zfs:zfs_immediate_write_sz" as recommended in the ZFS Evil Tuning Guide. References: The ZFS Intent Log Solaris ZFS, Synchronous Writes and the ZIL Explained ZFS Evil Tuning Guide: Cache Flushes ZFS Evil Tuning Guide: Tuning ZFS for Database Performance

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• What is bondib1 used for on SPARC SuperCluster with InfiniBand, Solaris 11 networking & Oracle RAC?

  - by user12620111

  A co-worker asked the following question about a SPARC SuperCluster InfiniBand network: > on the database nodes the RAC nodes communicate over the cluster_interconnect. This is the > 192.168.10.0 network on bondib0. (according to ./crs/install/crsconfig_params NETWORKS> setting) > What is bondib1 used for? Is it a HA counterpart in case bondib0 dies? This is my response: Summary: bondib1 is currently only being used for outbound cluster interconnect interconnect traffic. Details: bondib0 is the cluster_interconnect $ oifcfg getif            bondeth0  10.129.184.0  global  public bondib0  192.168.10.0  global  cluster_interconnect ipmpapp0  192.168.30.0  global  public bondib0 and bondib1 are on 192.168.10.1 and 192.168.10.2 respectively. # ipadm show-addr | grep bondi bondib0/v4static  static   ok           192.168.10.1/24 bondib1/v4static  static   ok           192.168.10.2/24 Hostnames tied to the IPs are node1-priv1 and node1-priv2  # grep 192.168.10 /etc/hosts 192.168.10.1    node1-priv1.us.oracle.com   node1-priv1 192.168.10.2    node1-priv2.us.oracle.com   node1-priv2 For the 4 node RAC interconnect: Each node has 2 private IP address on the 192.168.10.0 network. Each IP address has an active InfiniBand link and a failover InfiniBand link. Thus, the 4 node RAC interconnect is using a total of 8 IP addresses and 16 InfiniBand links. bondib1 isn't being used for the Virtual IP (VIP): $ srvctl config vip -n node1 VIP exists: /node1-ib-vip/192.168.30.25/192.168.30.0/255.255.255.0/ipmpapp0, hosting node node1 VIP exists: /node1-vip/10.55.184.15/10.55.184.0/255.255.255.0/bondeth0, hosting node node1 bondib1 is on bondib1_0 and fails over to bondib1_1: # ipmpstat -g GROUP       GROUPNAME   STATE     FDT       INTERFACES ipmpapp0    ipmpapp0    ok        --        ipmpapp_0 (ipmpapp_1) bondeth0    bondeth0    degraded  --        net2 [net5] bondib1     bondib1     ok        --        bondib1_0 (bondib1_1) bondib0     bondib0     ok        --        bondib0_0 (bondib0_1) bondib1_0 goes over net24 # dladm show-link | grep bond LINK                CLASS     MTU    STATE    OVER bondib0_0           part      65520  up       net21 bondib0_1           part      65520  up       net22 bondib1_0           part      65520  up       net24 bondib1_1           part      65520  up       net23 net24 is IB Partition FFFF # dladm show-ib LINK         HCAGUID         PORTGUID        PORT STATE  PKEYS net24        21280001A1868A  21280001A1868C  2    up     FFFF net22        21280001CEBBDE  21280001CEBBE0  2    up     FFFF,8503 net23        21280001A1868A  21280001A1868B  1    up     FFFF,8503 net21        21280001CEBBDE  21280001CEBBDF  1    up     FFFF On Express Module 9 port 2: # dladm show-phys -L LINK              DEVICE       LOC net21             ibp4         PCI-EM1/PORT1 net22             ibp5         PCI-EM1/PORT2 net23             ibp6         PCI-EM9/PORT1 net24             ibp7         PCI-EM9/PORT2 Outbound traffic on the 192.168.10.0 network will be multiplexed between bondib0 & bondib1 # netstat -rn Routing Table: IPv4   Destination           Gateway           Flags  Ref     Use     Interface -------------------- -------------------- ----- ----- ---------- --------- 192.168.10.0         192.168.10.2         U        16    6551834 bondib1   192.168.10.0         192.168.10.1         U         9    5708924 bondib0   There is a lot more traffic on bondib0 than bondib1 # /bin/time snoop -I bondib0 -c 100 > /dev/null Using device ipnet/bondib0 (promiscuous mode) 100 packets captured real        4.3 user        0.0 sys         0.0 (100 packets in 4.3 seconds = 23.3 pkts/sec) # /bin/time snoop -I bondib1 -c 100 > /dev/null Using device ipnet/bondib1 (promiscuous mode) 100 packets captured real       13.3 user        0.0 sys         0.0 (100 packets in 13.3 seconds = 7.5 pkts/sec) Half of the packets on bondib0 are outbound (from self). The remaining packet are split evenly, from the other nodes in the cluster. # snoop -I bondib0 -c 100 | awk '{print $1}' | sort | uniq -c Using device ipnet/bondib0 (promiscuous mode) 100 packets captured   49 node1-priv1.us.oracle.com   24 node2-priv1.us.oracle.com   14 node3-priv1.us.oracle.com   13 node4-priv1.us.oracle.com 100% of the packets on bondib1 are outbound (from self), but the headers in the packets indicate that they are from the IP address associated with bondib0: # snoop -I bondib1 -c 100 | awk '{print $1}' | sort | uniq -c Using device ipnet/bondib1 (promiscuous mode) 100 packets captured  100 node1-priv1.us.oracle.com The destination of the bondib1 outbound packets are split evenly, to node3 and node 4. # snoop -I bondib1 -c 100 | awk '{print $3}' | sort | uniq -c Using device ipnet/bondib1 (promiscuous mode) 100 packets captured   51 node3-priv1.us.oracle.com   49 node4-priv1.us.oracle.com Conclusion: bondib1 is currently only being used for outbound cluster interconnect interconnect traffic.

