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• Do your filesystems have un-owned files ?

  - by darrenm

  As part of our work for integrated compliance reporting in Solaris we plan to provide a check for determining if the system has "un-owned files", ie those which are owned by a uid that does not exist in our configured nameservice.  Tests such as this already exist in the Solaris CIS Benchmark (9.24 Find Un-owned Files and Directories) and other security benchmarks. The obvious method of doing this would be using find(1) with the -nouser flag.  However that requires we bring into memory the metadata for every single file and directory in every local file system we have mounted.  That is probaby not an acceptable thing to do on a production system that has a large amount of storage and it is potentially going to take a long time. Just as I went to bed last night an idea for a much faster way of listing file systems that have un-owned files came to me. I've now implemented it and I'm happy to report it works very well and peforms many orders of magnatude better than using find(1) ever will.   ZFS (since pool version 15) has per user space accounting and quotas.  We can report very quickly and without actually reading any files at all how much space any given user id is using on a ZFS filesystem.  Using that information we can implement a check to very quickly list which filesystems contain un-owned files. First a few caveats because the output data won't be exactly the same as what you get with find but it answers the same basic question.  This only works for ZFS and it will only tell you which filesystems have files owned by unknown users not the actual files.  If you really want to know what the files are (ie to give them an owner) you still have to run find(1).  However it has the huge advantage that it doesn't use find(1) so it won't be dragging the metadata for every single file and directory on the system into memory. It also has the advantage that it can check filesystems that are not mounted currently (which find(1) can't do). It ran in about 4 seconds on a system with 300 ZFS datasets from 2 pools totalling about 3.2T of allocated space, and that includes the uid lookups and output. #!/bin/sh for fs in $(zfs list -H -o name -t filesystem -r rpool) ; do unknowns="" for uid in $(zfs userspace -Hipn -o name,used $fs | cut -f1); do if [ -z "$(getent passwd $uid)" ]; then unknowns="$unknowns$uid " fi done if [ ! -z "$unknowns" ]; then mountpoint=$(zfs list -H -o mountpoint $fs) mounted=$(zfs list -H -o mounted $fs) echo "ZFS File system $fs mounted ($mounted) on $mountpoint \c" echo "has files owned by unknown user ids: $unknowns"; fi done Sample output: ZFS File system rpool/ROOT/solaris-30/var mounted (no) on /var has files owned by unknown user ids: 6435 33667 101 ZFS File system rpool/ROOT/solaris-32/var mounted (yes) on /var has files owned by unknown user ids: 6435 33667ZFS File system builds/bob mounted (yes) on /builds/bob has files owned by unknown user ids: 101 Note that the above might not actually appear exactly like that in any future Solaris product or feature, it is provided just as an example of what you can do with ZFS user space accounting to answer questions like the above.

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• Custom Lookup Provider For NetBeans Platform CRUD Tutorial

  - by Geertjan

  For a long time I've been planning to rewrite the second part of the NetBeans Platform CRUD Application Tutorial to integrate the loosely coupled capabilities introduced in a seperate series of articles based on articles by Antonio Vieiro (a great series, by the way). Nothing like getting into the Lookup stuff right from the get go (rather than as an afterthought)! The question, of course, is how to integrate the loosely coupled capabilities in a logical way within that tutorial. Today I worked through the tutorial from scratch, up until the point where the prototype is completed, i.e., there's a JTextArea displaying data pulled from a database. That brought me to the place where I needed to be. In fact, as soon as the prototype is completed, i.e., the database connection has been shown to work, the whole story about Lookup.Provider and InstanceContent should be introduced, so that all the subsequent sections, i.e., everything within "Integrating CRUD Functionality" will be done by adding new capabilities to the Lookup.Provider. However, before I perform open heart surgery on that tutorial, I'd like to run the scenario by all those reading this blog who understand what I'm trying to do! (I.e., probably anyone who has read this far into this blog entry.) So, this is what I propose should happen and in this order: Point out the fact that right now the database access code is found directly within our TopComponent. Not good. Because you're mixing view code with data code and, ideally, the developers creating the user interface wouldn't need to know anything about the data access layer. Better to separate out the data access code into a separate class, within the CustomerLibrary module, i.e., far away from the module providing the user interface, with this content: public class CustomerDataAccess { public List<Customer> getAllCustomers() { return Persistence.createEntityManagerFactory("CustomerLibraryPU"). createEntityManager().createNamedQuery("Customer.findAll").getResultList(); } } Point out the fact that there is a concept of "Lookup" (which readers of the tutorial should know about since they should have followed the NetBeans Platform Quick Start), which is a registry into which objects can be published and to which other objects can be listening. In the same way as a TopComponent provides a Lookup, as demonstrated in the NetBeans Platform Quick Start, your own object can also provide a Lookup. So, therefore, let's provide a Lookup for Customer objects.  import org.openide.util.Lookup; import org.openide.util.lookup.AbstractLookup; import org.openide.util.lookup.InstanceContent; public class CustomerLookupProvider implements Lookup.Provider { private Lookup lookup; private InstanceContent instanceContent; public CustomerLookupProvider() { // Create an InstanceContent to hold capabilities... instanceContent = new InstanceContent(); // Create an AbstractLookup to expose the InstanceContent... lookup = new AbstractLookup(instanceContent); // Add a "Read" capability to the Lookup of the provider: //...to come... // Add a "Update" capability to the Lookup of the provider: //...to come... // Add a "Create" capability to the Lookup of the provider: //...to come... // Add a "Delete" capability to the Lookup of the provider: //...to come... } @Override public Lookup getLookup() { return lookup; } } Point out the fact that, in the same way as we can publish an object into the Lookup of a TopComponent, we can now also publish an object into the Lookup of our CustomerLookupProvider. Instead of publishing a String, as in the NetBeans Platform Quick Start, we'll publish an instance of our own type. And here is the type: public interface ReadCapability { public void read() throws Exception; } And here is an implementation of our type added to our Lookup: public class CustomerLookupProvider implements Lookup.Provider { private Set<Customer> customerSet; private Lookup lookup; private InstanceContent instanceContent; public CustomerLookupProvider() { customerSet = new HashSet<Customer>(); // Create an InstanceContent to hold capabilities... instanceContent = new InstanceContent(); // Create an AbstractLookup to expose the InstanceContent... lookup = new AbstractLookup(instanceContent); // Add a "Read" capability to the Lookup of the provider: instanceContent.add(new ReadCapability() { @Override public void read() throws Exception { ProgressHandle handle = ProgressHandleFactory.createHandle("Loading..."); handle.start(); customerSet.addAll(new CustomerDataAccess().getAllCustomers()); handle.finish(); } }); // Add a "Update" capability to the Lookup of the provider: //...to come... // Add a "Create" capability to the Lookup of the provider: //...to come... // Add a "Delete" capability to the Lookup of the provider: //...to come... } @Override public Lookup getLookup() { return lookup; } public Set<Customer> getCustomers() { return customerSet; } } Point out that we can now create a new instance of our Lookup (in some other module, so long as it has a dependency on the module providing the CustomerLookupProvider and the ReadCapability), retrieve the ReadCapability, and then do something with the customers that are returned, here in the rewritten constructor of the TopComponent, without needing to know anything about how the database access is actually achieved since that is hidden in the implementation of our type, above: public CustomerViewerTopComponent() { initComponents(); setName(Bundle.CTL_CustomerViewerTopComponent()); setToolTipText(Bundle.HINT_CustomerViewerTopComponent()); // EntityManager entityManager = Persistence.createEntityManagerFactory("CustomerLibraryPU").createEntityManager(); // Query query = entityManager.createNamedQuery("Customer.findAll"); // List<Customer> resultList = query.getResultList(); // for (Customer c : resultList) { // jTextArea1.append(c.getName() + " (" + c.getCity() + ")" + "\n"); // } CustomerLookupProvider lookup = new CustomerLookupProvider(); ReadCapability rc = lookup.getLookup().lookup(ReadCapability.class); try { rc.read(); for (Customer c : lookup.getCustomers()) { jTextArea1.append(c.getName() + " (" + c.getCity() + ")" + "\n"); } } catch (Exception ex) { Exceptions.printStackTrace(ex); } } Does the above make as much sense to others as it does to me, including the naming of the classes? Feedback would be appreciated! Then I'll integrate into the tutorial and do the same for the other sections, i.e., "Create", "Update", and "Delete". (By the way, of course, the tutorial ends up showing that, rather than using a JTextArea to display data, you can use Nodes and explorer views to do so.)

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• Using Asset Groups

  - by Owen Allen

  I got a question about putting assets in groups: "I'm planning on installing some agents manually on existing systems, and I want to have them put in a specific asset group once they're discovered. I don't see any way to tell the install script to put the asset in a group. How can I add the assets to a group, either through the UI or the CLI?" There are a few ways. In the CLI, you can use groups mode, and use this command to add an asset to a group: attach -n| --gear <asset name> -g| --group <group> You can also use -U| --uuid <UUID> to specify the asset if you have multiple assets with the same name. In the UI, you have a couple of options. You can select an asset and click Add Asset to Group to add it to a group you select. Alternatively, if you're trying to make a group for assets with a specific characteristic, you can specify rules that will automatically add assets to a group based on that characteristic.

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• How to Calculate TCP Socket Buffer Sizes for Data Guard Environments

  - by alejandro.vargas

  The MAA best practices contains an example of how to calculate the optimal TCP socket buffer sizes, that is quite important for very busy Data Guard environments, this document Formula to Calculate TCP Socket Buffer Sizes.pdf contains an example of using the instructions provided on the best practices document. In order to execute the calculation you need to know which is the band with or your network interface, usually will be 1Gb, on my example is a 10Gb network; and the round trip time, RTT, that is the time it takes for a packet to make a travel to the other end of the network and come back, on my example that was provided by the network administrator and was 3 ms (1000/seconds)

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• API's

  - by raghu.yadav

  lets dump API's here .... // if you want to put/get something in/from the pageFlowScope, use thisMap pfsMap = AdfFacesContext.getCurrentInstance().getPageFlowScope(); pfsMap.put(key, value); // pfsMap.put("#{pageFlowScope.param}, "sample"); pfsMap.get(key); // pfsMap.get("#{pageFlowScope.param} // if you want to set bean's property value, use this MyBackingBean bean = (MyBackingBean)pfsMap.get("my_backing_bean_name"); // the name under which the bean is registered in the task flow bean.setMyParam(newValue);