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• MySQL tech writer position on Oracle jobs site

  - by stefanhinz

  Just in case you missed this, last week I announced that my team is looking for an experienced technical writer. Now the job offer has gone live on the Oracle website. Have a look! That's the EMEA job site, but the position is actually available for Europe or North America. The job offer should appear on the American site soon, too. If you want to join a great team, or if you know someone suitable who does, don't hesitate to contact me!

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• How to Set Up a MongoDB NoSQL Cluster Using Oracle Solaris Zones

  - by Orgad Kimchi

  This article starts with a brief overview of MongoDB and follows with an example of setting up a MongoDB three nodes cluster using Oracle Solaris Zones. The following are benefits of using Oracle Solaris for a MongoDB cluster: • You can add new MongoDB hosts to the cluster in minutes instead of hours using the zone cloning feature. Using Oracle Solaris Zones, you can easily scale out your MongoDB cluster. • In case there is a user error or software error, the Service Management Facility ensures the high availability of each cluster member and ensures that MongoDB replication failover will occur only as a last resort. • You can discover performance issues in minutes versus days by using DTrace, which provides increased operating system observability. DTrace provides a holistic performance overview of the operating system and allows deep performance analysis through cooperation with the built-in MongoDB tools. • ZFS built-in compression provides optimized disk I/O utilization for better I/O performance. In the example presented in this article, all the MongoDB cluster building blocks will be installed using the Oracle Solaris Zones, Service Management Facility, ZFS, and network virtualization technologies. Figure 1 shows the architecture:

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• Solaris 11

  - by user9154181

  Oracle has a strict policy about not discussing product features until they appear in shipping product. Now that Solaris 11 is publically available, it is time to catch up. I will be shortly posting articles on a variety of new developments in the Solaris linkers and related bits: 64-bit Archives After 40+ years of Unix, the archive file format has run out of room. The ar and link-editor (ld) commands have been enhanced to allow archives to grow past their previous 32-bit limits. Guidance The link-editor is now willing and able to tell you how to alter your link lines in order to build better objects. Stub Objects This is one of the bigger projects I've undertaken since joining the Solaris group. Stub objects are shared objects, built entirely from mapfiles, that supply the same linking interface as the real object, while containing no code or data. You can link to them, but cannot use them at runtime. It was pretty simple to add this ability to the link-editor, but the changes to the OSnet in order to apply them to building Solaris were massive. I discuss how we came to invent stub objects, how we apply them to build the OSnet in a more parallel and scalable manner, and about the follow on opportunities that have emerged from the new stub proto area we created to hold them. The elffile Utility A new standard Solaris utility, elffile is a variant of the file utility, focused exclusively on linker related files. elffile is of particular value for examining archives, as it allows you to find out what is inside them without having to first extract the archive members into temporary files. This release has been a long time coming. I joined the Solaris group in late 2005, and this will be my first FCS. From a user perspective, Solaris 11 is probably the biggest change to Solaris since Solaris 2.0. Solaris 11 polishes the ground breaking features from Solaris 10 (DTrace, FMA, ZFS, Zones), and uses them to add a powerful new packaging system, numerous other enhacements and features, along with a huge modernization effort. I'm excited to see it go out into the world. I hope you enjoy using it as much as we did creating it. Software is never done. On to the next one...