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• 64-bit Archives Needed

  - by user9154181

  A little over a year ago, we received a question from someone who was trying to build software on Solaris. He was getting errors from the ar command when creating an archive. At that time, the ar command on Solaris was a 32-bit command. There was more than 2GB of data, and the ar command was hitting the file size limit for a 32-bit process that doesn't use the largefile APIs. Even in 2011, 2GB is a very large amount of code, so we had not heard this one before. Most of our toolchain was extended to handle 64-bit sized data back in the 1990's, but archives were not changed, presumably because there was no perceived need for it. Since then of course, programs have continued to get larger, and in 2010, the time had finally come to investigate the issue and find a way to provide for larger archives. As part of that process, I had to do a deep dive into the archive format, and also do some Unix archeology. I'm going to record what I learned here, to document what Solaris does, and in the hope that it might help someone else trying to solve the same problem for their platform. Archive Format Details Archives are hardly cutting edge technology. They are still used of course, but their basic form hasn't changed in decades. Other than to fix a bug, which is rare, we don't tend to touch that code much. The archive file format is described in /usr/include/ar.h, and I won't repeat the details here. Instead, here is a rough overview of the archive file format, implemented by System V Release 4 (SVR4) Unix systems such as Solaris: Every archive starts with a "magic number". This is a sequence of 8 characters: "!<arch>\n". The magic number is followed by 1 or more members. A member starts with a fixed header, defined by the ar_hdr structure in/usr/include/ar.h. Immediately following the header comes the data for the member. Members must be padded at the end with newline characters so that they have even length. The requirement to pad members to an even length is a dead giveaway as to the age of the archive format. It tells you that this format dates from the 1970's, and more specifically from the era of 16-bit systems such as the PDP-11 that Unix was originally developed on. A 32-bit system would have required 4 bytes, and 64-bit systems such as we use today would probably have required 8 bytes. 2 byte alignment is a poor choice for ELF object archive members. 32-bit objects require 4 byte alignment, and 64-bit objects require 64-bit alignment. The link-editor uses mmap() to process archives, and if the members have the wrong alignment, we have to slide (copy) them to the correct alignment before we can access the ELF data structures inside. The archive format requires 2 byte padding, but it doesn't prohibit more. The Solaris ar command takes advantage of this, and pads ELF object members to 8 byte boundaries. Anything else is padded to 2 as required by the format. The archive header (ar_hdr) represents all numeric values using an ASCII text representation rather than as binary integers. This means that an archive that contains only text members can be viewed using tools such as cat, more, or a text editor. The original designers of this format clearly thought that archives would be used for many file types, and not just for objects. Things didn't turn out that way of course — nearly all archives contain relocatable objects for a single operating system and machine, and are used primarily as input to the link-editor (ld). Archives can have special members that are created by the ar command rather than being supplied by the user. These special members are all distinguished by having a name that starts with the slash (/) character. This is an unambiguous marker that says that the user could not have supplied it. The reason for this is that regular archive members are given the plain name of the file that was inserted to create them, and any path components are stripped off. Slash is the delimiter character used by Unix to separate path components, and as such cannot occur within a plain file name. The ar command hides the special members from you when you list the contents of an archive, so most users don't know that they exist. There are only two possible special members: A symbol table that maps ELF symbols to the object archive member that provides it, and a string table used to hold member names that exceed 15 characters. The '/' convention for tagging special members provides room for adding more such members should the need arise. As I will discuss below, we took advantage of this fact to add an alternate 64-bit symbol table special member which is used in archives that are larger than 4GB. When an archive contains ELF object members, the ar command builds a special archive member known as the symbol table that maps all ELF symbols in the object to the archive member that provides it. The link-editor uses this symbol table to determine which symbols are provided by the objects in that archive. If an archive has a symbol table, it will always be the first member in the archive, immediately following the magic number. Unlike member headers, symbol tables do use binary integers to represent offsets. These integers are always stored in big-endian format, even on a little endian host such as x86. The archive header (ar_hdr) provides 15 characters for representing the member name. If any member has a name that is longer than this, then the real name is written into a special archive member called the string table, and the member's name field instead contains a slash (/) character followed by a decimal representation of the offset of the real name within the string table. The string table is required to precede all normal archive members, so it will be the second member if the archive contains a symbol table, and the first member otherwise. The archive format is not designed to make finding a given member easy. Such operations move through the archive from front to back examining each member in turn, and run in O(n) time. This would be bad if archives were commonly used in that manner, but in general, they are not. Typically, the ar command is used to build an new archive from scratch, inserting all the objects in one operation, and then the link-editor accesses the members in the archive in constant time by using the offsets provided by the symbol table. Both of these operations are reasonably efficient. However, listing the contents of a large archive with the ar command can be rather slow. Factors That Limit Solaris Archive Size As is often the case, there was more than one limiting factor preventing Solaris archives from growing beyond the 32-bit limits of 2GB (32-bit signed) and 4GB (32-bit unsigned). These limits are listed in the order they are hit as archive size grows, so the earlier ones mask those that follow. The original Solaris archive file format can handle sizes up to 4GB without issue. However, the ar command was delivered as a 32-bit executable that did not use the largefile APIs. As such, the ar command itself could not create a file larger than 2GB. One can solve this by building ar with the largefile APIs which would allow it to reach 4GB, but a simpler and better answer is to deliver a 64-bit ar, which has the ability to scale well past 4GB. Symbol table offsets are stored as 32-bit big-endian binary integers, which limits the maximum archive size to 4GB. To get around this limit requires a different symbol table format, or an extension mechanism to the current one, similar in nature to the way member names longer than 15 characters are handled in member headers. The size field in the archive member header (ar_hdr) is an ASCII string capable of representing a 32-bit unsigned value. This places a 4GB size limit on the size of any individual member in an archive. In considering format extensions to get past these limits, it is important to remember that very few archives will require the ability to scale past 4GB for many years. The old format, while no beauty, continues to be sufficient for its purpose. This argues for a backward compatible fix that allows newer versions of Solaris to produce archives that are compatible with older versions of the system unless the size of the archive exceeds 4GB. Archive Format Differences Among Unix Variants While considering how to extend Solaris archives to scale to 64-bits, I wanted to know how similar archives from other Unix systems are to those produced by Solaris, and whether they had already solved the 64-bit issue. I've successfully moved archives between different Unix systems before with good luck, so I knew that there was some commonality. If it turned out that there was already a viable defacto standard for 64-bit archives, it would obviously be better to adopt that rather than invent something new. The archive file format is not formally standardized. However, the ar command and archive format were part of the original Unix from Bell Labs. Other systems started with that format, extending it in various often incompatible ways, but usually with the same common shared core. Most of these systems use the same magic number to identify their archives, despite the fact that their archives are not always fully compatible with each other. It is often true that archives can be copied between different Unix variants, and if the member names are short enough, the ar command from one system can often read archives produced on another. In practice, it is rare to find an archive containing anything other than objects for a single operating system and machine type. Such an archive is only of use on the type of system that created it, and is only used on that system. This is probably why cross platform compatibility of archives between Unix variants has never been an issue. Otherwise, the use of the same magic number in archives with incompatible formats would be a problem. I was able to find information for a number of Unix variants, described below. These can be divided roughly into three tribes, SVR4 Unix, BSD Unix, and IBM AIX. Solaris is a SVR4 Unix, and its archives are completely compatible with those from the other members of that group (GNU/Linux, HP-UX, and SGI IRIX). AIX AIX is an exception to rule that Unix archive formats are all based on the original Bell labs Unix format. It appears that AIX supports 2 formats (small and big), both of which differ in fundamental ways from other Unix systems: These formats use a different magic number than the standard one used by Solaris and other Unix variants. They include support for removing archive members from a file without reallocating the file, marking dead areas as unused, and reusing them when new archive items are inserted. They have a special table of contents member (File Member Header) which lets you find out everything that's in the archive without having to actually traverse the entire file. Their symbol table members are quite similar to those from other systems though. Their member headers are doubly linked, containing offsets to both the previous and next members. Of the Unix systems described here, AIX has the only format I saw that will have reasonable insert/delete performance for really large archives. Everyone else has O(n) performance, and are going to be slow to use with large archives. BSD BSD has gone through 4 versions of archive format, which are described in their manpage. They use the same member header as SVR4, but their symbol table format is different, and their scheme for long member names puts the name directly after the member header rather than into a string table. GNU/Linux The GNU toolchain uses the SVR4 format, and is compatible with Solaris. HP-UX HP-UX seems to follow the SVR4 model, and is compatible with Solaris. IRIX IRIX has 32 and 64-bit archives. The 32-bit format is the standard SVR4 format, and is compatible with Solaris. The 64-bit format is the same, except that the symbol table uses 64-bit integers. IRIX assumes that an archive contains objects of a single ELFCLASS/MACHINE, and any archive containing ELFCLASS64 objects receives a 64-bit symbol table. Although they only use it for 64-bit objects, nothing in the archive format limits it to ELFCLASS64. It would be perfectly valid to produce a 64-bit symbol table in an archive containing 32-bit objects, text files, or anything else. Tru64 Unix (Digital/Compaq/HP) Tru64 Unix uses a format much like ours, but their symbol table is a hash table, making specific symbol lookup much faster. The Solaris link-editor uses archives by examining the entire symbol table looking for unsatisfied symbols for the link, and not by looking up individual symbols, so there would be no benefit to Solaris from such a hash table. The Tru64 ld must use a different approach in which the hash table pays off for them. Widening the existing SVR4 archive symbol tables rather than inventing something new is the simplest path forward. There is ample precedent for this approach in the ELF world. When ELF was extended to support 64-bit objects, the approach was largely to take the existing data structures, and define 64-bit versions of them. We called the old set ELF32, and the new set ELF64. My guess is that there was no need to widen the archive format at that time, but had there been, it seems obvious that this is how it would have been done. The Implementation of 64-bit Solaris Archives As mentioned earlier, there was no desire to improve the fundamental nature of archives. They have always had O(n) insert/delete behavior, and for the most part it hasn't mattered. AIX made efforts to improve this, but those efforts did not find widespread adoption. For the purposes of link-editing, which is essentially the only thing that archives are used for, the existing format is adequate, and issues of backward compatibility trump the desire to do something technically better. Widening the existing symbol table format to 64-bits is therefore the obvious way to proceed. For Solaris 11, I implemented that, and I also updated the ar command so that a 64-bit version is run by default. This eliminates the 2 most significant limits to archive size, leaving only the limit on an individual archive member. We only generate a 64-bit symbol table if the archive exceeds 4GB, or when the new -S option to the ar command is used. This maximizes backward compatibility, as an archive produced by Solaris 11 is highly likely to be less than 4GB in size, and will therefore employ the same format understood by older versions of the system. The main reason for the existence of the -S option is to allow us to test the 64-bit format without having to construct huge archives to do so. I don't believe it will find much use outside of that. Other than the new ability to create and use extremely large archives, this change is largely invisible to the end user. When reading an archive, the ar command will transparently accept either form of symbol table. Similarly, the ELF library (libelf) has been updated to understand either format. Users of libelf (such as the link-editor ld) do not need to be modified to use the new format, because these changes are encapsulated behind the existing functions provided by libelf. As mentioned above, this work did not lift the limit on the maximum size of an individual archive member. That limit remains fixed at 4GB for now. This is not because we think objects will never get that large, for the history of computing says otherwise. Rather, this is based on an estimation that single relocatable objects of that size will not appear for a decade or two. A lot can change in that time, and it is better not to overengineer things by writing code that will sit and rot for years without being used. It is not too soon however to have a plan for that eventuality. When the time comes when this limit needs to be lifted, I believe that there is a simple solution that is consistent with the existing format. The archive member header size field is an ASCII string, like the name, and as such, the overflow scheme used for long names can also be used to handle the size. The size string would be placed into the archive string table, and its offset in the string table would then be written into the archive header size field using the same format "/ddd" used for overflowed names.

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• New Sample TaskFlow - ADF Survey

  Reusable polls taskflow and admin console

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• Notes for a NetBeans IDE 7.4 HTML5 Screencast

  - by Geertjan

  I'm making a screencast that intends to thoroughly introduce NetBeans IDE 7.4 as a tool for HTML, JavaScript, and CSS developers. Here's the current outline, additions and other suggestions are welcome. Getting Started Downloading NetBeans IDE for HTML5 and PHP Examining the NetBeans installation directory, especially netbeans.conf Examining the NetBeans user directory Command line options for starting NetBeans IDE Exploring NetBeans IDE Menus and toolbars Versioning tools Options Window Go through whole Options window Change look and feels Adding themes Syntax coloring Code templates Plugin Manager and Plugin Portal Dark Look and Feel Themes Toggle line wrap Emmet HTML Tidy NetBeans Cheat Sheets Creating HTML5 projects From scratch From online template, e.g., Twitter Bootstrap From ZIP file From folder on disk From sample Editing Useful shortcuts Alt-Enter: see the current hints Alt-Shift-DOT/COMMA: expand selection (CTRL instead of Alt on Mac) Ctrl-Shift-Up/Down: copy up/down Alt-Shift-Up/Down: move up/down Alt-Insert: generate code (Lorum Ipsum) View menu | Show Non-printable Characters Source menu Show keyboard shortcut card Useful hints Surround with Tag Remove Surrounding Tag Useful code completion Link tag for CSS, show completion Script tag for JavaScript, show completion Create code templates in Options window Useful HTML Palette items Unordered List Link Useful code navigation Navigator Navigate menu Useful project settings Project-level deployment settings CSS Preprocessors (SASS/LESS) Cordova support Useful window management Dragging, minimizing, undocking Ctrl-Shift-Enter: distraction-free mode Alt-Shift Enter: maximization Debugging JavaScript debugger Deploying Embedded browser Responsive design Inspect in NetBeans mode Chrome browser with NetBeans plugin Android and iOS browsers Cordova makes native packages On device debugging On device styling Documentation PHP and HTML5 Learning Trail: https://netbeans.org/kb/trails/php.html Contributing Social Media: Twitter, Facebook, blogs Plugin Portal Planning to complete the above screencast this week, will continue editing this page as more useful features arise in my mind or hopefully in the comments in this blog entry!

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• NightHacking Tour: Join the fun!

  - by terrencebarr

  My colleague and esteemed JavaFX hacker Stephen Chin is currently on the road on his NightHacking Tour through Europe, geeking with toys and projects, hacking code, and interviewing Java luminaries along the way. You might know the guy on the left – James Gosling was the first stop of the tour. What’s more, you can follow live on UStream at each stop along the way. Very cool! To learn all about the NightHacking Tour, check here.  Stephen will swing past my place in Freiburg, Germany, on Saturday (Nov 3). We’ll be chatting about all the stuff that’s happening in the embedded space these days and play with the latest small Java – if the demo gods allow For the latest UStream schedule and past recordings, go here. And follow #nighthacking on Twitter. Cheers, – Terrence Filed under: Mobile & Embedded Tagged: embedded, Java, Java Embedded, nighthacking

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• Is there a clean separation of my layers with this attempt at Domain Driven Design in XAML and C#

  - by Buddy James

  I'm working on an application. I'm using a mixture of TDD and DDD. I'm working hard to separate the layers of my application and that is where my question comes in. My solution is laid out as follows Solution MyApp.Domain (WinRT class library) Entity (Folder) Interfaces(Folder) IPost.cs (Interface) BlogPosts.cs(Implementation of IPost) Service (Folder) Interfaces(Folder) IDataService.cs (Interface) BlogDataService.cs (Implementation of IDataService) MyApp.Presentation(Windows 8 XAML + C# application) ViewModels(Folder) BlogViewModel.cs App.xaml MainPage.xaml (Contains a property of BlogViewModel MyApp.Tests (WinRT Unit testing project used for my TDD) So I'm planning to use my ViewModel with the XAML UI I'm writing a test and define my interfaces in my system and I have the following code thus far. [TestMethod] public void Get_Zero_Blog_Posts_From_Presentation_Layer_Returns_Empty_Collection() { IBlogViewModel viewModel = _container.Resolve<IBlogViewModel>(); viewModel.LoadBlogPosts(0); Assert.AreEqual(0, viewModel.BlogPosts.Count, "There should be 0 blog posts."); } viewModel.BlogPosts is an ObservableCollection<IPost> Now.. my first thought is that I'd like the LoadBlogPosts method on the ViewModel to call a static method on the BlogPost entity. My problem is I feel like I need to inject the IDataService into the Entity object so that it promotes loose coupling. Here are the two options that I'm struggling with: Not use a static method and use a member method on the BlogPost entity. Have the BlogPost take an IDataService in the constructor and use dependency injection to resolve the BlogPost instance and the IDataService implementation. Don't use the entity to call the IDataService. Put the IDataService in the constructor of the ViewModel and use my container to resolve the IDataService when the viewmodel is instantiated. So with option one the layers will look like this ViewModel(Presentation layer) - Entity (Domain layer) - IDataService (Service Layer) or ViewModel(Presentation layer) - IDataService (Service Layer)

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• Creating a Successful Cloud Roadmap

  - by stephen.g.bennett

  No matter what type of cloud services or deployment models you are considering as part of your overall IT strategy, you must have a cloud services adoption roadmap to guide your journey. A cloud services adoption roadmap provides guidance that enables multiple projects to progress in parallel yet remain coordinated and ultimately result in a common end goal. The cloud services adoption roadmap consists of program-level efforts and a portfolio of cloud services. The program-level effort creates strategic assets such as the cloud architecture, cloud infrastructure, cloud governance, risk, and compliance (GRC) processes, and security policies that are leveraged across all the individual projects. A feature article on this topic can be found in the latest SOA and Cloud Magazine.

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• Google Updates Picasa Web Albums; Emphasis on Sharing and Showcasing

  - by ETC

  Google has dusted off the Picasa Web interface and updated it with an emphasis on highlighting your photos and the photos of those you’re interested in. The new interface gives you speedy access to all the new photos you’ve uploaded and all the photos your friends, family, and others you’re following are sharing. Mixed in with that are popular photos from talented photographers across the service. It’s a nice change from the previously dull web interface and a definite step towards capturing some of the social power photo sharing site Flickr wields. Hit up the link below to read more. Showcasing Photos From People You Care About [The Official Google Photos Blog] Latest Features How-To Geek ETC Learn To Adjust Contrast Like a Pro in Photoshop, GIMP, and Paint.NET Have You Ever Wondered How Your Operating System Got Its Name? Should You Delete Windows 7 Service Pack Backup Files to Save Space? What Can Super Mario Teach Us About Graphics Technology? Windows 7 Service Pack 1 is Released: But Should You Install It? How To Make Hundreds of Complex Photo Edits in Seconds With Photoshop Actions Add a “Textmate Style” Lightweight Text Editor with Dropbox Syncing to Chrome and Iron Is the Forcefield Really On or Not? [Star Wars Parody Video] Google Updates Picasa Web Albums; Emphasis on Sharing and Showcasing Uwall.tv Turns YouTube into a Video Jukebox Early Morning Sunrise at the Beach Wallpaper Data Networks Visualized via Light Paintings [Video]

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• Best Of 2010

  - by Mike Dietrich

  Hi there, in Australia, Japan, Singapore and many other countries it's already 2011 - but Germany and the US is still some time until midnight :-) To round up the year you'll find a few off-topic pictures from 2010. You might click on the pictures to get a better resolution. Enjoy ... Moscow - Red Square Tokyo Train - Cell Phone Mania Great Chinese Wall near Beijing Hong Kong by Night Yearing Station Winery, Yarra - Victoria, Australia Dublin, Ireland - during the ash cloud - no comment - Liberty It's sometime foggy in SF Singapore Opera Stockholm - Gamla Stan Unbelievable white beach at Camps Bay, Clifton, Capetown Words fail me ... Mike