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• Security Access Control With Solaris Virtualization

  - by Thierry Manfe-Oracle

  Numerous Solaris customers consolidate multiple applications or servers on a single platform. The resulting configuration consists of many environments hosted on a single infrastructure and security constraints sometimes exist between these environments. Recently, a customer consolidated many virtual machines belonging to both their Intranet and Extranet on a pair of SPARC Solaris servers interconnected through Infiniband. Virtual Machines were mapped to Solaris Zones and one security constraint was to prevent SSH connections between the Intranet and the Extranet. This case study gives us the opportunity to understand how the Oracle Solaris Network Virtualization Technology —a.k.a. Project Crossbow— can be used to control outbound traffic from Solaris Zones. Solaris Zones from both the Intranet and Extranet use an Infiniband network to access a ZFS Storage Appliance that exports NFS shares. Solaris global zones on both SPARC servers mount iSCSI LU exported by the Storage Appliance.  Non-global zones are installed on these iSCSI LU. With no security hardening, if an Extranet zone gets compromised, the attacker could try to use the Storage Appliance as a gateway to the Intranet zones, or even worse, to the global zones as all the zones are reachable from this node. One solution consists in using Solaris Network Virtualization Technology to stop outbound SSH traffic from the Solaris Zones. The virtualized network stack provides per-network link flows. A flow classifies network traffic on a specific link. As an example, on the network link used by a Solaris Zone to connect to the Infiniband, a flow can be created for TCP traffic on port 22, thereby a flow for the ssh traffic. A bandwidth can be specified for that flow and, if set to zero, the traffic is blocked. Last but not least, flows are created from the global zone, which means that even with root privileges in a Solaris zone an attacker cannot disable or delete a flow. With the flow approach, the outbound traffic of a Solaris zone is controlled from outside the zone. Schema 1 describes the new network setting once the security has been put in place. Here are the instructions to create a Crossbow flow as used in Schema 1 : (GZ)# zoneadm -z zonename halt ...halts the Solaris Zone. (GZ)# flowadm add-flow -l iblink -a transport=TCP,remote_port=22 -p maxbw=0 sshFilter  ...creates a flow on the IB partition "iblink" used by the zone to connect to the Infiniband.  This IB partition can be identified by intersecting the output of the commands 'zonecfg -z zonename info net' and 'dladm show-part'.  The flow is created on port 22, for the TCP traffic with a zero maximum bandwidth.  The name given to the flow is "sshFilter". (GZ)# zoneadm -z zonename boot  ...restarts the Solaris zone now that the flow is in place.Solaris Zones and Solaris Network Virtualization enable SSH access control on Infiniband (and on Ethernet) without the extra cost of a firewall. With this approach, no change is required on the Infiniband switch. All the security enforcements are put in place at the Solaris level, minimizing the impact on the overall infrastructure. The Crossbow flows come in addition to many other security controls available with Oracle Solaris such as IPFilter and Role Based Access Control, and that can be used to tackle security challenges.