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• Performance Enhancement in Full-Text Search Query

  - by Calvin Sun

  Ever since its first release, we are continuing consolidating and developing InnoDB Full-Text Search feature. There is one recent improvement that worth blogging about. It is an effort with MySQL Optimizer team that simplifies some common queries’ Query Plans and dramatically shorted the query time. I will describe the issue, our solution and the end result by some performance numbers to demonstrate our efforts in continuing enhancement the Full-Text Search capability. The Issue: As we had discussed in previous Blogs, InnoDB implements Full-Text index as reversed auxiliary tables. The query once parsed will be reinterpreted into several queries into related auxiliary tables and then results are merged and consolidated to come up with the final result. So at the end of the query, we’ll have all matching records on hand, sorted by their ranking or by their Doc IDs. Unfortunately, MySQL’s optimizer and query processing had been initially designed for MyISAM Full-Text index, and sometimes did not fully utilize the complete result package from InnoDB. Here are a couple examples: Case 1: Query result ordered by Rank with only top N results: mysql> SELECT FTS_DOC_ID, MATCH (title, body) AGAINST ('database') AS SCORE FROM articles ORDER BY score DESC LIMIT 1; In this query, user tries to retrieve a single record with highest ranking. It should have a quick answer once we have all the matching documents on hand, especially if there are ranked. However, before this change, MySQL would almost retrieve rankings for almost every row in the table, sort them and them come with the top rank result. This whole retrieve and sort is quite unnecessary given the InnoDB already have the answer. In a real life case, user could have millions of rows, so in the old scheme, it would retrieve millions of rows' ranking and sort them, even if our FTS already found there are two 3 matched rows. Apparently, the million ranking retrieve is done in vain. In above case, it should just ask for 3 matched rows' ranking, all other rows' ranking are 0. If it want the top ranking, then it can just get the first record from our already sorted result. Case 2: Select Count(*) on matching records: mysql> SELECT COUNT(*) FROM articles WHERE MATCH (title,body) AGAINST ('database' IN NATURAL LANGUAGE MODE); In this case, InnoDB search can find matching rows quickly and will have all matching rows. However, before our change, in the old scheme, every row in the table was requested by MySQL one by one, just to check whether its ranking is larger than 0, and later comes up a count. In fact, there is no need for MySQL to fetch all rows, instead InnoDB already had all the matching records. The only thing need is to call an InnoDB API to retrieve the count The difference can be huge. Following query output shows how big the difference can be: mysql> select count(*) from searchindex_inno where match(si_title, si_text) against ('people')  +----------+ | count(*) | +----------+ | 666877 | +----------+ 1 row in set (16 min 17.37 sec) So the query took almost 16 minutes. Let’s see how long the InnoDB can come up the result. In InnoDB, you can obtain extra diagnostic printout by turning on “innodb_ft_enable_diag_print”, this will print out extra query info: Error log: keynr=2, 'people' NL search Total docs: 10954826 Total words: 0 UNION: Searching: 'people' Processing time: 2 secs: row(s) 666877: error: 10 ft_init() ft_init_ext() keynr=2, 'people' NL search Total docs: 10954826 Total words: 0 UNION: Searching: 'people' Processing time: 3 secs: row(s) 666877: error: 10 Output shows it only took InnoDB only 3 seconds to get the result, while the whole query took 16 minutes to finish. So large amount of time has been wasted on the un-needed row fetching. The Solution: The solution is obvious. MySQL can skip some of its steps, optimize its plan and obtain useful information directly from InnoDB. Some of savings from doing this include: 1) Avoid redundant sorting. Since InnoDB already sorted the result according to ranking. MySQL Query Processing layer does not need to sort to get top matching results. 2) Avoid row by row fetching to get the matching count. InnoDB provides all the matching records. All those not in the result list should all have ranking of 0, and no need to be retrieved. And InnoDB has a count of total matching records on hand. No need to recount. 3) Covered index scan. InnoDB results always contains the matching records' Document ID and their ranking. So if only the Document ID and ranking is needed, there is no need to go to user table to fetch the record itself. 4) Narrow the search result early, reduce the user table access. If the user wants to get top N matching records, we do not need to fetch all matching records from user table. We should be able to first select TOP N matching DOC IDs, and then only fetch corresponding records with these Doc IDs. Performance Results and comparison with MyISAM The result by this change is very obvious. I includes six testing result performed by Alexander Rubin just to demonstrate how fast the InnoDB query now becomes when comparing MyISAM Full-Text Search. These tests are base on the English Wikipedia data of 5.4 Million rows and approximately 16G table. The test was performed on a machine with 1 CPU Dual Core, SSD drive, 8G of RAM and InnoDB_buffer_pool is set to 8 GB. Table 1: SELECT with LIMIT CLAUSE mysql> SELECT si_title, match(si_title, si_text) against('family') as rel FROM si WHERE match(si_title, si_text) against('family') ORDER BY rel desc LIMIT 10; InnoDB MyISAM Times Faster Time for the query 1.63 sec 3 min 26.31 sec 127 You can see for this particular query (retrieve top 10 records), InnoDB Full-Text Search is now approximately 127 times faster than MyISAM. Table 2: SELECT COUNT QUERY mysql>select count(*) from si where match(si_title, si_text) against('family‘); +----------+ | count(*) | +----------+ | 293955 | +----------+ InnoDB MyISAM Times Faster Time for the query 1.35 sec 28 min 59.59 sec 1289 In this particular case, where there are 293k matching results, InnoDB took only 1.35 second to get all of them, while take MyISAM almost half an hour, that is about 1289 times faster!. Table 3: SELECT ID with ORDER BY and LIMIT CLAUSE for selected terms mysql> SELECT <ID>, match(si_title, si_text) against(<TERM>) as rel FROM si_<TB> WHERE match(si_title, si_text) against (<TERM>) ORDER BY rel desc LIMIT 10; Term InnoDB (time to execute) MyISAM(time to execute) Times Faster family 0.5 sec 5.05 sec 10.1 family film 0.95 sec 25.39 sec 26.7 Pizza restaurant orange county California 0.93 sec 32.03 sec 34.4 President united states of America 2.5 sec 36.98 sec 14.8 Table 4: SELECT title and text with ORDER BY and LIMIT CLAUSE for selected terms mysql> SELECT <ID>, si_title, si_text, ... as rel FROM si_<TB> WHERE match(si_title, si_text) against (<TERM>) ORDER BY rel desc LIMIT 10; Term InnoDB (time to execute) MyISAM(time to execute) Times Faster family 0.61 sec 41.65 sec 68.3 family film 1.15 sec 47.17 sec 41.0 Pizza restaurant orange county california 1.03 sec 48.2 sec 46.8 President united states of america 2.49 sec 44.61 sec 17.9 Table 5: SELECT ID with ORDER BY and LIMIT CLAUSE for selected terms mysql> SELECT <ID>, match(si_title, si_text) against(<TERM>) as rel  FROM si_<TB> WHERE match(si_title, si_text) against (<TERM>) ORDER BY rel desc LIMIT 10; Term InnoDB (time to execute) MyISAM(time to execute) Times Faster family 0.5 sec 5.05 sec 10.1 family film 0.95 sec 25.39 sec 26.7 Pizza restaurant orange county califormia 0.93 sec 32.03 sec 34.4 President united states of america 2.5 sec 36.98 sec 14.8 Table 6: SELECT COUNT(*) mysql> SELECT count(*) FROM si_<TB> WHERE match(si_title, si_text) against (<TERM>) LIMIT 10; Term InnoDB (time to execute) MyISAM(time to execute) Times Faster family 0.47 sec 82 sec 174.5 family film 0.83 sec 131 sec 157.8 Pizza restaurant orange county califormia 0.74 sec 106 sec 143.2 President united states of america 1.96 sec 220 sec 112.2  Again, table 3 to table 6 all showing InnoDB consistently outperform MyISAM in these queries by a large margin. It becomes obvious the InnoDB has great advantage over MyISAM in handling large data search. Summary: These results demonstrate the great performance we could achieve by making MySQL optimizer and InnoDB Full-Text Search more tightly coupled. I think there are still many cases that InnoDB’s result info have not been fully taken advantage of, which means we still have great room to improve. And we will continuously explore the area, and get more dramatic results for InnoDB full-text searches. Jimmy Yang, September 29, 2012

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• 5 Ways to Determine Mobile Location

  - by David Dorf

  In my previous post, I mentioned the importance of determining the location of a consumer using their mobile phone.  Retailers can track anonymous mobile phones to determine traffic patterns both inside and outside their stores.  And with consumers' permission, retailers can send location-aware offers to mobile phones; for example, a coupon for cereal as you walk down that aisle.  When paying with Square, your location is matched with the transaction.  So there are lots of reasons for retailers to want to know the location of their customers.  But how is it done? I thought I'd dive a little deeper on that topic and consider the approaches to determining location. 1. Tower Triangulation By comparing the relative signal strength from multiple antenna towers, a general location of a phone can be roughly determined to an accuracy of 200-1000 meters.  The more towers involved, the more accurate the location. 2. GPS Using Global Positioning Satellites is more accurate than using cell towers, but it takes longer to find the satellites, it uses more battery, and it won't well indoors.  For geo-fencing applications, like those provided by Placecast and Digby, cell towers are often used to determine if the consumer is nearing a "fence" then switches to GPS to determine the actual crossing of the fence. 3. WiFi Triangulation WiFi triangulation is usually more accurate than using towers just because there are so many more WiFi access points (i.e. radios in routers) around. The position of each WiFi AP needs to be recorded in a database and used in the calculations, which is what Skyhook has been doing since 2008.  Another advantage to this method is that works well indoors, although it usually requires additional WiFi beacons to get the accuracy down to 5-10 meters.  Companies like ZuluTime, Aisle411, and PointInside have been perfecting this approach for retailers like Meijer, Walgreens, and HomeDepot. Keep in mind that a mobile phone doesn't have to connect to the WiFi network in order for it to be located.  The WiFi radio in the phone only needs to be on.  Even when not connected, WiFi radios talk to each other to prepare for a possible connection. 4. Hybrid Approaches Naturally the most accurate approach is to combine the approaches described above.  The more available data points, the greater the accuracy.  Companies like ShopKick like to add in acoustic triangulation using the phone's microphone, and NearBuy can use video analytics to increase accuracy. 5. Magnetic Fields The latest approach, and this one is really new, takes a page from the animal kingdom.  As you've probably learned from guys like Marlin Perkins, some animals use the Earth's magnetic fields to navigate.  By recording magnetic variations within a store, then matching those readings with ones from a consumer's phone, location can be accurately determined.  At least that's the approach IndoorAtlas is taking, and the science seems to bear out.  It works well indoors, and doesn't require retailers to purchase any additional hardware.  Keep an eye on this one.

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• Transparent Technology from Amazon

  - by David Dorf

  Amazon has been making some interesting moves again, this time in the augmented humanity area.  Augmented humanity is about helping humans overcome their shortcomings using technology.  Putting a powerful smartphone in your pocket helps you in many ways like navigating streets, communicating with far off friends, and accessing information.  But the interface for smartphones is somewhat limiting and unnatural, so companies have been looking for ways to make the technology more transparent and therefore easier to use. When Apple helped us drop the stylus, we took a giant leap forward in simplicity.  Using touchscreens with intuitive gestures was part of the iPhone's original appeal.  People don't want to know that technology is there -- they just want the benefits.  So what's the next leap beyond the touchscreen to make smartphones even easier to use? Two natural ways we interact with the world around us is by using sight and voice.  Google and Apple have been using both in their mobile platforms for limited uses cases.  Nobody actually wants to type a text message, so why not just speak it?  Any if you want more information about a book, why not just snap a picture of the cover?  That's much more accurate than trying to key the title and/or author. So what's Amazon been doing?  First, Amazon released a new iPhone app called Flow that allows iPhone users to see information about products in context.  Yes, its an augmented reality app that uses the phone's camera to view products, and overlays data about the products on the screen.  For the most part it requires the barcode to be visible to correctly identify the product, but I believe it can also recognize certain logos as well.  Download the app and try it out but don't expect perfection.  Its good enough to demonstrate the concept, but its far from accurate enough.  (MobileBeat did a pretty good review.)  Extrapolate to the future and we might just have a heads-up display in our eyeglasses. The second interesting area is voice response, for which Siri is getting lots of attention.  Amazon may have purchased a voice recognition company called Yap, although the deal is not confirmed.  But it would make perfect sense, especially with the Kindle Fire in Amazon's lineup. I believe over the next 3-5 years the way in which we interact with smartphones will mature, and they will become more transparent yet more important to our daily lives.  This will, of course, impact the way we shop, making information more readily accessible than it already is.  Amazon seems to be positioning itself to be at the forefront of this trend, so we should be watching them carefully.

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• Twitte API for Java - Hello Twitter Servlet (TOTD #178)

  - by arungupta

  There are a few Twitter APIs for Java that allow you to integrate Twitter functionality in a Java application. This is yet another API, built using JAX-RS and Jersey stack. I started this effort earlier this year and kept delaying to share because wanted to provide a more comprehensive API. But I've delayed enough and releasing it as a work-in-progress. I'm happy to take contributions in order to evolve this API and make it complete, useful, and robust. Drop a comment on the blog if you are interested or ping me at @arungupta. How do you get started ? Just add the following to your "pom.xml": <dependency> <groupId>org.glassfish.samples</groupId> <artifactId>twitter-api</artifactId> <version>1.0-SNAPSHOT</version></dependency> The implementation of this API uses Jersey OAuth Filters for authentication with Twitter and so the following dependencies are required if any API that requires authentication, which is pretty much all the APIs ;-) <dependency> <groupId>com.sun.jersey.contribs.jersey-oauth</groupId>     <artifactId>oauth-client</artifactId>     <version>${jersey.version}</version> </dependency> <dependency>     <groupId>com.sun.jersey.contribs.jersey-oauth</groupId>     <artifactId>oauth-signature</artifactId>     <version>${jersey.version}</version> </dependency> Once the dependencies are added to your project, inject Twitter  API in your Servlet (or any other Java EE component) as: @Inject Twitter twitter; Here is a simple non-secure invocation of the API to get you started: SearchResults result = twitter.search("glassfish", SearchResults.class);for (SearchResultsTweet t : result.getResults()) { out.println(t.getText() + "<br/>");} This code returns the tweets that matches the query "glassfish". The source code for the complete project can be downloaded here. Download it, unzip, and mvn package will build the .war file. And then deploy it on GlassFish or any other Java EE 6 compliant application server! The source code for the API also acts as the javadocs and can be checked out from here. A more detailed sample using security and several other API from this library is coming soon!