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• How to Set Up a Hadoop Cluster Using Oracle Solaris (Hands-On Lab)

  - by Orgad Kimchi

  Oracle Technology Network (OTN) published the "How to Set Up a Hadoop Cluster Using Oracle Solaris" OOW 2013 Hands-On Lab. This hands-on lab presents exercises that demonstrate how to set up an Apache Hadoop cluster using Oracle Solaris 11 technologies such as Oracle Solaris Zones, ZFS, and network virtualization. Key topics include the Hadoop Distributed File System (HDFS) and the Hadoop MapReduce programming model. We will also cover the Hadoop installation process and the cluster building blocks: NameNode, a secondary NameNode, and DataNodes. In addition, you will see how you can combine the Oracle Solaris 11 technologies for better scalability and data security, and you will learn how to load data into the Hadoop cluster and run a MapReduce job. Summary of Lab Exercises This hands-on lab consists of 13 exercises covering various Oracle Solaris and Apache Hadoop technologies:     Install Hadoop.     Edit the Hadoop configuration files.     Configure the Network Time Protocol.     Create the virtual network interfaces (VNICs).     Create the NameNode and the secondary NameNode zones.     Set up the DataNode zones.     Configure the NameNode.     Set up SSH.     Format HDFS from the NameNode.     Start the Hadoop cluster.     Run a MapReduce job.     Secure data at rest using ZFS encryption.     Use Oracle Solaris DTrace for performance monitoring.  Read it now

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• Tip #19 Module Private Visibility in OSGi

  - by ByronNevins

  I hate public and protected methods and classes.  It requires so much work to change them in a huge project like GlassFish.  Not to mention that you may well have to support those APIs forever.  They are highly overused in GlassFish.  In fact I'd bet that > 95% of classes are marked as public for no good reason.  It's just (bad) habit is my guess. private and default visibility (I call it package-private) is easier to maintain.  It is much much easier to change such classes and methods around.  If you have ANY public method or public class in GlassFish you'll need to grep through a tremendous amount of source code to find all callers.  But even that won't be theoretically reliable.  What if a caller is using reflection to access public methods?  You may never find such usages. If you have package private methods, it's easy.  Simply grep through all the code in that one package.  As long as that package compiles ok you're all set.  There can' be any compile errors anywhere else.  It's a waste of time to even look around or build the "outside" world.  So you may be thinking: "Aha!  I'll just make my module have one giant package with all the java files.  Then I can use the default visibility and maintenance will be much easier.  But there's a problem.  You are wasting a very nice feature of java -- organizing code into separate packages.  It also makes the code much more encapsulated.  Unfortunately to share code between the packages you have no choice but to declare public visibility. What happens in practice is that a module ends up having tons of public classes and methods that are used exclusively inside the module.  Which finally brings me to the point of this blog:  If Only There Was A Module-Private Visibility Available Well, surprise!  There is such a mechanism.  If your project is running under OSGi that is.  Like GlassFish does!  With this mechanism you can easily add another level of visibility by telling OSGi exactly which public you want to be exposed outside of the module.  You get the best of both worlds: Better encapsulation of your code so that maintenance is easier and productivity is increased. Usage of public visibility inside the module so that you can encapsulate intra-module better with packages. How I do this in GlassFish: Carefully plan out at least one package that will contain "true" publics.  This is the package that will be exported by OSGi.  I recommend just one package. Here is how to tell OSGi to use it in GlassFish -- edit osgi.bundle like so:-exportcontents:     org.glassfish.mymodule.truepublics;  version=${project.osgi.version} Now all publics declared in any other packages will be visible module-wide but not outside the module. There is one caveat: Accessing "module-private" items outside of the module is controlled at run-time, not compile-time.  The compiler has no clue that a public in a dependent module isn't really public.  it will happily compile it.  At runtime you will definitely see fireworks.  The good news is that you don't have to wait for the code path that tries to use the "module-private" items to fire.  OSGi will complain loudly when that module gets loaded.  OSGi will refuse to load it.  You will see an error like this: remote failure: Error while loading FOO: Exception while adding the new configuration : Error occurred during deployment: Exception while loading the app : org.osgi.framework.BundleException: Unresolved constraint in bundle com.oracle.glassfish.miscreant.code [115]: Unable to resolve 115.0: missing requirement [115.0] osgi.wiring.package; (osgi.wiring.package=org.glassfish.mymodule.unexported). Please see server.log for more details. That is if you accidentally change code in module B to use a public that is really a "module-private" in module A, then you will see the error immediately when you try to test whatever you were changing in module B.

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