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• JSF 2.2 recent progress - Early Draft

  - by alexismp

  JSF specification lead Ed Burns has an update on the progress of JSF 2.2, another component which should be required as part of the upcoming Java EE 7 standard. This includes a reminder of the scope of this specification, the availability of the early draft and height specific features that are being worked on and split into "Mostly Specified Features" and "Not Yet Fully Specified Features" (I think you can read the latter as "at risk"). My favorite is "763-EverythingIsInjectable". Remember that JSF 2.2 is due out in the middle of 2012 which is in time to be integrated in the Java EE 7 platform JSR (currently scheduled for second half of 2012). In the mean time, JSF 2.2 nightly builds are available.

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• Protecting PDF files and XDO.CFG

  - by Greg Kelly

  Protecting PDF files and XDO.CFG Security related properties can be overridden at runtime through PeopleCode as all other XMLP properties using the SetRuntimeProperties() method on the ReportDefn class. This is documented in PeopleBooks. Basically this method need to be called right before calling the processReport() method: . . &asPropName = CreateArrayRept("", 0); &asPropValue = CreateArrayRept("", 0); &asPropName.Push("pdf-open-password"); &asPropValue.Push("test"); &oRptDefn.SetRuntimeProperties(&asPropName, &asPropValue); &oRptDefn.ProcessReport(&sTemplateId, %Language_User, &dAsOfDate, &sOutputFormat); Of course users should not hardcode the password value in the code, instead, if password is stored encrypted in the database or somewhere else, they can use Decrypt() api

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• Eine gelöschte APEX-Anwendung wiederherstellen ...? Das geht!

  - by carstenczarski

  Eine versehentlich gelöschte APEX-Anwendung lässt sich tatsächlich wiederherstellen; allerdings darf seit dem Löschen nicht allzuviel Zeit vergangen sein. Grundlage ist die Möglichkeit, Flashback-Funktionen beim Anwendungsexport zu nutzen. Doch wie soll man die zu exportierende Anwendung einstellen ...? In der Auswahlliste für die zu exportierende Anwendung fehlt sie natürlich, denn sie ist ja gelöscht. Hier hilft ein Trick: Legen Sie einfach eine neue Anwendung an - diese muss die gleiche ID haben, wie die, die versehentlich gelöscht wurde. Und voilá: Nun können Sie die Anwendung auswählen; tragen Sie bei As Of soviele Minuten ein, dass der Export zu einer Zeit stattfindet, als die "alte" Anwendung noch da war und exportieren Sie. Sie erhalten die verlorene Anwendung zurück. Wie weit Sie "in die Vergangenheit" zurückkommen, hängt von der Konfiguration des Datenbankservers (hier: der UNDO-Tablespace) durch den Administrator ab. Realistisch sind meist 10 bis 30 Minuten. Wenn Sie APEX-Entwicklungsstände auch über längere Zeiträume hinweg wiederherstellen möchten, bietet sich der regelmäßige, skriptgesteuerte Export per Kommandozeile und das Einchecken der Exportdateien in ein Versionskonstrollsystem an.

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• WebSocket Applications using Java: JSR 356 Early Draft Now Available (TOTD #183)

  - by arungupta

  WebSocket provide a full-duplex and bi-directional communication protocol over a single TCP connection. JSR 356 is defining a standard API for creating WebSocket applications in the Java EE 7 Platform. This Tip Of The Day (TOTD) will provide an introduction to WebSocket and how the JSR is evolving to support the programming model. First, a little primer on WebSocket! WebSocket is a combination of IETF RFC 6455 Protocol and W3C JavaScript API (still a Candidate Recommendation). The protocol defines an opening handshake and data transfer. The API enables Web pages to use the WebSocket protocol for two-way communication with the remote host. Unlike HTTP, there is no need to create a new TCP connection and send a chock-full of headers for every message exchange between client and server. The WebSocket protocol defines basic message framing, layered over TCP. Once the initial handshake happens using HTTP Upgrade, the client and server can send messages to each other, independent from the other. There are no pre-defined message exchange patterns of request/response or one-way between client and and server. These need to be explicitly defined over the basic protocol. The communication between client and server is pretty symmetric but there are two differences: A client initiates a connection to a server that is listening for a WebSocket request. A client connects to one server using a URI. A server may listen to requests from multiple clients on the same URI. Other than these two difference, the client and server behave symmetrically after the opening handshake. In that sense, they are considered as "peers". After a successful handshake, clients and servers transfer data back and forth in conceptual units referred as "messages". On the wire, a message is composed of one or more frames. Application frames carry payload intended for the application and can be text or binary data. Control frames carry data intended for protocol-level signaling. Now lets talk about the JSR! The Java API for WebSocket is worked upon as JSR 356 in the Java Community Process. This will define a standard API for building WebSocket applications. This JSR will provide support for: Creating WebSocket Java components to handle bi-directional WebSocket conversations Initiating and intercepting WebSocket events Creation and consumption of WebSocket text and binary messages The ability to define WebSocket protocols and content models for an application Configuration and management of WebSocket sessions, like timeouts, retries, cookies, connection pooling Specification of how WebSocket application will work within the Java EE security model Tyrus is the Reference Implementation for JSR 356 and is already integrated in GlassFish 4.0 Promoted Builds. And finally some code! The API allows to create WebSocket endpoints using annotations and interface. This TOTD will show a simple sample using annotations. A subsequent blog will show more advanced samples. A POJO can be converted to a WebSocket endpoint by specifying @WebSocketEndpoint and @WebSocketMessage. @WebSocketEndpoint(path="/hello")public class HelloBean {     @WebSocketMessage    public String sayHello(String name) {         return "Hello " + name + "!";     }} @WebSocketEndpoint marks this class as a WebSocket endpoint listening at URI defined by the path attribute. The @WebSocketMessage identifies the method that will receive the incoming WebSocket message. This first method parameter is injected with payload of the incoming message. In this case it is assumed that the payload is text-based. It can also be of the type byte[] in case the payload is binary. A custom object may be specified if decoders attribute is specified in the @WebSocketEndpoint. This attribute will provide a list of classes that define how a custom object can be decoded. This method can also take an optional Session parameter. This is injected by the runtime and capture a conversation between two endpoints. The return type of the method can be String, byte[] or a custom object. The encoders attribute on @WebSocketEndpoint need to define how a custom object can be encoded. The client side is an index.jsp with embedded JavaScript. The JSP body looks like: <div style="text-align: center;"> <form action="">     <input onclick="say_hello()" value="Say Hello" type="button">         <input id="nameField" name="name" value="WebSocket" type="text"><br>    </form> </div> <div id="output"></div> The code is relatively straight forward. It has an HTML form with a button that invokes say_hello() method and a text field named nameField. A div placeholder is available for displaying the output. Now, lets take a look at some JavaScript code: <script language="javascript" type="text/javascript"> var wsUri = "ws://localhost:8080/HelloWebSocket/hello";     var websocket = new WebSocket(wsUri);     websocket.onopen = function(evt) { onOpen(evt) };     websocket.onmessage = function(evt) { onMessage(evt) };     websocket.onerror = function(evt) { onError(evt) };     function init() {         output = document.getElementById("output");     }     function say_hello() {      websocket.send(nameField.value);         writeToScreen("SENT: " + nameField.value);     } This application is deployed as "HelloWebSocket.war" (download here) on GlassFish 4.0 promoted build 57. So the WebSocket endpoint is listening at "ws://localhost:8080/HelloWebSocket/hello". A new WebSocket connection is initiated by specifying the URI to connect to. The JavaScript API defines callback methods that are invoked when the connection is opened (onOpen), closed (onClose), error received (onError), or a message from the endpoint is received (onMessage). The client API has several send methods that transmit data over the connection. This particular script sends text data in the say_hello method using nameField's value from the HTML shown earlier. Each click on the button sends the textbox content to the endpoint over a WebSocket connection and receives a response based upon implementation in the sayHello method shown above. How to test this out ? Download the entire source project here or just the WAR file. Download GlassFish4.0 build 57 or later and unzip. Start GlassFish as "asadmin start-domain". Deploy the WAR file as "asadmin deploy HelloWebSocket.war". Access the application at http://localhost:8080/HelloWebSocket/index.jsp. After clicking on "Say Hello" button, the output would look like: Here are some references for you: WebSocket - Protocol and JavaScript API JSR 356: Java API for WebSocket - Specification (Early Draft) and Implementation (already integrated in GlassFish 4 promoted builds) Subsequent blogs will discuss the following topics (not necessary in that order) ... Binary data as payload Custom payloads using encoder/decoder Error handling Interface-driven WebSocket endpoint Java client API Client and Server configuration Security Subprotocols Extensions Other topics from the API Capturing WebSocket on-the-wire messages

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• Great Example of Community How-To Doc

  - by ultan o'broin

  Always on the lookout for examples of community doc, and here's a great one: Chet Justice (@oraclenerd) just launched an eBook version (PDF actually) of John Piwowar's (@jpiwowar) very popular multi-part E-Business Suite Installation Guide. You can obtain it using the PayPal buttons here. All in a good cause too. Creation of how-to information like this for functional or technical tasks, along with working examples about post-install steps, configurations and customizations, is what an applications community value-add is all about. Each community is different of course, an Adobe PhotoShop community might be more interested in templates. Great to see the needs of the community being met like this. If you have other examples you'd like to share, then find the comments.

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• MySQL and Hadoop Integration - Unlocking New Insight

  - by Mat Keep

  “Big Data” offers the potential for organizations to revolutionize their operations. With the volume of business data doubling every 1.2 years, analysts and business users are discovering very real benefits when integrating and analyzing data from multiple sources, enabling deeper insight into their customers, partners, and business processes. As the world’s most popular open source database, and the most deployed database in the web and cloud, MySQL is a key component of many big data platforms, with Hadoop vendors estimating 80% of deployments are integrated with MySQL. The new Guide to MySQL and Hadoop presents the tools enabling integration between the two data platforms, supporting the data lifecycle from acquisition and organisation to analysis and visualisation / decision, as shown in the figure below The Guide details each of these stages and the technologies supporting them: Acquire: Through new NoSQL APIs, MySQL is able to ingest high volume, high velocity data, without sacrificing ACID guarantees, thereby ensuring data quality. Real-time analytics can also be run against newly acquired data, enabling immediate business insight, before data is loaded into Hadoop. In addition, sensitive data can be pre-processed, for example healthcare or financial services records can be anonymized, before transfer to Hadoop. Organize: Data is transferred from MySQL tables to Hadoop using Apache Sqoop. With the MySQL Binlog (Binary Log) API, users can also invoke real-time change data capture processes to stream updates to HDFS. Analyze: Multi-structured data ingested from multiple sources is consolidated and processed within the Hadoop platform. Decide: The results of the analysis are loaded back to MySQL via Apache Sqoop where they inform real-time operational processes or provide source data for BI analytics tools. So how are companies taking advantage of this today? As an example, on-line retailers can use big data from their web properties to better understand site visitors’ activities, such as paths through the site, pages viewed, and comments posted. This knowledge can be combined with user profiles and purchasing history to gain a better understanding of customers, and the delivery of highly targeted offers. Of course, it is not just in the web that big data can make a difference. Every business activity can benefit, with other common use cases including: - Sentiment analysis; - Marketing campaign analysis; - Customer churn modeling; - Fraud detection; - Research and Development; - Risk Modeling; - And more. As the guide discusses, Big Data is promising a significant transformation of the way organizations leverage data to run their businesses. MySQL can be seamlessly integrated within a Big Data lifecycle, enabling the unification of multi-structured data into common data platforms, taking advantage of all new data sources and yielding more insight than was ever previously imaginable. Download the guide to MySQL and Hadoop integration to learn more. I'd also be interested in hearing about how you are integrating MySQL with Hadoop today, and your requirements for the future, so please use the comments on this blog to share your insights.

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• WebSocket Samples in GlassFish 4 build 66 - javax.websocket.* package: TOTD #190

  - by arungupta

  This blog has published a few blogs on using JSR 356 Reference Implementation (Tyrus) integrated in GlassFish 4 promoted builds. TOTD #183: Getting Started with WebSocket in GlassFish TOTD #184: Logging WebSocket Frames using Chrome Developer Tools, Net-internals and Wireshark TOTD #185: Processing Text and Binary (Blob, ArrayBuffer, ArrayBufferView) Payload in WebSocket TOTD #186: Custom Text and Binary Payloads using WebSocket TOTD #189: Collaborative Whiteboard using WebSocket in GlassFish 4 The earlier blogs created a WebSocket endpoint as: import javax.net.websocket.annotations.WebSocketEndpoint;@WebSocketEndpoint("websocket")public class MyEndpoint { . . . Based upon the discussion in JSR 356 EG, the package names have changed to javax.websocket.*. So the updated endpoint definition will look like: import javax.websocket.WebSocketEndpoint;@WebSocketEndpoint("websocket")public class MyEndpoint { . . . The POM dependency is: <dependency> <groupId>javax.websocket</groupId> <artifactId>javax.websocket-api</artifactId> <version>1.0-b09</version> </dependency> And if you are using GlassFish 4 build 66, then you also need to provide a dummy EndpointFactory implementation as: import javax.websocket.WebSocketEndpoint;@WebSocketEndpoint(value="websocket", factory=MyEndpoint.DummyEndpointFactory.class)public class MyEndpoint { . . .   class DummyEndpointFactory implements EndpointFactory {    @Override public Object createEndpoint() { return null; }  }} This is only interim and will be cleaned up in subsequent builds. But I've seen couple of complaints about this already and so this deserves a short blog. Have you been tracking the latest Java EE 7 implementations in GlassFish 4 promoted builds ?

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• JSR Updates and Inactive JSRs

  - by heathervc

   The following JSRs have made progress in the JCP program this week: JSR 342, Java Platform, Enterprise Edition 7 (Java EE 7) Specification, has posted an Early Draft 2 Review.  This review closes 30 November. JSR 338, Java Persistence 2.1, has posted an Early Draft 2 Review.  This review closes 30 November.  JSR 346, Contexts and Dependency Injection for Java, EE 1.1, has posted a Public Review.  This review closes 3 December.  JSR 352, Batch Applications for the Java Platform, has posted a Public Review.  This review closes 3 December. Inactive JSRs: In 2008, we initiated an effort to identify JSRs that had not continued to make progress in the JCP program.  We have reported on this topic since that time at JCP Executive Committee Meetings. The term 'Inactive JSRs' was introduced, and a process was developed with the guidance of the EC to reduce the number of Inactive JSRs  (reduced from over 60 to 2 JSRs) through either moving to the next JSR stage or being Withdrawn or declared Dormant.  This process has been formalized in JCP 2.8 and above, with the introduction of JSR deadlines.  The JSRs which were put to a Dormancy Ballot in September 2012  have been approved by the EC and are now declared Dormant.  You can view the results of the JSR Voting on JCP.org.  The latest Inactive JSRs report is available as part of the September 2012 JCP EC Face-to-Face Meeting Materials. 

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